dissertation1
TRANSCRIPT
i
SIMULATED TEST MARKETING AND ITS PRACTICAL APPLICATION
IN THE RUSSIAN FMCG MARKET
NIKOLAY KOROTKOV
Dissertation submitted to Oxford Brookes University
for the partial fulfillment of the requirement for the degree of
MASTER OF SCIENCE IN BUSINESS MANAGEMENT
September 2010
ii
DECLARATION
This dissertation is a product of my own work and is the result of
nothing done in collaboration.
I consent to the University’s free use including online reproduction,
including electronically and including adaptation for teaching and
education activities of any whole or part item of this dissertation.
Nikolay Korotkov
Word length: 36 437 words
iii
ACKNOWLEDGEMENTS
To my beloved wife, Zhenya, my dear parents, Natalia Nikolaevna
and Yuiry Vasilievich, my friends and colleagues
for their tremendous support throughout
this journey.
iv
“Simulated test marketing and its practical application in the Russian FMCG market”.
Nikolay Korotkov. September 2010
ABSTRACT
Currently, the world’s leading FMCG companies generate 30% to 50% of their
revenues by expanding into emerging markets. However, the approaches to
successful new product sales forecasting are not sufficiently well established in such
markets. The following study is an attempt to help in setting up effective Simulated
Test Marketing (STM) services in developing countries, particularly in Russia. This
dissertation reviews various theoretical and practical aspects of new product sales
forecasting, including STM, in the context of new product development (NPD) stages,
phases of market life-cycle and types of new product. A theoretical examination
undertaken has shown considerable structural differences between saturated
“western” markets and developing Russian FMCG market, rising the question about
applicability of traditional “western” STM models outside their origin countries. An
additional quantitative survey of marketing experts employed by the leading FMCG
companies in Russia has helped to develop actionable recommendations on effective
use of STM in the local environment. In particular, it is emphasized that traditional
Simulated Test Marketing is only effective in well-defined markets. Therefore, before
going with STM, it is advisable to examine available market knowledge across 8 sets
of characteristics, in particular - “consumer / shopper”, “products”, “market size and
dynamics”, “marketing / promotion”, “pricing”, “competitors / substitute products”,
“infrastructure / environment” and “finance”. For convenience, it is recommended to
refer to the template STM brief, developed on the basis of study findings. With that,
the research has revealed a number of areas for improvement of services provided
by the major vendors of Simulated Test Marketing in the Russia, in particular – taking
an individual approach to every project (i.e. shifting focus from “model-centered” to
“client-oriented” operations), “investments into local R&D - gathering local
benchmarks and learnings, performing validations”, “simplification of reporting and
modeling approaches”, “localized media models”, “reasonable timing and pricing”,
“openness in explaining modeling principles, client-friendly guidelines”, “flexibility in
forecasting”, “thoughtful and careful application of global standardized STM
techniques to local business situations”, “training local consultants”. The above
mentioned findings provide solid foundation for further development of Simulated
Test Marketing in the Russian FMCG market.
v
TABLE OF CONTENTS
DECLARATION ..................................................................................................... …ii
ACKLOWLEDGEMENTS ......................................................................................... iii
ABSTRACT .............................................................................................................. iv
TABLE OF CONTENTS ............................................................................................. v
LIST OF FIGURES .................................................................................................... vi
CHAPTER 1. INTRODUCTION ................................................................................ 1
1.1 Aim ................................................................................................................... 1
1.2 Justification ...................................................................................................... 1
1.3 Research objectives and success criteria ......................................................... 2
1.4 Academic and business rationale ...................................................................... 3
1.5 Limitations ........................................................................................................ 8
1.6 The structure of the work ................................................................................. 9
CHAPTER 2. NEW PRODUCT DEVELOPMENT IN THE FMCG SECTOR ........... 11
2.1 What is a new product? .................................................................................... 11
2.2 Introduction of new consumer products into modern markets:
Market insights, strategic purposes and paradoxes ......................................... 14
2.2.1 New-to-the-world products .................................................................... 25
2.2.2 New-to-the-firm products ........................................................................ 27
2.2.3 Brand stretching ................................................................................... 29
2.2.4 Line extensions .................................................................................... 30
2.2.5 Re-positioning / Re-branding ............................................................... 31
2.2.6 Product improvements and cost reductions ......................................... 32
2.2.7 Expansion to a new geographical market ............................................ 33
2.2.8 Paradoxes ............................................................................................ 34
2.3 How product innovations are managed by Western companies ...................... 34
2.3.1 Best practices for product development: key frameworks .......................... 36
vi
2.3.2 Best practices for new product launches: key success factors .................. 42
2.4 Major challenges for new product launches in the Russian market ................. 45
2.4.1 Market profile: a comparison versus mature western markets. .................. 46
2.4.2 Specifics of new product development and launches in Russia ................ 52
2.5 Key findings ........................................................................................................ 56
CHAPTER 3. MARKETING RESEARCH AND SALES FORECASTING FOR NEW
FMCG PRODUCTS .................................................................................................. 57
3.1 The role of marketing research in the NPD process ..................................... 57
3.2 Aligning marketing research with NPD process ............................................ 58
3.2.1 New-to-the-world products .................................................................... 61
3.2.2 New-to-the-firm products ........................................................................ 65
3.2.3 Brand stretching ................................................................................... 68
3.2.4 Line extensions .................................................................................... 70
3.2.5 Re-positioning / Re-branding ............................................................... 73
3.2.6 Product improvements and cost reductions ......................................... 75
3.2.7 Expansion to a new geographical market ............................................ 77
3.2.8 A summary on practical application of tools ........................................ 80
3.3 Methods to forecast sales for a new FMCG product ..................................... 81
3.3.1 An overview of sales forecasting instruments ......................................... 81
3.3.2 The history and evolution of Simulated Test Marketing ......................... 86
3.3.3 Inside Simulated Test Marketing: basic principles of calculation ............ 93
3.3.4 Review of the leading providers of Simulated Test Marketing ................ 98
3.3.5 Effective practical use of Simulated Test Marketing .............................. 104
3.3.6 The ways of further development of STM models ................................. 110
3.4 Key findings ................................................................................................. 112
CHAPTER 4. SALES FORECASTING IN THE RUSSIAN FMCG MARKET ........ 113
4.1 Forecasting sales of new consumer goods in Russia ................................. 113
4.2 Traditional STM approaches in the context of the Russian market ............. 120
4.3 Questions that require further investigation ................................................ 121
vii
CHAPTER 5. RESEARCH METHODOLOGY ...................................................... 123
5.1 Research objectives and success criteria ................................................... 123
5.2 Research framework ................................................................................... 124
5.2.1 Type of investigation ............................................................................. 124
5.2.2 Sample design and data collection method ......................................... 126
5.2.3 Questionnaire ........................................................................................ 127
5.2.4 Analysis plan ......................................................................................... 128
5.2.5 Limitations in terms of precision and confidence................................... 129
5.2.6 Limitations in terms of generalization .................................................... 129
CHAPTER 6. KEY FINDINGS .............................................................................. 130
6.1 Respondents’ profile ................................................................................... 130
6.2 New product types and marketing research methods employed in Russia . 131
6.3 Insights into forecasting in the Russian FMCG market ............................... 133
6.4 Awareness and usage of STM models in Russia ........................................ 136
6.5 Key areas of Simulated Test Marketing improvement in Russia ................. 143
6.6 Summary .................................................................................................... 144
CHAPTER 7. RECOMMENDATIONS .................................................................. 147
7.1 Recommendations for FMCG manufacturers ............................................. 147
7.2 Recommendations for agencies .................................................................. 152
REFERENCES ...................................................................................................... 154
APPENDICES ....................................................................................................... 169
viii
LIST OF FIGURES
Figure 1.1 Key drivers of change in FMCG markets .......................................... 3
Figure 1.2 Number of new products in global FMCG markets .......................... 5
Figure 1.3 New product success rate ................................................................ 6
Figure 1.4 The world’s leading FMCG companies in emerging markets ............. 7
Figure 1.5 The biggest TV advertisers in Russia in 2008 .................................... 7
Figure 1.6 Key factors of new product failure ...................................................... 8
Figure 2.1 New product components ................................................................ 12
Figure 2.2 New product types in the “western” markets .................................... 14
Figure 2.3 Modern views on market characteristics .......................................... 16
Figure 2.4 Eight sets of essential market characteristics .................................. 17
Figure 2.5 Market life-cycle and market characteristics .................................... 19
Figure 2.6 Market life-cycle and phases of innovation diffusion ........................ 19
Figure 2.7 The pyramid of market strategies ..................................................... 20
Figure 2.8 Market life-cycle and strategic directions ......................................... 21
Figure 2.9 Offensive business strategies .......................................................... 22
Figure 2.10 Defensive business strategies ......................................................... 23
Figure 2.11 Strategic focus and new product types ............................................ 25
Figure 2.12ab “New-to-the-world” products in the marketing context ................. 26,27
Figure 2.13ab “New-to-the-firm” products in the marketing context ........................ 28
Figure 2.14ab “Brand Stretching” in the marketing context ..................................... 29
Figure 2.15ab “Line Extensions” in the marketing context ....................................... 30
Figure 2.16ab “Re-branding / Re-positioning” in the marketing context .................. 31
Figure 2.17ab “Product / Price changes” in the marketing context .......................... 32
Figure 2.18ab “New-to-the-country” products in the marketing context ................... 33
Figure 2.19 Structuring NPD process: plans vs. reality ...................................... 35
Figure 2.20 Evolution of NPD theories ............................................................... 36
Figure 2.21 Stage-Gate® model ........................................................................ 37
ix
Figure 2.22 NPD process in the US FMCG market: costs, time, risks ............... 38
Figure 2.23abcd Consumer integration into NPD ............................................. 40,41,42
Figure 2.23 New product launches: typical patterns of marketing support ........ 45
Figure 2.24 Russia’s role in the global R&D process ......................................... 47
Figure 2.25 Russian economy profile ................................................................. 48
Figure 2.26abcd Russian FMCG market versus key developed markets .............. 49,50
Figure 2.27 Growth rates of Russian FMCG markets ........................................ 51
Figure 2.28ab Specifics of NPD and launches in Russia: literature review ........... 53
Figure 2.29 Specifics of NPD in Russia in terms of Stage-Gate® model ........... 54
Figure 3.1 Information needs addressed by marketing research in NPD ......... 58
Figure 3.2 Classification of marketing research methods ................................. 59
Figure 3.3 The link between marketing research and business processes ...... 60
Figure 3.4 The use of marketing research methods by new product type ........ 80
Figure 3.5ab Sales forecasting instruments ................................................. 82,83
Figure 3.6 Sales forecasting instruments and marketing research ............... 84
Figure 3.7 Sales forecasting instruments: cost, time, accuracy ...................... 85
Figure 3.8 Essentials of quality control in sales forecasting ............................ 86
Figure 3.9 Test marketing: two key approaches ............................................. 87
Figure 3.10 Simulated Test Marketing: data collection procedure .................... 89
Figure 3.11 Fourt-Woodlock approach .............................................................. 89
Figure 3.12 Simulated Test Marketing and the rise of retail in the U.S. ............ 90
Figure 3.13ab Evolution of Simulated Test Marketing ...................................... 91,92
Figure 3.14 Fourt-Woodlock approach and STM. ............................................. 94
Figure 3.15 Probability of purchase: “purchase intent” vs “preference share”. .. 95
Figure 3.16ab Principles of “purchased intent”-based approach ..................... 95,96
Figure 3.17ab Principles of “preference share”-based approach .................... 96,97
Figure 3.18 Key players in STM market ............................................................ 99
Figure 3.19 Key differences between STM models ......................................... 104
Figure 3.20 Essential control questions to ensure effective use of STM ......... 105
x
Figure 3.21 Forecasting instruments used in the process of STM .................. 106
Figure 3.22 STM accuracy at various stages of market development ............. 106
Figure 3.23 Cautions when using STM ........................................................... 108
Figure 3.24 Key areas for improvement of STM.............................................. 111
Figure 4.1 Marketing research and STM markets: Russia vs US vs World .. 115
Figure 4.2abcde Russian FMCG market and Simulated Test Marketing ........ 117-120
Figure 4.3 Key STM models in the context of the Russian market ............... 121
Figure 5.1 Research topic and methodological approach ............................ 124
Figure 5.2 Leading advertisers in FMCG sector in Russia ........................... 126
Figure 5.3 Sample design and methodology ................................................ 127
Figure 5.4 Analysis plan ............................................................................... 128
Figure 6.1 Respondents profile .................................................................... 131
Figure 6.2 New product types in the Russian FMCG market ....................... 132
Figure 6.3 The use of marketing research in NPD in Russia ....................... 133
Figure 6.4 Factors to consider when forecasting in Russia .......................... 134
Figure 6.5 Difficulties and issues that need to be addressed ....................... 135
Figure 6.6 NPD success rate and forecast accuracy in Russia .................... 135
Figure 6.7 Awareness and usage of STM in Russia .................................... 136
Figure 6.8 STM market in Russia: prices and trends ................................... 137
Figure 6.9 Perceived accuracy of STM in Russia.......................................... 137
Figure 6.10 Key factors of choice for STM (open-ended)................................ 138
Figure 6.11 Key factors of choice for STM (ranking) ....................................... 139
Figure 6.12ab Perception of various STM approaches ................................ 139,140
Figure 6.13 Customer satisfaction and factors that influence it ....................... 140
Figure 6.14 ACNielsen BASES: perceived strengths and weaknesses .......... 141
Figure 6.15 Ipsos DESIGNOR: perceived strengths and weaknesses ........... 142
Figure 6.16 Local STMs: perceived strengths and weaknesses ..................... 143
Figure 6.17 Other “western” STMs: perceived strengths and weaknesses ..... 143
Figure 6.18 Key areas of STM improvement in the Russian FMCG market ... 144
1
”Successful forecast is the culmination of knowledge” Nikolay Kondratiev (1926) 1
CHAPTER 1
INTRODUCTION
1.1 Aim
The ultimate aim of the present study is to enrich the theory of marketing with the
knowledge on practical use of sales forecasting techniques in the Russian FMCG
market. The secondary aim is to develop a set of recommendations on effective and
efficient use of Simulated Test Marketing in the Russia.
1.2 Justification
The high importance of dissertation topic has been recently recognized both by
academics and marketing practitioners due to a rapidly growing demand for reliable
new product sales forecasting technique in the Fast Moving Consumer Goods sector
(FMCG) in the emerging markets, particularly in Russia. For this reason, particular
interest is given to Simulated Test Marketing (STM), which is a recognized sales
forecasting tool in the US and Europe. According to “The Marketing Glossary”
(Clemente, 2002, p.391), a simulated test market (STM) is “a form of market testing
where consumers are exposed to a simulated purchase situation to gauge the
buyers’ reactions to a product, advertising or marketing mix variations. For example,
a company may create in a research facility a store-like environment to see how
consumers maneuver through the store and view the products on display. This
simulated test market would generate information on the marketing stimuli, with the
resultant data used in marketing planning, estimating market demand and sales
forecasting”. Nowadays, there’s a consensus among academics that international
markets are vastly different (Usunier, 2000). Many Russian marketing research
practitioners, argue that Russian FMCG market is quite unique (Kachalov, 2008).
Obviously, many traditional marketing research tools originally developed for the
1 (Kondratiev, 1993, p.120)
2
mature western markets are not directly applicable in the emerging markets, such as
Russia, without prior customization (Malhotra, 2007). Concerning the use of
Simulated Test Marketing in Russia, there is a visible lack of academic knowledge.
According to the recent study by Wherry (2006), the “global average” accuracy of
forecasts is about ±9%. However, the observed accuracy for the Russian market is
not reported and is not available from the independent reliable sources. Moreover,
the effectiveness of Simulated Test Marketing in Russia hasn’t been discussed yet in
the academic and business literature. Therefore, the following questions require
further investigation: (1) ”Which forecasting techniques are used to predict new
product sales in the Russian FMCG market and why?”; (2) “If traditional methods of
Simulated Test Marketing are used, what are their advantages and disadvantages in
the Russian market? What is their observed accuracy?”.
1.3 Research objectives and success criteria
Taking into account the need for information that is described above, the author
proposes a research program aiming at following three objectives:
1) Revisiting theoretical approaches to sales forecasting developed in the
“western” FMCG markets
2) Exploration into current practices of sales forecasting in Russia, in particular :
experience with various sales forecasting techniques for new products
perception of forecasting services provided by external suppliers
3) Identification of key factors influencing choice of forecasting method
4) Review of traditional STM approaches in terms of their applicability for the
Russian market, and elaboration of recommendations for effective and
efficient use of STM in the local environment.
As an outcome of the research, a set of clear findings and actionable
recommendations will be prepared and delivered to the participants of the survey,
who are NPD, business and consumer insights experts working for the biggest
3
FMCG manufacturers in Russia. It is supposed that the study results will support
decision making and business planning processes within these companies and will
drive further development and customization of STM models in emerging markets,
particularly, in Russia. Recruiting a market representative sample of n~30 experts
from at least 10 biggest FMCG companies makes this study a success.
1.4 Academic and business rationale The structure of the global market landscape, particularly in emerging markets of
BRIC countries, has been rapidly changing over the past few decades driven by the
number of socio-economical, technological and cultural trends, such as “blurring” age
differences in behavior, mixing traditional social roles, democratization of luxury, lack
of free time, anxiety about health, the need for excitement, individuality, comfort and
participation in social life. The rise and spread of these trends is heavily boosted by
the emergence of mass communication, such as Internet, mobile
telecommunications, satellite television as well as by considerable expansion of
global trade, i.e. rapid development of retail networks and distribution channels
(Datamonitor, 2005, 2006) (see Figure 1.1).
FMCG market landscapes are now changing rapidly driven by …
Age / Lifestage complexity: Age blurring
Gender complexity: Mixing & matching gender roles
Income complexity: Democratization of luxury
Convenience: Being time efficient
Health: Active steps to lead healthier lifestyles
Sensory: finding excitement and sensations in life
Individualism: seeking for individuality
Comfort: search for little symbols of stability, attempts to slow down and simplify lifestyles
Connectivity: growing importance of shared ethical and environmental values.
Culture fusion*: cross-cultural access, globalization,
emerging markets
Source: DataMonitor (2005)
Figure 1.1
4
Concerning the academic interpretation of innovation phenomena, i.e. “what” fuels
innovation boom nowadays, there are two traditional schools of thought: “market-
based” and “resource-based” (Trott, 2008, p.20). The “market-based” (or “market
pull”) point of view suggests that “market conditions provide the context which
facilitate or constrain the extent of firm innovation activity” (Porter, 1985, cited in
Trott, 2008, p.20). The “resource-based” (or “technology push”) view argue that
“market-driven orientation does not provide a secure foundation” for innovations and
listening to customers “may actually stifle technological innovation and be detrimental
to long-term business success”, therefore a better strategy for a firm is to shape
“markets in accordance to its own view” (Hamel and Prahalad, 1994, cited in Trott,
2008, p.9-20). By now it has been recognized, that the truth lies in between these
two extremes: obviously, technological invention is necessary but not sufficient for
successful new product launch (Trott, 2008). Consumer acceptance and marketing
plans should be tested in advance with tools such as Simulated Test Marketing, i.e. it
is crucial for the business to screen out commercially unsound technological
concepts. Urban and Hauser (1993) found out 13 major factors initiating the need for
innovations nowadays. These factors are: (1) financial goals of profit (“and the
resulting impact on stock price”), (2) growth in sales and market share (“this allows a
dominant share position to be established”), (3) competitive activities (“the standing
of an organization relative to its competitors is a strong motivational force”), (4) life
cycle (“as the product moves from maturity to decline, profits may fall. To regain
profitability, the organization direct its effort toward the new product”), (5) technology
(“technological change puts extreme pressures to organizations to innovate or
decline”), (6) globalization (“the advent of increased global trade has put … strong
forces on firms to develop new products”, (7) regulation (“in many cases new
government regulations or deregulation causes firms to consider new products”), (8)
material costs and availability (“as raw material costs and availability change
products must be revised or dropped”), (9) invention (“inventions create new
5
opportunities”), (10) demographic and lifestyle changes (“lifestyle generates
consumption changes”), (11) customer requests (“a source of many new products is
customer requests”), (12) supplier or distributor initiatives and reactions (“suppliers
and distributors can also be a force in innovation”), (13) alliances (“this can initiate
new product development by reacting to an offer to become a part of an alliance”)
(Urban and Hauser, 1993, pp.6-13). Urban and Hauser (1993) suggest that “if the
trends in the underlying initiating factors continue, we will see more new product
development activity in the future” (Urban and Hauser, 1993, p. 13). Nowadays, the
number of innovative products being brought to the global market is overwhelming
and shows dramatic double digit growth each consecutive year (Mintel, 2007) (see
Figure 1.2).
Record-breaking number of new products are flooding global FMCG shelves, addressing new trends…
# Launches went up by +17% vs. PY globally
182,000 new brands and line-extension products were introduced globally last year alone
105,000 new food and drink brands and line-extensions (~300 every day)
Key booming areas focused on mind, body, and general good health
Source: Mintel (2007)
“Low- / Minus claims”* are sharply on the rise Low/no trans-fat up to +120%
Gluten-free product launches up to + 86% with strong growth in North America,Europe and Latin America.
The ethical and organic boom + 100% growth for new products with an ethical positioning (making ecological
claims or linked to charitable concerns)
Double digit growth – organic products, organic non-food
Figure 1.2
A record-breaking number of new products are flooding global FMCG shelves, as a
result of processes discussed above. According to Mintel (2007), about 182 000
brands and line extensions were introduced globally in 2006, reaching the number of
500 items a day. The key booming areas are focused on mind, body, and general
good health. Sales of “Low/Minus claims”, organic products and products with ethical
6
positioning had doubled within a year. However, even nowadays the process of New
Product Development (NPD) is still dominated by failure. Research shows that, out of
all product prototypes available to a company, only 15% will actually appear on the
market (i.e. 85% will be dropped during development and testing stages), out of that
only 35% will remain on shelves in the next few years, and, finally, only 5% will reach
a break-even point (Booz Allen & Hamilton, 1988, Clancy et al., 2003, Armstrong et
al., 2009). In terms of finance, that means the process is extremely costly as 95% of
investment is wasted. (see Figure 1.3)
Available statistics show that , still, only 5% of new products are successful…
Development and launch of New products is dominated by failure not success.
15% of new product ideas are launched,
35% of those launched products remain actually in market.
~5% of new product ideas achieve the minimal level of
success,
95% of resources invested
with no positive
Return Of Investment !
Sources:Booz, Allen & Hamilton (1988), Armstrong et al (2009), Clancy et al (2003)
Figure 1.3
As for emerging markets, the rate of ROI for innovations has not been determined
yet: it may be lower due to the low market saturation or, vice versa, it may be higher
due to various hidden market entry barriers. According to corporate reports of the
global FMCG leaders, emerging markets are considered of highest importance due
to their significant contribution into overall corporate growth. The fact that more than
85% of global population live in developing countries, make these markets extremely
attractive (P&G, 2009). Nowadays industry leaders generate up to 50% of their
revenue in such countries. (see Figure 1.4 - overleaf)
7
Developing and emerging markets generate over 30% of revenue for the biggest multinationals
Sources: Colgate (2009), Unilever (2009), TCCC(2009), Nestle (2009), ABInBev (2009), Danone (2009), Henkel (2009), Pepsico(2009), Loreal (2009), P&G (2009), Kraft (2009), Reckitt Benckiser (2009)
Figure 1.4
52%
49%
45%
45%
42%
40%
38%
33%
33%
32%
26%
19%
0% 10% 20% 30% 40% 50% 60%
Colgate**
Unilever
The Coca-Cola Company
Nestle
AB InBev ***
Danone
Henkel
Pepsico
L'Oreal
P&G
Kraft
Reckitt Benckiser
Share of Emerging markets* in Total Revenue
Share of Emerging markets* in Total Revenue
13 bln $
40 bln Eur
31 bln $
108 bln CHF
36 bln$
15 bln Eur
14 bln Eur
43 bln $
16 bln Eur
79 bln $
48 bln $
8 bln GBP
Revenue, 2009
* - Eastern Europe, Africa/Middle East, Latin America, Asia and Pacific excluding Japan** - w/o petfood business, *** - w/o Export and Holding companies
In Russia, promotional budgets for new products may reach into tens of millions in
US dollars (see Figure 1.5).
The biggest TV advertisers in Russia in 2008
Source: TNS (2009)
Figure 1.5
302
191
144
124
123
123
117
112
107
96
81
77
68
59
55
53
- 50 100 150 200 250 300 350
P&G
L`OREAL
UNILEVER
DANONE
NESTLE
MARS-RUSSIA
RECKITT BENCKISER
HENKEL GROUP
COCA-COLA
WIMM-BILL-DANN
BALTICA
COLGATE-PALMOLIVE
PEPSI CO
WRIGLEY`S
KRAFT FOODS
SUN INBEV
TV ad spendings in 2008, mln $
TV ad spendings in 2008, mln $
Therefore, the amount of savings possible due to improvements in new product sales
forecasting in emerging markets, particularly in Russia (including improved NPD
protocols, research and forecasting methods) makes the study on this topic very
8
worthwhile. Given the continuous rise of costs, FMCG manufacturers show keen
interest in studying key reasons of failure and developing reliable research
techniques to reduce financial risks associated with innovations (Urban and Hauser,
1993, Clancy et al., 2006) (see Figure 1.6).
Key drivers of failure can be revealed before launch, in particular with Simulated Test Marketing …
Market potential is overestimated
The product is not really new / not Different
No real Consumer Benefit or Need
Poor Positioning
Competitive response & market changes
Insufficient ROI
Sources: Urban et al (1993) , Clancy et al (2006),
Figure 1.6
Particular attention is given to Simulated Test Marketing, a holistic research
approach that is able to help in addressing a majority of the issues listed above.
Therefore, the findings from the present study have practical implications of
considerable business importance, particularly in Russia.
1.5 Limitations of the study
Among the key features of thorough scientific investigation (purposiveness, rigour,
testability, replicability, precision and confidence, objectivity, generalizability,
parsimony) the latter two largely define the scope of limitations (Sekaran, 2003).
Generalizability, according to Sekaran (2003, p.149), is “to what extent would the
results found in the lab setting be transferrable or generalizable to actual…field
settings”. Parsimony is an “efficient explanation…through smaller, rather than a
larger” quantity of information (Sekaran, 2003, p.421). Therefore, while considering
9
recommendations from the study, the following limitations must be taken into
account:
The literature review provides only a snapshot of key ideas and approaches on
the research topic. Detailed description of statistical and mathematical
procedures goes beyond the scope of the present work.
The literature review contains information taken only from the open sources.
The findings from the study are based on an aggregated expert opinion of
Russian marketing practitioners employed by the major FMCG companies,
which are the end-users of sales forecasting techniques and Simulated Test
Marketing. These findings, therefore, are not transferrable to any market, other
than Russian FMCG (i.e. emerging markets are under-researched in this study),
or to any area of marketing research, other than Simulated Test Marketing.
In order to avoid “commercially” biased responses, marketing research agencies
are not surveyed.
1.6 The structure of the work
Besides introduction, this dissertation comprises six chapters, where every chapter is
divided into sections. Chapter 2 starts a literature review, introducing the terms of
“new product”, “new product development”, the types of new products and theoretical
frameworks in managing and marketing FMCG innovations, such as “stage-gate”.
This chapter also discusses various differences between established “western”
FMCG markets and Russian FMCG market. Chapter 3 explains the role of
marketing research in the process of new product development (NPD), focusing on
relevant research and forecasting techniques. In this chapter a considerable
emphasis is made on Simulated Test Marketing, its evolution and practical
application in the mature western markets. Chapter 4 starts discussion about sales
forecasting for new consumer products and its practical use in Russia. This chapter
presents various points of view on the topic, available in the literature. Chapter 5
10
describes a methodological approach to the study, outlining study objectives,
sampling design and questionnaire structure. The last Chapter 7 presents key
results and findings from the study, starting from sample profile and ending with
practical recommendations for effective and efficient use of Simulated Test Marketing
in the Russian FMCG market.
11
CHAPTER 2
NEW PRODUCT DEVELOPMENT IN THE FMCG SECTOR
2.1 What is a new product?
This is the question that is not very easy to answer as it may seem at first sight. First
of all, the phrase “new product” comprises two words: “new” and “product”. A broad
definition of “product” suggested by Oxford Dictionary is “an article or substance that
is manufactured or refined for sale” (Oxford University, 2010). The origin of the word
comes from Latin “producere” or “bring forth”. The meaning of the word “new” is
“produced, introduced or discovered recently or now; not existed before” (Oxford
University, 2010). However, nowadays, various agents of sales process (such as
consumers, retailers, engineers, technologists, marketers, manufacturers, suppliers,
distributors, stakeholders, state regulators) may have different views on what “new
product” is (Trott, 2008). Let us consider the term in the “marketing” context. As
pointed out by McDonald (2007, p.3), “the central idea of marketing is of a matching
between a company’s capabilities and the wants of customers in order to achieve the
objectives of both parties”. Consequently, the understanding of “product” in terms of
marketing is largely focused on bridging company’s technology and customer needs.
“The Marketing Glossary” identifies “a product” as “a manufactured good that
possesses objective and subjective characteristics, which are manipulated to
maximize the item’s appeal to consumers who purchase it to satisfy a given need”
(Clemente, 2002, p.322). Similar definitions are given by a majority of honorable
academics (Armstrong et al, 2009, Kotler, 1998, Dibb et al, 1997, Trott, 2008). Also,
nowadays it is a recognized fact that the nature of “product” is multi-faceted. ”A
product is a complexity of tangible and intangible attributes, including functional,
social and psychological utilities and needs” (Dibb et al, 1997, p.242). Despite minor
differences in academic concepts about the key forces shaping the “product”, there is
a consensus regarding “tangible” and “intangible” facets (Groucutt, 2005, p.169).
12
Dibb et al (1997, p.253), Kotler (1998) and Armstorng et al (2009) suggest that there
are three fundamental levels of product, each consisting of numerous sub-levels.
These levels are: (1) core product (“the level of a product that provides the perceived
or real core benefit or service”), (2) actual product, (“a composite of the features and
capabilities offered in a product: quality and durability, design and product styling,
packaging and brand name”), (3) augmented product (“support aspects of a
product”). With that, they point out that products may be organized in various forms in
terms of trade marks (“family brands”, “individual brands”, ”brand extensions”,
“product lines” etc.), that can be of various depth and width (see Figure 2.1).
A product is multi-dimensional. Some views on key “product” intrinsics in the context of marketing…
Sources: Adapted from Armstrong et al (2009), Dibb et al (1997), Trott (2008), Groucutt (2005), Drummond et al(2008)
Figure 2.1
By Trott (2008):
Augmented product
Actual product
Corebenefit
Brand
Design
Features
Quality level
Packaging
After-sale
service
WarrantyInstallation
DeliveryAnd
credit
By Armstrong et al (2009):
• Family / Company brands• Individual brands (“separate from the parent company”)• Brand extensions (“to diversify the brand towards different product categories and different target audiences”)• Range / Line extension (“adding a line of complementary products to the original brand”)• Generic brand (“the product having no brand name…Packaging merely states the contents of the package”)
Following the logic, one may conclude that if any changes in product components,
the whole product can be considered new (i.e. it becomes “a product innovation”).
This, however, is not true in every case. So, what makes the “product” new, or, in
another words, what makes it an “innovation”? As per “The Marketing Glossary”,
innovation is “the introduction of a product that is perceived to be new by the market”
(Clemente, 2002, p.197). According to Trott (2008, p.398), “the overwhelming
majority of so-called new products are developments and variations of existing
formats”. A research by Booz, Allen & Hamilton had shown that only 10% of new
13
product introductions are new both to the market and the company (Booz, Allen &
Hamilton, 1988). In that respect, a true successful innovation requires rather
consumer acceptance and effective use of modern technology, than fundamental
scientific discovery. Apple iPhone and Sony Walkman are examples of innovation
where existing technology is repackaged to better satisfy consumer needs.
According to Booz, Allen & Hamilton (1988) cited in Trott (2008, p.401) product
introductions may be classified into 6 groups: (1) New-to-the-world products (“they
are the first kind and create a new market. They are inventions that usually contain a
significant development in technology, such as new discovery or manipulate existing
technology in a very different way, leading to revolutionary new”), (2) New-to-the-firm
products (“although not new to the market place, these products are new to the
particular company. They provide an opportunity…to enter an established market for
the first time”), (3) Additions to existing lines, Line extensions (“this category is a sub-
set of new product lines above”), (4) Improvements and revisions to existing products
(“these new products are replacements of existing products in a firm’s product line”),
(5) Cost reductions (although ”this category may not be viewed as new from
marketing perspective,…it may be very significant from firm’s perspective”, this is
often driven by innovations in the process of production), (6) Repositioning, Re-
branding (“these new products are essentially the discovery of new applications for
existing products. This has as much to do with consumer perception and branding as
technological development”). A study by Griffin (1997) had revealed that, on
average, a majority of product introductions in western markets are “improvements or
revisions of existing products” (34%), followed by “additions to existing products”
(23%) and “new product lines” (20%). 4% of launches are “repositionings” and 9%
are “cost-reductions”. And, finally, only 10% are “new-to-the-world”, truly new
innovations, new both to consumers and companies. (see Figure 2.2 overleaf). A
survey by Schneider (2004) had confirmed the numbers recorded by Griffin (1997): a
portion of “new-to-the-world” product launches in the US accounts for only 14%.
14
A profile of new product introductions in western markets…
Sources: Adapted from Griffin (1997)
Figure 2.2
New products (New to the
world)
10%
New products (New to the firm)
20%
Line Extensions, Bran
d Stretching
23%
Product improvements
and cost reductions
43%
Repositionings / Re-branding
4%
Groucutt (2005, p. 171) divides all products into “two broad classifications, consumer
and industrial”. One particular group, “convenience consumer products” or so-called
Fast Moving Consumer Goods (FMCG) falls within the scope of this study.
“Convenience consumer products (or FMCG) are inexpensive, frequently purchased
and rapidly consumed products, that demand only minimum purchasing effort,
purchased to satisfy personal or family needs” (Dibb et al, 1997, p. 243). The next
section uncovers some fundamental insights on introduction of such products into
modern markets.
2.2 Introduction of new consumer products into modern markets:
Market insights, strategic purposes and paradoxes
Obviously, new products do not appear on shelves spontaneously without any
particular reason. New products are elements of strategies, which companies pursue
to achieve certain business objectives in the market. Therefore, it would be nearly
15
impossible to foresee future sales for each type of new product without considering
market realities and strategic context.
Let us start with the term ‘market’. As per “Market segmentation” by McDonald
(2008, p.71), “the general rule for ‘market definition’ is that it should be described in
terms of customer need in a way which covers the aggregation of all the alternative
products…which customers regard as being capable of satisfying the same need. It
is not therefore defined in terms of what company sells, but in terms of what
customers are setting to achieve”. Thus, McDonald (2008, p.71) gives the following
definition: “a market is the aggregation of all the products and services which
customers regard as being capable of satisfying the same need”.
There is no common point of view among leading marketing theorists about what are
the key distinctive features of ‘a market’ (i.e. how markets are distinguished from
each other). Although, it is well recognized that, markets can be described using
various characteristics. Noteworthy, that only one characteristic remains relatively
stable over time, which is a ‘core need’ (Dibb et al, 2008). The state of other
characteristics tends to vary. Thus, the recent studies in strategic planning by
McDonald (2008) show, that markets can be differentiated by the following attributes:
size, growth rate, predictability of growth potential, customers, distribution, product
and product lines proliferation, number of competitors, ease of entry, technology,
prices, costs and profits (see Figure 2.3). In the later work dedicated to marketing
planning, McDonald (2009) refines the list of initially proposed attributes: size,
growth, trends, customers / consumers, product characteristics, prices, physical
distribution, channels, communication, industry practices, competition (see Figure
2.3). Drummond et al. (2008) suggest that markets can be described in terms of size,
rate of growth, profitability, customers, price sensitivity, stage of life cycle,
predictability, pattern of demand (seasonality), potential for substitution, quality of
competition, likelihood of new entrants, bargaining power of suppliers and retailers,
16
bargaining power of customers, barriers to entry/exit (see Figure 2.3). Groucutt
(2005) has identified the following key properties of ‘a market’: size, consumers,
barriers to entry, competitors / shares, marketing, finance, operations, R&D /
technology, HRM (see Figure 2.3). Dibb et al (2008) point out such features as sales
(volume/value), number of consumers, number and size of competitors, basic
customer characteristics (demographic, geographic, socio-economics, personality,
lifestyles), consumer behavior (purchase and consumption behavior, occasions,
needs and benefits sough), accessibility, stability (see Figure 2.3). The most
respected theorists of Simulated Test Marketing, Glenn Urban and John Hauser
(Urban and Hauser, 1993) propose to consider seven key market parameters: (1)
growth potential (consisting of ‘size of the market’, ‘growth rate of sales’, ‘life cycle
stage’), (2) entry barriers (‘order of entry’, ‘time to be established’), (3) economies of
scale (‘cumulative sales volume’), (4) competitive attractiveness (‘vulnerability of
competition’, ‘share potential for product’, ‘rivalry intensity’), (5) investment (into
production, promotion, technology and managerial talent), (6) reward (profits, return –
on- investment), (7) risk (stability, probability of losses) (see Figure 2.3).
Modern views on market characteristics have much in common, although differ in details…
Sources: Adapted from McDonald (2008,2009), Drummond et al (2008), Groucutt (2005), Dibb et al (2008), Urban et al (1993)
Figure 2.3
McDonald (2009):Size
Growth, TrendsCustomers/ConsumersProduct Characteristics
PricesPhysical distribution
ChannelsCommunication
Industry practicesCompetition
McDonald et al (2008):Size / Growth rate
Predictability of growth potentialProduct line proliferationNumber of competitors
DistributionCustomer
Ease of entryTechnology
PriceCostsProfit
Drummond et al (2008):Size
Rate of growthProfitabilityCustomers
Price sensitivityStage of lyfe cycle
PredictabilityPattern of demand (Seasonality)
Potential for substitutionQuality of competition
Likelihood of new entrantsPower of suppliers and retailers
Power of customersBarriers to entry/exit
Groucutt (2005)Size
ConsumersBarriers to entry
Competitors / SharesMarketingFinance
OperationsR&D/Technology
HRM
Dibb et al (2008)Sales (volume/value)Number of consumers
Number and size of competitorsBasic customer characteristics
(demographic, geographic, socio-economics, personality, lifestyles)
Consumer behavior (purchase & consumption behavior, occasions,
needs and benefits sough) Accessibility
Stability
Urban et al (1993)Growth potential:
Size of marketGrowth rate of sales
Life cycleEarly entry:
Order of entryTime to be estabishedAdvantages to users
Economies of scale:Cumulative sales volume
LearningCompetetive
attractiveness:Vulnerability of
competitionShare potential for
productRivalry intensity
Investment:Investments in
production, technology and managerial talent
Reward:Profits, ROI
Risk:Stability, Probability of
losses
17
Obviously, there’s much in common between these point of views, for example, all
theorists mention ‘size of the market’, ‘customer’, ‘product’ and competitive
environment. However, there are minor, though important differences. Therefore, in
order to develop a consistent framework, that is required for further analysis, the
author had synthesized a list of essential market attributes based on the modern
theories discussed above. This list consists of eight sets of features (see Figure 2.4).
8 essential blocks of features characterizing modern markets: a synthesized list based on the modern marketing theories …
Sources: Adapted from McDonald (2008,2009), Drummond et al (2008), Groucutt (2005), Dibb et al (2008), Urban et al (1993)
Figure 2.4
Consumer:Needs
OccasionsProfile (Socio-Dem, Lifestyle)
Number of consumers involvedProduct awareness / experience / learning
Market size and dynamics:Size of market (volume/value)
Life cycle stageGrowth rate of sales
Granularity (structural complexity)Predictability
Variability / Variation
Promotional Marketing:Marketing ObjectivesDominant strategies
Communication / AdvertisingBranding
Products:Product characteristics / Technology
Potential for substitution / DifferentiationQuality
Pricing:Price levels and variationPrice elasticity / sensitivity
Competitive rivalry:Domination (shares of players)
Fragmentation (number of players)Quality of competition
Likelihood of new entrantsTime to be estabished
Order of entryMaximum share/sales potential for a new
entrant
Finance:Investments, Costs
Reward - Profitability, ROI, marginRisk - Probability of Loss
Infrastructure and Environment:DistributionSuppliers
Retail channelsRegulations
1
2
3
4
5
6
7
8
In particular: (1) Consumer (that includes needs, occasions, profile, number of
consumers involved), (2) Market size and dynamics (size of market (volume/value),
life cycle stage, growth rate of sales, granularity (structural complexity), predictability,
variability / variation), (3) Promotional marketing (dominant marketing objectives,
dominant strategies, communication / advertising, branding), (4) Products (product
characteristics and technology, potential for substitution and differentiation, quality),
(5) Pricing (price levels and price variation, price elasticity / sensitivity), (6)
Competitive rivalry ( domination - shares of players, fragmentation - number of
players, quality of competition, likelihood of new entrants, time to be established,
order of entry, maximum share/sales potential for a new entrant), (7) Finance
18
(investments and costs, reward - profitability, ROI, operational margin, risk -
probability of loss), (8) Infrastructure and Environment (distribution, suppliers, retail
channels, regulations).
Nowadays it widely recognized that markets undergo several stages in their
development. This is proven with empirical studies and even theoretically modeled
(Lilien et al, 1992, Urban and Hauser, 1993). The phenomena of ‘Life Cycle’ is
thoroughly described in fundamental marketing literature (Kotler, 1998, Dibb et al,
2003, 2008, McDonald, 2008, 2009, Armstrong et al, 2008, Clemente, 2002,
Drummond et al, 2008, Groucutt, 2005, Trott, 2008 and other). There’s a consensus
concerning the number and stages and their essence – “there is a universal
agreement … about life-cycle in marketing literature” (McDonald, 2009, p. 197).
According to Fowler and Thomas (1993) cited in Groucutt (2005, p. 198), the concept
of a life cycle “is used to predict the strategic needs associated with products as they
age within the marketplace. It allows for the development of strategies appropriate to
the life stage and anticipates the need for changes in strategy as progression from
one stage to the next occurs”. A typical life pattern of the market usually involves five
key stages: (1) introduction or embryonic stage, (2) growth, (3) early maturity, (4) late
maturity or saturation and (5) decline. A very detailed overview of stages and
managerial implications is given by McDonald (2009) and Drummond et al (2008). A
summary of that is presented on the Figure 2.5 (overleaf). An important extension of
the life – style concept is the model of ‘innovation’s diffusion’ (Lilien et al, 1992). The
model suggests that new products are adopted over time by consumers within social
systems, as encouraged by marketing. As pointed out by McDonald (2009), “diffusion
refers to the cumulative percentage of potential adopters of a new product or service
over time”. Each stage of adoption is directly linked to the particular life stage of the
market (or product) life cycle: innovators are pioneers on the market at the
‘embryonic’ life - cycle stage, while early adopters and early majority join them at the
‘growth’ stage. Late majority become consumers at the ‘maturity’ (or ‘saturation’)
19
stage, while maximum penetration is achieved with ‘laggards’ on the final stage (see
Figure 2.6). To summarize, both theories are very useful to help managers foresee
the future state of the market and avoid simply extrapolating next year’s sales from
last year’s sales.
‘Life Cycle’ is a universally recognized model used to predict strategic needs associated with products as they age within the marketplace.…
Sources: Adapted from McDonald (2009)
Figure 2.5
Market Embryonic Growth Early Maturity
Saturation/ Decline
A theory of ‘Innovation Diffusion’ is directly linked to the ‘Market Life Cycle’ .…
Sources: Adapted from McDonald (2009)
Figure 2.6
20
Further discussion about ‘life-cycle’ and ‘diffusion’ models goes beyond the scope of
the current study. The limitations of both models (such as different forms of shapes,
duration of stages and cycles, static nature) are summarized by Groucutt (2005).
As discussed earlier, modern companies operate in markets that are different in
terms of life stage. To succeed, they employ a variety of strategies applicable for
each particular stage of the market (see Figure 2.7). As literature review had shown,
these strategies can be classified into several layers, according to their scope and
institutional role. These layers include: corporate positioning strategies suggested by
Kotler (2000), strategic directions developed by Ansoff in 1957 (Ansoff, 2007),
business warfare strategies concept proposed by James (1984) and Porter’s generic
product positioning strategies (Porter, 1985, cited in Johnson et al, 2008)).
The pyramid of market strategies : 4 essential layers.…
Sources: Kotler (2000) cited in Groucutt (2005), Ansoff (2007), James B. (1984), Porter (1985) cited in Johnson et al (2008)
Figure 2.7
Market leaders, Market challengers,Market followers, Market nichers
Market penetration, Product development,Markets development, Diversification
ATTACKFrontal attackFlank attackEncirclement attackBypass attackGuerilla ("partisans") attack
DEFENSEPosition defenseFlank defensePre-emptive defenseCounter defenseMobile defenseContraction defense
Sequential, Cumulative, Indirect, Direct,Alliance, Counterforce, Countervalue
James (1984):
Ansoff (1957, 2007):
Kotler (2000):
Porter (1985):
Overall cost leadership, Differentiation, FocusProduct
Business
Strategic Directions
Corporate Positioning
According to Groucutt (2005, pp.113-117), firms can pursue four ‘corporate
positioning’ strategies: (1) Market leaders – “this is the position that many companies
seek to achieve: in essence to be leaders in their market segments”, (2) market
challengers – “these organizations continually challenge the dominance of the market
leader by attempting to win increased market share”, (3) market followers – “these
21
organizations do not seek to challenge either the market leaders or challengers, and
satisfied with their own profitable market segments and market share”, (4) market
nichers – “these organizations are able to dominate a small market segment or
segments and are unlikely to interest either the market leaders or challengers”. As
argued by Ansoff (2007), organizations of every type listed above can undertake
actions in four long-term strategic directions: (1) market penetration / consolidation
(i.e. penetrating currently existing market), (2) product development (i.e. moving by
developing new products for current markets), (3) market development (i.e. moving
by bringing existing products into new markets) or (4) diversification (i.e. creating new
markets by developing new products). According to McDonald (2009) strategic
directions are closely related with market evolution (see Figure 2.8)
Strategic Directions are closely related with the Life Cycle of the market .…
Sources: Adapted from Ansoff (2007), McDonald (2009)
Figure 2.8
1
2
3
4 44
In the context of chosen long-term strategic direction, firms implement the number of
mid-term business strategies, which are usually classified by analogy with warfare
strategies (James, 1984). The early research on the topic (James, 1984, p. 15) had
identified the following forms of strategies: (1) sequential - “successive steps, each
contingent on the preceding step, that lead to the final objective", (2) cumulative – a
22
collection of seemingly random actions with a single final objective, (3) indirect -
using “various indirect pressures to defeat an enemy and thereby avoid direct
conflict”, (4) direct – employing “direct force to achieve objectives…involving an
attack with a new marketing entrant, or defense of a market from competitive attack",
(5) alliance - "attempts to over-maneuver opponents through a combination of the
resources of several combatants with similar objectives". James (1984) suggested
that direct strategies can be offensive and defensive in nature. Drummond et al
(2008, pp.161-162) note that companies implement offensive strategies “by
aggressively pursuing market share, when the fight is taken to the competitors”, while
defensive strategies are employed “to protect your existing customer base and
ensure that market share is retained”. Nowadays this topic is broadly discussed in
the academic literature on strategic marketing planning (Johnson et al, 2008, Kotler,
2000, Groucutt, 2005, Armstrong et al, 2009, Drummond et al, 2008, McDonald,
2009). According to that discussion, a choice of offensive strategies include: frontal
and flanking attacks, encirclement, guerilla and bypass attacks (see Figure 2.9).
OFFENSIVE business strategies in the academic literature.…
Sources: James (1984, pp.15-170), Groucutt (2005, pp. 113-114), Kotler (1998) cited in Drummond (2008, p. 163)
Figure 2.9
Frontal attackAttack across all areas of
competitor's business
"The attacker needs a clearly defined advantage. For example,
the attacker may have a cost advantage or its brands may perceived more positively"
The major challenger builds resources and expertise to directly
attack the market leader in its major markets
Flanking attacksAttack on particular important areas of competitor's business
"the key to success here is to identify worthwhile underserved
segments"
Here the challenger seeks to exploit a weakness in the market leader's position. This permits the challenger to go around or flank the market leader and take some
market share.
Envelopment / Isolation / Encirclement
Encirclement, blocking opportunities for movement and
growth
"here we aim to offer a range of products that effectively encircle
the competitor"-
Unconventional offence (guerilla or"partisan's" warfare)
Used as a prime combat strategy in an aggressive role to harass competitors to the extent that a firm can win consessions in the form of increased market share"
Tactical (short term) marketing initiatives are used to gradually
weaken the opposition
This is a type of attack that is increasingly being used by new
entrants to the market. It’s a series of small ongoing actions that are
designed to (1) frustrate the market defenders (2) raise the company profile in the targeted
segment
Bypass attack -
"more a policy of avoidance as opposed to attack. The attacker
moves into areas where competitors are not active"
-
James (1984) Kotler (1998) Groucutt (2005)
23
A summary of views on defensive strategies is exhibited on Figure 2.10. These
involve: position defense, mobile defense, pre-emptive strike, flank positioning,
counter-offensive strategy and strategic withdrawal.
DEFENSIVE business strategies in the academic literature.…
Sources: James (1984, pp.15-170), Groucutt (2005, pp. 113-114), Kotler (1998) cited in Drummond (2008, p. 163)
Figure 2.10
Position defense
Involves the erection of barriers to entry (attack) around a product,
service or the company to protect against competitive aggression
A position defense aims to strengthen the current position…
Often dependent on brand management, service levels and
distribution
The market leader introduces a range of innovations to protect its
product position within the marketplace
Mobile defense
Focus around planned product replacement, product improvement
and changes to the length and duration of the product cycle"
"Flexible and adaptive response". It is achieved by broadening
current market or a diversifying into unrelated activities
Firms often seek expansion into new territories with the intention of
increasing their business opportunities, resources, size and
financial strength
Pre-emptive strikeIs used as an attempt to maintain
the status quo in favour of the devence
Striking at potential competitors before thay attack you (mainly threatening, i.e. price-cuts, ad
campaigns , promos)
Rather than waiting to be attacked, the firm may undertake proactive actions to gain market share from
the challenger
Flank positioning
Repositioning of the product or service in the marketplace or the
repositioning of resources to meet expected competitive thrusts
Protection of "weak spots" -
Counter offensive
Response to an attack where the objectives is to wrest the initiative
from the attacker by foiling the attack through a couter-strike
When attacked, most of organizations will respond to
counter attack. (for example a strong well-established brand
loyalty may see of a price cutting competitor)
The market share defender often will counter-attack with
overwhelming force to dislodge the market challengers attack
Strategic withdrawal
A defensive maneuvre with objective to extricate the maximum
amount of resources from untenable position and to provide an opportunity to regroup, rearm
and replenish
It may prove impossible to defend all operational activities. Therefore,
a selective strategic withdrawal could be the best option.
Such would protect valuable resources that could be moved to another product/service market
James (1984) Kotler (1998) Groucutt (2005)
The upper “product-related” layer of strategies (see Figure 2.7) comprises three
Porter’s generic product positioning strategies, which are: (1) overall cost leadership -
”being the low cost producer in an industry for a given level of quality“, (2)
differentiation – “development of product or a service that offers unique attributes that
are valued by customers”, (3) focus – “concentration on the narrow segment and
within that segment attempts to achieve either cost advantage or differentiation”
(Porter (1985) cited in Johnson et al (2008), p. 224). Also, it is worthy to mention a
set of “proactive” and “reactive” new product strategies defined by Urban and Hauser
(1993, p. 19-24). “Proactive” sub-set consists of the following: (1) Research and
Development - “investing in R&D to develop a technically superior products in order
to create new markets “, (2) Marketing – “foreseeing consumer needs and develop
products that provide benefits to satisfy these needs”, (3) Entrepreneurial – “a special
person - an entrepreneur - has an idea and makes it happen by building venture
24
enthusiasm and generating resources”, (4) Acquisition – “other firms are purchased
with products new to acquiring firm and perhaps to the market”, (5) Alliances - “non-
formal less rigid forms of co-operation between firms to put together a new product
portfolio…that lead to success in the market”. “Reactive” sub-set involve: (1)
Defensive strategies – “protects the profitability of existing products by countering
competitive new products”, (2) Imitative ("me too") – “is based on quickly copying a
new product before its maker is assured of being successful”, (3) Second but better –
“in this case the firm does not just copy the competitive product, but identifies ways to
improve the product and its positioning”, (4) Responsive – “purposively reacting to
customer's requests”.
At the bottom line, as pointed out by McDonald (2009, p. 298), all the marketing
strategies discussed above “are concerned with the four major elements of the
marketing mix, which are product, price, place and promotion”. According to the
concept of “phasing continuity spectrum” for innovations, developed by Saunders et
al (1994) cited in Trott (2008), strategic focus shifts on different marketing elements,
depending on the type of new product, as discussed in Section 2.1 (i.e. “new-to-the-
world” products, “new-to-the-firm” products, “brand stretching”, brand/range/line
extensions, brand repositioning / re-branding, product improvements and cost
reductions, “new-to-the-country” or “new-to-the-region” products). Thus, in cases
where changes are concerned with “product”, the emphasis should be laid on
technology, materials, manufacturing, product design and qualities, cost, price,
packaging, distribution and competition. In cases where changes touch upon
“branding”, a special attention should be given to generating awareness, establishing
unique and appealing image, encouraging product trial via promotion and advertising.
In case of “new market”, considerable effort is required to build the infrastructure,
educate consumers about new category and stimulate development of the core need,
related to the new product. These areas of emphasis are exhibited on Figure 2.11
(overleaf), grouped by the type of new product.
25
Change of strategic focus by new product type.…
Sources: Based on Saunders et al (1994) cited in Trott (2008)
Figure 2.11
Product Branding MarketNew to the world New New NewNew to the firm New New ExistingBrand Stretching New Changes ChangesLine Extensions New Changes ExistingBrand repositionings Existing Changes ExistingProduct / Price Changes Changes Existing ExistingNew to the region Existing New to the region New
Technology Name Target market Materials Brand Image Core need
Manufacturing Advertising PR + AdvertisingProduct qualities PR
CostsPrice
PackagingDistributionCompetition
Promotion (ATL/BTL)
If "New" or "Changes", then focus on:
The next few sections examine each type of new product introduction in view of
marketing and strategic insights discussed above in this section.
2.2.1 “New-to-the-world” products
A majority of these products are “technology-driven” innovations (Trott, 2008). At the
moment they appear there is no market (see Figure 2.12a), i.e. there is no
established or clearly articulated need for such products. At this early stage,
according to the recent studies by Hamel and Prahalad (1994), Martin (1995) and
Veryzer (2003) cited in (Trott, 2008, p.57), consumers have “difficulty in describing
and articulating their needs” and their frequent responses are along the lines “I want
my usual product only cheaper and better”. Research shows that “new-to-the-world”
products “require changes in thinking and behavior and hence require more from
consumer. Unsurprisingly, these products carry a high risk of failure” (Trott, 2007,
p.62). As indicated by respondents of the study by Schneider, “when you’ve got a
really new consumer product, you cannot launch a product - you have to launch a life
style” (Schneider, 2004, p.71). As pointed out by Martin (1995) consumers “can be
26
extremely unimaginative…trying to get people to change the way they do things is
the biggest obstacle facing many companies” (Martin, 1995, cited in Trott, 2008,
p.497). This is why product purchase intention measured on total population at this
stage may be dramatically low, and this is why it takes so much time to establish a
“new-to-the-world” product. This type of new product launch requires considerable
marketing effort to: (1) break through consumers’ conservatism and (2) build a
virtually new need from scratch. Although this is risky and sales are hardly
predictable, a future market leader has to lead the public from the very beginning,
rather than just follow consumers’ opinion (Akio Morita, Sony’s former charismatic
leader, cited in Trott, 2008). Indeed, ten or fifteen years ago, there weren’t many
consumers asking for cellular phones, portable computers, DVDs, flash cards, low
fat organic food, sport energy drinks etc.
A business situation concerned with the “new-to-the-world” product introduction is
examined below across key market parameters, identified in the previous section:
market life stage, consumer needs, market size and dynamics (see Figure 2.12a).
“New-to-the-world” products in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.12a
HereHere
New-to-the-World
Consumer
Needs Hidden, not established and poorly articulated, or at embrionic stage, or based on use of outdated
technologies or productsConsumption occasions Very rare, infrequent or sometimes do not even exist
Profile Innovators
Number of consumers involved Max 5% of total potential number of customers in the market
Products awareness / experience / Learning
Very low product awareness, no experience and therefore substantial learning effort is required, purchase intent on total population is very low, higher PI exhibited by Innovators
Market size and dynamics
Size of market (volume/value) Negligible
Life cycle stage Embryonic / Birth / Pioneering stageGrowth rate of sales Above 10% - "normally much greater than GNP (on small base)" - (McDonald, 2009, p.202)
Granularity (structural complexity) Simple plain market structure built around the pioneering product, usually no subcategories or niches
Predictability "Hard to define accurately both in the short- and longer-term. Market forecasts differ widely" (McDonald,
2009, p.202)Variability / Variation High variability due to unstable demand, Variation patterns (seasonality) can be hardly recognized
Promotional marketing, product, pricing, competitors, finance, market infrastructure
and environment analysis is shown on Figure 2.12b (overleaf).
27
“New-to-the-world” products in the marketing context:Figure 2.12bNew-to-the-World
Promotional Marketing
Marketing ObjectivesCreate product awareness and encourage trial (Armstrong et al, 2008), educate potential consumers while
introducing the product
Dominant strategiesMarket Leaders (Corporate positioning ), Diversification (Strategic Directions), Differentiation (Product),
Attack : Bypass, Flank, Alliance (Business), Sequential (Strategy types), Entrepreneurial and R&D (New product)
Communication / AdvertisingActivities such as advertising, sampling and special introductory offers, on-line, WOM campaigns, buzz are
used to increase customer AWARENESS, activities are often focused on innovators, "niche" marketing. Great emphasis is given to educating consumers at the end of introductory stage, establishing the need.
BrandingStrong focus on a single unique brand, very often the same name is used for corporate and product
branding. No or very few line extensions
Products
Product characteristics / TechnologyProduct offers unique superior qualities. Technology plays an important role in matching product
characteristics to market needs. Frequent product changes. The company must seek feedback on its product and carry out R&D to eliminate bugs
Potential for substitution / Differentiation
Potential for substitution is very LOW as differentiation is high and competitors are not in the market yet. However, there's a threat of resistance or switching back to old technology products
Quality Product quality varies a lot, however, usually is settled on high level by the end of introductory stage
PricingPrice levels and variation Perceived prices are very high, no particular differentiation by pricePrice elasticity / sensitivity Hardly recognized at early stages, however, normally consumers are price-sensitive
Competitive rivalry
Domination (shares of players)Very easy to enter. No one dominates. If barriers exist, they are usually technology, capital or fear of the
unknown
Fragmentation (number of players) Not fragmented, 0
Quality of competition No competition or very weakLikelihood of new entrants High at the final stage of introduction
Time to be established Depends on the nature of the product, for an FMCG category is up to 4 years (Urban et al, 1993)Order of entry 1
Maximum share/sales potential for a new entrant
Ranges from 100% at initial stage to 20% at maturity stage (with 1 competitor - 59% share, 2 - 44%, 3-36%, 4 -31%, 5 - 28% etc.)
Finance
Investments, Costs"Investment and other expenses will be high relative to revenues. Cash flow is negative. Established
companies may be able to finance new products from existing resources. New companies with new products will seek financing from venture capitalists and banks." (Groucutt, p.200)
Reward - Profitability, ROI, margin Negative cashf lows for a new entrant. In case of success this may be off set by higher profits in the long-run
Risk - Probability of Loss Extremely high Risks (Above 90%)
Infrastructure/Environment
Distribution Distribution network does not exist or unstable, Direct sellingSuppliers Unstable, no long-term high-volume contracts
Retail channelsDifficulties with retailers, new categories with unclear sales potential are not usually accepted by big
retailers, high cost of "entry ticket"Regulations Difficulties with product certification
As follows from the analysis above, developing and launching “new-to-the-world”
products might be a very challenging task that requires cautiousness when
forecasting future sales.
2.2.2 “New-to-the-firm” products
This kind of “new product” is often used by companies, known as “market
challengers” or “market followers”. They pursue reactive “imitative” or “second-but-
better” new product strategies (Urban and Hauser, 1993), which allow them to take
advantage of high profitability in the existing growing market without investing much
in research and market development. These companies usually do not achieve a
leading position in the market, yielding to originators of the product above 10%
market share (Urban and Hauser, 1993). However, they face significantly lower risks
when entering the market as compared to “new-to-the-world” product developers.
Typically, the market is well-defined and is at the growth stage, when a majority of
“new-to-the-firm” products appear on shelves. Further details are presented on
Figure 2.13a and Figure 2.13b (overleaf).
28
“New-to-the-firm” products in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.13a
HereHere
New-to-the-Firm
Consumer
Needs Emerging, often described as "exsessive" or "trendy"Consumption occasions Growing frequency, often defined as "special occasion", not perceived as a habit yet
Profile Early adopters (at growth stage) or Early Majority (at early maturity stage)
Number of consumers involvedUp to 20% of total potential number of customers in the market at growth stage, up to 50% - at early maturity
stage
Products awareness / experience / Learning
Considerable product awareness, though still low practical experience and therefore some effort on habit development is required. Consumers show some loyalty to chosen brands, however, seek for alternative
options
Market size and dynamics
Size of market (volume/value) Significant and measurable, up to 50% of total market potential has been realizedLife cycle stage Growth (generally), or Early Maturity
Growth rate of salesRapid growth, above 20% " sustained strong growth above GNP…Rate decelerates towards end of stage" -
(McDonald, 2009, p.202)Granularity (structural complexity) Very few layers or subcategories
Predictability "Greater percentage of demand is met and upper limits of demand becoming clearer. Discontinuities, such
as price reductions based on economies of scale may occur" (McDonald, 2009, p.202)Variability / Variation Sustainable upward general tendency, seasonal variations can be identified
“New-to-the-firm” products in the marketing context:Figure 2.13b
New-to-the-Firm
Promotional Marketing
Marketing Objectives Capture rapidly increasing sales opportunities, maximise sales and market share
Dominant strategiesMarket Challengers or Followers (Corporate positioning), Product Development (Strategic Directions), Any of the three Generic strategies (Product), Reactive (New Product): Imitative, Second - but-Better. Frontal,
Flank or Bypass attacks on the leaders (Business), Alliance strategy is possible
Communication / AdvertisingAggressive marketing using mass communications, heavy promotional expenses. Common tool is an IMC
campaign with focus on building brand awareness and distinctive brand imageBranding Focus on building powerful individual brands
Products
Product characteristics / TechnologyNeed for continued improvement in production processes to reduce costs and improve the product to
successfully differentiate it from competitors productsPotential for substitution /
DifferentiationSubstitution threat is MODERATE despite aggressive promotion of alternatives. This is due to low market
saturation and low category penetration Quality Quality usually is quite high
PricingPrice levels and variation High prices, allowing for price-skimmingPrice elasticity / sensitivity Visible price elasticity, however, this is the best opportunity for price skimming
Competitive rivalry
Domination (shares of players)Although first entrée's domination is challenged at this stage, market leadership has not been yet
established (may change instantly by 20%-30%). However, increased stability - a few competitors emerging as strong (above 20% share)
Fragmentation (number of players) Few strong competitors (3-5). However, the level of fragmentation may varyQuality of competition Despite high quality competition, there's a space for the growth without directly confronting competing firms.
Likelihood of new entrants Very HighTime to be established Up to 2 years for FMCG, much shorter than that for Introductory Stage (Urban et al, 1993)
Order of entry 2+Maximum share/sales potential for a
new entrant2nd entree - 41%, 3d entrée -25%, 4th entrée - 18%, 5th - 14%, 6th - 11%, 7th etc - less than 10%
Finance
Investments, Costs Lower initial investment than that for the 1st entrée
Reward - Profitability, ROI, marginVery high short-term profitability due to booming sales and relatively low investment in R&D (vs. first market
entrée) , Operational margins above 50% Risk - Probability of Loss High risk of failure (Above 70%)
Infrastructure/Environment
Distribution Mostly exclusive distribution,established logistics, building distribution networksSuppliers Exclusive suppliers, established operations
Retail channels Less strict barriers for entry, lower cost to get the product on shelfRegulations Less difficulties with certification, Product standards have been set up
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
At the “growth” stage of the life cycle, market dynamics becomes less volatile
exhibiting sustainable upward trend. This makes sales forecasting for new entrants a
less challenging task than that for “new-to-the-world” products. However, obviously
one should not expect highly accurate forecast at this stage.
29
2.2.3 “Brand stretching”
As new product progresses through its life cycle, there may be a need to exploit its
trademark and hence brand equity for another product on another existing growing
market. This can be caused by rising competitive threat in the origin market or this
may be due to its saturation and declining profits. (See Figures 2.14a and 2.14b).
“Brand Stretching” in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.14a
Target market
Originalmarket
Target market
Originalmarket
Brand Stretching
Consumer
Needs Needs are clearly identifiable in both “original" and "target" marketsConsumption occasions Established and frequent, " well-developed consumption patterns"
Profile Majority in both markets ("Early majority" on the target market, "Late majority" in the original market)Number of consumers involved Over 80% in the original market, up to 50% in the "target market"
Products awareness / experience / Learning
Consumers are aware of key brands, experienced with products. Sustainable brand loyalty. No particular education about the category is needed
Market size and dynamics
Size of market (volume/value)Significant and measurable, “original market" - up to 80% of total market potential has been realized, "target
market" - up to 50% has been realizedLife cycle stage Maturity or decline in "original" market, maturity or growth in the "target" market
Growth rate of sales Up to 10%, "Approximately equals GNP" - (McDonald, 2009, p.202)Granularity (structural complexity) Generally, both markets have complex structure
Predictability Good predictability. "Potential is well defined" for both markets - (McDonald, 2009, p.202)Variability / Variation The market may experience fairly predictable seasonal variations
“Brand Stretching” in the marketing context:Figure 2.14b
Brand Stretching
Promotional Marketing
Marketing Objectives
Maximizing profits and minimizing risks by defending market share on the saturated “original market", while capturing existing opportunities in the new growing markets by using established brand name. The objectiveis "to make consumers perceive the new product favourably by associating it with the company established
products" (Clemente,2002).
Dominant strategies
Applicable for all corporte positioning strategies, Product and Market Development Strategies (Strategic Directions), DEFENSE: Mobile Defense, Pre-emptive strike, Counter-offensive OFFENSE: Flanking attack,
Guerila attack, Encirclement attack, Bypass attacks on the leaders (Business), Acquisition strategy is possible
Communication / AdvertisingGenerally, a single advertising burst (IMC) aimed at building awareness about the new product. With
that,parent brand attributes are heavily utilized incommunication
BrandingIndividual or corporate brand. "Umbrella branding". To mazimize ROI the company capitalizes on the equity
of the already established brand name
Products
Product characteristics / Technology Product requirements well known and relatively undemandingPotential for substitution /
DifferentiationHigh due to aggressive competitive activity in both markets. Although established brand name provides
effective defense against competitive attacksQuality High in a majority of cases
PricingPrice levels and variation Price levels may vary a lotPrice elasticity / sensitivity High price sensitivity
Competitive rivalry
Domination (shares of players) One or few market leaders with shares above 20% each (in both markets)
Fragmentation (number of players)Considerable fragmentation of both markets (> 6 key players on the original market, > 3 players on ther
target market). Number of competitors reaches maximum
Quality of competition High. "Entrenched positions established. Further shakeout of marginal competitors in the base category"
(McDonald, 2009)Likelihood of new entrants High in the new markets, Low in the base market.
Time to be established Up to 2 years for FMCG in the new market (Urban et al, 1993)Order of entry (Target market) 4+
Maximum share/sales potential for a new entrant
4th entrée - 18%, 5th - 14%, 6th - 11%, 7th etc - less than 10%
Finance
Investments, Costs Lower investment on promotion (due to the use of umbrella brand) and R&D
Reward - Profitability, ROI, marginModerate profitability due to savings on brand promotion (vs. first market entrée) , Operational margins
above 30%
Risk - Probability of LossModerate risk (Above 60%), mostly related with damaged image of parent brand in case of new product
failure
Infrastructure/Environment
Distribution Mass distribution, Established distribution network, Ability to use current distributors for the new productSuppliers Choice of suppliers
Retail channelsBetter trade acceptance due to the existing relationships with the manufacturer and due to the established
brand name Regulations In a majority of cases regulatory rules are developed and introduced for both markets
30
2.2.4 “Brand -, Line, Range- Extensions”
According to the recent studies, “some high-value line extensions (in the current
category) offer consumers an important additional benefit and can contribute just as
much incremental growth to the parent brand, manufacturer, category or a whole
market as a totally new brand” (Schneider, 2004, p.73). (See Figures 2.15a, 2.15b).
“Brand-, Range-, Line- Extensions” in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.15a
Here
Here
Brand-, Range-, Line- Extensions
Consumer
Needs Precisely definedConsumption occasions Established and frequent, well-developed consumption and purchasing patterns
Profile Majority
Number of consumers involved Up to 80% (up to 50% in case of Early Maturity)
Products awareness / experience / Learning
Solid knowledge of brands, wide experience with products. Sustainable brand loyalty.
Market size and dynamics
Size of market (volume/value) Up to 80% of total market potential has been realized (up to 50% in case of Early Maturity)
Life cycle stage Maturity
Growth rate of salesUp to 10%, "Approximately equals GNP" - (McDonald, 2009, p.202), "Sales growth slow down" (Armstrong
et al, 2008)
Granularity (structural complexity) Very complex structure, various sub-segments and layers
Predictability High predictability. "Potential is well defined" - (McDonald, 2009, p.202)Variability / Variation The market may experience fairly predictable seasonal variations
“Brand-, Range-, Line- Extensions” in the marketing context:Figure 2.15b
Brand-, Range-, Line- Extensions
Promotional Marketing
Marketing Objectives1) Maintain and defend mature products on the current market 2) extend the range the company and try to
develop the almost saturated market further, looking for new users and niche segments and deliver incremental growth to the parent brand (Armstrong et al, 2008, Schneider, 2004)
Dominant strategiesApplicable for all corporte positioning strategies, Market Penetration and Product Development Strategies
(Strtategic Directions), Focused strategy (Product) DEFENSE: Mobile Defense, Pre-emptive strike,Counter-offensive OFFENSE: Flanking attack, Guerila attack, Encirclement attack, Bypass attack (Business)
Communication / AdvertisingA relatively short IMC campaign stressing new product differences and benefits. Aggressive sales
promotions may be undertakenBranding Brand, Line, Range extensions ("Umbrella" branding). High proliferation
Products
Product characteristics / Technology"Product characteristics are extended,or changed to attract new users and inspire more usage" (Armstrong
et al, 2008). "Product requirements well known and relatively undemanding, may be a slight thurst for novelties " (McDonald, 2008)
Potential for substitution / Differentiation
High due to highly competitive and granular market environment. Although existing loyalty to the parent brand is able to effectively support sales. There's a risk of cannibalizing products already existing in the
rangeQuality Varies from lower to higher quality. Often is "average" to the market
PricingPrice levels and variation Price levels may vary a lot, however usually a new product is priced in line with other products in the rangePrice elasticity / sensitivity High price sensitivity
Competitive rivalry
Domination (shares of players) Several established players with shares above 10% eachFragmentation (number of players) Considerable fragmentation (> 6 key players). Number of competitors reaches maximum
Quality of competition "The overcapacity leads to great competition" (Armstrong et al, 2008)
Likelihood of new entrantsModerate due to market saturation, stiff competition, considerable barriers. "Difficult to enter. New business
must be won from others" (McDonald, 2009, p.202)Time to be established Up to 1 year for a line extension FMCG (Urban et al, 1993)
Order of entry 6+ (in terms of product launches)Maximum share/sales potential for a
new entrantUp to 5% market share
Finance
Investments, CostsModerate investment, higher portion goes on promotion (savings achieved by use of existing technologies,
exploitation of parent brand name)Reward - Profitability, ROI, margin Moderate (Normally, no "break-throughs" should be expected)
Risk - Probability of LossRather low (Above 40%), related mostly with cannibalization and poor incremental sales delivered by the
new product. However, core business is often secured and is not put at risk
Infrastructure/Environment
Distribution Mass distribution, Established distribution network, Ability to use current distributors for the new productSuppliers Choice of suppliers
Retail channels Good trade acceptance due to established brand name and familiarity with the manufacturerRegulations In a majority of cases regulatory rules are in place
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
31
2.2.5 “Brand re-positioning / Rebranding”
According to Groucutt (2005, p.137), “the reasons underlying rebranding can be
many and varied”. However, the most important one is re-engineering (or “re-
inventing”) the brand in order to extend its life and retain consumer base within a
specific or multiple saturated markets. (See Figures 2.16a, 2.16b).
“Brand re-positioning / Rebranding” in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.16a
Here
Here
Revitalisation - Brand re-positioning / Rebranding
Consumer
Needs Precisely definedConsumption occasions Established and frequent, " well-developed consumption patterns"
Profile Total audience (Innovators, Adopters, Majority, Laggards)Number of consumers involved Up to 100%
Products awareness / experience / Learning
Solid knowledge of brands, wide experience with products. Sustainable brand loyalty.
Market size and dynamics
Size of market (volume/value) Up to 100% of total market potential has been realized Life cycle stage Maturity or Decline
Growth rate of sales "Stable or Negative. Declining demand. Market shrinks as users need change" - (McDonald, 2009, p.202)Granularity (structural complexity) Very complex structure, various sub-segments and layers
Predictability Very high predictability. "Known and limited" - (McDonald, 2009, p.202)Variability / Variation The market may experience absolutely predictable seasonal variations
“Brand re-positioning / Rebranding” in the marketing context:Figure 2.16b
Revitalisation - Brand re-positioning / Rebranding
Promotional Marketing
Marketing Objectives1) Maximize profit while defending market share (Armstrong et al, 2008) 2) " increase a brand's competitive position and therefore increase sales volume by seizing market share from rival products…" (Drummond et
al, 2008). Key brand repositioning areas involve: image and target audience
Dominant strategies
Used in all corporate positioning strategies (corporate positioning strategies), Market Penetration and Product Development Strategies (Strategic directions), Differentiation strategy (Product) DEFENSE: Position defense OFFENSE: Frontal attack (Business). Acquisition and Alliances strategies are often
employedCommunication / Advertising Massive IMC campaign (ATL) aiming at re-building brand equity and communicating new brand positioning
Branding Corporate or individual brands
Products
Product characteristics / Technology"Product requirements well known and relatively undemanding, may be a slight thurst for novelties "
(McDonald, 2008)Potential for substitution /
DifferentiationHigh due to highly competitive market environment. There's a risk of loosing share in case of re-positioning
failureQuality Varies from low to high. Often is "average" to the market
PricingPrice levels and variation Price levels may vary a lotPrice elasticity / sensitivity High price elasticity
Competitive rivalry
Domination (shares of players) One or few established market leaders with shares above 10% eachFragmentation (number of players) Usually considerable fragmentation, however this may vary dependting on the market
Quality of competition "The overcapacity leads to great competition" (Armstrong et al, 2008)
Likelihood of new entrantsLow due to HIGH market saturation, aggressive competition, declining profits. "Difficult to enter. New
business must be won from others" (McDonald, 2009, p.202)Time to be established In case of re-positiong, it takes longer than a 1 year to alllow the audience for absorbing new positioning
Order of entry Varies depending on the market, usually 6+Maximum share/sales potential for a
new entrantConcerning re-positioning, sales incremental may reach 10%, seized from rival brands
Finance
Investments, Costs Very high expenditures on advertising
Reward - Profitability, ROI, marginConsiderable, in case of success. The major reward is the ability to mainain sales with current profitability
for a longer period Risk - Probability of Loss High - above 70%. The whole business is put at risk with re-positioning
Infrastructure/Environment
Distribution Mass distribution, Established distribution network, Ability to use current distributors for the new productSuppliers Wide choice of suppliers
Retail channels Fairly good trade acceptance at the beginning due to familiarity with the manufacturerRegulations In a majority of cases regulatory rules are in place
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
32
2.2.6 “Product improvements and cost reductions”
As per Armstrong et al (2009), as the market reaches saturation and competitive
rivalry becomes incredibly intense, companies seek to modify their products by
changing their core characteristics such as quality, features and, particularly, price.
This helps them to protect their share in the mid-term. (See Figures 2.17a, 2.17b).
“Product / Price changes” in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.17a
Here
Here
Revitalisation - Product improvements and cost reductions
Consumer
Needs Precisely definedConsumption occasions Established and frequent, "extremely stable"
Profile Total audience (Innovators, Adopters, Majority, Laggards)Number of consumers involved Up to 100%
Products awareness / experience / Learning
Solid knowledge of brands, wide experience with products. Sustainable brand loyalty, customers less motivated to seek alternatives, extreme price sensitivity
Market size and dynamics
Size of market (volume/value) Up to 100% of total market potential has been realized Life cycle stage Late maturity, Decline
Growth rate of sales "Stable or Negative. Declining demand. Market shrinks as users need change" - (McDonald, 2009, p.202)Granularity (structural complexity) Very complex structure, various sub-segments and layers
Predictability Very high predictability. "Known and limited" - (McDonald, 2009, p.202)Variability / Variation The market may experience absolutely predictable seasonal variations
“Product / Price changes” in the marketing context:Figure 2.17b
Revitalisation - Product improvements and cost reductions
Promotional Marketing
Marketing Objectives
1) Reduce expenditure and milk the brand 2) Maintain or defend mature products, keep them on the market, continue harvesting profits, capture share from rival firms 3) inspire more usage by changing product
characteristics such as quality, features or modifying one or more marketing mix elements such as price ("face-lifts", "price-offs", trade deals, new TV commercials etc)
Dominant strategiesUsed in all corporate positioning strategies, Market Consolidation and Product Development Strategies
(Strategic Directions), Overall cost leadership (Product) DEFENSE: Position Defense (Business). Responsive (New product strategies).
Communication / AdvertisingWide range of advertising and promotional tools, with greater focus on BTL and Trade marketing than on ATL. Great emphasis is on price activities. At the "decline" stage ATL is conciderably reduced to the level
needed to retain hard-core loyals
BrandingCorporate or individual brands. Noteworthy, at the stage of market decline certain "commodization" is
observed (i.e.price becomes more important than branding). Product lines and ranges narrow as unprofitable products dropped
Products
Product characteristics / Technology "Technological content is known, stable and accessible" (McDonald, 2009, p.202)Potential for substitution /
DifferentiationAlmost none, weaker brands and products are repriced or just discontinued
QualityVaries from low to medium. "Premium" segment usually shrinks considerably, while "low-cost" and
"mainstream" remain stable or gradually decline
PricingPrice levels and variation Prices tend to lower drastically, no opportunities for price skimmingPrice elasticity / sensitivity Extreme price sensitivity
Competitive rivalry
Domination (shares of players) Either very fragmented (at late stages of decline) or dominated by few firms with shares above 10% eachFragmentation (number of players) Either very fragmented (at late stages of decline) or dominated by few firms with shares above 10% each
Quality of competition Focused on price, "price wars", weaker players leave the market (at the decline stage)Likelihood of new entrants Low to the declining profitability, "little or no incentive to enter"
Time to be established Concerning product or price changes, up to few months (2-3 months)Order of entry Varies depending on the market, usually 6+
Maximum share/sales potential for a new entrant
Concerning product/price changes, sales incremental may reach 5-10%, seized from rival brands
FinanceInvestments, Costs Minimum investment, low production costs, high production volumes
Reward - Profitability, ROI, margin Low. Margin is much lower than on previous stages , profit mainly comes from sales volumeRisk - Probability of Loss Moderate due to established, though declining market
Infrastructure/Environment
Distribution Mass distribution, Established distribution network, Ability to use current distributors for the new productSuppliers Wide choice of suppliers
Retail channelsGood trade acceptance, however low pricing is very important. There's also an issue with growing
commodization and competition from retailer's private labels. The product may be delisted.Regulations Regulatory rules are developed in detail
33
2.2.7 Expansion to a new geographical market
In this particular case a company follows strategic direction of “market development”
or “market extension”, starting a new regional (or country) market by launching a
product, which already exists in another market (Ansoff, 2007). The risks here are
related with various barriers analyzed below (See Figures 2.18a, 2.18b).
“New-to-the-Country (or Region)” in the marketing context:
Sources: Based on McDonald(2009), Armstrong et al (2009), Urban et al (1993), Drummond et al (2009)
Figure 2.18a
Target Market
Revitalisation - New-to-the-Region (Country)
Consumer
Needs Sometimes not established and poorly articulated, or at embrionic stage, or based on use of outdated
technologies,may often be described as "exsessive" or "trendy"Consumption occasions No clear patterns, infrequent or sometimes do not even exist. May be defined as "special occasion"
Profile Innovators, Early adoptersNumber of consumers involved Up to 20% of total potential number of customers in the market at growth stage
Products awareness / experience / Learning
Very low product awareness, no experience and therefore substantial learning effort is required, purchase intent on total population is very low, higher PI exhibited by Innovators
Market size and dynamics
Size of market (volume/value) Negligible, hardly measurable at the initial stageLife cycle stage Originalmarket = Late maturity or Decline; Local market (country) = Embryonic / Pionering
Growth rate of salesUsually much higher than that on the parent market , above 10% " sustained strong however in line withGNP…Rate decelerates towards end of stage". (McDonald, 2009, p.202). However,market growth may
decelerate due to overall market instability, cultural barriers and local habitsGranularity (structural complexity) Simple plain market structure built around the pioneering product, usually no subcategories or niches
Predictability Hard to define accurately both in the short- and longer-term. Market forecasts differ widely. However, predictability is better, than that at the same period in the parent market. Comparison to introduction
patterns on similar markets may help improve predictability ("analogies approach")
Variability / VariationHigh variability due to unstable demand. However variation patterns (seasonality) can be recognized, using
analogies from the similar markets
Target Market
OriginalMarket
OriginalMarket
“New-to-the-Country (or Region)” in the marketing context:Figure 2.18b
Revitalisation - New-to-the-Region (Country)
Promotional Marketing
Marketing Objectives1) Create product awareness and encourage trial, educate potential consumers while introducing the product 2) Create
and capture the market as soon as possible 3) Maximize share and secure future profits on the markets
Dominant strategiesMarket Leaders, Challengers and Nichers (Corporate positioning strategies), Market Development Strategy and Market
Penetration (Strategic Directions), Focus (Product), Proactive Attack : Bypass, Flank, Alliance or Acquisition (Business), Sequential (Strategy types)
Communication / Advertising
Aggressive marketing using mass communications, heavy promotional campaigns. A major tool is an IMC campaign with focus on building brand awareness and distinctive brand image. With that, considerable emphasis should be made on
educating consumers and development of consumption habitsBranding Corporate or individual brand
Products
Product characteristics "Technological content is known, stable and accessible" (McDonald, 2009, p.202)Potential for substitution /
DifferentiationPotential for substitution is MODERATE, since differentiation is high and some small local competitors maybe in the market already. However, there's a great threat of resistance or switching back to LOCAL old technology products
Quality Initially perceived as HIGH on the local market
PricingPrice levels and variation Perceived prices are very high, although prices may vary in case of cheap local alternatives are available on the market
Price elasticity / sensitivityVisible price elasticity. Growth stage is the best opportunity for price skimming. However, a firm should pursue balanced
pricing strategy, considering both profits and the speed of penetration.
Competitive rivalry
Domination (shares of players)
Very easy to enter. No one dominates. If barriers exist, they are usually cultural, economic or fear of the unknown
Fragmentation (# players) Either not fragmented, or fragmented, consisting of large number of small local competitors Quality of competition Very low, easy to enter
Likelihood of new entrants Very high, due to high potential profits in the long-term
Time to be established Depends on the nature of the product, cultural and economic barriers. It may be faster than that on the original market, or it may be not. Product customization is often required to appeal to local consumers. Introduction and market development
may take up to 4 years, same as it was on the original market (Urban et al, 1993)Order of entry 1
Maximum share/sales potential
Ranges from 100% at initial stage to 20% at maturity stage (with 1 competitor - 59% share, 2 - 44%, 3- 36%, 4 -31%, 5 -28% etc.)
Finance
Investments, CostsInvestment and other expences will be high relative to revenues. However, no fundamental R&D is needed, very often
adaptation is enough. Considerable expenditures on promotion and category development. Cash flow is negative at the beginning
Reward - Profitability, ROI, margin
Reward may be significant as price skimming is possible
Risk - Probability of Loss High risk associated with local consumption habits and cultural barriers
Infrastructure Environ
ment
Distribution Distribution network does not exist or unstable, Direct selling/Export through exclusive sales representatives
SuppliersChoice of suppliers on the original market, very few on the local, therefore local production is rarely possible in the
beginning.
Retail channelsDifficulties with retailers, new categories with unclear sales potential are not usually accepted by big retailers, high cost of
"entry ticket"Regulations Difficulties with product certification
34
2.2.8 Paradoxes
It’s worthy to mention, that continuous inflow of innovations into FMCG markets
produces paradoxes, which are of great interest to marketing science. An increasing
attention nowadays is drawn to the works of Barry Schwartz and to the so called
psychological “paradox of choice”. As Schwartz (2004) has shown, an overwhelming
amount of new products claiming new features (i.e. dramatic explosion in choice)
paradoxically generates more problems for customers, than it solves. The issue of
complex choice stifles trial and therefore undermines sales in general. According to
Trott (2008, p.62) this issue is a reflection of a major “control-chaos” paradox, which
states that innovation created to “facilitate order”, in reality can “lead to disorder”.
This paradox must be taken into consideration, when forecasting new product sales,
especially for line extensions. Mick and Fournier (1998) have identified few other
paradoxes related to the boom of new products. The most important are: (1)
freedom-enslavement (new products that are aimed to provide independence can
lead to dependence), (2) efficiency – inefficiency (new products that are aimed to
improve efficiency can lead to inefficiency), (3) assimilation – isolation (innovations
aimed to facilitate human togetherness can lead to human separation), (4) fulfils
needs – create needs (new products designed to fulfill needs create new desires).
These paradoxes has had a great impact on modern marketing, making FMCG
companies follow ‘lean’ and ‘focus’ principle in their new product development
practices. These practices are discussed in the next section.
2.3 How product innovations are managed by Western companies
How to succeed in managing new product development and new product launches?
What are the best practices in organizing this process within Western companies?
This question is tricky due to the nature of the problem: the process of innovation
requires creativity and freedom, which cannot be organized by its definition. With
that, as pointed out by Urban and Hauser (1993, p. 36), “new products must be
35
developed in an environment that allows innovation to flourish. At the same time, the
risk … must be controlled”. Urban and Hauser (1993) argue that it is a very
challenging task to develop disciplined and, at the same time, creative atmosphere
within an organization. “Organizations are not creative by nature. They have been
developed to manage on-going business. Even in new-product development
organizations often spend too much time on routine operational aspects rather than
on concentrating on developing the idea to its fullest potential”. Indeed, a survey of
the world’s largest companies by Christensen (1997) had shown that “proper”
management was the principal reason why they failed many of their innovation
initiatives. At the same time, significant innovation projects are rarely, if ever, tackled
by individuals. Nowadays NPD requires strong “collective genius”, which needs to be
structured somehow. A typical unsuccessful attempt to structure the process and fit it
into a “standardized linear framework” is depicted on Figure 2.19.
An example of failed attempt to structure NPD process:Plans versus Reality
Figure 2.19
Source: Urban et al (1993 , p. 50)
However, many Western companies had succeeded in their approaches to
developing and launching new products. This is discussed in the next section.
36
2.3.1 Best practices for new product development: key frameworks
Over the recent years it has become obvious that there’s no single formula of
success in new product development (NPD). As pointed out by Trott (2007),
regardless of 30 years of research in this area, each company approaches NPD
slightly differently than it is recommended by the theory. “For example, the
pharmaceutical industry is dominated by scientific and technological developments
that led to new drugs; whereas the food industry is dominated by consumer research
that leads to many minor product changes. And yet the vast majority of textbooks that
tackle this subject, present the NPD as an eight-stage linear model regardless of
these major differences” (Trott, 2007, p.403). Indeed, marketing literature review
shows a great commonality of views about major activities within NPD process. The
evolution of the theory and arrival at eight-stage linear model is shown on Figure
2.20.
Commercialization
Launch, Monitoring,Evaluation
NPD process: Theory evolution and standardizationFigure 2.20
Source: Adapted from Urban et al (1993), Dibb et al (1997), Groucutt (2005), Trott (2008), Armstrong (2009)
Idea generation
Armstrong et al
(2009)
Idea screening
Conceptdevelopmentand testing
Marketing StrategyDevelopment
Business Analysis
Test Marketing
Commercialization
1
2
3
4
5
6
7
Launch8
Groucutt
(2005)
Idea generation
Idea screening
Conceptdevelopmentand testing
Business Analysis
Productdevelopment and
testing
Test Marketing
Launch
1
2
3
4
5
6
7
Commercialization8
Trott
(2008)
Idea generation
Idea screening
Conceptdevelopmentand testing
Business Analysis
Productdevelopment and
testing
Test Marketing
1
2
3
4
5
6
7
8
Commercialization
Dibb et al
(1997)
Idea generation
Screening
Business Analysis
Productdevelopment and
testing
Test Marketing
1
2
3
4
5
6
Launch7
Urban et al
(1993)
Opportunity Identification:Market DefinitionIdea Generation
1
Design:Customer Needs, Product Positioning, Segmentation,
Sales forecasting, Engineering, Marketing Mix
Testing:Advertising and product testing,
pretest and prelaunch forecasting, Test Marketing
Introduction:Launch planning,
Tracking the launch
Life Cycle Management:Market response analysis, Competitive monitoring and
defense, Innovation at maturity
2
3
4
5
According to Armstrong et al (2009, pp.271) the typical NPD process comprises the
following stages: (1) idea generation (“the systematic search for new product ideas”
using internal and external sources, including consumers), (2) idea screening
37
(spotting good ideas and eliminating bad ones), (3) concept development and testing
(development a detailed version of an idea and testing with groups of target
consumers) , (4) marketing strategy development (designing a plan for introducing
the product to the market), (5) business analysis (forecasting of sales, costs, and
profits), (6) test marketing ( “the stage at which the product and marketing program
are tested in realistic marketing settings”), (7,8) commercialization and launch
(introducing the product to the market). With that, Urban and Hauser (1993), argue
that these activities may overlap in time. As mentioned earlier, in practice,
implementation details may vary from company to company. Trott (2008, p.409)
distinguishes the following organizational forms of NPD: (1) “departmental-stage”
models (particular tasks are delegated to specialized departments, leading to a
dilution of responsibility), (2) “stage–gate” models (“multifunctional teams must
successfully complete a prescribed set of related cross-functional tasks in each
stage prior to obtaining management approval to proceed to the next stage”, see
Figure 2.21), and (3) network models (the processes and teams are gradually built
Stage-Gate model is considered the best NPD practice nowadaysFigure 2.21
Source: Adapted from Cooper et al (2010)
Stage 0 - Discovery: Activities designed to discover opportunities and to generate new product ideas.
Stage 1 - Scoping: A quick and inexpensive assessment of the technical merits of the project and its market prospects.
Stage 2 - Build Business Case: This is the critical homework stage - the one that makes or breaks the project. Technical, marketing and business feasibility are accessed resulting in a business case which has three main components: product and project definition; project justification; and project plan.
Stage 3 - Development: Plans are translated into concrete deliverables. The actual design and development of the new product occurs, the manufacturing or operations plan is mapped out, the marketing launch and operating plans are developed, and the test plans for the next stage are defined.
Stage 4 - Testing and Validation: The purpose of this stage is to provide validation of the entire project: the product itself, the production/manufacturing process, customer acceptance, and the economics of the project.
Stage 5 - Launch: Full commercialization of the product - the beginning of full production and commercial launch.
38
around continuously accumulated shared knowledge). Other models are, in fact,
extensions of the “stage-gate” model. Trott (2008) points out that, at the moment,
despite some disadvantages, such as “administrative gates” (which may slow down
the process, shifting the focus from customer to management), the model of “stage-
gate” can be considered “best practice” in the “western” FMCG markets. According to
Cooper and Edgett (2010), 85% of North American companies utilize this model to
some extent. However, as argued by Tidd and Bessant (2009), the near future will
likely see the increasing popularity of “network” models, such as “open innovation”.
According to the data by Urban and Hauser (1993) (see Figure 2.22), the cost of
new product introduction in the US FMCG market may vary from $50 thousands to
$310 millions, where 86% is typically spent on promotion and only 14% is the cost of
development. The time allocated for NPD varies from 18 to 35 months. In particular,
design takes up to 8 months, research and testing – up to 15 months (including test
marketing), launch setup phase continues for up to 6 months.
NPD process in the US FMCG market: Costs, Time, RisksFigure 2.22
Source: Adapted from Urban et al (1993)
Cost (% of total*)Opportunity identification 1%Design 3%Testing 10%Total development 14%
Launch / Introduction 86%Grand Total 100%
*Total cost ranges from $58,000 to $310,000,000
Time range (months)
Opportunity identification -Design 4 - 8Testing 2 - 15
Consumer research 2 - 5Test market 6 - 12
Introduction setup 2 - 6Grand Total 18 - 35
Likelihood of Success in Phase
Opportunity identification 50%Design 57%Testing 70%Introduction setup 65%Grand Total 13%
However, these stages may overlap. The chance of success in all stages of
development is 13%, i.e. 50% chance of good opportunity identification, multiplied by
39
57% chance of successful design and prototype creation from the first run, multiplied
by 70% chance of no re-submissions to tests, multiplied by 65% chance of no
problems at setup stage.
Some practices of “passionate” new product development existing within firms attract
much criticism. Urban and Hauser (1993, pp. 47-48) draw attention to the following
typical cases: (1) “who’s got a new idea today?” - totally spontaneous and
undisciplined idea generation, not related to company’s goals and strategies, (2)
“here’s a new technical discovery” - profound ignorance of consumer needs (3) “me
too” – unproductive idea generation, unconscious copying of competitive products
due to the lack of own ideas, (4) “let’s run it up and see what will happen” – a
systematic generation of many ideas that are not well thought, (5) “we’ve got to do
something fast” – “sales are down and we need something new to help us by the end
of this year”. Trott (2008) criticizes certain inflexibility and rigidness of NPD protocols
that are utilized nowadays. Many companies run NPD only for the sake of it, not
linking the process (and, consequently, “innovation pipeline”) to their current
strategies. Finally, contemporary use of “stage gate” model is often criticized for a
visible lack of active consumer participation in the process (Von Hippel et al, 1998,
2002, 2005). Indeed, consumers are often viewed as an “information” resource
passively involved in the key stages of product development (See Figure 2.23a
overleaf). “Many industry analysts and business consultants are now arguing that
the devotion to focus groups and marketing research has gone too far”. (Christensen,
1997, Martin, 1995, Francis, 1994, cited in Trott, 2008). As confirmed by a series of
studies over the past decade, currently existing NPD protocols may stifle true
innovation at a very early stage and let minor product modifications to flourish (Von
Hippel, 2005). According to the theoretical modeling by Mahajan and Muller (1998),
which was later empirically confirmed by Voh Hippel (2005), targeting at conservative
majority and following their needs is sometimes financially more risky than
collaborating with ‘innovators’, which may be trend-setters of tomorrow.
40
So far consumer involvement into new product development has been very passive
Figure 2.23a
Source: Based on Based on Von Hippel et al (1998, 2002, 2005), Allam et al (2002)
Development stage Activities performed by the customer
1 Strategic planningInputs via consumer research: state needs, problems, criticise existing products, identify gaps in the market,
state requirements
2 Idea generationInputs via consumer research: same as in the strategic
planning phase
3 Idea screeningInputs via consumer research: show reactions to
concepts; show level of purchase intent for concepts; indication of sales and market size, pricing acceptance
4 Business analysis None
5Formation of cross-functional
teamsNone
6Design and process system
designNone
7 Test marketingInputs via consumer research: indication of sales, feedback on benefits and attributes, comments on
marketing mix
8 Commercialization Purchasing, participation in promotional activities
According to Von Hippel et al (1999, p.48), “there are two major findings by
innovation researchers. First, the researchers found that many commercially
important products are initially thought of and even prototyped by users rather than
manufacturers. Second, they discovered that such products tend to be developed by
‘lead users’ - (groups or individuals) that are well ahead of market trends and have
needs that go far beyond those of the average user”. A literature review has shown,
that nowadays NPD protocols should allow for active participation of consumers
through “fan clubs”, communities of “lead users and opinion leaders” and “innovation
toolkits”, at early and maturity stages of life cycle respectively. (Bilgram et al, 2008,
Buur and Matthews, 2008, Hoffman, 2007). (See Figure 2.23b overleaf). As argued
by Steirand and Lynch (2008), this approach is highly relevant for ‘true’ innovations
satisfying even basic consumer needs, such as food and drinks. The works of Von
Hippel et al (1988, 1999, 2002, 2005) have shown that “lead user method” can be
particularly effective for “ill-defined” or “fuzzy” markets, where consumer needs are
not clearly articulated and require further development. This is confirmed by
practitioners in the U.S. Product Development Association (Belliveau et al, 2002)
41
Consumer integration in NPD may change from passive to active, depending on business situation and company’s strategy
Figure 2.23b
Source: Adapted from Bilgram et al (2008), Buur et al (2008), Hoffman (2007)
Innovation communitiesCustomer toolkits
Passive & Distant
Actively Integrated
In terms of new product types, this is mostly related to “new-to-the-world” and “new-
to-the country” products. However, “traditional” approaches are still recommended for
use in well-defined saturated markets, where only incremental improvements are
possible (See Figure 2.23c).
Deep consumer involvement in NPD is crucial at early stages of market development to ensure successful product adaptation
Figure 2.23c
Source: Based on Von Hippel et al (1998, 2002, 2005)
Products types: “New-To-The-Firm”,“Line Extensions”,
“Repositioning”“Product / Price Change”
“well defined market, Articulated consumer needs”
“ill defined market, Unarticulated consumer needs”
Product types: “New-To-The-World”,
“New-To-The-Country”
All Users > 30% TA Lead Users, <10 % TA
42
With that, implementation of “lead users” approach raises the issue of identifying
“lead users” and bringing them together. Hassan (2008, p.54) has summarized some
unique characteristics, which distinguish “lead users” from mainstream consumers,
such as “perceived as having needs ahead of the trend”, “knowledge and
experience”, motivation, active networking for sharing modifications, search behavior,
high level of involvement, value and fashion conscious. At the same time, some of
“lead users” should act as future “opinion leaders”, showing such qualities as
“exposure to mass media”, “central to communication networks”, “social
accessibility”, “influence opinion”. According to Hassan (2008), Kim and Bae (2008)
establishing such social networks significantly boosts adoption of “new-to-the-world”
and “new-to-the-country” products. (see Figure 2.23d).
Starting up innovation diffusion process at early stage: How to identify LEAD USERS and OPINION LEADERS?
Figure 2.23d
Source: Hassen (2008)
2.3.2 Best practices for new product launches: key success factors
Product launch is the last, but at the same time is the most important phase of NPD
process. As discussed earlier, up to 86% of company’s resources are usually spent
at this stage. According to Schneider (2004, p. 226), “in Robert Cooper’s ‘stage-gate’
43
process production and launch are coupled as the final stage of new product
development. We believe launch should be its own stage because the elements that
make up launch are critical to a new product success”. As per Brody and Lord (2004,
p.462), these elements include “sourcing, production, distribution, sales force activity
and marketing”, which should be executed in proper time according to the launch
plan. Brody and Lord (2004, p.462) point out, that “the manufacturer must make sure
that product is in the store in the intended time frame and that advertising and
promotional events are scheduled accordingly. This means that retail sell-in and
distribution of the product will have to be scheduled with adequate lead-time to
ensure product availability in the store”. The study conducted by Schneider (2004,
p.226-229) had revealed 10 “success factors” for new product launches, which are as
follows: (1) “Treat launch as a separate phase”, (2) “Have a plan” (“timing is
everything with launch”), (3) “Don’t carve your plan in stone”, (4) “Learn to live with
inevitable delays”, (5) “Spend money on products that are ‘new’”, (6) “Assemble an
expert launch crew”, (7) “Brand / Product managers are the best team leaders”, (8)
“Bigger budgets fuel success”, (9) “Consumer-focused spending prevents crash
landings”, (10) “Don’t overlook PR”. A comprehensive launch start-up checklist
recommended by Schneider (2004) includes “client-to-agency” background activities,
systems and procedures development, program development and implementation,
external and internal communications, measurement, tracking and merchandising.
Detailed schedule of marketing activities is a vital component of every launch plan
(McDonald, 2009). As discussed earlier in Sections 2.2.1 – 2.2.7, marketing
objectives, strategies and engines used are quite different for each type of new
product. Although the topic of marketing and media planning goes beyond the scope
of the present study, it is necessary to consider “best practices” in this area in view of
their particular importance for sales forecasting. Rossiter and Percy (1987) suggest
that the amount of advertising, promotional, PR activities may vary depending on the
life stage of the product, product type and category, marketing objectives, strategy,
44
etc. In the later work, Rossiter and Danaher (1998) identified several typical “media
weight” patterns used to effectively support sales at various life stages. A set of
patterns applicable for new product launches includes (1) “blitz” pattern, (2) “wedge”
pattern, (3) “reversed wedge” pattern, (4) “awareness” and (5) “short fad” patterns.
The shape of “blitz” pattern is flat, meaning continuous activity for a certain period of
time. This type “will maximize the first-mover advantage if your brand is first in the
category” and “tend to suppress the effects of any competitors advertising by use of
sustained dominance” (Rossiter and Danaher, 1998, p.11). The “wedge” pattern is
“probably the most common for new product launches”, and implies heavy initial
“burst” of advertising “to create brand awareness for the new product and enable
prospective triers to learn new product benefits or acquire its intended image”
(Rossiter and Danaher, 1998, p.13). With the “reversed wedge” pattern, the audience
receives consistently increasing media “weight” with each “burst”. “In the most
effective application of this pattern the target audience consists of innovators and
then is broadened to the mass market”. (Rossiter and Danaher, 1998, p.14). The
“short fad” pattern is like a short “blitz” pattern. As for “awareness”, here “the
strategy is to keep consumers ‘aware’” at minimum cost (Rossiter and Danaher,
1998, p.18). This pattern consists of a series of “bursts” and exploits memory effects
and “decay” of advertising impact. Based on the findings by Rossiter et al (1983,
1998), Pickton and Broderick (2005), McDonald (2009) some general conclusions
can be drawn concerning the “best media and distribution build patterns” for different
types of new product launches (see Figure 2.23 overleaf). As it follows from the
chart: (1) “new-to-the-world” and “new-to-the-country” cases are quite similar and are
based on the “reversed wedge” pattern, with a heavy PR campaign preceding sales,
(2) “new-to-the-firm”, “brand stretching”, “line extension”, “re-branding” require the
opposite - “wedge”, “blitz” and “short fad” approaches, (3) “product or price changes”
need relatively low advertising support, an emphasis should be laid on sales
promotions and “place-of-purchase” actions.
45
New product launches: typical marketing “push” patternsFigure 2.23
Source: Based on Rossiter et al (1987), Rossiter et al (1998)
%
$
$
$
2.4 Major challenges for new product launches in the Russian market
Despite emerging globalization it has become obvious that international markets are
dramatically different (Armstrong et al, 2009). According to Usunier (2000, p.180),
“culture and languages are only part of marketing environment. A set of economic,
political, legal, social and cultural characteristics has a great influence on the
implementation of marketing decisions… For this reason, marketers have to
understand the local marketing environment before preparing strategy”. This
concerns the strategy of new product introductions a lot more than it concerns other
marketing issues. Armstrong et al (2009, p.516) argue that economic environment is
one fundamental factor affecting marketer’s decision “about which global market to
enter and how”. According to Armstrong et al (2009) economies are classified by
their types (“subsistence”, “raw material exporting”, “industrializing” and
“industrial”/”post-industrial”), and, what’s more important in marketer’s point of view –
income distribution and structure of needs in terms of Maslow’s pyramid (McDonald ,
2008). Usunier (2000) highlights dramatic differences in culture and consumption
46
habits between economies. Both Usunier (2000) and Armstrong et al (2009) argue
that ignorance of such differences can lead to some very expensive and
embarrassing mistakes. When deciding on new product introduction in different
markets, Kotler (1998) recommends to draw attention to the following factors: (1)
demographic characteristics (population size and growth, age profile, education), (2)
socio-cultural characteristics (consumer lifestyles, beliefs and values, business and
social norms, languages), (3) geographic scope (country size, climate, population
density, transportation structure), (4) Political and legal factors (national priorities,
political stability, bureaucracy, monetary and trade regulations), (5) economics (GPD
size and growth, income distribution, industrial infrastructure, natural resources,
financial resources). From marketing perspective this list of factors perfectly fits the
‘markets characteristics’ framework developed in Section 2.2 (see Figure 2.4). The
framework is therefore suitable for examination of the Russian FMCG market against
“western” FMCG markets, where Simulated Test Marketing technology was
developed and validated. This examination is performed in the next section.
2.4.1 Market profile: a comparison versus mature western markets
Concerning “western” FMCG markets, almost all of them are extremely saturated. In
70-ies these markets were less penetrated and less saturated then they are today
(Clancy,1992). Nowadays “whatever real growth there is, comes from population
increases, which never exceed 1 to 2 percent a year. To survive at all, a new product
must wrench market share from other, established brands”. (Clancy, 1994, p.7). As
for the Russian FMCG markets, before going into details, let us have a look at Russia
from a global economic perspective. Being the largest country in the world, Russia
holds a significant place in the very beginning of the global production chain,
exporting raw materials and natural resources (oil, gas, metals, woods) to more
developed economies (such as European Union, the United States etc) and being a
considerable market for imported ready-made products, including innovative products
47
(Economist Intelligence Unit, 2010). The structure of import/export operations allows
for classifying Russian economy as “raw material exporting” - “these economies are
rich in one or more natural resources but poor in other ways, these markets are good
markets for large equipment, tools, supplies as well as for consumer goods. If there
are many foreign residents or a wealthy upper class, they are also a market for luxury
goods” (Armstrong et al, 2009, p.515). To join the community of “industrializing”
economies Russia has to reach more than 10-20% share of manufactured products
in the total export value (Armstrong et al, 2009), however, currently that accounts
only for 6% of national export (Rosstat, 2010, Economist Intelligence Unit, 2010).
Investments into new product development remain at a very low level in comparison
to the developed countries (Ruvinsky, 2007) (see Figure 2.24).
Russia’s the one of the biggest suppliers of natural resources and an importer of ready-made products. NPD investments are marginal
Figure 2.24
Source: Based on Rosstat (2010) , Economist Intelligence Unit (2010), Ruvinsky J. (2007)
Global centres of New Product Development(Size of each country represents amount US$
spent on R&D and innovations in 2007 )
Russia’s exports, 2009, $302 bln
67%
13%
6%6%
8%
Oil, fuel & gas
Metals
Chemicals
Machinery & Equipment
Other (agriculture & woods)
43%
18%
17%
7%
15%
Machinery & equipment
Food
Chemicals
Metals
Other
Russia’s imports, 2009, $168 bln
R&D in Russia, 2007 is 1% of GDP($12 bln vs. $343 bln in the US)
According to Usinier (2000, p.179), the region of Eastern Europe and the former
USSR is “undergoing fundamental changes at all levels and everything is unstable
and negotiable”. These changes fuel rapid economic growth, which is more than
double than that for developed countries. (see Figure 2.25 overleaf). A detailed
comparison between the Russian market and the biggest markets of Northern
48
America, Europe and Asia clearly shows that they are at different stages of their
evolution. Considering FMCG sector, the needs of Russian consumers are rapidly
emerging (unlike that in the developed countries), shifting from the very basic to more
sophisticated desires (EIU,2010). Their disposable income grows at very high rates,
with that, a considerable portion of potential customers remain uncovered by
distribution networks.
Despite its size, emerging Russian economy exhibits growth rate whichis more than double than that for developed countries
Figure 2.25
Source: Economist Intelligence Unit (2010)
1 229
14 056
5 368
3 1072 084
0
2000
4000
6000
8000
10000
12000
14000
16000
Russia US Japan Germany UK
Nominal GDP, US$ bln, 2009
29% 31% 28%
-26%
27%
9%14% 12% 13%
-30%
-20%
-10%
0%
10%
20%
30%
40%
2006 2007 2008 2009 2010F 2011F 2012F 2013F 2014F
Russia US Japan Germany UK
GDP growth rate, vs. PY
29%33% 32%
-18%
17%13% 14% 13% 13%
-30%
-20%
-10%
0%
10%
20%
30%
40%
2006 2007 2008 2009 2010F 2011F 2012F 2013F 2014F
Russia US Japan Germany UK
Retail sales growth rate, vs. PY
Although product awareness across FMCG categories seems quite high, at the
moment, product experience is relatively low in many categories. According to
Economist Intelligence Unit (2010), Russian market, as a whole, is very far from
saturation and can be characterized as “growing”. As for the “western” FMCG
markets, the upper limit of their growth potential is expected to be nearly reached,
particularly in terms of sales volume. (See Figure 2.26a overleaf). Although Russian
retail market is considerably smaller than that in the US, its size is comparable to UK
or Germany. Moreover, it is expected Russian market to deliver at least 14% growth
by 2013, while major “western” FMCG markets will grow at a very low rate or
stagnate (EIU, 2010). (see Figure 2.26b overleaf).
49
Russian FMCG market is far from saturation, while “western” markets have almost reached the upper limit of their growth potential
Sources: Based on Economist Intelligence Unit (2010)
Figure 2.26a
Russia
US,UK, Germany,
Japan
US,UK,Germany,
Japan
Russia
Russia US Germany UK Japan
Consumer
Needs (Maslow's structure
represented by "Composition of
average household expenditures%")
Skewed towards basic needs (FMCG):
2009 - Food FMCG 29.2%, Non-Food FMCG - 12.5%, Other (residence, transport, clothing, services etc) -
58.3%, 2013F - 27.4 %, 11.7%, 60.9%
Bias towards services: 2009 - Food FMCG 9.3%,
Non -food FMCG 3.9%, Other (residence,
transport, services etc) -86.8%, 2013F - 9.1%,
2.9%, 87%
Bias towards services: 2009 - Food FMCG
14.2%, non-food FMCG -6.1%, Other (residence, transport, services etc) -79.7%, 2013F - 13.9%,
5.9%, 80.1%
Bias towards services: 2009 - Food FMCG
12.6%, non-food FMCG -5.4%, Other (residence, transport, services etc) -81.9%, 2013F - 12.2%,
5.2%, 82.5%
Bias towards services: 2009 - Food FMCG
17.5%, non-food FMCG - 7.5%, Other
(residence, transport, services etc) - 74.9%, 2013F - 16.6%, 7.1%,
76.3%
OccasionsEmerging FMCG consumption and
shopping patterns
Established and rigid FMCG consumption and
shopping patterns
Established and rigid FMCG consumption and
shopping patterns
Established and rigid FMCG consumption and
shopping patterns
Established and rigid FMCG consumption and
shopping patterns
Profile
"Majority", rapid income growth: No. of households with annual
earningsabove US$10,000 in 2009 - 54%, in
2013F - 81%
All, stable high income:No. of households with
annual earningsabove US$10,000 in 2009
- 95%, in 2013F - 96%
All, stable high income: No. of households with
annual earningsabove US$10,000 in 2009 - 100%, in 2013F - 100%
All, stable high income: No. of households with
annual earningsabove US$10,000 in
2009 - 100%, in 2013F -100%
All, stable income: No. of households with
annual earningsabove US$10,000 in
2009 - 100%, in 2013F -100%
Number of consumers involved
Up to 80% of 53,707,000 households in 2009
Up to 100% of 115 mln HH in 2009
Up to 100% of 39,958,000 HH in 2009
Up to 100% of 26,139,000 HH in 2009
Up to 100% of 49,935,000 in 2009
Products awareness / experience /
Learning
High awareness and fairly good experience in the main centers of
distribution, limited awareness and availability in rural area. Weak loyalty,
high level of switching. Further learning is required about niche
categories
High awareness and experience, considerable brand loyalty (although
undermined by aggressive competitive pricing)
Same as in the US Same as in the US Same as in the US
Figure 2.26b
Russia US Germany UK Japan
Market size and dynamics
Size of market (value, total retail)
in 2009 - $457 bln, in 2013F -$782 bln
in 2009 - $3442 bln, in 2013F -$3969 bln
in 2009 - $467 bln, in 2013F -$516 bln
in 2009 - $389 bln, in 2013F -$492 bln
in 2009 - $1424 bln, in 2013F -$1627 bln
Life cycle stageGrowth (although
saturation is observed in some FMCG categories)
Highly saturated Highly saturated Highly saturated Highly saturated
Growth rate of sales2009-2013 CAGR 14%
(above GDP)
2009-2013 CAGR 4% (in line with GDP), There is a general stagnation in the most highly developed
sectors
2009-2013 CAGR 3%(in line with GDP)
2009-2013 CAGR 6% (in line with GDP)
2009-2013 CAGR 3%(in line with GDP)
Granularity (structural complexity)
Moderate granularity, visible development of
sub-categories and segments
Highly granular and complex, a lot of niches
and micro-segments
Highly granular and complex, a lot of niches
and micro-segments
Highly granular and complex, a lot of niches
and micro-segments
Highly granular and complex, a lot of niches
and micro-segments
Predictability of market reactions and factors
Moderate (still unstable market structure and
demand)
Very high (very stable market structure and
demand)
Very high (very stable market structure and
demand)
Very high (very stable market structure and
demand)
Very high (very stable market structure and
demand)
Variability / Variation
Moderate (still unexpected fluctuations
caused by internal market factors)
Low / Very small unexplained variations
Low / Very small unexplained variations
Low / Very small unexplained variations
Low / Very small unexplained variations
A comparison of the Russian market versus major developed markets: Market size and dynamics
Sources: Based on Economist Intelligence Unit (2010)
To succeed in the Russian and “developed” markets firms pursue different marketing
strategies. In the Russian FMCG market, the most popular set of business strategies
include proactive attacks with an objective of market penetration or price skimming,
while defensive price-focused strategies are dominant in the US, UK, Germany and
Japan. (See Figure 2.26c overleaf)
50
Figure 2.26c A comparison of the Russian market versus developed markets: Dominant Marketing Strategies
Sources: Based on Economist Intelligence Unit (2010)
Russia Developed markets (US, UK, Germany, Japan)
Promotional Marketing
Dominant Marketing Objectives
1) Create product awareness and encourage trial, educate potential consumers while introducing the product 2) Create
and capture the market as soon as possible 3) Maximize share and secure future profit
1) Reduce expenditure and milk the brand 2) Maintain or defend mature products, keep them on the market, continue harvesting profits, capture share from rival firms 3) inspire more usage by changing product offer, i.e. price, quality, features ("face-lifts", improved formula, "price-offs", trade
deals)
Dominant strategies
Market Development and Market Penetration (Strategic Directions), Focus (Product), Proactive ATTACK: Bypass,
Flank, Alliance or Acquisition (Business), Sequential (Strategy types)
Market consolidation and Product Development Strategies (Strategic Directions), Overall cost leadership (Product)
DEFENSE: Position Defense (Business). Responsive (New product strategies).
Communication / Advertising
Aggressive marketing using mass communications, heavy promotional activities. A major tool is an IMC campaign with
focus on building brand awareness and distinctive brand image. In some cases, considerable emphasis is made on
educating consumers and development of consumption habits
Wide range of advertising and promotional activities, with greater focus on BTL and Trade marketing than on ATL. Great
emphasis is on pricing activities.
BrandingCorporate or individual brands, line extenstions, brand
differentiation
Line extensions of great magnitude with a tendency to narrow as uprofitable products are quickly dropped. Visible
"commodization", high share of private labels.
Products
Product characteristics / Technology
Technologies are sometimes new for the local market but ordinary for the markets of developed countries
Technological content is known in a majority of cases
Potential for substitution / Differentiation
Moderate. Despite highly differentiated brand image of new products there's a significant threat from "me-too" products
Moderate. Despite existing brand loyalty, price has become more important than brand in terms of purchase decision
QualityVariable (from very low to very high). For new products
imported from developed countries is initially perceived as VERY HIGH
Stable good quality
Pricing
Price levels and variationHigh variation. Perceived prices are very high especially for new imported products. Very cheap local alternatives are
generally available. Low variation. A tendency for prices to fall
Price elasticity / sensitivity Visible price elasticity. Price skimming is observed. High price elasticity
Figure 2.26d A comparison of the Russian market versus developed markets: Differences in terms of business environment
Sources: Based on Economist Intelligence Unit (2010)
Russia Developed markets (US, UK, Germany, Japan)
Competitive rivalry
Domination (shares of players)Many foreign multinational companies are now market leaders orimportant players in non-alcoholic drinks, bottled water, alcoholic
drinks (excluding vodka), confectionery, coffee and tea. Domestic players consist mainly of small and medium-sized
companies. Although large domestic companies dominate the juice, meat and vodka markets, other segments (bakery, dairy, cereals, and fruit and vegetables) remain relatively fragmented.
A majority of segments is dominated by few multinationals
Fragmentation (number of players)
Considerable number of local small players with minor shares
Quality of competition Varies across categories, however, still very easy to enter Very high
Likelihood of new entrants Very high Very low for traditional segments
Time to be established Depends on the nature of the product, branding, cultural and
economic barriers (typically from 1 to 5 years)Hardly possible to stay in the market within 2 years
Order of entry Varies across categories. Early entry is still possibleVery late for traditional categories, early entry is possible for
niche segments
Maximum share/sales potential for a new entrant
Ranges from 100% at initial stage to 20% at maturity stage (with 1 competitor - 59% share, 2 - 44%, 3- 36%, 4 -31%, 5 - 28%
etc.) Negligible (<2%) for traditional markets
Finance
Investments, CostsGenerally low investments in R&D,while high in advertising, promotion and distribution. In case of domestic production -
direct investment in manufacturing facilitiesPotentially very high
Reward - Profitability, ROI, margin
High margins. Reward may be significant due to price skimming Very low margin due to highly competitive environment
Risk - Probability of LossHigh risk associated with local consumption habits and cultural
barriersHigh due to competition and despite of high market predictability
and use of marketing research
Infrastructure /Environment
DistributionThe country's vast territory and underdeveloped distribution
channels limit sales opportunities Distribution have become more concentrated among fewer
players, owing to mergers and acquisitions
Suppliers
Vast majority of FMCG is imported (food FMCG is around 40%, non-food FMCG is above 50%). Russia is a net importer of food products and raw agricultural goods fordomestic processing. The
main imports are meat, poultry, fish, milk and milk products, cheese, vegetables, and raw sugar.
The US has a strong and diversified agricultural sector and numerous well established food-processing and manufacturing
companies that supply most domestic needs.
Retail channels
The Russian retail market is extremely fragmented, and only in Moscow and St Petersburg has there been consolidation. Nationwide, the top five grocers have a combined share of
around 15% of modern grocery distribution.
Retail distribution is concentrated among few national chains. Wal-Mart is by far the leader in retailing. Smaller independent
players have become less important in terms of retail sales and distribution.
Regulations Possible difficulties with product certification Regulatory pressures play considerable role
A comparison of business environments has shown, that launching a new product is
very risky both in the Russian and “developed” markets, because of various reasons,
though (See Figure 2.26d). In the case of “developed” market the risk is mainly due
to the aggressive competitive environment and the lack of market niches. While in
the case of the Russian market the risk is associated with consumer acceptance and
51
market infrastructure. New product launches in both markets require substantial
investment, particularly in promotion.
An analysis of trends in the particular FMCG markets has revealed that a large
majority of FMCG markets in Russia are not yet saturated as compared to the US,
UK, Germany and Japan (Datamonotor, 2010). In terms of volume, the only
saturated FMCG markets in Russia are beer and spirits, while the expected growth
by 2013 for some other categories varies from +5% (traditional milk) to 37%
(snacks). Just for comparison, the largest increase expected on the “developed”
market is 14% for soft drinks in the UK. Concerning Russian food market growth in
value terms, it is expected to reach +67% by 2013%, which is more than threefold
higher than that in Europe and more than tenfold higher than that in the US and
Japan. (See Figure 2.27).
Unlike developed markets of US, UK, Germany and Japan, a large majority of Russian FMCG markets are at the growth stage and are expected to deliver a strong double-digit growth by 2013, both in volume and value terms
Figure 2.27
Source: Datamonitor (2010)
Sales in 2009
Growth by 2013
Sales in 2009
Growth by 2013
Sales in 2009
Growth by 2013
Sales in 2009
Growth by 2013
Sales in 2009
Growth by 2013
Savory Snacks kg p/c 1.7 +36% 5.9 +7% 3.2 +9% 7.2 +11% 6.8 +10%Soft Drinks litres p/c 108.9 +29% 403.1 +10% 312.8 +9% 203.6 +14% 140.0 +9%Hair care units p/c 4.3 +23% 5.6 -10% 6.2 -0% 8.2 -1% 7.1 +3%Make up units p/c 1.5 +21% 2.5 -1% 1.9 +5% 2.9 +11% 2.5 +4%Confectionery kg p/c 9.3 +17% 12.1 +4% 13.9 +6% 14.9 -0% 3.9 +4%Meat kg p/c 46.8 +12% 91.4 +0% 84.0 +2% 72.9 +0% 34.4 +3%Fish kg p/c 19.5 +7% 23.4 +2% 14.4 +1% 19.2 -2% 65.7 +2%Fruit kg p/c 75.3 +6% 124.2 +3% 136.2 +1% 145.3 +5% 59.1 +2%Hot Drinks (Tea, Coffee) kg p/c 2.0 +6% 2.5 -3% 4.3 +7% 2.6 -2% 2.1 +5%Vegetables kg p/c 114.5 +5% 125.4 +2% 134.0 +1% 135.2 +5% 130.0 +2%Milk litres p/c 170.0 +5% 268.9 +3% 260.3 +3% 236.7 -2% 68.1 +3%Alcoholic Drinks litres p/c 90.2 +0% 99.5 -1% 144.4 -3% 128.3 -11% 70.1 +1%Beer litres p/c 70.0 -3% 83.5 -2% 109.0 -4% 84.0 -18% 51.4 +1%
All food US$ p/c 1 400.3 +57% 2 974.6 +7% 3 297.1 +16% 2 785.1 +23% 4 327.3 +4%Savory Snacks US$ p/c 8.7 +38% 59.7 +14% 27.8 +13% 84.0 +18% 87.3 +12%Make up US$ p/c 11.1 +38% 20.1 +2% 22.3 +12% 28.9 +17% 34.1 +6%OTC Pharma US$ p/c 38.9 +36% 79.9 +8% 109.5 -1% 92.1 +1% 74.7 +7%Dairy (all products) US$ p/c 95.5 +33% 154.3 +14% 285.0 +6% 279.5 +14% 133.8 +14%Soft Drinks US$ p/c 91.9 +25% 391.1 +6% 413.0 +10% 415.2 +18% 288.7 +6%Hair care US$ p/c 9.2 +25% 24.0 -7% 31.3 +2% 29.2 +0% 38.6 +5%Household products US$ p/c 24.8 +24% 56.8 +0% 60.4 +5% 101.8 +5% 51.1 +9%Confectionery US$ p/c 73.6 +22% 110.6 +7% 155.8 +7% 219.7 +5% 61.4 +5%Alcoholic Drinks US$ p/c 445.5 +14% 502.0 +2% 1 402.1 +0% 1 126.1 -5% 412.0 -2%Hot Drinks (Tea, Coffee) US$ p/c 30.4 +11% 30.3 +3% 84.5 +9% 54.9 +3% 27.4 +6%Tobacco US$ p/c 118.8 +10% 324.5 +6% 306.8 +8% 368.3 +5% 305.3 +17%Beer US$ p/c 200.1 +6% 259.6 -0% 742.8 -0% 664.0 -15% 221.9 -2%
Color coding for growth rates: Green Yellow Red
VOLUME PER CAPITA:
VALUE PER CAPITA:
= Strong Growth, > 11%
= Close to saturation (3-10%) = Saturation / Decline (<3%)
Russia US Germany UK Japan
The analysis above has confirmed that Russian FMCG market is dramatically
different in comparison to “developed” markets, such as the US, the UK, Germany
and Japan. While the latter markets are at the stage of “saturation or decline”,
Russian market is at the stage of strong growth. The promotional strategies
52
employed in these markets and the types of new product launches are dramatically
different as well. Moreover, as discussed earlier, Russian market has become an
arena for expansion of the biggest multinationals that pursue “market development”
strategies. In the “developed” markets, a number of new products are regarded as
“product/price improvements” and “brand-extensions” - as it was shown on Figure
2.2. The analysis performed above allows to hypothesize about: (1) considerable
skew towards “new-to-the-country”, “new-to-the firm” products in the growing Russian
FMCG market (2) differences in the process of launching new products (3)
differences in sales forecasting techniques, involving differences in the use of test
marketing, and Simulated Test Marketing in particular. This will be considered in the
next chapters.
2.4.2 Specifics of new product development and launches in Russia
As discussed earlier, the concept of “stage gate” is widely used in the “developed”
markets for effective management of NPD. Indeed, a thorough time-consuming
elaboration of new product concept is required in order to overcome strong
competitive barriers. The Russian market is very specific due to its size, booming
demand, low saturation and underdeveloped infrastructure, therefore, the key issue
here is not the competition, but uncertainty about sales mechanics, infrastructure,
general economic environment and consumer acceptance. Although there are not
many academic sources available on that topic, the analysis of available information
has given some vital clues about new product launching in Russia, such as: (1)
absence or a very short R&D stage, (2) time-consuming efforts around sales
infrastructure, (3) short turnover cycles and short-term horizon of planning, (4)
importance of “branding” and its adaptation (see Figure 2.28a overleaf). Also, it is
worthy to note the following specifics of the Russian market: (1) lower conservatism
of consumers than that in the developed markets, (2) high importance of social
networking, (3) lack of reliable market information, that undermines general trust in
53
marketing research, (4) rising need for trustworthy research techniques applicable to
the local market, (5) considerable regulation barriers, (6) issues with confidentiality
(see Figure 2.28b)
Specifics of new product development and launches in Russia (1)Figure 2.28a
Source: Economist Intelligence Unit (2010), Rosstat (2010), Schorsch (2009)
WHAT MAKES THE LAUNCH SO DIFFERENT IN RUSSIA? IMPLICATIONS: Economist Intelligence Unit (2010), Rosstat (2010):
High demand for quality imported products, a considerable portion of products is directly imported (e.g. 40% of food is imported, over 70% non-
food)None or a very short R&D stage. Low focus on local NPD
Underdeveloped infrastructure, retail and distribution channels (e.g. Small independent food stores and open markets still account for 75% of
retail market)
Significant time-consuming effort into building infrastructure, setting up operations, sales and distibution, other market entry procedures
Price inflation (e.g. food price inflation 22% per year) and overall market volatility
Short turn-over cycle, quick selling, short-term horizon of planning (<3 years)
Unsaturated market, consumer boom (e.g. expected retail sales growth at around 5% p.a. from 2009 onwards)
The need to be quick in capturing long-term market opportunities. Less aggressive competitive environment. Lower risk of marketing mistake,
"everything sells". No established need for sophisticated time-consuming marketing research before the launch
A tendency for customization of products to local needs (e.g. soft drinks -kvas, mors, traditional dairy)
Rising need for local R&D
Lagging behing "developed markets" and some "emerging" markets Sales forecasting is often based on the "analogy" principleSchorsch (2009):
"Compettition and customer habits are not the foremost challenges on the Russian market. Instead, companies struggle with infrastructure deficit, bureaucracy, corruption and labor shortage… These challenges is first
priority for companies, marketing comes second" (p.72)
Higher emphasis on setting up sales infrastracture than on marketing
Many multinationals sell the same product as in the original market, only packaging and commercials are customized
Local consumer preferences and culture should not be ignored, though
Russian consumers "live in constant fear of fake or in other ways inferior products", "firmly believe in the relationship between price and quality", ready to pay extra for "a brand", "through buying brands, the Russian consumer wishes to show his social status and integrete himself into
society", "there's no such thing as 'smart shopper' and very little space for individualism", share of private labels does not exceed 4% (p.70-75)
Nevertheless, effective marketing and relevant communication are very strong drivers of new product's success
Specifics of new product development and launches in Russia (2)Figure 2.28b
Source: Schorsch (2009), Kachalov (2008), Usinier (2000), PricewaterhouseCoopers (2010)
WHAT MAKES THE LAUNCH SO DIFFERENT IN RUSSIA? IMPLICATIONS: Schorsch (2009):
"Russian consumers are usually well informed, interested in technical details open for innovations and full believers in technological progress.
The later is heritage of the Soviet Union" (p.71)
Considerable group of "innovators" boosts the adoption of new product
"the quality of Russian advertising is usually mediocre to horrible, especially in greater cities with western business communities"
High quality adaptation of product offer is essential.
"Another heritage of Soviet Union is the mindset of population…Social relations, social networks became a mean of survival, providing the safety
the state is unable or unwilling to give", "networking for promotion is essential for Russia" (p.80-86)
Social networking is crucial in sales or any other business operations. In sales planning social relationships MUST be taken into account. In
terms of mass marketing Wors-Of-Mouth is very important, i.e. attractiveness of new product must be tested in that regard
"Russia is a country with a mixed European and Asiatic roots" (p.53). Asiatic to some extent, but heavily influenced by European culture
Local consumer preferences and cultural specifics must be taken into account in the process of marketing planning
Kachalov (2008):Lack of reliable information about the market and competitors. Low
accuracy of available sources (40-50% on average)Lack of thrust to marketing research and market information. Lower
accuracy of forecast than that for developed markets.Hardly predictable and unstable local economic environment
90% accuracy of sales planning for next year can be achieved with quite simple and straighforward approaches
Growing need for reliable sales forecasting and planning techniques. Effectiveness of quick simple transparent techniques. Recognition of the fact that high accuracy is hardly achievable in the russian market
Usinier (2000):It is accepted that local consumption pattern is not exactly the same as in the "developed" markets. It is argued that there's certain cultural similarity
between Russia and Slavic Eastern European and Central Asian countries
Local consumer preferences and cultural specifics must be taken into account in the process of marketing planning
PricewaterhouseCoopers (2010):Considerable regulation barriers (e.g. export, customs related ussies, excessive , underdeveloped legal framework - pointed out by 86% of
surveyed companies)
Significant time-consuming effort into building infrastructure, setting up operations
Lack of procedures for assessing risk and return on investments Growing need for reliable sales forecasting and planning techniques. Insufficient protection of intellectual property, leaks of confidential
informationHigh security is required in the process of NPD and launch
54
Although there is a visible lack of academic research on NPD in Russia and no
reliable statistics, the topic is being extensively discussed in the local periodical
business literature (such as “Sales Business”, “Advertising Industry”, “Advertising
Ideas”, “Company” magazines) as well as in the professional internet forums (such
as www.sostav.ru). It becomes evident from the discussion that “stage-gate” model
is rarely employed in Russia, and a majority of actual users are largest
multinationals. However, even if the framework is used there are significant
differences in the process as compared to “developed” countries (Burdey et al, 1999,
Agaeva, 2008). As per discussion, in Russia, the standard flow of “Stage-Gate”
activities is heavily influenced by the sense of business urgency, driven by desire to
capture rapidly emerging market opportunities as soon as possible and skim
considerable short-term profit (see Figure 2.29).
The standard flow of “Stage-Gate” activities is heavily influenced by the business urgency, i.e. “market penetration” and gaining short-term profit
Figure 2.29
1. R&D is omitted2. Preliminary need
assessment is very rough and market analysis is poor
3. No systematic approach to generating new product ideas
4. Ideas are simply transferred from the developed markets
Very often: Very often:
1. Arbitrary2. Not systematic3. Consumer
opinion is ignored or overemphasized
4. Inadequate research tools used
1. Lack of market information
2. Operational and infrastructure complexities
Very often: Very often:
1. Skipped or half-done due to urgency and rapidly changing market environment
1. Rough short-term plans, clashes
2. Difficulties with establishing sales infrastructure
3. Lack of investment
4. Stopped if no immediate profit
Very often:
Source: Based on Agaeva M. (2008), Burdey K. et al (1999), Belotserkovskaya, O (2005),
For example, at the current stage of market development in Russia, the need for
marketing research is still being discussed (Belotserkovskaya et al, 2005, Agaeva,
2008). Still, the culture of its use is not widely established (Burdey et al, 2010).
According to Burdey et al (1999), a co-founder of an independent local marketing
55
research agency, the specifics in developing and launching new products in Russia
are the following: (1) tight time schedules, sometimes lasting for only 2-3 months
from the moment of project idea, (2) arbitrary decisions, i.e. authoritative style of
decision making about what product is to be launched, when and how, (3) prioritizing
“product” over “consumer”, i.e. building promotional strategy around the product, and
not around consumer needs, “inventing” and developing consumer needs that may
suit product features, (4) focus on “western” products, i.e. imported products as well
as “imported” lifestyles, (5) “pseudo-new” products, i.e. changing the original product
formula in order to cut the cost, but selling it as a novelty, (6) volatile economic
environment negatively affects domestic production of new products. As practice has
shown (Burdey et al, 2010), the major reasons of failures in the Russian FMCG
market are: (1) “inadequate” product idea supported and pushed by the top-
management, (2) loss of focus on consumer needs, too much emphasis on product,
(3) ignorance of marketing research or its very poor quality or its improper use or
misinterpretation of findings, (4) lack of support from top-management, (5) high
profits expected immediately, (6) chaotic process of new product development and
launching, (7) unclear responsibilities within the company, resulting in appearance of
a new product as a compromise solution between departments, good for the
company but a definite failure in the marketplace, (8) inappropriate pricing , too high
or too low (9) poor quality control, i.e. inability to maintain consistent quality, (10)
wrong time for product launch, i.e. market is not ready or not established, (11) issues
with retailers, distribution or supply chain, i.e. high entrance fees, poor coverage, out-
of-stocks or over-stocks, inconsistency between advertising campaign and product
appearance on trade. This leads to a conclusion that currently existing practice of
NPD is dramatically different to that in the “developed” economies, and is a result of
rapid economic development in Russia.
56
2.5 Key findings
A thorough analysis performed in Chapter 2 above has resulted in the following key
findings about new product development in Russia:
Russian market is dramatically different as compared to “developed” markets in
terms of its global role (import of ready-made FMCG products vs. export), life
stage (“growth” vs. “saturation decline”), consumer needs (“products” vs.
“services”), dominant marketing strategies (“attack” vs. “defense”) as well as in
terms of promotional tools, infrastructure and environment;
The number of “new-to-the-country”, “new-to-the-firm” product launches is
significantly higher than that in the “developed” markets;
The process of new product development is radically different as compared to
“developed” markets, driven by the sense of business urgency and desire to
capture emerging market opportunities. Due to the “import-oriented” nature of
operations, in a majority of cases, the process of new product introduction is
characterized by a very short R&D stage, transferring product ideas (or products)
from other markets, inadequate use of marketing research tools, high emphasis
on building sales infrastructure, short-term horizon of planning, focus on
immediate profit skimming.
57
CHAPTER 3
MARKETING RESEARCH AND SALES FORECASTING
FOR NEW FMCG PRODUCTS
3.1 The role of marketing research in the NPD process
As defined in “The Marketing Glossary”, effective marketing decision-making
“requires accumulating information that relates to a specific market situation or
problem. Marketing research refers to the process of collecting, analyzing and
reporting this information” (Clemente, 2002, p.246). This is “one of the most
important and fascinating facets of marketing” and one of the vital functions within all
organizations involved in the FMCG business (Malhotra, 2007, p.2). According to
Malhotra (2007, p.13), “the task of marketing research is to assess the information
needs and provide management with relevant, accurate, reliable, valid, current and
actionable information. Today’s competitive marketing environment and the ever-
increasing costs attributed to poor decision-making require marketing research to
provide sound information. Sound decisions are not based on gut feeling, intuition or
even pure judgement”. Tull and Hawkins (1993) argue that a single purpose of
marketing research is “providing information that assist marketing managers to make
better decisions”. The information required can be obtained from various primary and
secondary sources, using qualitative and quantitative techniques (Malhotra, 2007).
These sources include consumers, retailers, distributors and wholesalers, suppliers,
competitors, officials, experts, technicians and scientists, other business agents
(Clemente, 2002). However, consumer opinion remains central to marketing
research, particularly in the process of new product development (Groucutt, 2005).
The past few decades saw an enormous popularity of marketing research as well as
its dramatic influence on decision making in the FMCG sector. The negative side of
such a process was standardization and poorly thought out application of
“productized” marketing research, that undermined the effectiveness of NPD in many
58
leading multinational companies (Christensen, 1997). As pointed out by von Hippel
and Thomke (2002, p.74), the marketing mantra that has dominated the market so
far, i.e. “listen carefully to what your customers want and then respond with new
products that meet or exceed their needs" does not always work in case of new
product development. As it was discussed earlier in Chapter 2, NPD processes may
differ depending upon the type of new product. Following that logic, Hart et al (1999)
and Trott (2001, 2008) suggested that standardized approach to marketing research
is not equally effective in all cases of new products. Although the great majority of
authors (Trott, 2008, von Hippel et al, 2002, 2005, Christensen, 1997, Malhotra,
2007, Tull and Hawkins, 1993 etc) recognize the fundamental role of marketing
research in the process of NPD, they point out its twofold effect: marketing research
may be either destructive in the case of improper use or extremely helpful in the case
of thoughtful application.
3.2 Aligning marketing research with NPD process: key tools and approaches
The key information needs addressed by marketing research in the traditional “stage-
gate” process are summarized by Hart et al (1999) (See Figure 3.1).
Information needs addressed by Marketing Research in the NPDFigure 3.1
Source: Based on Hart et al (1999), Cooper et al (2010)
Stage of developmentMarket Research Information needed for the
stage; Nature of informationSources of information Likely output of stage
Explicit Statement of strategyPreliminary market and technical analysis;
company objectives
Generated as part of continuous Management Information System and
corporate planning
Generated as part of continuous Management Information System and
corporate planning
Assembling Knowledge and Idea Generation
Customer needs and technical developments in previously identified markets
Inside company: salesmen, technical functions. Outside
company: customers, competitors, inventors
Body of initially acceptable ideas
Screening ideas: finding those with most potential
Assessment of whether there is a market for this type of product, and the company can make
it. Assessment of financial implications. Knowledge of company goals and assessment
of fit
Main internal functions: R&D, Sales, Marketing,
Finance, Production. Outside the company:
customers
Ideas which are acceptable for further
development
Scoping (Concept development and Screening): turning an idea
into a recognizable product concept, with attributed and
market position identified
Explicit assessment of customer needs to appraise market potential. Explicit assessment
of technical requirements
Extensive research with customers; Input from
marketing and technical functions
Identification of: key attributes that need to be
incorporated in the product, major technical
costs, target markets and potential
Build business case: full analysis of the proposals in
terms of its business potential
Fullest information thus far: detailed market analysis, explicit technical feasibility - and cost-
production implications - corporate objective
Main internal functions; Customers
Development plan and budget specification;
marketing plan options
Development: crystallizing the product into semi-finalized
shapeProduction information to check "makeability" Production Product Options
Testing and validation: small-and large-scale tests with
customers
Profile of new product performance in light of competition, promotion and marketing mix
variables
Customers; Surveys (Tests and Test
Marketing); Production; Sales
Final go/no go for launch; Final marketing plan
LaunchTest market results and report; Monitoring
launch performanceCustomers;Sales
Full-scale launch: Sales performance; Customer
acceptance
59
As it seen from the figure, marketing research is crucial to success of a new FMCG
product. The industry of marketing research has developed numerous tools and
approaches aimed to deliver information that is required at each stage of product
development (Belliveau et al, 2002, Malhotra, 2007, Tull and Hawkins, 1993, Davis,
1997, Duboff and Spaeth, 2000, Wind and Green, 2004, Bagozzi, 1997, Burton et al,
1986, Shim, 2000, Hanke et al, 2001, Webb , 1992, Franses and Paap, 2007, Urban
and Hauser, 1993, Lilien et al, 1992, 2007, Clancy et al, 1994, 2006, Farris, 2006,
Davis, 2007, Thomas, 1993 etc.). Regardless of general methodology and data
collection principles, these tools can be divided into several groups and sub-groups,
according to their functional role in the business process: (1) “problem identification
research” or “exploratory”, and (2) “problem-solving research” or “confirmatory”
(Malhotra, 2007, Hart et al, 1999). One of the most rigorous classifications is offered
by Malhotra (2007) (See Figure 3.2)
The most rigorous classification of Marketing Research by MalhotraFigure 3.2
Source: Adapted from Malhotra (2007, p.8,9)
Marketing Research
Problem SolvingProblem Identification
• Market Potential research• Market Share research• Image Research• Market Characteristics
research• Segmentation research• Sales Analysis research• Shopper/Retail research• Forecasting research• Business Trends research
• Determine basis of segmentation• Establish market potential for segments• Select target markets • Create consumer profiles
Segmentation
Product, Positioning, Pack
Pricing
Promo / Advertising
Retail/ Distribution
• Test concept • Optimal product design• Package tests• Product modification• Brand positioning • Test marketing• Control store tests
• Importance of price in brand selection• Pricing policies• Product line pricing• Price elasticity of demand• Response to price changes
• Optimal promotional budget• Sales promotion relationship• Optimal promotional mix• Copy decisions• Media decisions• Creative advertising testing• Claim substantiation• Evaluation of advertising
effectiveness
• Type of distribution• Attitude of channel members and
shoppers• Retail coverage• Channel margins• Trade research • In-store observations• Location of outlets
It is worthy to note that despite of serious conceptual differences, “exploratory” and
“confirmatory” techniques “go hand in hand, and a given marketing research project
may combine both types of research”. (Malhotra, 2007, p. 9). For example, complex
60
studies such as market segmentation can be classified as both “problem
identification” and “problem solving”. Insights provided by “exploratory” studies are
often used at the “strategic planning” phase of NPD due to their strategic nature,
while “problem solving” techniques are employed at later stages. The major task of
“exploratory” studies is to clearly identify the issue and address it with the launch of
specific new product, such as “new-to-the-firm”, etc. (See Figure 3.3)
The types of new product developed by the company must be strictly determined by the strategy
Figure 3.3
Source: Malhotra (2009), Tull et al (1993), Groucutt (2005), Shim(2000), Webb et al (1992),Trott (2008)
What’s the current state of the business?What should be the next steps?
Business Process: Marketing Research:
Business Review.Strategic Planning
“Problem Identification” Research:Market Potential Research (Quant / Expert),
Market Share Research (Quant / Expert),Market Characteristics Research (Quant / Expert)
Sales Analysis Research (Quant)Segmentation Research (Quant / Qual)
Image Research (Quant / Quali),Shopper / Retail Research (Quant / Qual)
Forecasting research (Expert / Quant)Business Trends Research (Expert / Quant / Qual)
Long-term vision, Mid- or short-term goals,
Strategies (Corporate positioning, Strategic
directions, Business and Product)
Provides relevant information to address business questions and support decision making
“Ne
w-T
o-T
he-
Wo
rld
”
“Ne
w-T
o-T
he-
Fir
m”
“New
-To
-Th
e-C
ou
ntr
y”
“Bra
nd
Str
etch
ing
”
“Lin
e E
xten
sio
ns”
“Bra
nd
re-l
au
nch
”
“Pro
du
ct /
Pri
ce
Ch
ang
e”
“Problem Solving” Research:Segmentation Research (Qual / Quant),
Concept / Product Research (Qual / Quant) ,Pricing research (Quant)
Advertising / Promotional research (Qual/Quant)Distribution Research (Quant/ Expert)
Shopper / Retail Research (Quant / Qual)Forecasting research (Expert, Quant)
A very detailed review of marketing research tool is beyond the scope of the present
work, however, as literature review has shown (Belliveau et al, 2002, Malhotra, 2007,
Peng and Finn, 2008, Tull and Hawkins, 1993, Davis, 1997, Duboff and Spaeth,
2000, Wind and Green, 2004, Bagozzi, 1997, Burton et al, 1986, Shim, 2000, Hanke
et al, 2001, Webb , 1992, Franses and Paap, 2007, Urban and Hauser, 1993, Lilien
et al, 1992, 2007, Clancy et al, 1994, 2006, Farris, 2006, Davis, 2007, Trott, 2008,
Groucutt, 2005, Rossiter and Percy, 1987 etc.)., there are several broad groups of
research techniques utilized in the process of new product development and
launching. These are: (1) Qualitative tools - providing insights, helping to generate
ideas and understand the nature of the problem; these tools include focus‐groups,
61
brainstorming and ideation sessions, in-depth interviews etc., (2) Quantitative
diagnostic tests - seeking to quantify consumer reaction to particular marketing mix
elements with no attempt to forecast sales directly - such as concept tests,
advertising tests, product tests, price tests, pack tests, (3) Quantitative tests for
particular marketing mix elements comprising both diagnostic research and sales
assessment, such as conjoint – the “joint” analysis of various attributes, e.g. price
and product characteristics and their impact on preferences and market share, (4)
Simulated Test Marketing (STM) or Volumetric Bundle Tests – a quantitative test for
a full set of marketing mix components that simulates the launch of a new product
upon a representative sample of potential buyers and provides sales forecast with an
accuracy of over +/- 30% (another definition by U.S. Advertising Research
Foundation is as follows – “An STM is a marketing research project which attempts to
combine consumer interviews with a standardized data-gathering procedure, usually
in the form of a questionnaire, with a computer model of behavior to produce an
estimate of actual sales volume for a new product or line extension” (Baldinger, 1988,
p.3), (5) Traditional test market – controlled sales of new product in a sample of real
stores during a period of several months, (6) Econometric sales forecasting based
on historical market data, trends and accumulated periodical business statistics
(retail audit, consumer tracking, household panel, industry statistics, media etc), (7)
Strategic “exploratory” studies, such as consumer segmentations, (8) Expert analysis
and assessments, such as Delphi (or “consensus”) forecasting. However, as
discussed above, the set of marketing research tools employed may be different for
various types of new products. The specifics are discussed below.
3.2.1 Specifics of marketing research for “New-to-the-world” products.
The analysis of available literature sources listed above has helped to crystallize and
develop a number of specific recommendations on marketing research for “new-to-
the-world” products, for each particular stage of new product development process.
62
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: It is important to make sure that expert assessments and research on
emerging social and technological trends in the areas of company’s strategic interest
are available (i.e. the areas of business where a firm is planning to develop their new
competences). The sources may include such services as Trendwatching,
Datamonitor etc. It is essential to collect and structure available information on recent
technological developments and patens in the area of interest as well as gather
competitive intelligence. It is advisable to seek suggestions from employees, partners
and suppliers. The most important piece of useful information should come from
strategic economic reviews on emerging markets, industries and consumer needs
(for example, official statistics, expert estimations, strategic “exploratory” consumer
surveys such as Target Group Index, see www.tgisurveys.com). Concerning
revealed trends, it is vital to identify “lead users” or “opinion leaders” within each
trend (i.e. find individuals that are on top of the trend) and study in advance their
lifestyle and product preferences.
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
Discovery Stage – Ideation.
Tools: Qualitative Ideation Sessions, Brainstorming.
Specifics: While conducting ideation sessions, it is recommended to incorporate “lead
users” in the mixed groups of specialists from various fields of expertise. It is
advisable to assign each team a specific trend to work on.
Output: Ideas in the textual and graphic forms, above 20 items.
Gate 1: Idea screening
Tools: Screening by consumers as well as by experts (Technical, Business).
63
Specifics: Although researchers usually seek for ideas, which score very high in
terms of Purchase Intention (PI), in the case of “new-to-the-world” product there will
be only a few or none such ideas – due to a natural conservatism of majority of
consumers. Instead, it is recommended to focus on ideas received low PI, but very
high “differentiation” scores. These ideas should be double-tested on the basis of
“lead users”, using the same screening technique. In case of positive feedback from
“lead users” it is advisable to consider further development of the product involving
“lead users” community, as suggested by von Hippel (2002, 2005). At this stage it is
worthy to save “less promising” ideas for further use in the next rounds of idea
generation.
Output: Ideas in textual and graphic forms, up to 10 items.
Stage 1: Scoping / Concept Generation
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers).
Specifics: The best results may be achieved with an active participation of “lead
users”, given the freedom to drive the process.
Concepts: Up to 10 concepts in the standard format, i.e. product name/headline,
draft product picture, product statement (insight, reason-to-believe, benefit, end line),
preliminary product information (price, weight, features).
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening), Qualitative and
Quantitative ad hoc studies
Specifics: Same recommendations as for idea screening. In this particular case the
stringency of selection can be reduced. If the concept is perceived positively only by
“lead users” and not by a majority of consumers, it is advisable to identify barriers
using concept test data or conduct a separate study (qualitative or quantitative). It is
recommended to make rough estimates of diffusion rate, roughly project long-term
sales potential using expert techniques and modeling.
64
Output: up to 5 concepts
Stages 2,3: Build business case / Development
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers),
Strategic “exploratory” marketing research (media, retail, consumer)
Specifics: While planning, it is important to remember that aggressive national launch
will likely lead to failure. It is recommended to consider: (1) significant amount of
time, required to create the need for a completely new product in consumers’ minds,
(2) high financial risks associated with investments in infrastructure, production and
promotion, (3) unstable sales in the beginning of introduction, (4) high rate of product
modifications, (5) niche or focus product positioning in a majority of cases, (6) limited
number of actual consumers at early stage of market (i.e. innovators, early adopters,
up to 10% of total potential consumer base). Concerning product development and
communication, the inputs from “lead users” should be significant to ensure rapid
adoption and avoid unnecessary iterations with screening.
Output: up to 5 product prototypes and business plans
Stage 4: Testing and Validation
Tools: Test marketing, Quantitative tests of marketing mix elements, Strategic
“exploratory” marketing research (media, retail, consumer)
Specifics: In many cases of “new-to-the-world” products, traditional test markets may
actually be considered as product launch (or vice versa) due to a very narrow target
market. If using quantitative tests, researchers have to be ready for a number of re-
iterations due to frequent changes in product characteristics, positioning and
consumer requirements. Therefore, employing costly “large-scale” research
techniques designed to test the whole marketing mix in a single “bundle” (e.g.
Simulated Test Marketing) makes little practical sense. Instead, it is advised to run a
few simple separate studies in order get basic consumers’ response (including
feedback from “lead users’”) on product, pricing, positioning, possible barriers for
65
adoption. While sales forecasting, it is advised to focus on short-term projections
utilizing expert techniques - due to high market fluctuations. Nevertheless, marketers
need to try to identify the moment of “crossing the chasm” (Moore, 2002), i.e. the
point where the market enters the stage of rapid growth.
Output: up to 2 new product offers.
Stage 5: Launch / Post Launch validation
Tools: PR, Customer satisfaction, Media tracking, Sales statistics
Specifics: According to Trott (2008), it takes much longer time to launch a “new-to-
the-world” product as compared to the other types of new products. The focus in
promotional plan needs to be made on the community of “lead users” and “opinion
leaders”, viewing them as “trend-setters”. In a majority cases, at this stage there is no
need in conducting expensive tracking studies (such as advertising effectiveness,
retail audit etc). However, considerable attention should be paid to gathering direct
feedback from consumers, retailers, production, sales representatives. Their
suggestions on product improvement and promotional tactics must be immediately
processed to ensure rapid and widespread product adoption. Also, it is
recommended to track evolving consumer perceptions by monitoring discussion
around the product in the mass media.
Output: sales, feedback from key agents of product launch (consumer, retailer etc).
3.2.2 Specifics of marketing research for “New-to-the-Firm” products.
The synthesis of views on marketing research for “new-to-the-firm” products, which is
presented below, is based on the literature sources listed in Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
66
Specifics: The central role at this stage is played by the information about recent
novelties on the market (e.g. Datamonitor MarketWatch), successful product
launches, patents and competitive activities. It is important to utilize strategic
“exploratory” studies (such as segmentations or expert reviews) and gather relevant
data on market size, dynamics, growth potential, structure and consumers. A
particular attention should be given to suggestions of employees, partners and
suppliers. It is desirable, but not obligatory to collect information about “lead users”
and “opinion leaders”, i.e. lifestyle, product preferences etc. However, it is worthwhile
making an estimate of their incidence, which should normally exceed 20% of total
potential audience, comprising “innovators”, “early adopters” and “early majority”.
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
Discovery Stage – Ideation.
Tools: Qualitative Ideation Sessions, Brainstorming.
Specifics: Participation of “lead users” in ideation group works is desirable, though
not obligatory in case information about their lifestyle and preferences is available
(qualitative and quantitative studies, observations).
Output: Ideas in textual and graphic forms, 10-20 items.
Gate 1: Idea screening
Tools: Screening by consumers (Quantitative Idea Screening) as well as by experts
Specifics: It is recommended to go ahead with ideas showing high purchase intent
(PI) on total audience, however, paying attention to those with high level of
differentiation. It is advisable to consider a boost of “lead users” for separate analysis
of this group. When necessary, consider additional qualitative sessions with “lead
users”.
Output: Ideas in the textual and graphic forms, 5-10 items.
Stage 1: Scoping / Concept Generation
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers).
67
Specifics: It is possible not to involve “lead users” in the concept generation, if
sufficient information about their preferences is collected during previous stages.
Concepts: Up to 10 concepts in the standard format (see Section 3.2.1).
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening).
Specifics: Same recommendations as for idea screening. A successful concept
should exhibit high sales potential on total audience, however, “lead users” opinion
must be considered as well.
Output: 2-3 concepts
Stages 2,3: Build business case / Development
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers),
Strategic “exploratory” marketing research (media, retail, consumer)
Specifics: In case of sustainable and rapid growth of the market, it is advised to start
with the development of a long-term promotional strategy, performing “break-even”
analysis etc. The next step is usually a mid-term planning of product launch (i.e.
media planning, sales and production plans).
Output: 1-2 product offers, along with 2-3 marketing plans per offer
Stage 4: Testing and Validation
Tools: Quantitative tests of marketing mix elements (Product, Pricing,
Advertising/Communication, Pack / Place-of-purchase, Positioning), Simulated Test
Marketing
Specifics: Rapidly growing markets may experience dramatic changes in terms of
structure, number of players - over a relatively short period of time. However, at this
stage the market is nearly shaped (i.e. consumer needs are generally identified) and,
therefore, market potential can be roughly estimated. With that, market testing needs
to be quick, delays with the launch may result in missing opportunities and losing
market share to competitors. Therefore, at this stage the system of testing should
68
produce accurate assessment of marketing mix components and assist in revealing
barriers for further sales growth. The process should be flexible, fast and confidential.
At the same time, it should not be costly or demanding in terms of study materials
(i.e. ready commercials, packs, quantity of products to test). The option of precise
sales forecasting is not mandatory as normally projections does not exceed 30%
accuracy due to significant market fluctuations and lack of important pieces of
information (as discussed above). The key recommended techniques are:
quantitative tests of marketing mix elements, volumetric “bundle” tests or simple
flexible models of Simulated Test Marketing providing detailed diagnostics of
performance in competitive environment along with preliminary projection of future
sales. Traditional Test Marketing is not always the best solution at this stage due to
its long duration, expensiveness, limited capabilities in terms of consumer
diagnostics and unclear accuracy of sales forecast (e.g. difficulties in making national
projection etc.). The horizon of business planning at this stage is either short or mid-
term, however, developing a view on the future market tendencies is essential. This
is usually performed by using expert techniques or in-house quantitative analyses.
Output: 1-2 product offers with a 2-3 marketing plans.
Stage 5: Launch / Post Launch validation
Tools: Advertising / Brand tracking or post-tests, Retail audit, Media tracking (TV,
Press, Outdoor etc), Promo tracking, other quantitative strategic “problem
identification” studies.
Specifics: It is desirable to continuously monitor the launch with the range of special
techniques, such as advertising effectiveness tracking, retail audit etc. It is vital for
success at this stage to gauge and understand consumer response (as well as
competitors’, retailers’ etc) and make adjustments accordingly. To move the business
further it is recommended doing a second wave of the segmentation study (Usage
and Attitudes, Market Landscape etc).
Output: sales, feedback from key agents of product launch (consumer, retailer etc).
69
3.2.3 Specifics of marketing research in case of “Brand Stretching”.
The review below is based on the literature sources listed in Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: A critical component in decision-making about “brand stretching” is the
information about business attractiveness of the target market, which is typically at
the stage of rapid growth. The choice of the market is often made with “BCG matrix”
analysis, using available data on market potential, dynamics and structure. It is
imperative to conduct segmentation studies for the target market (i.e. U&A, Market
landscaping) in order to reveal “white spaces” and quantify the size of unexploited
business niches. It is highly recommended to run a series of Brand Stretching studies
(qualitative and quantitative) aimed at identification of categories (or markets)
suitable for extension in terms of brand image. Within these studies, it is important to
assess “substitutability” of products from the original category by the products from
the target category - to avoid excessive cannibalization. The risks concerned with
brand image “dilution” due to the presence in several markets should be measured
as well. Also, it is critical for successful idea generation to have information on
macro-and competitive environment, business infrastructure as well as on recent
technological developments, new product launches and patents.
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
From “Discovery Stage” to “Stages 2,3: Build business case / Development”
Tools, Specifics, Output: See “New-to-the-firm”.
Stage 4: Testing and Validation
Tools: See “New-to-the-firm”.
Specifics: Similar to “New–to-the-firm”. The recommended research methods are:
quantitative test of marketing mix components, simple and flexible Simulated Test
70
Marketing techniques. One of the key requirements is rich diagnostics of consumer
response in competitive environment, particularly, in terms of brand image,
positioning and communication. It is crucial to accurately gauge the impact of
“cannibalization” in case of significant substitutability between products from original
and target markets. Traditional Test Marketing is seen as not ideal tool in case of
“brand stretching” due to its limited capabilities to capture consumer feedback on
brand positioning.
Output: See “New-to-the-firm”.
Stage 5: Launch / Post Launch validation
Tools: See “New-to-the-firm”.
Specifics: It is recommended to employ a full range of monitoring techniques
(customer, retail, media etc), covering both original and target markets. Also, in order
to provide support in further development of the target market (e.g. possible range
extensions), it is desirable to repeat the segmentation study within 2-3 years after
entering the target market.
Output: See “New-to-the-firm”.
3.2.4 Specifics of marketing research for “Line/Range extensions”.
The summary below reflects the discussion on research methods for
“Brand/Line/Range extensions”, according to the literature sources mentioned in
Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: Product line extension is typical for late stages of market development.
This is done in order to promote further sales growth, satisfy increasingly
sophisticated needs of consumers and capture empty space on the market. At the
71
initial stage it is important to gather information on the emerging needs of consumer
in a given market. In particular, it is imperative to reveal and quantify opportunities
with need-based segmentations (i.e. U&A, Market Landscaping, Shopping Behavior).
It is desirable to monitor new product launches in the local and global markets (e.g.
Datamonitor Market Watch) and track competitor activities. Finally, it is vital to obtain
recent data on market size, its dynamics and structure (e.g. expert forecasts,
quantitative econometric analysis, retail audit etc). It is worth considering suggestions
from R&D department, employees and partners.
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
Discovery Stage – Ideation.
Tools: Qualitative Ideation Sessions, Brainstorming.
Specifics: At this stage, it is recommended to engage a wide variety of specialists,
including external consultants form advertising, branding and research agencies.
Output: Ideas in the textual and graphic forms, 10-15 items.
Gate 1: Idea screening
Tools: Screening by consumers (Quantitative Idea Screening) as well as by experts.
Specifics: Promising ideas should show capabilities for defending and growing
market share for the total product range, i.e. exhibit high purchase intent on the basis
of total audience.
Output: Ideas in the textual and graphic forms, up to 5 items.
Stage 1: Scoping / Concept Generation
Tools: Expert team work (R&D, Sales, Finance, Marketing, Consultants).
Specifics: Similar as for Idea Generation.
Concepts: Up to 10 concepts in the standard format, i.e. product name/headline,
draft product picture, product statement (insight, reason-to-believe, benefit, end line),
preliminary product information (price, weight, features).
72
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening), ad hoc studies
Specifics: Similar as for Idea Screening. Additionally, it is recommended to use such
techniques as TURF (to optimize the number of SKUs in the range) and Conjoint (to
achieve a balanced offer in terms of product characteristics and price).
Output: 2-3 concepts in a standard format
Stages 2,3: Build business case / Development
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers),
Strategic “exploratory” marketing research (media, retail, consumer)
Specifics: In this case it is necessary to develop a detailed mid-term business plan
(2-3 years), focusing on the product launch and promotional tactics. The plan should
include accurate assessments of incremental sales and sources of volume.
Therefore, this stage may overlap with testing and validation.
Output: 1-3 product prototypes along with 2-3 marketing plans per each
Stage 4: Testing and Validation
Tools: Quantitative tests, Conjoint, TURF, Simulated Test Marketing, Test Marketing
Specifics: A technique which is widely recommended in case of line extensions is
Simulated Test Marketing, providing accurate estimation of market share as well as
powerful diagnostic for marketing mix elements in competitive environment. In case
of low financial risks, it is possible to conduct individual advertising, product, pack or
price tests. It is advisable to consider using Conjoint and TURF, if this is not done on
the previous stages. Traditional Test Marketing may help in sales forecasting and
testing trade acceptance, however, it usually fails to deliver consumer insights
(especially those related to purchasing drivers).
Output: 1-2 product offers with recommendations on marketing plans.
Stage 5: Launch / Post Launch validation
Tools, Specifics, Output: Same as for “New-to-the-firm”.
73
3.2.5 Specifics of marketing research for “Brand re-launch / repositioning”.
A summary of views on marketing research for brand repositioning is based on the
literature sources listed in the Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: Changing brand positioning or re-launching the brand is normally
recommended at the late stages of market lifecycle in order to proactively prevent
sales decline and defend market share. This is usually achieved through renovation
of brand architecture (intangible characteristics such as image), keeping the name
unchanged. However, sometimes repositioning involves changing some tangible
product features as well. Therefore, at the stage of assembling knowledge it is
necessary to gather insights on emerging trends in consumer behavior (expert and
qualitative studies), drawbacks in current brand positioning (positioning and
preference “exploratory” studies), relationship between consumer requirements and
market share. Such studies may be conducted as a part of U&A segmentation,
allowing for in-depth study of consumer lifestyles and needs. It is important to have
information about competitive activities, macro-economic environment and business
infrastructure.
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
Discovery Stage – Ideation.
Tools: Qualitative Ideation Sessions, Brainstorming.
Specifics: Ideation sessions are most productive when run in diverse groups of
specialists, including external consultants form advertising, branding and research
agencies.
Output: Ideas in the textual and graphic forms, up to 15 items.
74
Gate 1: Idea screening
Tools: Screening by consumers (Quantitative Idea Screening) as well as by experts.
Specifics: The typical reason for idea selection is high purchase intent, exceeding
that for the current idea of positioning. This should be measured on the basis of total
audience, since the objective is to preserve and grow market share.
Output: Ideas in the textual and graphic forms, up to 10 items.
Stage 1: Scoping / Concept Generation
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers).
Specifics: Similar approach as for ideation.
Concepts: Up to 10 concepts in the standard format.
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening), ad hoc studies
Specifics: Similar to idea screening. It is advised that each concept is evaluated
separately from other (i.e. monadic approach to testing).
Output: up to 3 concepts
Stages 2, 3: Build business case / Development
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers),
Strategic “exploratory” marketing research (media, retail, consumer)
Specifics: In this case it is required to carry out short- and long-term business
planning activities, develop a re-positioning marketing plan, accurately assess
incremental sales potential. The stages of business planning may overlap with the
phase of testing and validation.
Output: 1-3 positioning options with 2-3 business plans per each
Stage 4: Testing and Validation
Tools: Quantitative tests of marketing mix elements, Simulated test marketing
Specifics: It is strongly advised to employ advanced Simulated Test Marketing
technique, providing accurate estimate of market share in a stable market conditions,
75
as well as delivering rich diagnostics of positioning in competitive environment.
Carrying out Traditional Test Marketing is not recommended due to its limited
diagnostic capabilities and lack of confidentiality.
Output: up to 2 new product offers.
Stage 5: Launch / Post Launch validation
Tools, Specifics, Output: Same as for “New-to-the-firm”
3.2.6 Specifics of marketing research for “Product/Price changes”.
The following set of insights is based on the literature sources listed in Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: Changing product characteristics or price is quite common for late stages
of the market lifecycle. This is usually done in response to competitive actions with
the purpose to defend market share. At this stage, the central role is played by
technical ideas from R&D department as well as market data delivered by various
tracking studies, which monitor competitive activities in media and retail (e.g.
consumer tracking, media monitoring, retail audit etc). Gathering news on product
novelties in international markets is quite helpful. Also, it is advisable to utilize
information provided by market segmentations (consumer and shopper), and it is
imperative to conduct strategic pricing study to identify successful approaches to
pricing improvements. Suggestions from employees (especially from sales
representatives), clients, partners, suppliers and retailers may be extremely useful.
Output: a set of materials for scoping, prepared in a concise and clear form.
Discovery Stage – Ideation, Gate 1: Idea screening, Scoping / Concept Generation
Tools: Expert team work (Technical / R&D, Sales, Finance, Marketing)
76
Specifics: Typically treated as one-stage due easiness of idea generation and high
time pressure.
Output: 2-3 concepts in a standard format.
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening), ad hoc studies
Specifics: Using Conjoint is a common practice to develop the most appealing
product offer. Concepts are usually tested individually (monadic approach).
Output: 1-2 concepts in a standard format.
Stages 2,3: Build business case / Development
Tools: Expert team work, Strategic “exploratory” marketing studies
Specifics: In this case it is often required to develop a short-term business plan (up to
1 year), which includes projection of market share and sources of volume. The latter
is often obtained from Conjoint or Test Marketing (Stage 4).
Output: up to 5 product prototypes and business plans
Stage 4: Testing and Validation
Tools: Quantitative tests, Traditional and Simulated Test marketing
Specifics: In case of high financial risks it is recommended to go with Simulated Test
Marketing, while in other cases using Conjoint and quantitative tests of marketing mix
is sufficient. Conducting traditional test market is possible, in case of limited
advertising support and considerable number of in-store activities. However,
researchers have to consider long duration of traditional approach, its complexity in
execution as well as high cost.
Output: 1 new product offer.
Stage 5: Launch / Post Launch validation
Tools, Specifics, Output: Same as for “New-to-the-firm”. Information from retail audit
is particularly important.
77
3.2.7 Specifics of marketing research for “New-to-the-Country” products.
The following review of approaches to marketing research for “new-to-the country”
products is based on the literature sources listed in Section 3.2.
Discovery Stage – Assembling Knowledge.
Tools: Interactive knowledge base / Marketing Information system (MIS), Strategic
“problem identification research” (Expert / Qualitative / Quantitative).
Specifics: When considering international sales expansion, it is essential to
determine the stage of development for the market of such products in the target
country. Researchers must make sure that they have collected all available
information about the target market: its structure, dynamics, competitive and
macroeconomic environment (e.g. Economist Intelligence Unit , Euromonitor,
Datamonitor, PricewaterhouseCoopers etc.), current consumer needs, preferences
and lifestyles (see Section 2.2, Figure 2.4) . It is important to obtain historical data
and background information on the recent product launches and do the same for the
similar markets. After having collected market statistics, it is advisable to perform
market attractiveness analysis (“BCG matrix”) (Johnson et al, 2008). It is imperative
to be aware of cultural differences as well as be prepared to overcome cultural
barriers while promoting the product (qualitative ethnographic research). As
discussed earlier in Section 2, “new-to-the-country” launches are typical for “lagging”
economies, where markets are either embryonic or entering the stage of growth (see
“new-to-the-world” ,“new-to-the-firm” products). However, normally there is an
emerging segment of “lead users”, living the same lifestyles as a majority of
consumers in the country of product origin, and acting as trend setters in the local
culture. As discussed above, the role of “lead users” is central to market development
at early stages, therefore it is important to gather all available information about them
(as well as to estimate their number in the total target group).
Output: a set of materials for ideation sessions, prepared in a concise and clear form.
78
Discovery Stage – Ideation.
Tools: Qualitative Ideation Sessions, Brainstorming.
Specifics: To ensure productive ideation sessions, it is necessary to conduct a series
of qualitative studies aiming at getting current consumer perception about the original
product offer. In case of significant attitudinal barriers or no established need
revealed, it is recommended to engage “lead users” and “opinion leaders” in the
product adaptation (ideation sessions, qualitative research etc).
Output: Ideas of the “localized” product in the textual and graphic forms, 5-10 items.
Gate 1: Idea screening
Tools: Screening by consumers (Quantitative Idea Screening) as well as by experts
(Technical, Business).
Specifics: Typically the most promising ideas selected at this stage have high
purchase intent (PI) on the basis of total audience or among “lead users”. During the
test, it is advisable to get preliminary information on possible barriers for product
promotion, especially in the case of low PI. This issue may require additional in-depth
study with a series of focus groups (i.e. qualitative research).
Output: Ideas in the textual and graphic forms, up to 5 items.
Stage 1: Scoping / Concept Generation
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers).
Specifics: It is desirable to finalize the concept using feedback from “lead users”
obtained during the previous stages.
Concepts: 2-3 concepts in the standard format, i.e. product name/headline, draft
product picture, product statement (insight, reason-to-believe, benefit, end line),
preliminary product information (price, weight, features).
Gate 2: Second Screen / Concept Screening
Tools: Screening by consumers (Quantitative Concept Screening), ad hoc studies
79
Specifics: In the case of a completely new market the stringency of concept selection
may be decreased. If the concept is perceived positively only by “lead users” and not
by a majority of consumers – it is recommended to estimate the rate of adoption and
identify the barriers towards it.
Output: up to 3 concepts in case of embryonic market, 1-2 concepts in the case of
growing market.
Stages 2,3: Build business case / Development
Tools: Expert team work (Technical, Sales, Finance, Marketing, Consumers),
Strategic “exploratory” marketing research (media, retail, consumer)
Specifics: In the case of very early market entry, it is recommended to follow
recommendations outlined for “new-to-the-world” products, while in the case of
rapidly growing market it is advisable to refer to that for “new-to-the-firm”. It is worthy
to note, that it is commonly accepted practice to use “analogies” methods to foresee
market perspectives in “lagging” economies (i.e. considering experience in more
advanced markets that are similar in terms of consumer profile and culture).
Output: 1-2 products (in case of growing market) and up to 4 product options (in
case of new market) along with 2-3 marketing plans for each.
Stage 4: Testing and Validation
Tools: Test marketing, Quantitative tests of marketing mix elements, Strategic
“exploratory” marketing research (media, retail, consumers).
Specifics: It is advised to follow recommendations for “new-to-the-world” or “new-to-
the-firm” products, depending on the state of the market. In the first case, large-scale
Simulated Test Marketing (STM) study have little effectiveness in terms of sales
forecasting and may easily lead to wrong conclusions about new product
perspectives in the market. However, it may provide some powerful diagnostics, but
at a very high price. Concerning the second case, the only STMs that can be
80
recommended, should meet requirements outlined in respective section for “new-the-
firm” products.
Output: 1-2 products (in case of growing market) and up to 4 product options (in case
of new market) along with 2-3 marketing plans for each.
Stage 5: Launch / Post Launch validation
Tools: PR, Consumer/Media/Advertising tracking and post-tests, Retail audit and
Distribution studies, Sales statistics
Specifics: See “new-to-the-world” or “new-to-the-firm”, depending on the state of the
market.
Output: sales, feedback from key agents of product launch (consumer, retailer etc).
3.2.8 A summary on practical application of marketing research tools
The table exhibited in Figure 3.4 summarizes the discussion above. Thus, it is
evident that research programs differ significantly depending upon the type of new
product. The more developed is the market, the greater is the role of quantitative
research techniques and Simulated Test Marketing in particular.
The role of large-scale quantitative tests increases with market development
Figure 3.4
Source: Based on Malhotra (2009), Tull et al (1993), Groucutt (2005), Shim(2000), Webb et al (1992)
New-To-The-World
New-To-The-Firm
New-To-The Country
Brand Stretching
Line Extensions
Re-Positionings
/ Relaunches
Product / Price
changes
Qualitative tools (e.g. focus‐groups, brainstorming and ideation sessions, in-depth interviews )
See either "New-to-
the-World" or "New-to-the-Firm", depending on state of the market
Quantitative diagnostic tests (e.g. concept tests, advertising tests, product tests, price tests, pack tests)
Quantitative tests with elements of sales assessment (e.g. conjoint)
Simulated Test Marketing (STM) or Volumetric Bundle Tests
-
Traditional test market -
Econometric sales forecasting / Statistical modeling
Strategic “exploratory” studies (e.g. segmentations, trackings,trend analysis etc)
Expert analysis (internal and external)
- Not recommended / Serious Limitations Rarely used - Low importance Frequently used - Medium importance Extensively used - Critical importance
81
3.3 Methods to forecast sales for a new FMCG product
The true function of marketing research is not just gathering consumer and business
insights, but shaping a future vision of the market and the company’s place in that
future by forecasting sales, minimizing uncertainty and risk in decision-making. A
reliable forecast creates the foundation of a business plan and, consequently, the
future success of the company - “if you cannot foresee the company’s future, don't
invest in it” (Dorsey, 2004, p.11). In particular, according to Shim (2000, p.3) “sales
forecasts give the expected level of sales for the company’s goods… throughout
some future period; they are instrumental in the company’s planning and budgeting
functions, and are key to other forecasts and plans”. As defined by the “Marketing
Glossary” (Clemente, 2002, p.370), sales forecast is “a projection of the amount of
sales a company expects to achieve over a future period of time, as determined by a
specific marketing program”. Dibb and Simkin (2008), and MacDonald (2008) point
out that sales forecasting and marketing research are tightly integrated into each
other. Thus, a number of forecasting tools (or instruments) are an essential part of
various marketing research techniques considered above. These instruments will be
discussed in the following section.
3.3.1 An overview of sales forecasting instruments
A forecasting instrument, as per “Marketing Glossary”, is “a model used in predicting
product sales, market share levels and other marketing variables. An example of
forecasting model is an econometric model, which offers diagnostic insights into
effects of variations in marketing mix variables (e.g. the size of advertising budgets,
sales force composition, distribution channels)” (Clemente, 2002, p.163). However,
besides such well-known approaches as econometrics, there are many other sales
forecasting tools developed for a variety of business cases. A review of the
fundamental literature on the topic (Burton et al, 1986, Berenson and Levine, 1986,
Lilien et al, 1992, Aivazian and Mkhitarian, 1998, Draper and Smith, 1998, Baker,
82
1999, Hanke et al, 2001, Shim, 2000, Franses and Paap, 2001, Wind and Green,
2004, Mentzer and Moon, 2006, Slutskin, 2006, Koop, 2008, Gujarati and Porter,
2009, Rossi et al, 2009, Byrne, 2010 etc) allows to suggest that forecasting
instruments can be divided into two main groups, according to the use of historical
data. Instruments comprising the first group do not directly utilize historical
information. Instead, they rely on primary data, expert analysis, short-term
projections of consumer behavior and indirect sources. An overview of such
instruments is presented on Figure 3.5a.
Sales forecasting instruments which does not utilize historical data directly
Figure 3.5a
Sources: Based on Chambers et al (1971), Shim (2000) and the following review of Burton et al (1986), Berenson et al (1986), Lilien et al (1992), Aivazian et al (1998), Draper et al (1998), Hanke et al (2001), Franses et al (2001), Wind et al (2004), Slutskin (2006), Koop (2008), Gujarati et al (2009), Rossi et al (2009), Byrne (2010)
Expert opinions
Based on the assumption that several experts can arrive at a better forecast than can one person. There is no secrecy, and communication
is encouraged. Forecasts are sometimes influenced by social factors and may not reflect a true consensus.
Forecasts of long-range and new product sales;
technological forecasting
Information from a panel of experts is openly in group meetings to arrive at a consensus
forecast. Minimum is two sets of reports over time
Delphi ("consensus") approach
A panel of experts is interrogated by a sequence of questionnaires in which the responces to one questionnaire are used to produce the next questionnaire. Any information available to some experts and not to the others is thus passed on to the others, enabling all the experts to have
access to all information for forecasting
Forecasts of long-range and new product sales;
technological forecasting
A coordinator issues the sequence of questionnaires, editing and consolidating the
responses
Sales force polling Based onsales force polling; tends to be optimisticForecasts of short-term
salesData by regional and product line
breakdowns
PERT-DerivedBased on three estimates provided by experts: pessimistic, most likely
and optimisticSame as expert
opinionsSame as expert opinions
Consumer SurveysBased on direct interviews with consumers regarding purchasing
behaviorForecasts of short-term
salesPersonal interviews, questionnaires
Stochastic modeling (e.g. Markov approach / Bayesian approach /
Game theory etc )
Models based on learned behavior: consumers tend to repeat their behaviour
Forecasts of sales and cash collections
Data required for transaction probabilities
Industry (Market) surveys
Typically a B2B survey that captures information from various market agents (such as partners, retailers, manufacturers, distributors, regulators etc).about their actual performance and future plans
Inputs to quantitative analysis of secondary
informationPersonal interviews, questionnaires
Input - Output (Structural flows)
analysis
Concerned with the interindustry or intermarket flow of goods or services. It shows what flow of inputs must occur to obtain outputs
Forecasts of company sales by sectors
Considerable amounts of cross-category sales data
Leading indicators / Analogy / Normative
Leading indicators, analogies or normatives tends to predict future trend in sales
Forecast of sales by product class
An observation (e.g. consumer survey) plus a databese of similar cases
3) Indirect information
Description Typical Application Data collection
1) Primary information (Judgemental / Qualitative)
2) Learned behaviour
According to Shim (2000, p.5), these models are indispensable “when patterns or
relationships do change” and the advantage of such methods is that they are “able to
identify systemic change more quickly and better interpret its effects on the future”.
These instruments are useful for both long- and short- terms forecasting in case of
considerable uncertainty, although their “numerical” accuracy is typically lower than
that for the methods which handle historical data. The latter methods work superbly
on developed markets as long as considerable amount of knowledge is already
accumulated and there is little or no systemic change in the environment (See Figure
3.5b overleaf).
83
Sales forecasting instruments which utilize historical informationFigure 3.5b
(a) Time series
Moving average / Naïve methods
Averages are updated as the latest information is received; weighted average of a number of consequtive points of the series
Forecast of sales with no other input data
A minimum of 2 years of sales history by month if seasonals are present; otherwise fewer data. The more history the better. The moving average must be specific
Exponential Smoothing
Similar to moving average, except that more recent data points are given more weight. Effective when there is random demand and no seasonal fluctuation in the data series
Forecasts of sales, financial data, inventory control
The same as for moving average
Trend AnalysisFits a trend line to time series data. There are two variations, linear and nonlinear
New produt forecasts and products in the growth and maturity stages of life cycles; inventory control
Varies with the technique used. However, a good rule of thumb is to use a minimum five years annual data to start and, thereafter, the complete history
Life cycle analysisAnalysis and forecasting new product growth rates based on S-curves
Forecasts of new product salesAs a minimum, the annual sales of the product being considered or a similar product are necessary
DecompositionDecomposes a time series into seasonals, trend cycles and irregular elements. Primarily used for detailed time series analysis (including estimating seasonals)
Forecasts of sales and financial data; tracking and warning
A minimum 3 years of history to start; thereafter, the complete history
Box-Jenkins
Iterative procedure that produces an autoregressive, integrated moving average model, adjusts for seasonal and trend factors, estimates appropriate weighting parameters, tests the model, and repeats the cycle as appropriate
Production and inventory control for large volume items, forecasts of cash balances and earning
The same as for classical decomposition
(b) Causal
Simple / Multiple Regression
Functionally relates sales to other variables and estimates an equation using the least-squares technique
Forecasts of sales by product classes, forecasts of earnings and other financial data
At least 30 observations are recommended for acceptable results
Econometric modelingA system of independent regression equations that describe some sector of sales or profit activity. The parameters of the regression equations are usually estimated simultaneously
The same as for regression The same as for regression
4) Secondary information (present and past data)
Instrument Description Typical Application Data collection
Sources: Based on Chambers et al (1971), Shim (2000) and the following review of Burton et al (1986), Berenson et al (1986), Lilien et al (1992), Aivazian et al (1998), Draper et al (1998), Hanke et al (2001), Franses et al (2001), Wind et al (2004), Slutskin (2006), Koop (2008), Gujarati et al (2009), Rossi et al (2009), Byrne (2010)
Therefore, the choice of sales forecasting technique is heavily influenced by the
stage of market lifecycle. As pointed out by Shim (2000, p.141), “the proper choice of
forecasting methodology depends on where the market is”. Typically, at the
“introduction” stage no historical data is available and, therefore, the researcher has
to rely on primary sources and “judgemental” methods. At the “growth” stage some
data is already available for analysis, and the focus shifts to quantitative methods
which help to measure “cause-effect” relationships and make more accurate
projections (e.g. regressions, time series, life cycle analysis). On the “maturity” stage
the amount of data accumulated is significant. However, still, there is a need for
short- and long-term projections, although trends may change only slightly. Here,
quantitative techniques are extremely useful (e.g. classic decomposition, other time
series methods, causal techniques). At the late stages of the lifecycle, “judgemental”
tools may help to identify signals for decline.
As discussed above, sales forecasting instruments are integral parts of marketing
research studies (and vice versa, market research may provide inputs to forecasting
model). Some of research methodologies, such as Simulated Test Marketing, often
84
incorporate a broad range of forecasting techniques (Clancy et al, 2003, 2006). The
use of forecasting instruments in various marketing research methodologies in the
context of market development is analyzed on Figure 3.6. Thus, it clearly shows that
“judgemental”, “learned behavior” and “indirect” instruments are often used at the
early stages within qualitative studies and expert analyses (i.e. for “new-to-the-world”,
“new-to-the-firm”, ”new-to-the-country” products), while time-series and causal
instruments can be effectively applied at late stages within strategic quantitative
studies, such as segmentations and trackings, STMs, econometric analyses and
modeling (i.e. “brand relaunches”, “line extensions”, “product / price changes” etc).
The use of sales forecasting instruments in marketing research for NPD throughout the market lifecycle: the input provided by quantitativeinstruments increases with knowledge accumulation
Figure 3.6
Sources: Based on Chambers et al (1971), Shim (2000), Malhotra (2009), Tull et al (1993), Webb et al (1992), Wind et al (2004)
Acc
ura
cy
Ho
rizo
n o
f f
ore
cast
Qua
litat
ive
too
ls
Exp
ert a
naly
sis
Tra
ditio
nal t
est
mar
ket
Qua
ntita
tive
dia
gnos
tic
test
s
Qua
ntita
tive
test
s w
ith
elem
ents
of s
ale
s as
sess
me
nt
Str
ateg
ic “
expl
orat
ory”
st
udi
es
Sim
ulat
ed
Tes
t M
arke
ting
(ST
M)
Eco
nom
etric
sal
es
fore
cast
ing
/ Sta
tistic
al
mod
elin
g
Expert opinions Delphi ("consensus") approach - - -
Sales force polling - PERT-Derived - - -
Consumer Surveys -
Stochastic modeling (e.g. Markov approach / Bayesian approach / Game theory etc )
-
Industry (Market) surveys Input - Output (Structural flows) analysis Leading indicators / Analogy / Normative -
Time series - Causal -
Marketing research
Sal
es f
ore
cas
tin
g
Primary information (Judgemental / Qualitative)
Indirect information (no back data)
Secondary information (present and past data)
Learned behaviour
Market growth Knowledge accumulation
- Not applicable Rarely incorporated - Low contribution Frequently incorporated - Medium contribution Always incorporated - Critical contribution
Although the stage of market development is critical for the choice of forecasting
instrument, the final selection is also determined by several other factors, such as
accuracy (i.e. “how the forecast will be used?”), cost (i.e. “how much money is
involved?”), timing (i.e. “when will the forecast be used?”), users of forecast (i.e. “who
will use the forecast?”), available data (i.e. “what data are available?”). In addition,
Shim (2000, p.8) recommends considering the following six questions, while
choosing the forecasting instrument: “(1) How much will it cost to develop the
85
forecasting model compared with the potential gains resulting from its use? The
choice is one of benefit-cost trade-off, (2) How complicated are relationships that
must be forecast ?, (3) Is the forecast for short-run or long-run purposes?, (4) How
much accuracy is desired?, (5) Is there a minimum tolerance level of error?, (6) What
data are available? Techniques vary in the amount of data they require?”. Chambers
et al (1971) developed a detailed overview of various forecasting instruments. An
updated and refined version of that summary is presented on Figure 3.7 below.
Identification of "turning point"
Short-term (0-3 mon)
Mid-term (3 mon-2yr)
Long-term (2yr+)
Expert opinions Minimal 2 weeks 2.5 2.5 2.5 2Delphi ("consensus") approach Expensive 1 month 3.5 4 3.5 3.5
Sales force polling Minimal 2 weeks 3 3.5 2 2PERT-Derived Minimal 2 weeks 2.5 3 2 2
Consumer Surveys Expensive > 1 month 3.5 3.5 2 2
Stochastic modeling (e.g. Markov approach / Bayesian approach / Game theory etc )
Expensive > 1 month 4 5 2 2
Industry (Market) surveys Expensive > 1 month 4 - 5 3.5Input - Output (Structural flows) analysis Expensive > 1 month 3 - 4.5 3.5Leading indicators / Analogy / Normative Varies 1 month 4 3 3 1
(a) Time series Moving average / Naïve methods Very minimal 1 day 2 3 2 1
Exponential Smoothing Minimal 1 day 2.5 4 3 1Trend Analysis Varies 1 day 2.5 5 4 3.5
Life cycle analysis Varies 1-5 days 3 2 3 3Decomposition Minimal 1 day 5 5.5 4 1
Box-Jenkins Varies 2 days 3 5.5 3 1 (b) Causal
Simple / Multiple Regression Varies Depends on 4 4.5 4.5 2Econometric modeling Expensive 1 month+ 6 4.5 5.5 3.5
4) Secondary information (present and past data)
Observed Accuracy (1="Very poor" to 6 ="Excellent")
1) Primary information (Judgemental / Qualitative)
2) Learned behaviour
3) Indirect information (no historical data)
Instrument Cost Time
A choice of sales forecasting instruments in terms of cost, time and observed accuracy
Figure 3.7
Sources: Based on Chambers et al (1971), Shim (2000) and the following review of Burton et al (1986), Berenson et al (1986), Lilien et al (1992), Aivazian et al (1998), Draper et al (1998), Hanke et al (2001), Franses et al (2001), Wind et al (2004), Slutskin (2006), Koop (2008), Gujarati et al (2009), Rossi et al (2009), Byrne (2010)
“Very Poor” to “Poor” “Fair” “Good” to “Excellent”
Traditionally, high accuracy is considered as an important factor of choice. As
pointed out by Hanke et al (2001, p.1) “a particular focus of (management) attention
is on the errors that are inherent part of any forecasting procedure. Predictions as to
future outcomes rarely are precisely on the mark; the forecaster can only endeavor to
make the inevitable errors as small as possible”. Choosing the right instrument in
terms of market environment significantly reduces the error, however, following the
standards of execution is equally important. The quality control (QC) procedures in
sales forecasting involve testing and validation of the model, controlling accuracy of
sources and inputs (e.g. “market growth” etc). While forecasting, it is worth using a
86
variety of approaches and sources to deliver the final prediction as a result of
convergence of interim forecasts (See Figure 3.8). It is important to bear in mind,
that accuracy of the final forecast is heavily dependent on the accuracy of its
components, i.e. data sources and models (i.e. predictions about macro-
environment, market size and dynamics etc.).
Essentials of Quality Control in sales forecasting:Right process execution allows to arrive at reliable results
Figure 3.8
Sources: Based on Shim (2000) , Hanke et al (2001), Kachalov (2008)
Compliance with high standards of execution:
Accuracy control for each componentand for the whole model :
The need for forecast
Determine the objective – what is the dependent variable
Ascertain likely explanatory independent variables
Develop a model or choose a forecasting method
Test the model
Apply the model
Evaluate and revise the model
Determine need for ongoing forecasting of variable
Reliability of INFORMATION SOURCES
Accuracy of MACRO ENVIRONMENT
forecasting approach
Accuracy of MARKET DYNAMICSforecasting technique
FINAL FORECASTACCCURACY
Accuracy of NEW PRODUCT SALES
forecasting model
X
X
X
Use varioussources
Use various
approaches
Use various
techniques
Use various models
Cross-check,Analyze,
Converge
1 2
=
As mentioned above, the methods of test marketing are central to NPD and utilize a
wide range of forecasting instruments to deliver a highly accurate final forecast.
Important aspects of their design as well as benefits and drawbacks of their practical
application will be discussed in the next section.
3.3.2 The history and evolution of Simulated Test Marketing
By the end of 60-ies an examination of a new product in a “test market” prior to the
launch had been formally implemented in NPD protocols of the largest FMCG
companies in the US and Europe (Urban et al, 1987, Clancy et al, 1994). According
to the classical definition by Armstrong et al (2009, p.271), test marketing is – ” a
stage at which the product and marketing program are introduced into more realistic
87
marketing settings… it lets the company test the product and its entire marketing
program – positioning strategy, advertising, distribution, pricing, branding and
packaging and budget levels”. It is advisable to conduct test marketing “when stakes
are high...This could be when a large company is introducing a new product that
requires a big investment or when management is not sure about the product or
marketing program”.
There are two basic approaches to test marketing studies at the moment: (1)
traditional test marketing (2) simulated test marketing (Clancy et al., 2006). In the first
case, a new product is simply put on shelves of a limited number of stores located
usually in a distant area. Next, sales performance is usually tracked for a period of
two or three months. The second approach implies conducting an ad hoc quantitative
consumer survey, which simulates the process of a new product launch in a
laboratory environment (including advertising exposure, shopping opportunity and
product test) for a representative sample of potential purchasers. The key outcome
of the study is a mid-term sales forecast (Clancy et al, 2006) (See Figure 3.9).
Test Marketing – two key approaches
Source: Based on Clancy et al (2006)
Figure 3.9
Traditional Test Markets
Testing new product in the real life
marketplace (sample of stores)
Simulated Test Markets
Methodology to simulate new product
launch under a laboratory environment
Provides mid-term sales forecast
88
The major advantage of traditional test markets is that they allow to examine trade
acceptance and to reveal issues related to distribution mechanics prior to the launch.
Clancy et al (2006) point out that tradition approach is extremely useful for testing: (1)
In-store promotional tactics, (2) Distribution channels, (3) Transportation
infrastructure, (4) Merchandising standards / Product location in the store. Traditional
test marketing offers a real-life opportunity to monitor sales performance of the new
product and measure such business indicators as sales incremental to parent brand,
cannibalization etc. Also, traditional testing allows for minor improvements in
marketing mix during the process at a relatively low cost (as compared to the national
launch). However, the key limitation of this approach is that it visibly fails: (1) to
simulate the entire environment of a new product launch (i.e. advertising campaigns
on national TV channels etc.), (2) to register consumer response correctly and,
therefore, to explain the observed sales effect. According to Urban and Hauser
(1993) and Clancy et al (1994) there are seven major drawbacks of traditional test
marketing: (1) limited capabilities to capture consumer feedback, i.e. reasons for
purchase, (2) expensiveness, (3) long time period for results (often a year), (4) lack
of confidentiality, (5) competitors can sabotage the results, (6) allows for testing only
one marketing plan, (7) difficulties with national projection.
A simulated test marketing study incorporates consecutive assessment of entire
range of marketing mix components in the order of their interaction with the
consumer (i.e. communication, place, price, product formula) (Armstrong et al.,
2009). A set of indicators often consists of communication and advertising metrics
(memorability, content recall, message evaluation, enjoyment, key likes and dislikes
about tested commercials), image positioning (brand offer against consumer needs
and expectations), product and pack characteristics (likes, dislikes, performance
across various attributes, visibility on shelf) and price elasticity coefficients. The
method of data collection can be classified as two-stage quantitative central location
test (CLT). At the first stage data collection is usually conducted in-hall face-to-face,
89
while the typical method at the second stage is telephone interview (Clancy et al,
2006). (See Figure 3.10).
STM typical data collection procedure- replication of new product launch in a laboratory environment. Quantitative in-hall test with call-back that that captures trial and repeat….
Trial purchase(before product use, after concept /ad exposure)
Repeat purchase(after product use)
Figure 3.10
Source: Clancy K. et al (2006, p.110)
An STM study provides a mid-term sales forecast that is typically based on Fourt -
Woodlock model (Fourt and Woodlock, 1960), which involves separate assessment
of trial and repeat components contributing to the total sales. (See Figure 3.11)
Sales volume decomposition suggested by Fourt and WoodlockFigure 1.9Figure 3.11
Market size
xTrial rate
Trial + Repeat volumesper trialist
x
x
Weighted Distribution%
Consumer survey
Marketing plan effect:Awareness projection
The market
x
Awareness %
Target audience #
(
)
At 100% awareness & 100% distribution:
Availability
Source: Fourt and Woodlock (1960)
Projected Sales Volume
90
A similar approach was suggested by Parfitt and Collins in 1968 for decomposing
sales data obtained from household panel studies (Parfitt and Collins, 1968).
According to Clancy et al (2006) there are numerous advantages of Simulated Test
Marketing over traditional approach. In particular, STMs: (1) are less expensive , (2)
deliver results in a shorter period of time (often two months), (3) are confidential, (4)
are capable to test multiple marketing plans and scenarios (advertising, brand
positioning, product, pack, price etc), (5) results may be projected nationally, (6)
provide accurate estimates of cannibalization and source of volume, (7) allow for
“what-if” simulations. Clancy et al (2006) argue that a majority of modern STM
techniques are superior in terms of deliverables than traditional test marketing.
The history of STM techniques development is very closely linked to the evolution of
FMCG market, retail infrastructure, media and marketing in the US (Kratt, 2009,
Clancy et al, 2006) (See Figure 3.12).
Rise of retail chains in U.S.A. triggered development of STMs in early 70-ties
Mid 50-ties
Transition to supermarkets from traditional groceries was
largely complete.
Source: Based on Kratt (2009) , Clancy et al (2006)
Early suburbanShopping Centers
Aggressive rise of retail chains.
Mid 60-ties 80-90 ties70-ties Current decade
Discounters and Warehouse
Stores
Rapid consolidation of
retail
Upscale Stores, Warehouses, and
Mergers,Private labels
The re-emergence of superstores,
featuring general merchandise and groceries under
one roof
Further International expansion,E-stores.
Test marketsOnly (rare)
Test markets +advanced analysis
Early developmentsof STMs
Rise of advancedSTMs: Bases, Assessor
High demand for fast,accurate and flexible STMs (in terms of shopping process, targets, media-modeling)
Figure 1.9Figure 3.12
Rise of retail sales Saturation
By mid 60-ies, a rapid consolidation of self-service outlets in retail industry, emerging
advertising opportunities (i.e. national TV, radio, press, outdoor etc.), sophistication
of promotional techniques had triggered development of early STMs and gradual
91
cutover from using classic traditional test markets. From here on the concept of “trial”
and “repeat” purchases was used as a foundation for every STM model, starting from
the model called DEMON, the pioneer in the industry. DEMON was a “transitional”
model that utilized traditional test market outcome to make a national sales projection
(Charnes et al., 1966) (See Figure 3.13a).
Evolution of Simulated Test Markets. Key milestones:
1960
Fourt-Woodlock equation
(Also known as “Trial & Repeat purchases” approach)
BY
Joseph Woodlock (MRCA, Market Research Corporation of America)
Louis Fourt (MRCA)
1966 1966
DEMON (BBDO)
(Decision Mapping Via Optimal Go-Not Go Networks – A model for marketing )
BY
Dr. David Learner (Research Director BBDO), James DeVoe (Dep.Director BBDO) Prof. Abraham Charnes, Prof. William Cooper
Complementary study to real test market. Exit-poll+Call-back. Reveals trial and repeat rates for national projections. Complicated, doesn’’tallow marketing plansimulations
Sales Volume is afunction of Trial andsubsequent Repeat
purchases
11-point Juster’s Purchase Probability
Scale BY
Thomas Juster (U.S. Chamber of Commerce)
The Juster scale in its many applications has been found to be superior as a predictive measure of future purchase behaviour than other intentions scales
1968-1970
NEWS (BBDO)
(New Product Early Warning
System) BY
Larry Light (in 2003 Excutive
Vice-President at McDonalds)
Prof. Lewis Pringle
(University of Miamy)
Simplified and improved “DEMON”
LTM (YankelovichConsulting)
(Laboratory Test Market)
BY
Yankelovich, Skelly & White (Sponsored by P&G and Pillsbury)
First “laboratory” test market,no need for real test market.Sample n=500. Replicates purchase behaviour: ad exposure, shopping in real outlets (although purchase is allowed only in test category, test product in-home trial).“PURE” BEHAVIOURAL MODEL
Figure 1.9Figure 3.13a
Source: Based on Cancy K. et al (1994, 2006)
A considerable contribution to the model development process was made by Thomas
Juster, who suggested to use 11-points purchase intent scale for estimation of
purchase probability (Juster, 1966) (See Figure 3.13a). As it was shown by Juster,
the scale provides the means for highly predictive measurement of purchase
probability in the US FMCG markets, although, to arrive at an unbiased estimate
some adjustments are required as consumers tend to over-claim their behavior. The
adjustment for “over-claim” is usually made with the use of a normative database that
is unique for each particular country and market (Juster, 1966). In the beginning of
70-ies the two advanced models, LTM (Laboratory Test Market) and ESP (Estimating
Sales Potential), based on entirely opposite principles (“behavioral” and “attitudinal”,
92
respectively), were introduced to the market (Clancy et al., 1994, Souder and
Sherman, 1994, Eskin and Malec, 1976). ESP was designed to estimate purchase
probability for a new brand using purchase intent scale without taking into account
competitive environment (Morwitz, 2007). It was used later by Lynn Y.S. Lin for the
development of BASES, the most popular STM model nowadays - due to its “relative
simplicity” (Clancy et al, 2006, p.62). Indeed, some simple illustrations of BASES
calculation principle can be found even in popular literature for marketing and brand
managers (Farris et al., 2006). As it was mentioned earlier, the main requirement for
accurate modeling with the “purchase intent”-based approach is availability of
extensive normative database for making an “over-claim” adjustment (Lin, 1986). All
STM models that are built on “purchase intent” platform firstly forecast sales volume,
and then derive volume share from this initial projection (Clancy et al, 2006). In the
mid 70-ies the first academic articles on relationship between preference and market
share were published. The most remarkable success in this area was achieved by
scientists from Massachusetts Institute of Technology (MIT), who developed their
Perceptor model in 1975 (Urban, 1970,1975) (See Figure 3.13b).
1972
ESP (Pillsbury, NPD
group)
(Estimating sales potential)
BY
Gerald J. Eskin (Pillsbury, co-founder of IRI), John Malec (NPD Group), Lin Y.S. Lynn (Pillsburry)
1972-1977
First “Laboratory”test market based
on Fourt-Woodlock equation, 11-point Juster’s scale to
estimate trial / repeat.“PURE”
ATTITUDIONALMODEL (based on
declared behaviour).
PREDICTS VOLUME
BASES(Booz, Allen, & Hamilton Co. merged with
Burke Marketing Research)
(Booz Allen Sales Estimation System)
BY
Lin Y.S. Lynn (ex-Pillsburry)
ADVANCED ESP
PREDICTS VOLUME
1979
Assessor(MIT Sloan Business
School, Massachusetts Institute of Technology)
BY
Prof. Glen L. Urban, Prof. Alvin J. Silk
A FIRST MIX OF BEHAVIORAL AND ATTITUDIONAL MODELS(PERCEPTOR).
Behavioral algorithm is complemented by attitudional. Replicates purchasing behavior (ad exposure, realistic shelf, product trial etc.). In parallel, projections are adjusted with outputs from Perceptor.
PREDICTS SHARE
Predecessor of BASES Predecessor of Designor
1975
Perceptor(MIT Sloan
Business School, Massachusetts
Institute of Technology)
BYProf. Glen L. UrbanJacques Blanchard
“A Model for Product Positioning”
A model explaining reasons behind
consumer’s choice. Estimates share of
requirement (or even market share) through attitudes toward brands
and in-market price. Preference score is
based on ratings obtained from paired brand comparisons
(brand vs. brand using 11-point scale)
RECOVERS SHARE
1986
Designor(Novaction)
BY
Jacques BlanchardAnd Novaction team(post. Graduate of MIT Sloan, ex-student of prof. Glen L.Urban, )
ADVANCED ASSESSOR,
Enriched with powerful media model. Sales
projections are adjusted with
normative-based model. Benchmarking
on key success criterias
(IDQV), Powerful Perceptor-based
diagnostics
PREDICTS SHARE
Evolution of Simulated Test Markets. Key milestones:
Source: Based on Cancy K. et al (1994, 2006)
Figure 3.13b
93
Perceptor was designed to derive purchase probabilities from purchaser’s brand
preferences, measured in a competitive context. Despite the fact that Perceptor was
built on a totally different principle than “purchase intent” models, similarly to them, it
remained an “attitudinal” model (i.e. based on declarations), that undermined forecast
reliability. In 1979, on the basis of Perceptor a new model called Assessor was
developed. That model comprised both “attitudinal” (consumer preferences) and
“behavioral” components (observation of shopping behavior in a realistic simulated
store). An unmatched accuracy of the model was achieved by the convergence
(“self-calibration”) of both components the individual respondent level (Urban, 1978).
Later the model was significantly improved by Jacques Blanchard and his colleagues
at Novaction by incorporating proprietary awareness and normative models, by
introducing special designs for brand re-launches, line extensions, and by developing
modifications for early pre-testing. Currently, this version of Assessor is owned and
marketed by Ipsos under the brand of Designor (Clancy et al, 2006). Despite
considerable sophistication and commercialization, no visible breakthroughs in
Simulated Test Marketing have been achieved since early 90-ies. Still the models are
based on a few fairly simple principles (Willke, 2002, Wherry, 2006), which are
considered below.
3.3.3 Inside Simulated Test Marketing: basic principles of calculation
According to Clancy et al (1994, p.49), “all major STM research models are similar
enough that they exhibit some common strengths and weaknesses” due to the use of
similar calculation principles. Although detailed theoretical background is available in
Lilien et al (1992, 2007), Urban et al (1978,1970,1975,1993), Lin (1986,1997) and
Clancy et al (1994, 2006), these principles can be easily demonstrated without
involving sophisticated mathematical apparatus, which is beyond the scope of the
present work (see Section 1.5 “Limitations”). As discussed above, the first principle
of contemporary STM modeling is that sales are decomposed according to Fourt-
94
Woodlock approach, i.e. “trial” and “repeat” components are estimated separately
(See Figure 3.14 overleaf).
Fourt-Woodlock Equation (1960) forms a foundation of contemporary Simulated Test Marketing model
The Fourt-Woodlock equation is a market research tool to describe the total volume of consumer productpurchases per year based on households which initially make trial purchases of the product and those households which make a repeat purchase within the first year.
The Fourt-Woodlock equation itself is
Source: Based on Fourt and Woodlock (1960).
V = (HH * TR * TU) + (HH * TR * MR * RR * RU)
V - sales volume (in units) in a given period of time (usually taken to be one year).
HH - total number of households in the geographic area of projection.
TR - "trial rate“, is the percentage of those households which will purchase the product for the first time in a
given time period..
TU - "trial units" is the number of units purchased on this first purchase occasion
MR - "measured repeat," % of trialists who will purchase it at least one more time within the first year of the
product's launch
RR - “repeats per repeater”: the number of repeat purchases within that same year
RU - number of repeat units purchased on each repeat event
“Trial” volume “Repeat” volume
Figure 3.14
In other words, the precision of the forecast generated by each particular model is
determined by the accuracy of estimates for “trial” and “repeat” components.
According to Farris at al (2006, p.93), modern “system of trial and repeat calculations
… works on the principle that everyone buying the product will either be a new
customer (a ‘trier’) or a repeat customer”. Therefore, the key challenge for each STM
is to deliver accurate assessments of trial and repeat.
The second principle is concerned with the fundamental way of estimating the
probability of purchase. According to available reviews of Simulated Test Marketing
(Wherry, 2006, Lilien et al, 1992, Mahajan and Wind, 1988, Shocker and Hall, 1986),
all currently existing models are based on the two approaches described above – (1)
“purchase intent” and (2) “preference share”. In the first case, the probability of
purchase is generally measured monadically using a purchase intent scale (e.g.
Juster’s scale), while in the second case the probability is always measured against
competitors (See Figure 3.15 overleaf). Therefore, a majority of “purchase intent”
95
models initially generate sales volume projection, while “preference share” firstly
estimate market share (Clancy et al, 2003). However, nowadays there is a trend
among STM vendors for incorporating aspects of both approaches in their proprietary
models “in order to hone their forecasting” (Wherry, 2006, p.3).
Principal difference in estimating of PROBABILITY OF PURCHASE
“Purchase Intent” model:Consumer’s claims, monadic
“Preference Share” model :Consumer’s preferences, direct comparison
Paired brand comparisons on 11 –point scale, across all brands in relevant set. Collected data are as follows:
Pair A B B C A CChips 7 : 4 6 : 5 8 : 3
An example of theoretical scores computed as arithmetic averages of these data, out of a total of 33 chips :
Brand A 7 + 8 = 15 / 33 = 45 %Brand B 4 + 6 = 10 / 33 = 30 %Brand C 5 + 3 = 8 / 33 = 25 %
“Raw” probability of purchase(Brand A) = 45%
45% repsponded “9” and ”10”
“Raw” probability of purchase (Brand A) = 45%
Total chips :33
Figure 3.15
Source: Based on Urban and Silk (1978), Urban and Hauser (1993), Lilien et al (1994, 2007), Farris et al (2006), Clancy et al (1994)
Score Verbal Equivalent
0 No chance, almost no chance (1 in 100)
1 Very slight possibility (1 change in 10)
2 Slight possibility (2 chances in 10)
3 Some possibility (3 chances in 10)
4 Fair possibility (4 chances in 10)
5 Fairly good possibility (5 chances in 10)
6 Good possibility (6 chances in 10)
7 Probable (7 chances in 10)
8 Very probable (8 chances in 10)
9 Almost sure (9 chances in 10)
10Certain, practically certain (99 chances in
100)
An illustration of the PI-based algorithm is given on Figures 3.16a and 3.16b.
An illustration of calculations for “Purchase Intent “-based model. “Trial” component
105 000
50%30%
= 15%Trial
Volume 40%
= 5.9%
2.0
= 12 390
Premium Baby Food
Population in HH
x Projected Awareness x Projected WDistiribution
% HHs aware and able to find on sale
x Trial units (TU)
Trial Volume
x Trial Rate at full awareness and Distribution
Trial rate (TR)
Volume models are based on declared purchasing behavior: “People tend to overclaim their future purchases but they do so with great consistency”
Derived from AWARENESS model. Media model simulates awareness generated by a media plan.Inputs to media model: GRP, number of billboards etc.Outputs: % Awareness
Client’s projection
Derived from the study. Asked before Product trial,after monadic concept exposure: “Would you like to buy this product?”
Step 1. Raw data % TOP2 score 11-points Justers scale:
Certainly will buy (99 chances in 100).............. -10Almost sure will buy (90 chances in 100) …….-9
Then, adjusted for overclaim (coefficients are derived from global normatives database, collected by country/category etc.)
Step2. Adjustment coefficient: from those who claim, only 80% will buy.
50% x 80% = 40%
50%
Based on claimed volume, adjusted using normatives
Source: Based on Farris et al (2006), Clancy et al (2006), Lin (1986)
Figure 3.16a
*
IMPORTANT NOTE ! This illustrates only the basic principle of calculation. In fact, the structure of leading traditional “purchase intent” based models
, such as BASES is FAR more complex
*
96
Triers 6 195Repeat x 1st Repeat (MR) 62%Volume x Repeats Per Repeaters (RR) 2.4
x Repeat Units (RU) 2.2 = Repeat Volume 20 280
=
Total = Total Volume 32 670
% Trial Volume 38%% Repeat Volume 62%
Premium Baby Food
Same approach as for trial…. “People tend to overclaim their future purchases but they do so with great consistency”
Estimated on the previous stage (5.9% trialists * 105 000)
Asked after product trial:“Would you like to buy this product in the future?”
Step 1. Raw data % TOP2 score 11-points Justers scale:
Certainly will buy (99 chances in 100).............. -10Almost sure will buy (90 chances in 100) …….-9
Then, adjusted for overclaim (coefficients are derived from global normatives database, collected by country/category etc.)
Step2. Adjustment coefficient: from those who claim, only 80% will buy.
77% x 80% = 62%
77%
Based on claimed volume / frequency , adjusted using normatives
An illustration of calculations for “Purchase Intent “-based model. “Repeat” component
Source: Based on Farris et al (2006), Clancy et al (2006), Lin (1986)
Figure 3.16b
IMPORTANT NOTE ! This illustrates only the basic principle of calculation. In fact, the structure of leading traditional “purchase intent” based models
, such as BASES is FAR more complex
*
*
In the case of “preference share”, the trial rate observed during the realistic shelf
experiment is adjusted using the outcome from the preference model, as described
on Figure 3.15. The repeat component is calculated in a very similar way. The flow
of the algorithm is shown on Figure 3.17a and Figure 3.17b (overleaf).
105 000
50%30%
= 15%Trial
rate % 40%
= 5.9%
Premium Baby Food
Population in HH
x Projected Awareness x Projected WDistiribution
% HHs aware and able to find on sale
x Trial Rate at full awareness and Distribution
Trial rate (TR)
Estimated trial rate (% of trialists) is obtained from realistic shopping exercise (behavioral component) , then calibrated against preference model (attitudinal component)
Derived from media model. Media model simulates awareness generated by a media plan.Inputs to media model: GRP, number of billboards etc.Outputs: % Awareness
50%
Derived from the study: Captured before product usage, after concept exposure (in competitive environment).Step 1. Raw data. Observed trial from realistic shelf
Step 2. Calibration . Adjustment with the preference model
50%40%
Client’s projection
50%Raw trial%
Estimatedtrial%
An illustration of calculations for “Preference share“-based model. “Trial” component
Figure 3.17a
Source: Based on Urban and Silk (1978), Urban and Hauser (1993), Lilien et al (1994, 2007),
IMPORTANT NOTE ! This illustrates only the basic principle of
calculation. In fact, the structure of leading traditional “preference share” models , such as
Assessor or Designor is FAR more complexand comprise a variety of models (“convergence
principle”)
*
*
97
After product use:
Step 1. Raw data – Declared Purchase Basket in the category in the next month (3 months etc.) ,
Step 2. Calibration . Convergence with the preference model
Trial Rate 5.9%Repeat x Projected volume share 40%
(Volume among trialists share) ("share of requirement")
= Volume share 2.4%
x Total Market Size 1 384 322
Total = Total Volume 32 670
Premium Baby Food
At this stage, share models estimate VOLUME SHARE AMONG TRIALISTS (i.e. , “share of requirement (SOR)”, in volume)
Estimated on the previous stage
45%
AGGREGATED ACROSS SAMPLE
Raw Share of Requiement
(Volume share)
45%40%
EstimatedSOR %
An illustration of calculations for “Preference share“-based model. “Repeat” component
Figure 3.17b
Source: Based on Urban and Silk (1978), Urban and Hauser (1993), Lilien et al (1994, 2007),
*
IMPORTANT NOTE ! This illustrates only the basic principle of
calculation. In fact, the structure of leading traditional “preference share” models , such as
Assessor or Designor is FAR more complexand comprise a variety of models (“convergence
principle”)
*
Noteworthy, that both approaches receive a lot of criticism. Although “purchase
intent”-based approach has proven to be a simple and a highly accurate technique in
“western” markets (Lin, 1986, Bemmaor, 1995), it is often criticized for its inability to
consider competitive environment, over-reliance on norms, a tendency to penalize
true innovations (especially at early stages of development) . In particular, Markovitz
(2007) point out the following disadvantages: (1) Purchase intent advances line
extensions and incremental innovations and kills breakthrough and targeted
products, (2) Purchase intent fails to discriminate differences in concept positioning,
(3) Purchase intent does not reflect the competitive context…and databases cannot
be relied upon as competitive benchmarks, (4) Purchase intent is not diagnostic as it
fails to provide understanding how to rework concepts. At the same time, the leading
providers of “purchase intent” based models, such as BASES, recognize these
drawbacks and try to improve their models (Adrien and Mooth, 2009). “Relying on the
performance standards from the past without adjusting for current market conditions
is likely to result in blind spots that can get in the way of success… The current key
measures of success—such as purchase intent…—continue to be critically important
98
and are key to accurate estimations of volume potential. But there are a host of new
factors—such as breaking through clutter, generating buzz and offering true
innovation—that also need to be considered” (Adrien and Mooth, 2009, p.1).
Concerning “share-preference” models, they are better documented in the academic
sources and sometimes show superior accuracy as compared to “purchase intent”
models (Urban and Hauser, 1993, Bell et al, 2004, Lilien et al, 1992, 2007, Clancy et
al, 1994). However, they are often considered as far too complex (information which
is not understood has no value and is not transferrable within an organization), time-
consuming and demanding in terms of inputs (market data, finished commercials,
shelf schemes, competitive packs etc) (Clancy et al, 1994, Fader and Hardie, 2005).
Despite the seeming simplicity of calculations shown above, the precision of
Simulated Test Marketing is achieved only with the use of advanced modeling
instruments and accurate market data. Typically, an STM model is a “state-of-art”
intellectual product in terms of marketing engineering, statistics and mathematics,
particularly the theory of probabilities. As pointed out by Clancy et al (1994, p.28),
“simulated test marketing is the single best validated tool in all of marketing research.
For new packaged goods, the better STMs can forecast what will happen in the real
world, plus or minus 15 percent”. An overview of such STM techniques is provided in
the next section, with the special attention given to their potential capabilities to
forecast sales in emerging markets.
3.3.4 Review of the leading providers of Simulated Test Marketing
According to the recent study by Wherry (2006, p.6), “today the industry is highly
consolidated. Most of the STM techniques are similar with validation rates that they
highly effective. The major vendors are able to differentiate their services through
customized models, the experience of senior researchers and experimentation with
the latest research trends”. The leading STM models that were originally based on
99
purchase intent are VNU BASES and RI Microtest (marketed as TNS eValuate since
the merge of TNS and RI). The major provider of “share of preference”-based models
is Ipsos. Currently the market is dominated by BASES, particularly in the U.S., as
shown on Figure 3.18.
In terms of market share, BASES is considered the leader, with about 50% of the global STM market, with a higher estimate within the U.S.
Ipsos (Designor)
20%
Other
30% VNU (BASES)
50%
Source: Based on Wherry (2006)
Leading “Volume”-based models Leading “Share”-based models
• VNU Bases (Pre-BASES, BASES I, BASES II)• Research International (MicroTest) / TNS eValuate• Aegis Copernicus (Discovery)• Aegis Synovate (MarketQuest, MarkeTest)
• Ipsos (Designor)• M/A/R/C Research (Assessor)
Figure 3.18
The two leading models are considered below, according to the information obtained
from the independent academic sources (Wherry, 2006, Clancy et al, 1992, 2006,
Lilien et al, 1992, Mahajan and Wind, 1988, Shocker and Hall, 1986, Lin, 1986, 1997,
Urban et al , 1978,1970,1975,1993).
BASES is an STM service offered by VNU holding, which acquired ACNielsen, the
owner of the model at that moment, in early 2000-ies. BASES has separate
forecasting solutions for every NPD stage from concept evaluation to in-market
testing and tracking, in particular – Snap Shot, Pre-BASES, BASES I and BASES II.
A number of additional tools are available, such as Restager, Decision Point, Price
Advisor, Launch Advisor, Find Time, Franchise Growth Analysis etc. Commercial
brochures can be obtained from www.en-us.nielsen.com . As mentioned earlier,
originally BASES is built around the use of attitudinal scales (such as purchase
100
intent) and normative data (in particular, extensive databases provided by IRI and
ACNielsen). Although an article by Lin (1986) provides some sketchy information
about BASES design, the model has not been well documented in the academic and
business literature. So far it has been a “black box” for the academic community and
forecast users. However, it is worthy to mention the following specifics:
Test product and packaging requirements. BASES require at least a picture of
packaging (including brand name and logo). The test product must be
production-line quality. No competitive packs are typically required.
Advertising requirements. BASES works with a finished concept board in a
standard form (see above Section 3.2.1), an animatic or a ready commercial.
No competitive concepts or commercials are required.
Other information required. Detailed market data, marketing plans (media,
distribution, promotional activities)
Design of the shopping environment. BASES approach does not include a
realistic shopping experiment (unless it is not needed for additional
diagnostics). Instead, it gauges consumer reactions on the tested concept in
the form of “purchase intent” and attitudinal scales.
Sampling. Typically the sample represents a broad audience (total
population), however, boosts are possible. Sample size normally exceeds
400 respondents, with callback data collected from about 200 triers.
The initial interview. An initial brief interview is conducted as part of the “mall-
intercept” procedure. Few questions are asked about category and brand
usage for several categories, including the test brand category.
Exposure to advertising. BASES respondents are typically exposed to
concept boards, and not ready commercials. This is usually done in no
competitive context.
Post-Purchase and Usage Experience. Only those with positive purchase
intent are given with a test product for home use. Then, within the two or four
101
weeks, BASES survey these respondents by phone regarding their future
purchasing intentions, frequencies and amounts. Also, the interview includes
questions on product satisfaction, consumption occasions etc.
Sales forecasts. Typically BASES output includes: Sales volume in the first
and the second year (optionally), trial rate (for the first year), repeat rate (for
the first repeat purchase), repeats per repeater, average time between
purchases, average number of units per trial and repeat purchase occasions,
awareness.
Diagnostic Information. Mandatory outputs are: Respondent likes and dislikes
of the concept, profiles of triers, test brand image diagnostics based on
ratings
Timing and costing. On average, BASES provides a volume estimate within 6-
10 weeks from the field start. An approximate price for a full BASES test is
approximately $50,000-$60,000 (Clancy et al, 2006).
Validity. ”It is the large number of product launches in the database (over
60,000) that contribute to the BASES edge in the market place, with an
accuracy level within 9%” (Wherry, 2006, p.9). According to the company
brochures, validation statistics are not split by the stage of the studied market,
however, there is breakdown by geographical area. According to the
company’s validation record, an overwhelming majority of validations (above
85% of 1400+ cases) were made in the developed markets of Northern
America, Europe and Japan.
Designor is a sophisticated Simulated Test Marketing service provided by Ipsos, a
French research company, which bought Novaction from its founder Jacques
Blanchard in 2001. Similar to BASES, Ipsos offers a wide range of tools developed to
assist the NPD process, from the stage of idea generation to the launch tracking. In
particular, the newly-introduced Designor NextGen, which utilizes normative
database of original Designor, can be built-in to almost every quantitative test
102
conducted by Ipsos. However, traditional Designor STM remains the flagship
forecasting product in the Ipsos’ range due to its superiority as compared to Designor
NextGen. Further information is available at www.ipsos.com. As discussed above,
Designor is an enhanced version of Assessor, which comprises attitudinal
(“preference share”) and behavioral components (realistic shopping experiment) and
therefore is “self-calibrated”. Unlike BASES, normative databases are only
supplementary to the Designor model. According to Wherry (2006, p.13), this allows
Ipsos claiming that Designor is suitable for any countries and categories “where has
been no previous testing”. However, according to Urban and Hauser (1993) and
Clancy et al (1994), Assessor-based models are suitable only for a well-defined
market (or category), therefore a very detailed market data for Designor is a must. In
fact, Assessor is definitely the best documented STM model (Urban et al,
1978,1970,1975,1993). This really helps in understanding Designor and somehow
offsets its structural complexity. Besides that, there are the following points to
consider:
Test product and packaging requirements. Unlike BASES, Designor needs
competitors’ packs in the same format as tested (images or mockups). The
test product must be production-line quality.
Advertising requirements. Designor typically works with a wide range of
materials, such as concept boards in a standard format (see above Section
3.2.1), animatics or ready commercials. Competitors’ materials must be in
the same format.
Other information required. Very detailed market data, marketing plans
(media, distribution, promotional activities)
Design of the shopping environment. Designor incorporates a realistic
shopping experiment with the real shelf and “a voucher” (substitute for
money). This allows to “capture” the moment of purchase decision in store.
103
Sampling. Typically, the sample consists of category users. However, non-
users are often recruited to gauge category incremental volume. Sample size
ranges from 250 to 600 respondents, with callback data collected from 70%
of sample.
The initial interview. Technically is similar to BASES, although involve deeper
study of consumer preferences and repertoire.
Exposure to advertising. This is always done in the competitive context. For
all commercials (or concepts), Designor measures advertising impact,
relevance and differentiation. A set of diagnostics includes brand recall,
content recall and several secondary metrics.
Post-Purchase and Usage Experience. Typically this procedure is considered
as sampling, i.e. all respondents are given with a test product for home use.
The rest is similar to BASES in terms of data collection.
Sales forecasts. Typically the output includes: Year 1 and Year 2 volume
sales build – up, volume and value market shares, price elasticity,
cannibalization estimates, awareness modeling.
Diagnostic Information. Mandatory outputs are: 1) measures of IDQV (impact,
differentiation, quality and value), 2) Perceptor analysis based on a factor
regression of attributes across respondent’s unique brand consideration set.
These tools provide useful insights for further improvements.
Timing and costing. In line with BASES (Clancy et al, 2006).
Validity. Similar to BASES, the average accuracy reported by Ipsos is 9%
(Wherry, 2006, p.12). Like BASES, an overwhelming majority of validations
(above 85% of 600+ cases) were made in the developed markets of Northern
America, Europe and Japan.
The other popular STM approaches on the market are TNS eValuate (former
Researh International’s MicroTest), Aegis Copernicus’s Discovery, Synovate’s
MareTest / MarketQuest, M/A/R/C Group’s Assessor FT, TNS FYI (which is no
104
longer marketed, but still used by some loyal clients), GfK MarketingLab / Volumetric
TESI (which holds considerable share in Germany) and In-Vivo MarketMind (which is
a significant vendor in France). A comparison table for the leading STM models is
shown on Figure 3.19 below.
Provider Industry Data collection Diagnostics ForecastClaimed Accuracy
Model Positioning Site
VNU BASES (Pre-Bases, BASES I,
BASES II)
Mainly FMCG (dominated in the US)
Centralized locations, e-panel, phone
Target analysis, cannibalization analysis, price
sensitivity testing
Yr1, Yr2 sales (Yr1 with Pre-Bases)
+/- 9%
Purchase intent for trial; attitudinal scales and
normative databases for repeat
Brand name (industry leader), forecast accuracy, validated,
high level consultancy, extensive databases and wide expertise
http://en-us.nielsen.com/
Ipsos Novaction (Designor, Designor
Next Gen)FMCG, OTC
Centralized locations, mall, phone
Brand recall, shelf visibility and competitive comparison.
Sensitivity analysis. Forecasing module
(Next Gen)
Yr1, Yr2 sales, share,
(Yr1 with Next Gen)
+/- 9%
Originally a convergent model based on
competitive set (enhanced ASSESSOR). Depending
on the option, utilizes attitudinal, preference,
normative, observational data
Comprehensive diagnostics, realistic shelf experiment,
perceptor sensitivity analyses, self-calibration, modular
approach (Next Gen)
http://www.ipsos.com
TNS (eValuate) / RI (MicroTest)
FMCG, services (financial, air travel, entertainment) and
pharma (product with mid-late stage development)
Centralized locations, phone, internet.
Respondent data collection vs aggregate.
Intercept
Provides diagnostics to
improve marketing plan: target market,
price sensitivity, position,
Yr1, Yr2 sales, share
and incidence; quarterly
sales
+/- 9%
Purchase intent forecasting at concept
level with in-home placement. Uses priced
competitive set
Respondent level forecasting; calibrates level of customer inertia within each category;
strong non-US presense
http://www.tnsglobal.com
TNS FYI* (Foresight, Insight, RePurchase)
*) no longer in the market
DTS prescription pharma (leader), FMCG, financial
sservices, durables
Internet on-line samples; calibratd b/c different
responses for purchase intent from mall
respondents
New methods for cannibalization and key driver analysis
Yr1 sales, trial, repeat
+/- 15%Based on Purchase Intent
and rating scales for concept testing
Diagnostics using new methods, expertise within pharmaceutical
industry
http://www.tnsglobal.com
M/A/R/C Group (ASSESSOR FT)
Move towards financial services,
pharma, still in FMCG
Centralized locations, phone, internet.
Price sensitivity; competitive
response; lloks at cannibalization
when forecasting for line extension
Yr1 sales and share;
trial, 1st and 2d repeat forecast
+/- 10%
Concept exposure with product placement, improved version of ASSESSOR. Uses
diffusion modeling for longer-term forecasts
Based on competitive set; highly customizable; strong forecast
and diagnostics
http://www.marcresearch.com
Copernicus (Discovery)
FMCG, DTC, financial services, durables -
focuses on evaluating / improving in-market
performance
Centralized locations for store mockup, phone and on-line (consumer, b2b,
physicians)
Provides info & recommentations re: target market,
positioning, media, pricing etc - all to
improve marketing
Yr1-Yr3 sales,
consumer awareness,
trial and repeat,
+/- 10%
Combination of various models, but includes
additional in-home periods for "wear-in/wear-out"
phenomena
Takes into account consumer memory decay and new entrants. Incorporates
interaction of awareness drivers. Employs defensive response
modeling
http://www.copernicusmarketing.c
om
Synovate (MarkeTest, MarketQuest)
FMCG, non-food consumer products (toys, magazines)
On-line consumer panel. ION system in malls (multimedia kiosk)
Cannibalization analysis; target market for line
extensions, restaged products
Yr1 trial and sales;
awareness, trial and
repeat units
n/d
Purchase intent; always priced, but not necessarily
competitive set. Uses attitudinal model for
repurchase
A suite of models and simulators for quantifying the market
potential of a product or service. Measures product performance on product attributes; also uses local professionals to take into
account local culture and market when forecasting. Interactive
simulator
http://www.synovate.com/marketq
uest
Comparison chart of STM provider differences
Source: Based on Wherry (2006) , Clancy et al (1994, 2006), Lilien et al (1992, 2007)
Figure 3.19
Apart from the leading international STM products, some locally developed solutions
are offered in the Russian market, in particular by A/R/M/I Marketing (see www.armi-
marketing.com/), Comcon (see www.comcon-2.ru) and MASMI (see
www.masmi.com). Given the existing wide range of the models, it is essential to
study how companies chose and apply them. This is discussed in the next section.
3.3.5 Effective practical use of Simulated Test Marketing
It is imperative that a forecaster has a deep understanding of the market, which is to
be modeled, as well as modeling principles, which are to be applied. It is important to
remember that there is no such thing as “magic crystal ball”, and the quality of
forecast is entirely determined by the quality of inputs. In particular, as discussed
earlier, the quality of Simulated Test Marketing project is a function of that of its
105
components - market model, marketing planning system, awareness model, trial
purchasing model, repeat purchasing model and competitive response model.
Therefore, while deciding on “go/no go” with STM or selecting the most effective
approach, a forecaster must be prepared to answer some essential questions related
to each particular component. These questions are summarized on Figure 3.20.
Essential control questions to ensure effective use of STM
Source: Clancy et al (1994), Lilien et al (1992)
Figure 3.20
Market modeling (see Chapter 2, Figure 2.4)Needs, occasions, product characteristics etc
Size of Potential Market (People/Units)
Market Growth / Decline
Seasonality
Purchase cycle
Segments / Sub-segmentsCompetitive Intelligence (shares, domination, fragmentation, brands, products, positioning,
Marketing Planning:
Marketing planning - Effective Distribution, Media (TV, Outdoor
etc), PR, Sampling, POP activitiesClient's marketing planning system
Does the marketing plan use the same terms, variables and formats as compared to the market model ? What is the justification for claiming that this
planning approach will work best in this particular case / market ?
Modeling consumer response:
Awareness Generation (Function of Multiple Awareness States - W-O-
M, Advertising (TV, Outdoor, Internet), Couponing, Sampling, Distribution)
Awareness model
What makes sure that this model is adequate in terms of country specifics (cultural, educational, economic differences, media infrastructure) as well as
market specifics (stage of development, consumer experience and qualifications, purchase cycles, sales infrastructure ?)
Trial% Due To: Advertising, Couponing, Sampling, Distribution
Trial Volume
Repurchase Levels%, Repeat Cycles
Repeat Volumes
Modeling competitors response:
Competitive Intelligence (strategies, potential response, threats)
Competitive response modelAre there identifiable strategies pursued by competitors ? What is their likely
response, what kind of action to expect ? How probable is it? What is the possible damage?
Market model / Business environment model
How clear and strict is the market definition in terms of consumers needs, consumption occasions and product characteristics? Are there any close or
"substitute" categories? At what stage is the market ? How stable is the market infrastructure, how predictable is it? Is there enough knowledge and
data accumulated? How accurate is this information? Are there known drivers and barriers to market development? Are there similar markets in other
countries? Are there competitors, what is their market weight ?
Trial purchasing model. Consumer response to communication
and positioning, pack (on-shelf performance), price
What are justifications for the use of the chosen model in THIS PARTICULAR market ? Is there enough evidence of high accuracy in similar cases (country, category, stage of market development)? What is the level of accuracy obsereved in such cases? Is the chosen method of data collection appropriate ? Is the sample representative? Does the model provide enough
information to make quality decisions in time? Repeat purchasing model: Consumer
response to product, pack (in-use preformance), price
Component of Simulated Test Marketing Model Typical questions that should be answered during the process
As discussed earlier in Section 3.3.1, Simulated Test Marketing incorporates a
variety of forecasting instruments (See Figure 3.6). However, in fact, very few of
them can be applied at “early” stage of market development due to the lack of
information. Quantitative time-series instruments may be employed at “growth” stage
as the amount of historical data considerably increases. As market saturates, a
majority of instruments become available for use, including econometric modeling.
(See Figure 3.21 overleaf). The analysis of accuracy for particular forecasting
instruments (see Figure 3.7), leads to a conclusion that the accuracy of Simulated
Test Marketing at “early” stage of market development is “poor” in the mid-term (1-2
years). Taking into account the amount of time required to conduct the study, this
makes the forecast practically negligible, although, the study may have some value in
106
terms of diagnostics (i.e. revealing barriers for adoption, dislikes etc). As discussed
earlier, poor quality forecasting at this stage may produce misleading conclusions
about long-term market success of the new product. The analysis has shown that
Simulated Test Marketing is only effective at the later stages of market development.
(See Figure 3.22).
Forecasting instruments employed in STM (marked by “”) at “early”, “growth” and “saturation” stages of market development
Source: Based on Clancy et al (1994), Lilien et al (1992)
Figure 3.21
Pri
mar
y o
r
Jud
gem
enta
l
Ind
irec
t
Lea
rned
beh
avio
r
Tim
e S
eri
es
Cau
sal
Pri
mar
y o
r
Jud
gem
enta
l
Ind
irec
tL
earn
ed
beh
avio
rT
ime
Seri
es
Cau
sal
Pri
mar
y o
r
Jud
gem
enta
l
Ind
irec
tL
earn
ed
beh
avio
rT
ime
Seri
es
Cau
sal
Market modeling (see Chapter 2, Figure 2.4)Needs, occasions, product characteristics etc
Size of Potential Market (People/Units)
Market Growth / Decline
Seasonality
Purchase cycle
Segments / Sub-segments Competitive Intelligence (shares, domination, fragmentation, brands, products, positioning,
Marketing Planning:
Marketing planning - Effective Distribution, Media (TV, Outdoor etc),
PR, Sampling, POP activitiesClient's marketing planning system
Modeling consumer response:
Awareness Generation (Function of Multiple Awareness States - W-O-
M, Advertising (TV, Outdoor, Internet), Couponing, Sampling, Distribution)
Awareness model
Trial% Due To: Advertising, Couponing, Sampling, Distribution
Trial Volume
Repurchase Levels%, Repeat Cycles
Repeat Volumes
Modeling competitors response:
Competitive Intelligence (strategies, potential response, threats)
Competitive response model
Market model / Business environment model
Trial purchasing model. Consumer response to communication
and positioning, pack (on-shelf performance), price
Repeat purchasing model: Consumer response to product, pack (in-use
preformance), price
Component of Simulated Test Marketing Model
Early stage Growing Saturation
Estimated STM accuracy across stages of market development:Quite poor at early stages, good at “growth” and “saturation”
Source: Based on Clancy et al (1994), Lilien et al (1992)
Figure 3.22
“Very Poor” to “Poor” “Fair” “Good” to “Excellent”RANGING FROM 1= “VERY POOR” TO 6=“EXCELLENT”
Short-term (0-3 mon)
Mid-term (3 mon-2yr)
Long-term (2yr+)
Short-term (0-3 mon)
Mid-term (3 mon-2yr)
Long-term (2yr+)
Short-term (0-3 mon)
Mid-term (3 mon-2yr)
Long-term (2yr+)
Market model 3.3 2.4 2.3 4.0 3.6 2.5 4.4 4.6 2.7Needs, occasions, product characteristics etc 3.3 2.4 2.3 3.3 4.2 2.7 3.3 4.2 2.7
Size of Potential Market (People/Units) 3.3 2.4 2.3 4.2 4.2 2.7 4.5 5.0 2.8Market Growth / Decline 3.3 2.4 2.3 4.2 4.2 2.7 4.5 5.0 2.8
Seasonality 3.3 2.4 2.3 4.2 3.2 2.3 4.5 5.0 2.8Purchase cycle 3.3 2.4 2.3 5.0 3.2 2.3 5.0 5.0 2.8
Segments / Sub-segments 3.3 2.4 2.3 3.3 2.4 2.3 4.2 3.2 2.3
Competitors 3.3 2.4 2.3 4.2 4.2 2.7 4.5 5.0 2.8Marketing Planning: 3.3 2.4 2.3 3.3 4.2 2.7 4.5 5.0 2.8
Marketing planning 3.3 2.4 2.3 3.3 4.2 2.7 4.5 5.0 2.8Modeling consumer response: 4.6 2.8 2.4 4.6 4.2 2.7 4.9 5.0 2.8
Awareness Generation 3.0 4.2 2.7 3.0 4.2 2.7 4.5 5.0 2.8Trial% Level 5.0 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8Trial Volume 5.0 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8
Repurchase Levels%, Repeat Cycles 5.0 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8Repeat Volumes 5.0 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8
Modeling competitors response: 3.3 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8Competitive Intelligence (strategies, potential
response, threats) 3.3 2.4 2.3 5.0 4.2 2.7 5.0 5.0 2.8
TOTAL ACCURACY 3.6 2.5 2.3 4.2 4.0 2.6 4.7 4.9 2.7
Growing SaturationEarly Stage
Component of Simulated Test Marketing
Poor to Fair Fair to Good Fair to Excellent
107
This has been practically confirmed over the past decades in the Western markets.
Thus, Gundee (1982, p.3) points out that “increased use of the technique has created
a pressing need to avoid potential problems. Starting with the premise that the
simulated systems are only as good as the data they receive, great care must be
taken with the use of the techniques – greater care, in fact, than with many other
conventional types of studies. The reason is that the inputs eventually result in ‘The
Number’, a market share or a sales volume projection. Herein lies the danger”.
Gundee (1982) advises the following when planning a Simulated Test Marketing
study: 1) don’t use models too early - in the market or product development cycle, 2)
correctly define the target audience, 3) don’t load the attitudinal measure to favor the
new product, avoid having attributes which read like the test products copy points, 4)
don’t be overly optimistic about attainable levels of awareness and distribution, 5)
don’t ask the models to measure sales beyond their accuracy range, 6) define goals
before the test begins, 7) don’t rely solely on observed purchases in the test center.
According to Prince (1992), 1) marketing plan assumptions should be linked to the
realities of market history and, if possible, should be modeled, 2) the test product
must fit into an existing product category,3) the accuracy of projections is heavily
dependent on assumptions about market variables. Prince (1992) has identified that
the following factors are particularly important for STM services selection: 1) external
validity, i.e. ability to predict accurately, 2) cost and timing, 3) quality of diagnostics,
4) coordinated thinking and judgements of a company’s researchers, agency
researchers and brand managers, 5) compatibility with company’s standards of
research 6) capabilities for simulation, 6) word-of-mouth, vendor’s reputation, reviews
by consultants. Wilson (1990, p.20) draws particular attention to the quality of market
data – “90% of forecasting error is traceable to marketing input”. He suggests that in
case of poor market information, simpler research techniques must be employed.
Fader (2005) argues that the model should be simple and clear enough to be
effectively used in client’s organization. Mahajan and Wind (1988) argue that use of
108
Simulated Test Marketing is inappropriate when the product class is new or the
market is growing (see Figure 3.23).
STMs are generally not recommended at early stage of the market (“new-to-the-world”). A simpler diagnostics test should be considered instead
Figure 3.23
Source: Mahajan and Wind (1988)
(STM) (Traditional approach)
There are few independent studies on effective use of Simulated Test Marketing in
the “western” markets. The most recent study conducted by Advertising Research
Foundation surveyed 42 marketing research experts employed by the largest FMCG
manufacturers in the U.S. (Baldinger, 1988, 1991). The study produced the following
findings:
Companies are “largely using STMs to reduce the test market failure rate by
eliminating poorer performing ideas before the high costs of test market are
incurred” (Baldinger, 1988, p.5). 94% of respondents use STM for new
products, 78% - for line extensions.
The key benefits of using STM are: 1) Risk reduction (55% of responses), 2)
Diagnostics/What if capability (33% of responses), 3) Confidentiality (14% of
responses), 4) Cost effectiveness vs traditional test marketing (10% of
responses), 5) Product optimization (7% of responses), 6) Speed (7% of
responses)
109
The predominant negatives include: 1) Validity, “with a typical complaint that
STMs don’t reflect highly complex situations accurately” (Baldinger, 1988,
p.7) (40% of responses), 2) Analytical / communication issues such as “black
box” approach and poor communication of the meaning of estimates to
marketing (31% of responses), 3) Methodological limitations related to
category appropriateness/experience (17% of responses) and estimation of
long-term repeat rates in long purchase cycle categories (24% of responses)
Perceived STM validity is moderate: 52% of forecasts were confirmed in
reality, 41% of respondents claimed that actual sales were lower than
predicted, 8% reported that sales were higher than the forecast.
The 20/80 rule definitely applies to STM market. A few large manufacturers
account for a disproportionally high share of STM activity.
81% of respondents participated in at least one STM project during one year,
62% participated in 1-4 such projects, 20% participated in more than 5 STMs,
Average STM cost is around $45 000
To use Simulated Test Marketing effectively a client-side researcher must carefully
select an external vendor of this service. Thus, Malhotra (2007) advises that a firm
should put together a list of prospective suppliers, based on trade publications,
professional directories (such as www.greenbook.org or www.esomar.org) and word
of mouth. When selecting, an in-house researcher must consider the following
questions: “1) What is the reputation of supplier? Do they complete projects on
schedule? Are they known for maintaining ethical standards? Are they flexible? Are
their research projects of high quality? What kind and how much experience does the
supplier have? Has the firm had experience with the projects similar to this one? Do
the suppliers personnel have both technical and non-technical expertise? … Are the
personnel assigned to the task sensitive to the client’s needs, and do they share the
client’s ideology? Can they communicate well with the client?” (Malhotra, 2007, p.21).
Also, Malhotra (2007) points out that this decision should be well-informed and not
110
only price-based. In addition to that, the studies on client satisfaction in B-2-B sector
have confirmed the importance of collaborative relationships between client and
supplier (Boughton, 1996, Wetzels et al, 1998, Wuyts et al, 1998, Woodruff and Flint,
2003).
Therefore, the discussion above leads to a conclusion that an effective application of
STM is fully determined by the following factors: (1) General quality of services
(‘highly accurate forecast’, ‘professional presentation of findings and
recommendations’), (2) Supplier credibility (‘recommended by management or
company protocols’, ‘positive previous experience’, ‘well‐known agency’), (3) Model
characteristics (‘simple and easy‐to‐understand’, ’suitable for particular market or
category’, ‘a lot of diagnostic information’, ‘publications in professional literature’), (4)
Communication quality (‘high quality service and project management’,’ individual
approach for every project’), (5) Price (‘affordable price’, ‘transparent and flexible
pricing’), (6) Technological and organizational factors (‘high speed of research’,
‘superior data collection’, ‘use of modern technologies’).
3.3.6 The ways of further development of STM models
According to one of the most influential opinion leaders in Simulated Test Marketing,
Joseph Willke, The President of ACNielsen BASES worldwide, “current STM models
are not well suited for the future marketing world of one-to-one consumer targeting
nor are they well suited for the changing retail environment. They are not well
adapted to the increasing granularity with which businesses are managed – at the
SKU level, at individual store level, week by week. As the world changes, all the
current forecasting models will begin to break”. (Willke, 2002, p.1). Willke identified
the following challenges to address in the future STM models: 1) adaptation to ‘one-
to-one’ marketing driven by the use of modern communication technologies, shifting
away from ‘mass’ marketing approach, 2) sample sizes of contemporary STMs
111
(n=300-400) are far too small for reliable forecasting in modern markets, 3) STMs will
need to forecast at the weekly/monthly level to effectively support production and
inventory control, 4) STMs will have to produce projections at SKU level 5) STMs will
assist in optimizing marketing plans and developing long-term marketing strategies
(including modeling competitor responses). Wherry (2006) highlights the following
trends: 1) STMs are being frequently applied to new dynamic industries and markets,
2) The Internet has become the main channel for data collection, 3) Recent decades
have seen considerable advancements in diagnostics, particularly in the area of key
driver analysis and in-store observations, 4) new methods of estimation such as
hierarchical Bayes and other methods beyond standard regression. Wherry (2006)
shares a Willke’s point of view with respect to challenges that lie ahead of STM: 1)
developing forecasting capabilities on niche and new markets, at the respondent and
SKU levels, 2) evaluating sensitivities to complex marketing programs that involve
new media such as online communities, word-of-mouth, in-store communication 3)
forecasting data in more granular time periods, 4) foreseeing competitive reaction.
The summary of this discussion is presented on Figure 3.24.
Developing solutions for the new dynamic emerging markets is among key areas for improvement
Key areas for improvement Internet will become the main channel for data collectionDiagnostics and Consultancy quality analysis will be improved Incorporating Conjoint techniques, new methods of estimation such as
hierarchical Bayes, discrete choiceSpeed and accuracyReflecting rapid changes in retail / shopping environment Accurate forecasting in the new dynamic emerging markets
Challenges that lie ahead of STMSmaller Markets Require More Customization and Granularity
Smaller consumer markets and segmentsComplex targeting in media plansForecast data in more granular time periods
Improve New Product Success Rate (still at best less than 20%)New Tools Required for Simulating Awareness (Buzz marketing, Word-of-
Mouth effects simulation: multi-media, internet, blogs etc.)
Figure 3.24
Source: Wherry (2006) , Willke (2002)
112
3.4 Key findings
The analysis performed in Chapter 3 leads to the following key conclusions about
marketing research role in FMCG NPD process:
Marketing research is essential for NPD process, regardless of the stage of
market development
Inappropriate use of marketing research in NPD (in particular, at early stages
of market development) can give rise to misleading conclusions about future
success of true innovations
Considerable differences between research programs for various types of
new products are determined by the stage of market development and
company’s strategy.
Traditional and Simulated Test Marketing are important elements in NPD
marketing program, particularly for those new products that are launched at
late stages of market development
In case of “new-to-the-country” product, the design of Simulated Test
Marketing is heavily influenced by peculiarities of the local market and the
stage of its development
The accuracy of forecast increases with the development of the market: the
use of techniques shifts from “judgemental” to “time-series” and “causal”.
Simulated Test Marketing is a highly complex research process, comprising
the use of wide range of forecasting instruments
Test Marketing evolved with the “western” markets. Simulated Test Marketing
was originally developed for saturated FMCG markets in 70-ies and 80-ies.
“Purchase intent” and “preference share” are two key principles of modeling
consumer response to a new product offer, which underlie the foundations of
the two respective models, ACNielsen BASES (50 % global STM market
share) and Ipsos Designor (20% global STM market share)
113
The accuracy of STM is heavily dependent on the quality of market data
(especially, in case of Designor), as well as on the quality of accumulated
historical information (especially, in case of BASES).
Claimed average accuracy of Simulated Test Marketing in the “western”
markets is +/-9% (“Good”)
The analysis undertaken has proven “poor” accuracy of STM forecast at the
“early stage” of market lifecycle, while that at the later stages is estimated to
be “fair to excellent”.
Developing solutions for emerging markets is among key challenges for the
STM vendors at the moment
114
CHAPTER 4
SALES FORECASTING IN THE RUSSIAN FMCG MARKET
4.1 Forecasting sales of new consumer goods in Russia As discussed in the previous chapters, Russian consumer goods market has shown
tremendous progress over the past decade and is expected to deliver solid double-
digit growth in the next few years (see Figure 2.26b). A majority of FMCG markets
are at the “growth” stage and only few of them can be considered “saturated” (see
Figure 2.27). Although Russian FMCG market offers great sales opportunity in the
long-run, there is a considerable level of uncertainty related to hidden consumer and
business insights. Uncovering these insights with relevant marketing research and
forecasting tools may decrease the level of uncertainty and bring a sustained profit in
the long-run. The need in such tools has been widely recognized in the Russian
market as discussed in Section 2.4.2. According to ESOMAR Industry Study
(2004,2008,2009) Russian marketing research industry grows by +25% per annum
(on average), while that for North America and World are +5% and +9% respectively.
“Russia continues its dynamic expansion” and is ranked among most promising
research markets in the world, being classified to group ‘A’ as growing at double-digit
rate (ESOMAR, 2008, pp.7, 34-35). All major players of the global marketing
research industry (such as Nielsen, Ipsos, GfK, TNS / Kantar Group, Synovate) are
already in the Russian market (ESOMAR, 2010). The Pricing Study by ESOMAR
(2010) shows that a majority of marketing research tools discussed in the Section
3.2 (such as qualitative studies, quantitative tests, strategic exploratory stidies etc.)
are also available in Russia. Moreover, there are several strong local players, such
as Comcon and A/R/M/I-Marketing (affiliated with Kantar Group). The study by
Baldinger (1988) suggests that the size of STM market in Russia can be estimated of
about $USD 5 million a year. (See Figure 4.1 overleaf).
115
In Russia, strong demand for reliable information resulted in a rapid development of local marketing research industry
Figure 4.1
Source: Based on ESOMAR (2008, 2009) *) based on estimates by www.oirom.ru **) Based on Baldinger (1988,1991), 1.7% of MR TO
Russia,CAGR +25%
US andCanada,CAGR +5%
World,CAGR +9%
Marketing research turnover (mln $US)
STM turnover ** (mln $US)
85 110144
198270
325265
050
100150200250300350
2003 2004 2005 2006 2007 2008* 2009*
1.4 1.92.4
3.44.6
5.54.5
0.0
1.0
2.0
3.0
4.0
5.0
6.0
2003 2004 2005 2006 2007 2008* 2009*
7137 7824 8306 8890 9494 9629
0
2000
4000
6000
8000
10000
12000
2003 2004 2005 2006 2007 2008
121 133 141 151 161 164
020406080
100120140160180
2003 2004 2005 2006 2007 2008
1923721957234822473728235
32462
05000
100001500020000250003000035000
2003 2004 2005 2006 2007 2008
327 373 399 421480
552
0
100
200
300
400
500
600
2003 2004 2005 2006 2007 2008
Noteworthy, that despite its visibly small size as compared to the mature “western”
research markets, Russian research industry is considered to be on the edge of
modern marketing science, driving methodological innovations in the global research
industry (Burgess and Steenkamp, 2006). However, as shown by the literature
review, a very limited number of quality publications is available concerning the local
practice of sales forecasting. Some valuable insights provided by marketing
practitioners were discussed in Section 2.4.2 (Burdey et al, 1999, 2010,
Belotserkovskaya et al, 2005, Agaeva, 2008). However, the work of Kachalov (2008)
is recognized as the best available summary of forecasting expertise in the Russian
FMCG market. The key points, highlighted by Kachalov (2008) are: (1) the typical
problems that Russian marketers face are “lack of reliable information about market
and competitors” and “high uncertainty about future” (Kachalov, 2008, p.16), (2)
widespread opinion that luck is a key factor of business success, (3) Russian market
is ruled by the same laws as “western” markets, (4) high accuracy of forecasting is
principally achievable (10% error margin vs. reality), (5) forecasting process in the
Russian market involves four stages, in particular (a) refinement of information
116
sources, (b) market size projection, (c) sales forecasting and planning, (d) detailing
sales plan by periods, regions and SKUs. Kachalov (2008, p.32-36) distinguishes
three types of forecasts: 1) visionary or long-term, above five years, 2) detailed or
short-term, one year and 3) mid-term (up to three years), which “has no individual
value” and makes “no sense”. This indirectly confirms a significant skew to short-term
projects in the Russian market as compared to “western” markets. Concerning the
quality of information available in the Russian market, Kachalov (2008) argue that the
error is typically 40%-50% due to (1) poor representativeness of studies, (2)
irrelevant responses received. Kachalov (2008, pp.41-27) suggests to employ
various judjemental and indirect instruments to address this issue, such as expert
opinions (“intuition”, workshops, “adjustment coefficients” ranging from 10% to
200%), multiple sources and assessments (“three sources”, “five assessments”),
input-output analysis (“right model”, “macro-approach”), lifecycle analysis, leading
indicators (CEE markets, “per capita” analysis), quantitative techniques (mostly time-
series, such as “moving average” and “trend” analysis – causal models are not
mentioned), consumer and industry surveys, official statistics . With that, it is
recommended that “change-points” be identified with the mix of “expert” and
“quantitative” instruments. However, the work of Kachalov has serious limitations. In
particular, the role of marketing (in general) and promotional techniques (in
particular) is not considered. This reveals implicit assumptions underlying Kachalov’s
recommendations: (a) the market is not saturated (i.e. there’s no need in stimulating
demand as it is already there) and (b) the dominant business strategy is “market
penetration”. According to Kachalov (2008, p.47), ad-hoc consumer research should
contribute not more than “10-20% to company’s knowledge about the market”, which
is obviously far from the rate recommended in “western” markets (McDonald, 2008).
The methods of quantitative tests, traditional test marketing, simulated test marketing
and causal (econometric) modeling are not mentioned. This indicates relatively minor
role of such methods at the current stage of market development, in comparison to
117
developed markets. However, the use of sales forecasting techniques (in general)
and Simulated Test marketing (in particular) in the Russian FMCG market requires
more thorough examination in the context of findings presented in Chapters 2 and 3.
Thus, Sections 2.4.1 and 2.4.2 revealed important insights about Russian FMCG
market, while Chapter 3 summarized existing knowledge on basic principles of
marketing research, sales forecasting and simulated test marketing. In view of these
findings, further analysis can be carried out. Thus, the key conclusion about STM in
the Russian FMCG market is that there are no fundamental barriers to using it,
although some drawbacks need to be considered - in particular, limitations on use at
early stage of development and the need for local customization (see Figure 4.2a).
Reality of the Russian FMCG market and its implications for sales forecasting and STM. (Consumers)
Figure 4.2a
Forecasting, in general Simulated Test Marketing, in particular
Needs (Maslow's structure represented by "Composition of average
household expenditures%")
Skewed towards basic needs (FMCG):
2009 - Food FMCG 29.2%, Non-Food FMCG - 12.5%, Other
(residence, transport, clothing, services etc) - 58.3%, 2013F - 27.4
%, 11.7%, 60.9%
(+) A variety of ready-to-use forecasting solutions (available in
the Western FMCG markets); (-) These solutions need to be
validated in the Russian FMCG market, which is different in terms of needs and drivers of product choice.
(+) No fundamental barriers to using STM, which were originaly
developed for FMCG markets; (-) With that, some drawbacks of
STM need to be considered - in paticular, limitations to effectively deal with very dynamic markets at early stage of development
OccasionsEmerging FMCG consumption and
shopping patterns
(+) Can be revealed by using "Judgemental" instruments
(Expert opinions, consumer surveys), it is essential to get regular
updates on consumer needs and shopping behavior; (-) The
need in frequent updates, rapid aging of information and models;
(-) The accuracy of mid-term and log-term forecasts is lower
than that in developed markets
(+) Focusing on current consumer preferences and behavior
may improve foracasting accuracy; (-) Relying solely on
historical data and local benchmarks may decrease accuracy;
(-) Applying STM at a very early stage of market development
may result in misleading conclusions about long term success of
the tested product; (-) Generally, frequent changes in
consumption patterns may deteriorate the accuracy of STM
Profile
"Majority", rapid income growth: No. of households with annual
earningsabove US$10,000 in 2009 - 54%, in
2013F - 81%
(+) Considerable market growth in the long-run, at least in
value terms; (-) Structural changes in consumer demand must
be considered (e.g. price, brand factor etc); (-) Trading up,
probable stagnation of "low-price" markets and mass segments;
(-) Unpredictable fluctuations are possible; (-) Considerable
inequality in terms of income distribution must be taken into account
(+) Possiblitiy to integrate price elasticity models developed in
the Western FMCG markets; (-) In general, this factor has a
negative impact on STM accuracy
Number of consumers
involved
Up to 80% of 53,707,000 households in 2009
(+) Significant number of actual consumers allows for using
quantitative forecasting techniques; (+) Well known instruments
to assess incidence are consumer surveys and official statistics;
(-) Probable lack of information, unreliable sources, outdated
information, low coverage of surveys, the need to apply adjustment coefficients or expert estimations ("judgemental"
instruments)
(+) Principal possibility to use STM in case of significant
number of consumers; (-) Inaccurate estimation of incidence
may seriously undermine forecast accuracy
Products awareness / experience /
Learning
High awareness and fairly good experience in the main centres of
distribution, limited awareness and availability in rural area. Weak loyalty, high level of switching.
Further learning is required about niche categories
(±) Slower adoption as compared to developed countries; (+) No technical barriers to applying "western" approaches of
awareness modeling in Russia (the development of media in
Russia lags behind developed countries); (-) the need for
extensive use of consumer research to tailor awareness models to the Russian market (i.e. country-individual response rates to
various marketing activities etc)
(+) Developed approaches to awareness and repeat purchasing
modeling are applicable to Russia; (-) Local adjustments are
required
Market specifics Implications
Co
ns
um
er
In terms of “market size” and “product characteristics” the main implications are: (1)
lack of reliable market data and a high number of underdeveloped categories
significantly narrows the use of STM, (2) higher chance of forecasting for “new-to-
the-firm” and “new-to-the-country” products, as compared to developed markets, (3)
a strong long-term upward trend - a majority of markets exhibit sustainable double-
digit growth , (4) using norms and historical data from similar markets may improve
118
forecast accuracy, (5) overall STM accuracy is lower as compared to developed
markets, (6) the need for a fast and flexible STM, which is able to simulate a variety
of scenarios (pricing, positioning, advertising etc), (7) importance of testing in the
competitive context (see Figure 4.2b).
Reality of the Russian FMCG market and its implications for sales forecasting and STM. (Market size and Product)
Figure 4.2b
Forecasting, in general Simulated Test Marketing, in particular
Size of market (value, total retail)
in 2009 - $457 bln, in 2013F -$782 bln
(+) Sustainable growth in a majority of FMCG markets, many of
them are well-defined and quantified; (-) The accuracy of market
size estimation is often low, therefore adjustments are often made ("judgemental" techniques); See "Number of consumers
involved"
(+) STM is recommended to use if market is well-defined and
quantified; (-) High number of undeveloped markets and lack of
market data are barriers for STM
Life cycle stageGrowth (although saturation is
observed in some FMCG categories)
(+) Market life stage can be identified with traditional lifestyle
models (typically "growth") (-) lack of market data in many
cases (-) considerable number of "embryonic" markets
(+) No limitations to conducting STM at the late stages of the
lyfecycle (such as beer etc); (-) STM is not recommended for
"early" stage, at the "growth" its accuracy is lower as compared
to "saturated"; (±) Considerable skew to "new-to-the-firm",
"new-to-the-country" products as compared to Western markets
Growth rate of sales
2009-2013 CAGR 14% (above GDP) See "Size of the Market" (-) Inaccuracy in estimation of this factor leads to lower forecast
accuracy
Granularity (structural
complexity)
Moderate granularity, visible development of sub-categories and
segments, wide territory
(+) In terms product segments - a simpler forecasting model
as compared to developed markets; (-) The need for separate
modeling for regional markets
(+) A simpler market model as compared to developed markets
- fewer sub-segments; (-) Difficulties with selection of cities
representing national market
Predictability of market reactions
and factors
Moderate (still unstable market structure and demand), "lagging" development vs Western markets
(+) A possibility for forecasting using "analogue" (similarities)
approach (e.g. Central Eastern European markets, Western
markets); (-) Generally, limited accuracy of long-term
forecasts, the horizon of forecasting is typically 3-5 years
(+) Using norms and historical data from similar markets may
improve forecast accuracy; (-) Still the accuracy is lower as
compared to developed markets
Variability / Variation
Moderate (still unexpected fluctuations caused by internal
market factors)
(-) Uncertainty related to possible seasonal variations,
unpredictable impact of external factors; See "Predictability"
(-) The need for a fast and flexible STM, which is able to
simulate a variety of scenarios; See "Forecasting" for "Granularity"
Product characteristics /
Technology
Technologies are sometimes new for the local market but ordinary for the
markets of developed countries
(+) The need to use consumer research to guage consumer
reactions to the product; (+) It is advisible to apply "analogue"
(similarity) principle in forecasting (Eastern European markets, some Western markets etc)
(+) The use of benchmarks and historical data from the similar
markets may help forecasting; (+) integrated product in-use test
module is highly recommended
Potential for substitution / Differentiation
Moderate. Despite highly differentiated brand image of new
products there's a significant threat from "me-too" products
(+) Competitors and substitute products must always be taken
into account (Consumer Research. Indirect methods)
(+) Importance of testing in competitive context; (-) In case of
no competitors or product substitutes (i.e. when the market is at early stage) monadic evaluation is possible. However, this leads
to lower accuracy
Quality
Variable (from very low to very high). For new products imported from developed countries is initially
perceived as VERY HIGH
(+) The need for examining quality perception, measuring price
elasticity, performing "quality vs price" trade off analysis or conjoint
(+) Integrated "quality vs price" positioning analysis, price
elasticity analysis
Pro
du
cts
Market specifics Implications
Ma
rke
t s
ize
The analysis of implications in terms of “marketing” facet (promotion, pricing), has
shown the following: (1) lower accuracy due to high speed of forecasting is generally
accepted by users, (2) rich diagnostics of marketing mix is essential, although
materials are often not finished, (3) validated local media model is a plus, (4) in case
of no communication simpler methods should be used, such as CPTs or traditional
test marketing (as an initial phase of national roll out), (5) high importance of pricing
information in STM , in particular - modeling various price scenarios is highly
advisable (see Figure 4.2c overleaf).
The analysis allows to hypothesize about high importance of testing in the
competitive context. In case of no competitors or product substitutes (i.e. when the
market is at early stage) monadic evaluation is possible. From financial perspective,
STM is highly recommended to justify substantial investments into advertising and
119
infrastructure in a well-defined market. Generally, the price of STM is supposed to be
lower than that in Western markets due to the limited marketing budgets, lower STM
accuracy, lower expenditures on R&D and higher probability of profits (see Figure
4.2d).
Reality of the Russian FMCG market and its implications for sales forecasting and STM. (Marketing and Pricing)
Figure 4.2c
Forecasting, in general Simulated Test Marketing, in particular
Dominant Marketing Objectives
1) Create and capture the market as soon as possible 2) Maximize share and secure future profit 3) Create product awareness and encourage trial, educate potential consumers
while introducing the product
(+) Lower requirements to forecast accuracy as compared to
Western markets; (-) Higher requirements to the speed of
forecasting; (+) No barriers to use Western techniques of
Consumer Research and Indirect methods for data collection (market size estimation, barriers for entry, consumer response) as well as quantitative forecasting instruments (typically, time-
series). Some saturated markets allow the use of causal models
(+) Lower forecast accuracy is acceptable; (-) high speed;
(+) lower cost as compared to the West; (+) essential delivery
of rich diagnostic information (barriers, consumer perception and
response to advertising, product, packaging, positioning etc); (-) test materials are not always finished (commercial, pack etc)
Dominant strategies
Market Development and Market Penetration (Strategic Directions),
Focus (Product), Proactive ATTACK: Bypass, Flank, Alliance or
Acquisition (Business), Sequential (Strategy types)
(+) It is advisable to employ methods of market monitoring
(Consumer Surveys, Indirect - Industry Surveys); (-) higher
requirements to the flexibility of the model (reviews and updates
in the course of strategy implementation); (-) low quality of initial
information, low predictability of competitive response
(-) highly flexible model is required; (-) capabilities to
simulate various scenarios of market development and compatitive reposnse
Communication / Advertising
Aggressive marketing using mass communications, heavy promotional
activities. A major tool is an IMC campaign with the focus on building
brand awareness and distinctive brand image. In some cases,
considerable emphasis is made on educating consumers and
development of consumption habits
(+) No limitations for use of proven western techniques of media
modeling, i.e. econometric (causal) modeling of relationship
between media exposure and awareness; (±) Awareness
model needs to be localized; (±) To develop accurate
forecasts, it is important to actively utilize consumer research
information (advertising tests etc); (±) In case of no
advertising support and direct import it is advisable to employ standard comcept/product tests or traditional test marketing (as an initial phase of roll out plan). In this case, forecasting horizon
is short-term (limited to 1 year)
(±) validated local media model; (±) communication
diagnostics and advertising tests; (±) In case of no
communication simpler methods should be used such as CPTs and traditional test marketing (as part of the product launch)
BrandingCorporate or individual brands, line extenstions, brand differentiation
(+) Lower requirements for the level of product detail in
forecasts (typically, brand or sub-brand, while that is SKU in the
Western markets); (±) The need in consumer research
information for positioning diagnostics
See "Forecasting" for "Branding"
Price levels and variation
High variation. Perceived prices are very high especially for new
imported products. Very cheap local alternatives are generally available.
(+) There are retail audit suppliers on the market (Industrial
surveys) - prices are measuread at SKU, segment, channel, city,
region levels; (-) Many markets are not audited, or coverage is
limitied or accuracy is very low
(+) High importance of pricing information in STM (Industrial
studies)
Price elasticity / sensitivity
Visible price elasticity. Price skimming is observed.
(+) Analysis of price elasticity is imperative (Consumer
Research), by consumer and product segments
(+) Modeling various price scenarios in STM is a highly
advisable
Pro
mo
& M
ark
eti
ng
Pri
cin
g
Market specifics Implications
Reality of the Russian FMCG market and its implications for sales forecasting and STM. (Competitors and Finance)
Figure 4.2d
Forecasting, in general Simulated Test Marketing, in particular
Domination (shares of players)
Fragmentation (number of
players)
Quality of competition
Varies across categories, however, still very easy to enter
Likelihood of new entrants
Very high See "Dominant Strategies" See "Dominant Strategies"
Time to be established
Depends on the nature of the product, branding, cultural and
economic barriers (typically from 1 to 5 years)
Order of entryVaries across categories. Early entry
is still possible
Maximum share/sales
potential for a new entrant
Ranges from 100% at initial stage to 20% at maturity stage (with 1
competitor - 59% share, 2 - 44%, 3- 36%, 4 -31%, 5 - 28% etc.)
Investments, Costs
Generally low investments in R&D,while high in advertising,
promotion and distribution. In case of domestic production - direct
investment in manufacturing facilities
Reward - Profitability, ROI,
margin
High margins. Reward may be significant due to price skimming
Risk - Probability of Loss
High risk associated with local cultural barriers in case of significant
ad investment. Low risk in case of limited investment and diversity of
offered products
(+) Importance of testing in competitive context; (-) In case of
no competitors or product substitutes (i.e. when the market is at early stage) monadic evaluation is possible. However, this leads
to lower accuracy
(+) short-term horizon of forecasting as conmpared to western
markets; (+) strong probability to capture significant share in
the market using first entree advantage (with 1 competitor - 59%
share, 2 - 44%, 3- 36%, 4 -31%, 5 - 28% etc.); (-) tight
forecasting schedules
See "Forecasting" for "Time to be Established"…"Maximum share"
Fin
ance
(-) In case of considerable investment (advertising,
infrastructure) - it is recommended to gather all available information (Industrial surveys, Judgemental) to reduce the level
of uncertainty and produce a financial forecast of satisfactory
accuracy; (+) In case of underdeveloped market lower
accuracy of forecasting may be offset by high probability of
profits; (±) In case of entirely new product category, a phased
launch is often recommended (by regions, by channels). This allows the risks to be managed as market development goes
through the stages and becomes more predictable. Traditional test markets are useful in this regard (as part of the product
launch).
(+) High importance of STM in case of substantial investment;
(-) the cost of STM is lower than that in Western markets due to limited marketing budgets, lower STM accuracy, lower expenditures on R&D and higher probability of profits
Co
mp
eti
tors
Many foreign multinational companies are now market leaders
orimportant players in non-alcoholic
drinks, bottled water, alcoholic drinks(excluding vodka), confectionery,
(+) Competitive Intelligence information is available for the
largest markets, such as soft drinks etc (i.e. Industrial surveys,
Expert optinions, Consumer Research); (-) Generally this
knowledge is often very limited; (+) In a majority of markets
there's lower competitive pressure as compared to Western markets
Market specifics Implications
120
The analysis from the macro-environmental point-of-view shows that: (1) the quality
of information about retail is variable depending upon the market and data supplier,
(2) very high risks are typically related to underdeveloped sales infrastructure, (3) the
need to forecast for at least 1 year ahead to plan orders from suppliers or/and
production, (4) it is required that STM model has enough flexibility to review
forecasts in case of unexpected environmental changes, which affect prices,
channels, communication, consumers (see Figure 4.2e).
Reality of the Russian FMCG market and its implications for sales forecasting and STM. (Infrastructure and Environment)
Figure 4.2e
Forecasting, in general Simulated Test Marketing, in particular
DistributionThe country's vast territory and
underdeveloped distribution channels limit sales opportunities
(+) Existing suppliers of retail audit information (Industrial
surveys); (-) High risks related to underdeveloped sales
infrastructure; (-) Considerable regional and channel
differences must be taken into account; (-) Inaccurate
estimation of distribution level, i.e. adjustment coefficiants are often applied ("Judgemental" instruments)
(-) Imprecise estamates of distribution build up for the new
product (very often)
Suppliers
Vast majority of FMCG is imported (food FMCG is around 40%, non-
food FMCG is above 50%). Russia is a net importer of food products and raw agricultural goods for domestic processing. The main imports are meat, poultry, fish, milk and milk
products, cheese, vegetables, and raw sugar.
(±) The need to forecast for at least 1 year ahead to plan orders
from suppliers or/and production See "Forecasting" for "Suppliers"
Retail channels
The Russian retail market is extremely fragmented, and only in
Moscowand St Petersburg has there been consolidation. Nationwide, the top
five grocers have a combined share of around 15% of modern grocery
distribution.
See "Distribution" See "Distribution"
RegulatorsPossible difficulties with product
certification
(-) High risks related to poor quality of trade regulation and
related infrastructure. The need to examine these risks with Judgemental methods (expert opinions) and Indirect sources
(-) It is required that STM has enough flexibility to review forecasts in case of unexpected environmental changes, which
affect prices, channels, communication, consumers
Infr
ast
ruct
ure
/ E
nv
iro
nm
en
t
Market specifics Implications
4.2 Traditional STM approaches in the context of the Russian FMCG market
Following findings outlined above, it is appropriate to undertake further theoretical
examination of traditional STM approaches in the context of the Russian market. The
results of comparison analysis are presented on Figure 4.3 (overleaf). As it seen
from the table, both STM approaches are not recommended at early stages of
market development due to the lack of market data, competitive products, normative
databases etc. With that, their performance in the Russian market is not clear, given
the low number of validations in both cases (as to that in developed countries).
121
However, in the case of “purchase intent”-based models the clear advantages are:
quick and simple “setup” stage, “easy-to-understand” approach. While that in the
case of “preference share” models - rich diagnostics for the tested proposition and
“self-calibrated” approach .
Examination of the two leading STM approaches in the context of the Russian FMCG market
Figure 4.3
Positives (+) Negatives (-) Positives (+) Negatives (-)
Rich diagnostics of tested proposition in competitive context (
advertising, place-of-purchase, positioning)
Highly demanding for quality market data
Monadic evaluation of tested proposition ensures quick and
simple preparation stage. No need in test materials for competitors
(concepts, reels, packs etc)
Highly demanding for quality market data
Proven average accuracy on developed markets (+-9%)
Low accuracy in underdeveloped markets. Highly complex or
impossible to conduct in case of entirely new market and no substitute products or close
categories.
Proven average accuracy on developed markets (+-9%)
Lack of validations in the Russian FMCG market
"Self-calibration", i.e. capabilities for accurate assessment of consumer
response without involving normative databases
Time-consuming process of test materials preparation (test product and competitors advertising, packs,
shelf design etc)
"Easy-to-understand". Relative simplicity of the model
Perceived as "high-priced" (as compared to Western markets)
Modular approach to address various business issues
Perceived as "high-priced" (as compared to Western markets)
Theoretical possibility to apply the model at earlier stages of market development with no significant
competition
Limited amount of diagnostic information (especially in
competitive context)
Perceived as "slow" (as compared to Western markets)
Access to extensive normative databases
Perceived as "slow" (as compared to Western markets)
Pereceived as a "black box", limited flexibility (design and structural
modifications)
Modular approach to address various business issues
Pereceived as a "black box", limited flexibility (design and structural
modifications)Overall modeling complexity,
difficult to manageProprietary "closed" awareness
modelLack of validations in the Russian
FMCG marketCompetitive strategies and responses are not analyzed
Proprietary "closed" awareness model
Competitive strategies and responses are not analyzed
Preference share (e.g. Designor) Purchase Intent - based (e.g. BASES)
4.3 Questions that require further investigation
The findings from theoretical analysis presented above in Sections 4.1 and 4.2 do
raise many questions that require further investigation. A majority of findings needs to
be validated empirically in the follow-up study. Thus, it is particularly important to be
certain about the following:
What is the proportion of “new-to-the-country”, “new-to-the-world”, “new-to-
the-firm” products, “brand stretching”, “repositionings” and “product-price
changes” in the Russian FMCG market?
What kind of marketing research studies are typically conducted in such
cases, if any?
122
What are critical success factors of sales forecasting in the Russian FMCG
market?
What makes it difficult to forecast sales in the Russian FMCG market?
What is the experienced accuracy of sales forecasts for the new products in
the Russian FMCG market?
What is the share of successful product launches (i.e. reached the target, set
in the business plan, i.e. met the forecast)?
What are the reasons of not using Simulated Test Marketing?
What are the levels of awareness and usage for the most popular “western”
models of Simulated Test Marketing?
What is the average price per study?
What is the trend in usage of Simulated Test Marketing? Will it become more
popular?
What are the key drivers of choice in the Russian STM market (in terms of
factors discussed in Section 3.3.5)?
What are perceived qualities of particular STM services (in terms of factors
discussed in Section 3.3.5)?
To tackle these questions an additional research has been undertaken, which is
described in chapters that follow.
123
CHAPTER 5
RESEARCH METHODOLOGY
5.1 Research objectives and success criteria
The previous chapters have identified a visible gap in scientific knowledge
concerning the use of Simulated Test Marketing in the Russian FMCG market,
raising a number of questions outlined above in Section 4.3. At the same time, the
literature review have revealed increasing business interest in new product sales
forecasting models specifically tailored for the Russian FMCG market (see Section
4.1). Taking into account the need for information that is described above, an
additional study is proposed towards the following objectives:
1) Exploration into sales forecasting practices in Russia, in particular,
experience with various sales forecasting techniques for innovations
perception of forecasting services provided by external suppliers
2) Identification of key factors influencing the choice of forecasting method,
particularly, Simulated Test Marketing
3) Review of traditional STM approaches in terms of their applicability for the
Russian market, and elaboration of recommendations for effective and
efficient use of STM in the local environment.
As an outcome of the research, a set of clear findings and actionable insights should
be made available to the participants of the survey, who are NPD, business and
consumer insights experts working for the biggest FMCG manufacturers in Russia.
As revealed in Section 3.3.4, the 20/80 rule is definitely applicable for STM market,
reflecting the greater proportion of experience accumulated within the biggest
manufacturers, as compared to smaller companies. Therefore, surveying a market
representative sample of n~30 experts from at least 10 biggest companies will make
this study a success. A summary of research objectives and methodological
approach is presented on Figure 5.1 overleaf.
124
Research topic and methodological approach
A literature review has revealed considerable lack of academic knowledgeconcerning the effectiveness of traditional techniques of sales forecastingfor new products in the Russian FMCG market. With that, a high practicalimportance of such information has been identified, particularly, in thearea of Simulated Test Marketing (STM).
There are 3 key objectives of the research, which are as follows:1) Exploration into sales forecasting practices in Russia, in particular :
• experience with various sales forecasting techniques for innovations• perception of forecasting services provided by external suppliers
2) Identification of key factors influencing choice of forecasting method3) Review of traditional STM approaches in terms of their applicability for the
Russian market, and elaboration of requirements for the most effective andefficient technique.
Quantitative B2B U&A study (descriptive research)
Research findings will be shared with the participants of the study (i.e. RussianFMCG manufacturers) and will support their decision making and businessplanning processes. It is supposed that the study results will drive furtherdevelopment and customization of STM models in emerging markets,particularly, in Russia.
Actionable recommendations based on a representative survey (n~30 expertsfrom at least 10 leading FMCG companies)
Researchissue
Objectives
How?
Contribution
Success criteria
Figure 5.1
5.2 Research program framework
5.2.1 Type of investigation
According to Sekaran (2003), the proposed methodological approach falls under the
general category of “descriptive studies”. “A descriptive study is undertaken in order
to ascertain and be able to describe the characteristics of the variables of interest
…The goal of a descriptive study, hence, is to…describe relevant aspects of the
phenomena of interest from an individual, organizational, industry-oriented
perspective…Such information might be vital before considering certain corrective
steps, as ‘should the organization consider changing its practices?’” (Sekaran, 2003,
pp. 121-122). As noted by Malhotra (2007, p.83), “the major objective of descriptive
research is to describe something, usually market characteristics or functions”. As it
seen, this type of investigation is appropriate for the study objectives stated above in
Section 5.1, particularly in view of research implications, i.e. ‘should the
organizations working in the Russian FMCG market considering changing their
practices of using traditional sales forecasting techniques for NPD, such as
125
Simulated Test Marketing?’. Sekaran (2003, p.122) suggests that descriptive studies
are helpful in the following: 1) understanding “the characteristics of the group in a
given situation”, 2) obtaining systematic knowledge about aspects of a given
situation, 3) “offer ideas for further probe and research, and / or (4) help make certain
simple decisions”.
The scope of research objectives justifies the use of quantitative approach to data
collection. Gill and Johnson (1991, 2010) suggest that this approach is the more
suitable than qualitative methods in the case of descriptive business study. Burton et
al. (1986) strongly advise to employ quantitative methods for descriptive studies to
ensure better quality decision-making based on the research findings. According to
Malhotra (2007, p.144), quantitative approach seeks to “quantify the data and
generalize the results from the sample to the population of interest”, the sample
should be large enough to allow statistical analysis, the form of data collection is
“structured”, and the outcome is recommendations concerning a final course of
action.
Due to the profile of respondents engaged, the study can be considered as a
business-to-business (B2B). As discussed above, it is planned to survey local
marketing experts, therefore, the research does not go outside of the business world.
According to McNeil (2005, p. 3), “B2B research includes all research where the
product or service is being used in a business environment”.
The type of questions that the study is seeking to answer suggests that it is a Usage
and Attitude survey (U&A). According to Parameswaran (2005), such studies cover
the following areas: 1) awareness and usage of categories and brands, 2)
consideration set, 3) attitude towards the category, attributes looked for in the
category, 5) brand image (beliefs and attitude towards various brands).
The discussion above suggests that the type of investigation can be defined as
descriptive quantitative business-to-business usage and attitudes study.
126
5.2.2 Sample design and data collection method
As it follows from research objectives, the target population can be defined as NPD
managers, business and consumer insight managers, category and brand managers,
strategic planning managers, i.e. in-house experts engaged in sales forecasting for
new products in the FMCG market. With that, according to 20/80 rule (discussed
above in Section 3.3.4) a skew towards biggest advertisers is required in order to
achieve a representative sample of marketing research users, particularly, the users
of Simulated Test Marketing. Therefore, “quota sampling” is required (Malhotra,
2007, p.340), i.e. 80% of interviews should be conducted with employees of FMCG
companies from the Top-100 advertisers list (See Figure 5.2).
The following FMCG companies are among the Top-100 advertisers in Russia :
Figure 1.9Figure 5.2
Source: TNS (2009)
Rating CompanyTotal ad budget ,
mln $USDRating Company
Total ad budget , mln $USD
1 PROCTER & GAMBLE 301,59 36 BEIERSDORF AG (BDF) 40,35
2 L`OREAL 190,51 37 HEINEKEN 38,82
3 UNILEVER 144,11 38 LEBEDYANSKY 37,59
6 DANONE 123,92 46 DIROL CADBURY LLC 32,76
7 NESTLE 123,01 55 EFES BREWERY 27,01
8 MARS-RUSSIA 122,94 69 S.C.JOHNSON 22,97
10 RECKITT BENCKISER 116,62 70 TRANSMARK (SAB MILLER) 22,96
11 HENKEL GROUP 111,91 75 ROLLTON 21,65
12 COCA-COLA 106,72 76 ORIFLAME COSMETIC 20,65
13 WIMM-BILL-DANN 96,42 77 KIMBERLY CLARK 19,64
16 BALTICA 81,22 78 CAMPINA 19,43
17 COLGATE-PALMOLIVE 76,70 79 DOUWE EGBERTS 19,00
19 PEPSI CO 68,43 81 FABERLIC 18,74
21 WRIGLEY`S 59,20 83 SCA HYGIENE PRODUCTS 18,33
23 KRAFT FOODS 55,49 88 UNIMILK 16,06
25 SUN INBEV 53,33 89 ORIMI TRADE 15,20
27 NEFIS COSMETICS 51,85 90 HOCHLAND 15,04
30 JOHNSON & JOHNSON 47,49 91 PEPSI LIPTON INTERNATIONAL 14,92
31 KALINA 45,91 93 KAZANSKY FAT FACTORY 14,31
32 FERRERO 44,82 100 UNITED CONECTIONERS 12,18
35AVON BEAUTY PRODUCTS COMPANY
41,11
It was initially planned to contact at least 25 biggest and medium-size FMCG
companies, assuming response rate 40%-50%. As the entire universe of client-side
experts is estimated to be about 300 people, a sample size of n~30 respondents can
be considered as minimum number to arrive at fair conclusions. In this case, the
margin of error varies from ±5% to ±14% (for 5% and 50% respectively), assuming
finite universe population N=300 and confidence level 90% (Malhotra, 2007). With
127
that, it is worthy to note that it is difficult to achieve large sample size in such studies.
Thus, a similar study by the U.S. Advertising Research Foundation was able to
survey only n=42 experts (Baldinger, 1988). The method of sampling employed can
be qualified as “non-probability”, which has certain limitations discussed below in
Section 5.2.2. However, the use of random sampling techniques is not quite
appropriate in this case due to the small size of the target population.
Concerning the method of data collection, Web-interviewing was chosen assuming
that this is the only possible way to reach the target group and get sufficient number
of quality responses (given a complex structure of the questionnaire). To ensure
smooth execution of the fieldwork, the best-in-class software was used to facilitate
data collection (Sawtooth CiW, grant ID is 1302678, see Appendix). The summary
information on survey methodology is shown on Figure 5.3.
Sample design and methodology:Figure 1.9Figure 5.3
In-house experts working for major Russian FMCG companies, thosewho are responsible for new product sales forecasting and marketingresearch: NPD managers, business and consumer insight managers,category and brand managers, strategic planning managers
N= 30+ interviews, non probability quota sample:
Involved in sales forecasting for NPD At least 10 FMCG companies covered 80% interviews with employees of the biggest FMCG manufacturers
(among top 100 biggest advertisers) * 20% interviews with employees of the medium-size FMCG manufacturers *
Personal self-administered web-interviews
Data collection: June-July 2010Data analysis: August 2010Presentation of results: End of September 2010
Russia, Moscow
Business-to-Business Usage and Attitude study
Who?
How?
When?
Where?
What ?*) reflects proportion of total spending on advertising and marketing research
5.2.3 Questionnaire
The questionnaire is designed to specifically address research questions and
incorporates the following main sections: (1) screener, (2) the frequency of occasions
128
when sales forecasting in required, (3) most popular research techniques and
information sources used while forecasting (by type of marketing occasion), (4)
awareness and usage of STM techniques, (5) key factors driving the choice of STM
model, (6) perceived qualities of various STM techniques, (7) assessment of
customer satisfaction with various types of Simulated Test Marketing, (8) perceived
accuracy of Simulated Test Marketing. The questionnaire consists of 25 questions
and is estimated to take 25-30 minutes (according to the results of two pilot tests).
The method of data collection is Web - interview, therefore the questionnaire is self-
administered. The final version of the questionnaire is available in Appendix.
5.2.4 Analysis plan
The analysis plan is designed to answer research questions directly, uncovering
insights in a sequential manner, narrowing the scope of investigation. The table
below shows preliminary analysis plan consisting of 16 research items, starting from
profiling of the audience and finishing with sophisticated positioning analysis (see
Figure 5.4). As mentioned earlier, a special emphasis in the analysis is placed on
significance testing under assumption of finite target population (n=300).
Analysis planFigure 1.9Figure 5.4
Research question Key Variables Analysis approachAnalysis
unitObservations
requiredWho are the experts we are talking to? Is the sample
representative enough?Q0-Q4. Profile of the people involved in business
forecasting for NPD (Nominal)Frequency Respondent 30+
What is the proportion of “new-to-the-country”, “new-to-the-world”, “new-to-the-firm” products, “brand stretching”, “repositionings” and “product-price
changes” in the Russian FMCG market?
Q5. Number of occasions per year when sales forecasting is required, by type of marketing task (new brand launch, line extension, brand stretching, brand
re-launch etc.) (Scales)
Frequency, Descriptive: Weighted to represent the
market Occasion 100+
What kind of marketing research studies are typically conducted in such cases, if any?
Q7. Research tools employed to accomplish each marketing task (new brand etc). Popularity and share
of STM techniques
Frequency, Descriptive: Weighted to represent the
market Occasion 100+
What are critical success factors of sales forecasting in the Russian FMCG market?
Q7a. Critical success factors for sales forecasting for NPD (open-ended)
Frequency Respondent 30+
What makes it difficult to forecast sales in the Russian FMCG market?
Q7b. Difficulties in sales forecasting for NPD (open-ended)
Frequency Respondent 30+
What is the experienced accuracy of sales forecasts for the new products in the Russian FMCG market?
Q7c. Perceived accuracy of sales forecasting for NPD (scale)
Frequency, Descriptive Respondent 30+
What is the share of successful product launches (i.e. reached the target set in the business plan, i.e. met
the forecast)?Q7d. Perceived success rate for NPD (scale) Frequency, Descriptive Respondent 30+
What are the reasons of not using Simulated Test Marketing?
Q8, Q16. Key barriers for using STM (open-ended) Frequency Respondent 30+
What are the levels of awareness and usage for the most popular Western models of Simulated Test
Marketing?Q9-Q12. Awareness, usage of various STM
techniques (nominal)Frequency Respondent 30+
What is the average price per study? Q14. Average STM price per study (scale) Frequency, Descriptive Respondent 30+
What is the trend in usage of Simulated Test Marketing? Will it become more popular? Q15. Future usage of STM (scale)
Frequency, Descriptive Respondent 30+
What are the key drivers of choice in the Russian STM market (in terms of factors discussed in Section
3.3.5)?
Q17-21, Q22 Consumer preferences and choice drivers for STM services (Ranking), Likes, Dislikes
(open-ended)Frequency Respondent 30+
What are perceived qualities of particular STM services (in terms of factors discussed in Section
3.3.5)?
Q23. STMs brand image (5-point scale, 16 statements)
Correspondence analysis ( Analsysis of residuals, Perceptual mapping)
(Malhotra, 2007,p.673)
Assessment (statement)
100+
Q24. Customer satisfaction by STM brand (Scale) Frequency, Descriptive Respondent 30+
Q25. Perceived STM accuracy (Scale) Frequency, Descriptive Respondent 30+
Q22, 23, 24 SWOT analysis for major STM modelsFrequency, Regression (Malhotra, 2007,p.666)
Assessment (statement,
brand)100+
129
5.2.5 Limitations in terms of precision and confidence
Sekaran (2003, p.24) suggests that precision and confidence are key parameters
that measure ‘closeness’ of research findings to the ‘reality’. While precision “refers
to a degree of accuracy on the basis of the sample”, confidence is “the probability
that estimations are correct”. As mentioned earlier, the size of the sample (n=30) can
be considered as minimum sufficient to produce reliable findings. The expected error
margin may go up to ±14%, assuming finite universe N=300 and confidence level
90% (Malhotra, 2007). With that, the use of advanced significance testing techniques
(based on the assumption of a finite small target population) may help to improve the
quality of findings.
5.2.6 Limitations in terms of generalization
According to Sekaran (2003) there are two common understandings of the term
‘generalization’. The first one is related to the representativeness of sample and the
ability to project the findings to the total target population – “the researcher should be
able to draw conclusions that would be generalizable to the population of interest”
(Sekaran, 2003, p.266). In view of this definition, it is important to note, that the
chosen sampling approach falls within the group of “non probability” techniques due
to the use of ‘judgement’ (pre-defined contact lists) and ‘snowball’ recruiting
(Malhotra, 2007, p.340). The above factor can be recognized as a serious drawback
to the generalization of study findings. The second meaning of generalization is that it
”refers to the scope of applicability of the research findings in one organizational
settings to other settings” (Sekaran, 2003, p.35). In that respect, it is important to
emphasize that findings of the study are not transferrable to any market, other than
Russian FMCG.
130
CHAPTER 6
KEY FINDINGS:
THE USE OF SIMULATED TEST MARKETING IN RUSSIA
6.1 Respondents profile
In course of the study 48 quantitative interviews were taken with experts working at
largest FMCG companies in Russia (see Figure 6.1 overleaf). From these
interviews, the number of completes is 33, incompletes (i.e. interrupted at some
stage due to various reasons) – 14. One interview was dropped from the analysis by
respondent’s request. As seen from the data, a very large majority of research
participants (above 70%) have over six years of experience in the FMCG market,
while 30% of the sample have above ten years of experience. More than a half of
interviewed experts hold management positions in marketing or marketing research
departments of their firms. In particular, 36% are senior managers and 29% are team
leaders. In terms of representativeness at company level, the survey reflects opinion
of employees working at 27 FMCG companies, specializing in various categories,
such as food (52% of companies), beverages (non alcoholic and alcoholic, 20% and
13%, respectively), non-food, including tobacco (21%). The average number of
interviews per company is 1.77. In the case of one particular company the “weight”
assigned to each respondent was reduced in order to ensure fair and equal
representation of companies in the sample. About 60% of the companies surveyed
are listed among the top 100 television advertisers (see Figure 5.2).Their
contribution into total amount of television advertising expenditure made by all FMCG
companies, accounts for over 50%. Therefore, respondents’ profile as well as the
number of interviews collected are generally satisfying requirements set above in
Section 5.2.2. Also, this supports the conclusion that the quality of collected data is
acceptable for further analysis.
131
53%
12%
35%
SURVEY PARTICIPANTS
NON‐PARTICIPANTS:
PROCTER & GAMBLE
NON‐PARTICIPANTS:
OTHER FMCG COMPANIES
Respondents’ profileFigure 1.9Figure 6.1
Number of interviews:
Total …………………..………........…...............................48Completed…….……..……………………………..………..33Incomplete (stopped at some point of interview)……….14Dropped from the analysis by respondent’s request..…..1
By company:
Companies ranked among Top-100 TV advertisers ...60%
Total number of companies surveyed…………….…...27Average number of respondents per company……...1.77
Television advertising expenditures in 2008 (FMCG companies in the Top-100 group, see Figure 5.2):
SOV among the biggest FMCG TV advertisers...........53%
Category profile of surveyed companies (% of total) :
52%
21%
20%
13%
Food
Non‐food (inc. tobacco)
Non alcoholic beverages
Alcoholic beverages
Experience:
Q3a. How long do you work in FMCG sector?
Less than 2 years …..………........…...............................3%2-5 years…….……..……………………………..……….23%6-10 years………………………………………...……….45%More than 10 years…………………………………...….29%
Department / Position :
Q2. In which department do you currently work?Marketing research / Insights…..…...............................60%Marketing (Category or Brand management) …………20%Strategic planning and forecasting …………………….12%Marketing (NPD / Innovations )……………………...….4%Trade marketing / Sales……………………….……...….4%
Q3. What is your role (position) in the department?Department director / Senior manager .........................36%Team/Working group manager …………………….……29%Specialist / Expert ………………………..……………….35%
6.2 New product types and marketing research methods employed in Russia
As discussed in previous chapters, it was revealed that there are seven major types
of new products in the developed markets. As it was shown in Chapter 2 (see Figure
2.2), as per estimates of western researchers in late 90-ies, the share of ”new-to-the-
world” and “new-to-the-country” products in the total number of new product launches
in the U.S. FMCG markets accounts for 10%, “new-to-the-firm” - 20%, “line
extensions” and “brand stretching” - 23%, “brand re-launch / repositioning” - 4%,
”Product/price changes” – 43%. As per results of theoretical analysis performed in
Chapter 2 (Section 2.4.1), the portion of “new-to-the-country” and “new-to-the-firm”
is significantly higher in the emerging Russian market as compared to saturated
“western” markets. However, the analysis of data collected reveals the opposite
conclusion (see Figure 6.2 overleaf). Thus, as per study results, the share of “new-
to-the-country” and “new-to-the-firm” totaled only 10% in the Russian FMCG market
in the year 2009, while contribution of “product/price change” exceeded 60% (the
respective numbers for “western” markets are 30% and 43%, as measured in 90-ies).
132
The shares for “line extensions / brand stretching” and “brand re-launches” are
consistent with that in “western” markets (23% and 4% respectively). It may be
hypothesized, that this situation is unusual and is largely caused by the world’s
economic downturn (which peaked in the middle of 2009). Due to that reason, the
number of true product innovations had dramatically decreased both in the Russian
and global FMCG markets as companies shifted their focus to cost reduction for
existing products (e.g. price-offs, formula revision, savings, applying “lean” principle
etc). It is therefore advisable to perform additional assessment in the period of
expected market growth between 2011 and 2013 years in order to finally determine
whether the hypothesis discussed above is correct.
Extremely high share of “product/price changes” registered for Russia in 2009 as compared to that in Western markets in the end of 90-ies
Figure 1.9Figure 6.2
Q5. Concerning these categories (or brands), how often did the task of sales forecasting arise in the following situations in your department in 2009 ?
N=870 occasions(estimated from responses on Q5)
Source: Griffin (1997)
New‐to‐the‐Country or World;
4%
New‐to‐the‐Firm;
6%Line
Extensions, Brand
Stretching;
23%
Brand relaunch /
Re‐positioning;
4%
Product / Price change;
62%
Russia,FMCG,2009 (as reported by respondents): Western markets in the end of 90-ies:
New‐to‐the‐Country or World;
10%
New‐to‐the‐Firm;
20%
Line Extensions,
Brand Stretching;
23%
Brand relaunch /
Re‐positioning;
4%
Product / Price change;
43%
Types of new product launches according to results of the similar studies in the U.S. market
The study results indicate that respondents are quite familiar with the fundamental
methods of marketing research, which confirms theoretical expectations discussed in
Chapters 3 and 4. The most popular method of marketing research in 2009 was
expert analysis (both internal and external) based on accumulated historical market
data and newly obtained statistics - 79% of respondents report that they used this
method in 2009 (see Figure 6.3 overleaf). Also, qualitative techniques were
133
extensively employed by 72% of respondents. A majority of respondents participated
in a Simulated Test Marketing project at least once during the year 2009 (62% of
surveyed experts). This confirms theoretical prediction that STMs can potentially be
exploited in the Russian FMCG market (see Figure 4.2a). The data analysis
performed indicates that STM is more often used in the case of true product
innovation, while in the case of ”product/price changes” simpler quantitative
diagnostic tests are frequently applied.
The use of marketing research tools for sales forecasting during new product development process
Figure 1.9Figure 6.3
Q7.Which research techniques or information sources did you or your colleagues use while forecasting in each of the following business situations in 2009?
MARKET RESEARCH METHODS EMPLOYED IN THE RUSSIAN FMCG MARKET FOR NPD:
* BASE: N=39, Weighted to
ensure equal representation of
companies
* ADJUSTED STANDARDIZED RESIDUALS, i.e. difference from expected average measured in Standard Deviations Shaded boxes indicate differences exceeding 2 Std.Dev.
BASE: N=418 cases, Significant at 5% error level(p<0.05, p=0.024 ,Chi-Square 23.465)
TOTAL
(any type of new
product)
New‐to‐the‐
Country / World
/Company
Line Extensions /
Brand Stretching
Changes in Marketing mix
(Positioning / Product /
Price change
(% of Total Answering*)
Any expert analysis (time series based on
available statistics, internal, external)79% +0.1 ‐0.5 +0.4
Qualitative studies 72% ‐0.1 ‐0.2 +0.4
Quantitative diagnostic tests ‐ w/o sales
forecasting64% ‐2.5 +0.4 +2.2
Simulated Test Marketing 62% +3.0 +0.5 ‐3.7
Quantitative tests with elements sales
forecasting55% +0.1 +0.3 ‐0.5
Strategic quantitative exploratory studies 47% ‐0.2 +0.3 ‐0.1
Traditional Test Marketing 42% +0.1 +0.7 ‐0.8
Deviation **
6.3 Insights into forecasting in the Russian FMCG market
According to study results, the key factor that must be taken into account when
forecasting sales in the Russian FMCG market is quality and the amount of available
market data – 39% of experts mentioned that answering question Q7a. Thus, for
example, according to respondent #4, ”it is critically important to get reliable market
data, such as household panel and retail audit”, while respondent #29 emphasizes
that ”the forecast is built on retail audit data, which is of variable quality in Russia”.
This is consistent with the hypothesis about high importance of market data, its
134
limited availability and variable quality in the emerging Russian FMCG market (see
Chapters 3 and 4, in particular Figure 4.2b). Also, experts stress the importance of
the following: clarifying forecasting objectives, requirements, and action standards
beforehand - in terms of incremental sales and cannibalization (22%); competitive
intelligence (19%); accurate targeting, i.e. clear and concise definition of the market
and target group (17%); gathering and using historical data (17%); choice of
appropriate forecasting technique (17%); considering various scenarios of market
development (17%). The details are given below on Figure 6.4.
Reliable information is key to forecasting success in Russia Figure 1.9Figure 6.4
7a. From your experience, what are the factors that need to be taken into account when forecasting in the Russian FMCG market? /OPEN-ENDED/
FACTORS TO CONSIDER WHEN FORECASTING IN THE RUSSIAN FMCG MARKET
BASE: N=36, Weighted to ensure equal representation of companies
Q7a (% from answering)
Reliable information about market size, trends, drivers of development, white spaces and
sales opportunities 39%Business objectives, action standards (e.g. incremental sales, cannibalization etc) 22%Competitors and their likely response 19%Targeting ‐ defining the audience and sourcing product categories 17%Similar cases, benchmarks, historical data 17%Forecasting approach (structure, components, inputs, outputs, limitations) 17%Various business scenarios 17%Consumer and shopper insights (barriers to adpotion, attractiveness of offer etc) 17%Business and Organizational insights (execution capbilities, sales infrastructure,
distribution, retail, media etc) 14%Realistic marketing plans 11%Readiness of new product offer (communication, formula, marketing plans etc) 11%Proven method 11%Forecasting ROI, ratio between investments (time, efforts, budget) and project importance 11%Price context, price elasticity and dynamics 8%
Monitoring performance during the launch / Validation / Calibration / Correction upon in‐
market results 8%Strategy and long‐term vision 6%End ‐ users (i.e. who and how will use the result) 6%Internal communication 6%New product type (new‐to‐the‐firm, new‐to‐the‐country etc) 3%Horizon of forecasting (short‐, mid, long‐term) 3%Using several alternative approaches to increase accuracy 3%
Respondents point out the following actual problems in sales forecasting, that need
to be addressed: (1) accuracy of market data (39%), tailoring existing approaches to
the local market reality - in terms of design, accuracy, flexibility, cost (36%),
validations, acquiring historical data, benchmarks, working out evaluation criteria
(29%), improvement of business- and media- planning processes (25% and 21%
respectively), lack of knowledge about forecasting and research techniques among
end users (e.g. brand teams, sales teams etc.). The complete list of answers is
shown of Figure 6.5 overleaf.
135
Reliable market data, customized models, extensive local normative databases and effective methods of marketing planning are key areas for improvement in the Russian FMCG market
Figure 1.9Figure 6.5
7b. What are the difficulties that you usually face when forecasting sales for a new product? What issues require further exploration? /OPEN-ENDED/
DIFFICULTIES AND ISSUES THAT NEED TO BE ADDRESSED
BASE: N=36, Weighted to ensure equal representation of companies
Q7b (% from answering)
Reliable, accurate and up‐to‐date information about the market (size, structure, trends and
factors of development, macro environment and sales infrastructure, e.g. sales channels) 43%
Tailoring western models to local market conditions ‐ improving forecast accuracy (especially
short‐term), speed of delivery, cost, simplification , developing alternative approaches36%
Normative database / Developed evaluation criteria / Historical information / Validations
database / Knowledge base for comparative analysis29%
Effective methods of business and marketing planning in the local market (setting realistic goals,
effective marketing approaches)25%
Local media‐model (relationship between ad investments and consumer response, detailing by
channel)21%
Methods of forecasting in highly volatile markets 18%
Education of internal clients (to overcome senior managers' lack of trust and avoid difficulties
with brand teams)18%
Competitors ‐ competitive environment, competitors response 14%
Targeting ‐ defining the market and target group 14%
Consumer and Shopper insights in the local market 14%
High cost and duration of forecasting, especially STM 14%
Approaches to cannibalization and source of volume assessment 11%
Manegerial and infrastructure aspects of NPD and launch in the local market (best practices of
NPD, infrastructure setup: distribution, media, retail etc)11%
No problems if marketing research tools are used correctly 7%
Methods of forecasting for absolutely new products, non existing markets 4%
Accurate and clear description of modeling approaches ,their limitations and terms of application 4%
Flexibility of the model (quick reruns of revised scenarios, adaptability etc ) 4%
Methods of forecasting for incomplete of not final marketing mix at early stage of NPD 4%
Concerning successful new product launches, over 70% of respondents reported
that, in their experience, more than 25% of launches can be considered successful
(i.e. actual sales were in line or above business plan). This is significantly above that
in “western” markets – 5%. Also, as per experience of 70% of experts, the accuracy
of forecasts does not exceed 20% on average (See Figure 6.6 below).
100%
83%
70%
39%
25%
0% 50% 100% 150%
More than 0%
More than 10%
More than 25%
More than 50%
More than 75%
100%
95%
68%
23%
10%
0% 50% 100% 150%
More than 5%
More than 10%
More than 20%
More than 40%
More than 50%
In comparison to Western markets: greater rate of successful new product launches (25%+ vs. 5%*) and considerable error of sales forecasting for NPDFigure 1.9Figure 6.6
Q7d . In your experience, how many product launches would you consider successful
(i.e. actual sales were as planned or above) ? (% OF TOTAL ANSWERING, CUMULATIVE)
PERCEIVED SUCCESS RATE OF LAUNCHES:
BASE: N=27 (N=37 excluding DK/NA), Weighted to ensure equal representation of companies
Q7c. In your experience, what is the average error of sales forecasting for a new product ?
(% OF TOTAL ANSWERING, CUMULATIVE)
BASE: N=29 (N=37 excluding DK/NA), Weighted to ensure equal representation of companies
PERCEIVED ERROR OF FORECASTING (NPD):
*See Figure 1.3 Significant differences between Russian and Western Markets – Hypotheses tested with Z-Test with correction for finite population , assuming same sample for Western markets – ( 70% vs. 50% ,Significant at 10% error level (p<0.01) 2-tailed Z-Score =1.66)
Med
ian
Med
ian
136
Generally, research findings above confirm theoretical expectations discussed in
Chapter 4 (see Figure 4.2a and 4.2b).
6.4 Awareness and usage of STM models in Russia
The research undertaken has shown that respondents are very much aware of the
leading approaches in Simulated Test Marketing. A majority of experts had gained
practical experience with particular STM models. More than a half of experts
indicated that they had previous experience with the most popular “western”
approaches – ACNielsen BASES and Ipsos DESIGNOR (see Figure 6.7).
Generally high awareness of various STM models. ACNielsen BASES and IpsosDesignor lead the market. Figure 1.9Figure 6.7
Q9 . Which of the following “simulated test marketing” techniques have you ever heard of, at least by the
name? (% OF TOTAL ANSWERING)
AWARENESS OF STM MODELS
BASE: N=36, Weighted to ensure equal representation of companies
Q10. Which of the following “simulated test marketing” techniques have you or your colleagues have ever
used? (% OF TOTAL ANSWERING)
EXPERIENCE WITH STM MODELS
BASE: N=36, Weighted to ensure equal representation of companies
92%
86%
71%
90%
85%
54%
64%
58%
55%
40%
29%
28%
19%
14%
13%
5%
4%
4%
IPSOS DESIGNOR (ANY)
Designor Shelf, Desighor D'Light
NextGen, Innoscreen Forecast
ACNielsen BASES (ANY)
BASES I или BASES II
Snapshot или Pre‐BASES
A/R/M/I Marketing ‐ STM
GfK ‐MarketingLab / TESI
Comcon Sales Vision
TNS / RI ‐ Microtest
MASMI ‐ Simulated Test Market
TNS / RI ‐ eValuate
Synovate ‐MarketQuest MVP
InVivo ‐MarketMind
TNS / RI ‐ FYI
Other
Aegis Copernicus ‐ Discovery
M/A/R/C ‐ Assessor
65%
65%
21%
56%
56%
21%
19%
16%
15%
14%
9%
7%
6%
5%
4%
IPSOS DESIGNOR (ANY)
Designor Shelf, Desighor D'Light
NextGen, Innoscreen Forecast
BASES (ANY)
BASES I или BASES II
Snapshot или Pre‐BASES
Comcon Sales Vision
TNS / RI ‐ Microtest
A/R/M/I Marketing ‐ STM
GfK ‐MarketingLab / TESI
TNS / RI ‐ eValuate
MASMI ‐ Simulated Test Market
InVivo ‐MarketMind
Other
Synovate ‐MarketQuest MVP
About 75% of respondents stated that average budget of STM studies they had
recently participated in was above $30 000. This indicates that general price level for
Simulated Test Marketing in Russia is comparable to that in developed markets.
However, despite this considerable price barrier (see also theoretical analysis in
Section 4.2, Figure 4.3), more than 60% of respondents believe that the market of
STM in Russia will grow in the mid-term perspective - i.e. in the next 3-5 years (see
Figure 6.8 overleaf).
137
STM pricing is in line with that in Western markets*. With that, STM market is expected to grow within the next 3-5 years Figure 1.9Figure 6.8
Q14 . Concerning the last few STM studies, that you have participated recently, would you please estimate the
average budget per study ? (% OF TOTAL ANSWERING, CUMULATIVE)
BUDGET PER STUDY STM MARKET TREND
* see p.109, $45000 for Western markets
Q15. Speaking about mid-term perspective (3-5 years), can you please estimate the change in number of STM studies
conducted p/a with your participation? (% OF ANSWERING)
BASE: N=23 (N=36 excluding DK/NA), Weighted to ensure equal representation of companies
100%
93%
75%
64%
43%
25%
0% 50% 100% 150%
Above $ 20 000
Above $ 20 000
Above $ 30 000
Above $ 40 000
Above $ 50 000
Above $ 60 000
Med
ian
BASE: N=30 (N=36 excluding
DK/NA), Weighted
Will
increase;
65%
Will remain
on the same level;
25%
Will decline;
5%
Not going to
conduct;
4%
KEY REASONS FOR NOT-CONDUCTING STM
BASE: N=7
Q8. Why didn't you conduct STM in 2009? Q16. Why areyou not going to conduct STM in the next 3-5 years? (COUNT)The cost does not match its quality / Poor value for money / There are more cost efficient solutions.............................6No business need…………………………………….………4Prefer traditional test marketing……..………………….......2Other……………………………..……..………………….……...1
Study results suggest that Simulated Test Marketing provides acceptable level of
accuracy in key Russian FMCG markets. However, the number of precise forecasts
is slightly below to that in “western” markets (see Figure 6.9).
Many respondents have confirmed that STM results are within claimed global accuracy . This is slightly below than that in Western Markets
Figure 1.9Figure 6.9
Q25 . From your personal experience, would you please evaluate relative accuracy of <STM >forecasts
observed in the Russian market? (% OF ALL STM ASSESSMENTS)
PERCEIVED ACCURACY OF STM (RUSSIA, 2009):
BASE: N=42 assessments by STM model type(N=73 excluding N=31 “Unable to evaluate”),
Weighted to ensure equal representation of companiesBASE: N=241 assessments
PERCEIVED ACCURACY OF STM (U.S., early 90-ies):
Source: Baldinger (1988, 1991)
STM Perceived Validity in the U.S. market (1988-1991)
Sales results
are in line with claimed
global accuracy;
46%
Sales are
higher or lower then
predicted (below
claimed global
accuracy) ;
54%
Sales results confirmed the STM;
52%
Sales are
higher or lower then
predicted;
48%
To certain extent, this suggests that the hypothesis about lower accuracy of STM in
the Russian market is likely true (see Figure 4.2a and 4.2b). However, the
138
significance of that difference cannot be technically tested within the current study
and requires additional exploration.
Answering the question on why particular STM model can (or cannot) be
recommended, respondents mentioned such factors as: (1) reliable and accurate
forecast, proven accuracy in the local market, (2) professional performance, quality
service and consulting, individual approach (3) simplicity (or complexity) of modeling
approach, (4) positive previous experience, (5) number of validations and learnings
collected, as well as size of normative database in particular FMCG category, (6)
price, (7) breadth of diagnostic capabilities, (8) duration (see Figure 6.10).
Accuracy in the local market a and level of expertise are key factors of STM choice
Figure 1.9Figure 6.10
Q19-21. Why some STM models can (or cannot) be recommended ? /OPEN-ENDED/
FACTORS OF CHOICE FOR SIMULATED TEST MARKETING IN THE RUSSIAN FMCG MARKET
BASE: N=30, Weighted to ensure equal representation of companies
Q19‐21 (% from
answering)
Reliable forecast, accurate in the Russian market 36%
Level of expertise, quality service, professional consulting, individual approach 32%
Simplicity of model 21%
Previous experience 21%
Number of Validations / Benchmarks / Cases / Learnings ‐ particularly, in the
specific FMCG category18%
Cost 14%
Rich diagnostics, a lot of parameters 11%
Duration 11%
Project importance, business risks 7%
International recognition 7%
Types of market and new product (i.e. saturated market, absolutely new product) 4%
Ability to control the process 4%
At the same time, ranking key factors in order of their importance had helped to
reveal some other drivers, which determine selection of Simulated Test Marketing
model, in particular: (1) suitability for the Russian market, (2) recommendations from
management or requirements by company research protocols (see Figure 6.11
overleaf).
139
Forecast accuracy, suitability for the local market, professionalism and positive previous experience as well as recommendations from management are key determinants in choice of Simulated Test Marketing approach
Figure 1.9Figure 6.11
Q22 . You have mentioned that you are going to conduct STM in the next 3-5 years. Which of the following 5 (five) factors are more important for you, when you are making your decision about technique or agency? (% OF TOTAL)
KEY DRIVERS OF CHOICE – TOP 5 KEY DRIVERS OF CHOICE – TOP 1
Q22. Which single factor out of these five is of a top importance? (% OF TOTAL)
BASE: N=28 Weighted to ensure equal representation of companies
BASE: N=28 Weighted to ensure equal representation of companies
88%
53%
53%
53%
53%
45%
44%
31%
22%
19%
17%
8%
6%
5%
4%
Highly accurate forecast
Suitable for Russian market
Professional presentation of findings
Positive previous experience
High quality of data collection
High speed of research
Individual approach for every project
A lot of diagnostic information
High quality service and project management
Recommended by management / protocols
Simple and easy‐to‐understand
Transparent and flexible pricing
Well‐known agency
Affordable price
Use of modern technologies
53%
15%
14%
8%
8%
3%
Highly accurate forecast
Suitable for Russian market
Recommended by management or company
protocols
Professional presentation of findings and
recommendations
Simple and easy‐to‐understand
Positive previous experience
Comparative analysis uncovered perceived positioning of the leading STM
approaches from experts’ point of view. Thus, BASES is often recommended by
management or global protocols, while DESIGNOR produces a lot of diagnostic
information. Locally developed STMs are suitable for the Russian market and offer
individual service at affordable price (see Figure 6.12a, 6.12b).
Perception of various Simulated Test Marketing approachesFigure 1.9Figure 6.12a
Q23. We would like to know your opinion about practical use of STM techniques. Would you please evaluate to what extend you agree or disagree with each of the following statements regarding <STM> , using 5-point scale below
(Correspondence Analysis based on TOP2 scores)
PERCEIVED IMAGE OF STM APPROACHES / CORRESPONDENCE ANALYSIS – PERCEPTUAL MAP *
* Correspondence analysis measures the distance between nominal variables on a map, where each variable is associated with each other. BASE: N=570 cases, Significant at 10% error level (p<0.1, p=0.064 ,Chi-Square 56.77)
Highly accurate forecast
Professional presentation of findings and recommendations
Recommended by management or company
protocols
Positive previous experience
Well‐known agency
Simple and easy‐to‐understand
Suitable for Russian market
A lot of diagnostic information
Publications in professional literature
High quality service and project management
Individual approach for every project
Affordable price
Transparent and flexible pricing
High speed of research
High quality of data collection
Use of modern technologies
ACNielsen BASES
Ipsos DESIGNOR
Local STMs
Other Western STMs
‐0.6
‐0.4
‐0.2
0
0.2
0.4
0.6
‐0.8 ‐0.6 ‐0.4 ‐0.2 0 0.2 0.4 0.6 0.8
‐‐axis F2 (28.91 %) ‐‐>
‐‐ axis F1 (60.95 %) ‐‐>
Symmetric Plot (axes F1 and F2: 89.86 %)
140
Perception of various Simulated Test Marketing approachesFigure 1.9Figure 6.12b
Q23. We would like to know your opinion about practical use of STM techniques. Would you please evaluate to what extend you agree or disagree with each of the following statements regarding <STM> , using 5-point scale below
(Adjusted standardized residuals based on TOP2 scores)
PERCEIVED IMAGE OF STM APPROACHES / CORRESPONDENCE ANALYSIS – RESIDUALS *
BASE: N=570 cases, Significant at 10% error level (p<0.1, p=0.064 ,Chi-Square 56.77)* RESIDUALS ANALYSIS, is a descriptive technique designed to analyze two-way and multi-way tables measuring
correspondence between the rows and columns. Shows deviation between observed and expected (average) values measured in Standard Deviations
Shaded boxes indicate differences exceeding
1 Std.Dev. ACNielsen BASES Ipsos DESIGNOR Local STMs
Other
Western STMs
Highly accurate forecast +0.5 ‐0.8 ‐0.6 ‐0.1
Professional presentation of findings and recommendations +0.7 +0.8 ‐0.8 ‐1.1
Recommended by management or company protocols +3.2 ‐1.3 ‐2.2 +0.9
Positive previous experience +0.8 +0.4 ‐1.0 ‐0.2
Well‐known agency +1.4 +0.4 ‐1.4 ‐0.5
Simple and easy‐to‐understand ‐0.3 ‐1.8 +0.8 +2.0
Suitable for Russian market ‐1.1 ‐0.2 +1.3 ‐0.1
A lot of diagnostic information ‐1.3 +1.7 ‐0.1 ‐0.9
Publications in professional literature +0.1 ‐0.5 +0.2 +0.4
High quality service and project management +0.0 +0.4 ‐0.6 +0.1
Individual approach for every project +0.3 ‐0.1 +1.1 ‐1.6
Affordable price ‐1.8 ‐1.4 +3.1 +0.3
Transparent and flexible pricing ‐0.6 ‐0.7 +1.6 ‐0.4
High speed of research ‐2.1 +0.6 +1.4 ‐0.2
High quality of data collection ‐1.5 +0.4 +0.5 ‐0.2
Use of modern technologies ‐0.0 +0.9 ‐0.7 ‐0.4
Analysis of correlation between particular image attributes and general customer
satisfaction revealed significant correlation between satisfaction and such factors as:
(1) individual approach for every project, (2) high quality service and project
management, (3) positive previous experience and (4) professionalism (see Figure
6.13)
Customer satisfaction and factors that influence itFigure 1.9Figure 6.13
24. For each STM model that you have ever used, would you please tell us, how satisfied were you with the quality of service provided by the agency. For your answers, please use 10-point scale, where 1 means “COMPLETELY DISSATISFIED” and 10 means “COMPLETELY SATISFIED”
CORRELATION BETWEEN IMAGE ATTRIBUTES AND CUSTOMER SATISFACTION
BASE: N=73 cases, **Correlation is significant at the 0.01 level (2-tailed)* Correlation is significant at the 0.05 level (2-tailed).
Customer Satisfaction
(Mean 6.17, Std.Dev. 1.40)
Correlation
Individual approach for every project 0.333 **High quality service and project management 0.319 **Positive previous experience 0.301 **Professional presentation of findings and recommendations
0.297 **
Well‐known agency 0.275 *A lot of diagnostic information 0.222 *
Recommended by management or company protocols 0.187 *
Highly accurate forecast 0.175
Simple and easy‐to‐understand 0.169
Publications in professional literature 0.143
Use of modern technologies 0.119
Transparent and flexible pricing 0.103
High speed of research 0.055
High quality of data collection 0.053
Suitable for Russian market 0.028
Affordable price ‐0.059
141
The following analysis explores main strengths and weaknesses of each particular
STM model. As it seen from the maps depicted on Figures 6.14-6.17 below, image
attributes are located in a two-dimensional space, where horizontal axis represents
the power of association between particular attribute and a given STM model, while
vertical axis shows attribute’s importance in terms of customer satisfaction. Тhus, the
principal strengths of ACNielsen BASES are: ”recommended by management or
company protocols”, “well-known agency”, “positive previous experience”,
“professional presentation of findings and recommendations”. The key weaknesses,
that need to be addressed are ”diagnostic information”, “high speed of research”,
“transparent and flexible pricing”, ”affordable price”, “quality of data collection”,
“suitability for Russian market” (See Figure 6.14)
Highly accurate forecast
Professional presentation of findings and
recommendations
Recommended by management or company
protocols
Positive previous experience
Well‐known agency
Simple and easy‐to‐understand
Suitable for Russian market
A lot of diagnostic information
Publications in professional literature
High quality service and project management
Individual approach for every project
Affordable price
Transparent and flexible pricing
High speed of research
High quality of data collection
Use of modern technologies
‐0.10
‐0.05
‐
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
‐3.00 ‐2.00 ‐1.00 0.00 1.00 2.00 3.00 4.00
Principalweaknesses
ACNielsen BASES: Perceived strengths and weaknessesFigure 1.9Figure 6.14
Co
rrel
atio
n w
ith
Sat
isfa
ctio
n**
Principal strengths
Secondaryweaknesses
Level of Associations*
* Residuals, see Figure 6.13b
** S
ee F
igu
re 6
.14
Secondarystrengths
According to experts’ opinion, derived from the data, the key strengths of Ipsos
DESIGNOR are: “a lot of diagnostic information”, “professional presentation of
findings and recommendations”, “well-known agency”, “positive previous experience”,
“high quality data collection”. However, the following perceived weaknesses were
identified: model complexity, lack of management support and recommendations in
142
company protocols, lower accuracy of forecast, less ”transparent and flexible pricing”,
less ”affordable price” and questionable “suitability for Russian market” (See Figure
6.15).
Highly accurate forecast
Professional presentation of findings and
recommendations
Recommended by management or company
protocols
Positive previous experience
Well‐known agency
Simple and easy‐to‐understand
Suitable for Russian market
A lot of diagnostic information
Publications in professional literature
High quality service and project managementIndividual approach for every
project
Affordable price
Transparent and flexible pricing
High speed of research
High quality of data collection
Use of modern technologies
‐0.10
‐0.05
‐
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
‐2.00 ‐1.50 ‐1.00 ‐0.50 0.00 0.50 1.00 1.50 2.00
Principalweaknesses
Ipsos DESIGNOR: Perceived strengths and weaknessesFigure 1.9Figure 6.15C
orr
elat
ion
wit
h S
atis
fact
ion
**
Principal strengths
Secondarystrengths
Level of Associations*
* Residuals, see Figure 6.13b
** S
ee
Fig
ure
6.1
4
Secondaryweaknesses
Similar analysis undertaken for locally developed STMs had shown that their major
strengths are: “individual approach for every project”, “simple and easy to
understand”, “flexible pricing”, “high speed”, “high quality of data collection”, “suitable
for the Russian market”. However, there are some drawbacks such as ”well-known
agency”, “highly accurate forecast”, ”recommended by management or company
protocols” (see Figure 6.16 overleaf).
As for other “western” STM models, in the eyes of surveyed experts their perceived
advantages are “simplicity” and “recommendations by management or company
protocols”, while weaknesses lay in the area of ”individual approach for every
project”, “highly accurate forecast”, “professional presentation of findings and
recommendations”, “diagnostic information” , transparent and affordable pricing, high
quality of data collection and suitability for the Russian market (see Figure 6.17
overleaf).
143
Highly accurate forecast
Professional presentation of findings and
recommendations
Recommended by management or company
protocols
Positive previous experience
Well‐known agency
Simple and easy‐to‐understand
Suitable for Russian market
A lot of diagnostic information
Publications in professional literature
High quality service and project management
Individual approach for every project
Affordable price
Transparent and flexible pricing
High speed of research
High quality of data collection
Use of modern technologies
‐0.10
‐0.05
‐
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
‐3.00 ‐2.00 ‐1.00 0.00 1.00 2.00 3.00 4.0
Principalweaknesses
Local STMs: Perceived strengths and weaknessesFigure 1.9Figure 6.16
Co
rrel
atio
n w
ith
Sat
isfa
ctio
n**
Principal strengths
Secondarystrengths
Level of Associations*
* Residuals, see Figure 6.13b
** S
ee
Fig
ure
6.1
4
Secondaryweaknesses
Highly accurate forecast
Professional presentation of findings and
recommendations
Recommended by management or company
protocols
Positive previous experience
Well‐known agency
Simple and easy‐tounderstand
Suitable for Russian market
A lot of diagnostic information
Publications in professional literature
High quality service and project managementIndividual
approach for every project
Affordable price
Transparent and flexible pricing
High speed of research
High quality of data collection
Use of modern technologies
‐0.10
‐0.05
‐
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
‐2.00 ‐1.50 ‐1.00 ‐0.50 0.00 0.50 1.00 1.50 2.00 2.50
Principalweaknesses
Other Western STMs: Perceived strengths and weaknessesFigure 1.9Figure 6.17
Co
rrel
atio
n w
ith
Sat
isfa
ctio
n**
Principal strengths
Level of Associations*
* Residuals, see Figure 6.13b
** S
ee
Fig
ure
6.1
4
Secondaryweaknesses
Secondarystrengths
6.5 Further ways of STM development in Russia
According to surveyed experts, the key areas for improvement and further
development of Simulated Test Marketing in the Russian FMCG market at the
moment are: 1) more “client-oriented”, individual approach, greater focus on client’s
144
business; 2) local R&D, i.e. gathering local historical information, benchmarks,
learnings, performing validations, developing evaluation criteria relevant to the
Russian FMCG market; 3) simplification of reporting and modeling approaches; 4)
localized media models, gauging effectiveness of various marketing instruments in
the local market; 5) reducing project timing; 6) openness in explaining modeling
principles, client friendly guidelines on terms of use, inputs, outputs, research
implications, limitations, requirements to study materials etc.; 7) flexibility; 8) price
(see Figure 6.18).
Key areas for improvement of Simulated Test Marketing in Russia are: agency involvement, individual approach , benchmarks and validations
Figure 1.9Figure 6.18
Q17. How, in your opinion, STM services provided by local agencies can be improved? /OPEN-ENDED/
AREAS FOR IMPROVEMENT OF SIMULATED TEST MARKETING IN THE RUSSIAN FMCG MARKET
BASE: N=33, Weighted to ensure equal representation of companies
Q17 (% from answering)
More agency involvement into particular business issue / market analysis 33%
Gathering local benchmarks / evalution criteria / validations / learnings / historical
information 33%
Individual approach ‐ customization to particular markets and particular projects 27%
Model simplicity ‐ more clear reports,concise and accurate set of terms 17%
Media and marketing mix model. Local assessment of marketing impact on sales 17%
Timing (should be reduced) 13%
Description of model, terms of application and limitations to use, requirements to
model inputs, formats and use of results13%
Flexibility ‐ forecasting multiple scenarios, making adjustments 13%
Success stories and cases obtained from the local market 13%
Cost 13%
Thoughtful application of global practices / guidelines 10%
Qualification of agency specialists 10%
Accuracy of forecast (including components such as inctremental sales, 7%
Software (simulators) 3%
Fieldwork quality 3%
Approaches to targeting, i.e. market definition, source of volume 3%
6.6 Key findings
The research undertaken leads to the following main conclusions about current
practices of sales forecasting for new products and the use of Simulated Test
Marketing in the Russian FMCG market:
The hypothesis about greater portion of ”new-to-the-country” and “new-to-the-
world” products in the Russian market (as compared to Western markets) has
not been confirmed. Instead, higher share of “product/price” changes was
145
registered due to the economic downturn in 2009. The question is still open
and requires additional study in the period of 2011-2013. For other types of
new products, their shares are line with that in “western” markets.
Methods of marketing research are extensively used by the major players in
the market. Methods of Simulated Test Marketing are relatively popular and
often used in case of “new-to-the-country” and ”new-to-the-firm” products.
Reliable market data is key to accurate sales forecasting in the Russian
market.
The key issues researchers face when forecasting are: lack of market data,
research approach is not adapted to the local market (or not proven), lack of
historical data, validations and poor normative databases
Higher rate of successful new product launches and lower accuracy of
forecast – as compared to “western” markets
High awareness of the leading STM models. Relatively equal experience with
ACNielsen BASES and Ipsos DESIGNOR
The market of STM is expected to grow despite such limiting factor as high
pricing (i.e. prices are in line with “western” markets)
BASES and DESIGNOR have certain important advantages in the Russian
market, such as well-known global brand, professionalism, proven validation
record in developed markets. However, their key weaknesses are related to
insufficient focus on local market insights, pricing and flexibility. These
weaknesses of international suppliers are successfully exploited by local
competitors.
As indicated by experts, the key areas for STM improvement in the Russian
market are mainly related to agencies competences, e.g. more involvement to
clients’ business, individual approach, R&D, pro-active “research-on-
research” (i.e. gathering of benchmarks, validations, local market
information), price and timing optimizations
146
The majority of hypotheses about practical application of Simulated Test
Marketing in the Russian FMCG market (discussed in Chapter 4) have been
confirmed. Research questions stated in Chapter 5 have been answered
(with the exception of new product shares - by type).
The study undertaken and the amount of information collected allows to make
actionable recommendations concerning effective application of Simulated
Test Marketing in the Russian FMCG market (see point 6, Secrion 6.5
above), which is done in the following Chapter 7.
147
CHAPTER 7
RECOMMENDATIONS
7.1 Recommendations for FMCG manufacturers.
The research undertaken has led to a principal conclusion about feasibility of using
Simulated Test Marketing in the Russian FMCG market (see Chapters 4,6). With
that, the study has shown that making decision about STM in Russia requires more
thorough market examination than that in developed countries. It is important to bear
in mind, that the quality of STM outputs is entirely determined by the quality and the
amount inputs, i.e. market data and tested materials (see Chapter 3). Indeed, as
discussed earlier in Chapter 3, it is hardly possible to simulate dynamic market
environment, especially at early stage of life-cycle, provided with insufficient or poor
quality market data. In such cases, any attempt of modeling employing traditional
“western” STM techniques may not only lead to inaccurate forecast, but wrong
business decisions due to misleading outputs (see Chapter 3). Unlike predictable
and well-defined “western” FMCG markets, a majority of Russian markets are at
growth stage, and only few of them can be considered mature or saturated (see
Chapter 2). Therefore, only few are ready for quality modeling with standardized
“western” STM approaches (see Chapter 3). This leads to recommend employing
traditional STM methods only if the following general criteria are satisfied:
Business need: New product is of a high strategic importance to the company
(i.e. is a part of offensive or defensive business strategy). Strategic direction
is concerned with operations in existing markets (i.e. “market penetration” and
“new product development” – see Chapter 2). Considerable financial risk is
tied to substantial reward in case of success (i.e. gaining market share,
increasing revenue and profitability, defeating competitors, capturing empty
market space, improving brand image to maintain market leadership etc);
148
Decision-makers: Senior management (country, region, global), key users –
marketing and strategic planning departments;
Application area: Mid-term business planning;
Horizon of forecast: 1-2 years;
Interval of forecast : 1 year (very rarely – quarter or month);
Decision options: (a) National product launch, investing in production and
support (”go national”); (b) additional testing with traditional test markets (”test
market”); (c) postponing the launch due to temporary market barriers or due
to improvement of marketing mix (”recycle”); (d) dropping the initiative
(“discontinue”);
Decision making criteria: These should involve not only sales business
metrics like in the case of traditional test market (e.g. sales volume and value,
market share, incremental sales, cannibalization etc), but measures of
consumer response (e.g. awareness, brand image and advertising
perception, trial, repeat etc) (see Chapters 3,4). Action standards are usually
based on company’s objectives and benchmarks (similar cases in the past or
in similar markets);
Level of detail : Usually, by brand or SKU
Time to market / Timing: Product launch is typically within a year after making
decision about STM, which usually takes about 2-3 months to conduct (see
Chapter 3)
Accuracy to expect: In case of a well-defined saturated market it is up to
±10%, while that for a rapidly growing market is considerably lower ±20%
(See Chapters 4,6)
Price to expect – In case of true STM it is above $30 000 (See Chapters 4,6)
Geographical scope – national, stratified by regional centers of distribution
149
Number of forecasts – up to 10 (2-3 marketing mix options, times 2-3
marketing plan options) (See Chapter 4)
As noted in Chapter 4, classic approaches to Simulated Test Marketing are most
effective when the following requirements to the tested product offer are met:
Product newness: The novelty of the product is some of its “actual” or
“augmented” components (e.g. product, brand, pack, price etc), satisfying
already existing “core need”. Strategic focus should be placed on product and
branding, rather than market or need development. (see Chapter 2);
Product types: “New-to-the-Firm”, “Line extensions”, “Brand stretching”, “Re-
positioning / re-branding”. In case of “product / price changes” it is advisable
to employ less costly and more appropriate solutions, like Conjoint.
Considerable difficulties could be expected in the case of “New-to-the-world”
or “New-to-the-country” - as market does not exist yet, hence there’s nothing
to simulate. (see Chapter 3 for more details on marketing research
procedures);
Stage of NPD: It is expected for STM that the phase is “Test Marketing and
Validation” (in terms of Stage-Gate® framework). It follows right after
“development and testing” of separate components of marketing mix (i.e.
concept or commercial, product formula, pack etc). In other words, it is
usually required that marketing mix come in final form. (see Chapters 2, 3, 4);
Range of tested materials / stimuli: Ready-made advertising materials
(commercials, prints, although concepts or animatics are often accepted),
product samples, final packs or mockups, prices. In case of testing in
competitive context (e.g. preference share approach) - the same is required
for competitors. (see Chapters 3,4);
Product launch / Marketing support: Typically, there is an IMC campaign
(integrated marketing communications), i.e. a variety of traditional mass
media are employed (e.g. television, outdoor, press etc) as well as standard
150
retail channels (hyper and supermarkets, c-stores, traditional trade etc). It is
imperative that performance of these channels is measured beforehand (e.g.
media monitoring, trackings, retail audit). (see Chapter 4)
The use of traditional STM approaches requires precise targeting (i.e. accurate target
market definition). As suggested earlier, the market can be defined using 8 distinctive
sets of characteristics (see Chapters 3, 4). In particular:
Consumer / Shopper: It is highly recommended to consider conducting
qualitative and quantitative consumer studies, such as segmentation or
market landscaping (see Chapter 3), to clearly define the following: (1) what
is exactly the core need ?; (2) in what occasions it might arise? how is it
satisfied by consumers? how often does this occasion occur? - the frequency
must be high; (3) who will experience such a need during the horizon of the
forecast (1-2 years), i.e. to identify socio-demographic, psychological and
cultural profiles of potential consumers and shoppers, in particular – life
styles, structure of values, decision-making process when satisfying the need;
(4) the number of potential consumers and shoppers – it should be measured
at least in ten thousands of people, (5) the level of experience with the
product category should be medium to high – consumers must be aware
about key brands, product features etc.
Product: It is advisable to determine key distinguishing characteristics of
products in the market and identify potential substitutes (i.e. “neighbor”
markets, categories of products with similar qualities satisfying similar needs)
(see market landscaping, Chapter 3).
Market size and dynamics: It is helpful to employ industrial and quantitative
methods to identify: (1) actual size of “core” and “neighbor” markets; (2)
market life-cycle stage – the use of STM is effective only at “sustainable
growth” and “saturation” stages (as shown in Chapter 3, the market is
considered “saturated” if growth is in line with GDP or below. Thus, beer
151
market in Russia is almost “saturated”) ; (3) Growth rate, predictability ,
variability – the market must be sustainable and predictable; (4) Granularity /
Structural complexity – when applying STM, it is important to make sure that
market structure is homogenous across regions, channels etc. If not, the
sample is often divided into several cells, reflecting the reality of each case.
Promotion / Marketing: A majority of STM models are able to simulate
standard mechanisms of marketing support, e.g. television, outdoor, press,
sampling, simple promo-activities. However, it is essential that only proven
advertising and promotion vehicles are used, i.e. their performance must be
measured locally with reliable methods.
Pricing: It is imperative in a highly dynamic market environment to consider
possible price changes in the market. Before going with STM, it is helpful to
obtain price sensitivity scores from the pricing study (see Chapter 3). Based
on that, it is advisable to produce several price scenarios for testing with
STM.
Competitive rivalry: Unlike stable “western” markets, competitive response in
the Russian market can be very quick and aggressive. Therefore, if possible,
it is worthy to consider competitive environment in the planned STM study.
Infrastructure / Environment – When forecasting sales in the Russian market,
one must take into account a number of difficulties related to infrastructure
setup (distribution, supply, manufacturing, logistics, retail) and volatile macro-
environment (political and economic risks, possible changes in regulating
policy, law, issues with certification, customs control, local authorities etc.)
Finance / Action standards – STM action standards must be developed
involving strategic accounting methods (i.e. “break-even” etc).
Market characteristics listed above are usually requested by STM vendors to build
the model of the market. Therefore, it is necessary to ensure high quality and
sufficient amount of the data.
152
Also, when selecting a vendor for Simulated Test Marketing, it is important to keep in
mind business objectives as well as other criteria outlined above. Thus, according to
research findings, the following strengths of STM vendors operating in Russia can be
highlighted:
ACNielsen BASES – ”recommended by management or company protocols”,
“well-known agency”, “positive previous experience”, “professional
presentation of findings and recommendations”;
Ipsos DESIGNOR - “a lot of diagnostic information”, “professional
presentation of findings and recommendations”, “well-known agency”,
“positive previous experience”, “high quality data collection”
Local providers - “individual approach for every project”, “simple and easy to
understand”, “flexible pricing”, “high speed”, “high quality of data collection”,
“suitable for the Russian market”
The research undertaken has helped to develop a standardized form of request for
Simulated Test Marketing study, which is presented in the Appendix.
7.2 Recommendations for agencies.
As it follows from the analysis of the Russian research market (see Chapters 4,6), it
is expected that the number of STM projects carried out with classic techniques will
rise in the future due to increasing number of well-defined saturated FMCG markets.
However, as shown in Chapters 1 and 2, this process might take long time, while at
the moment there is a considerable demand for high quality forecasting service - as
noted in Chapter 1, nearly a half of earnings for the world’s leading FMCG
manufacturers come from emerging markets like Russia. In that respect, it is highly
recommended that STM vendors operating in Russia (as well as in some other
emerging markets) pay greater attention to local market insights and improve their
forecasting / servicing models in view of the following:
153
More “client-oriented”, individual approach, greater focus on client’s business
Investing in local R&D - gathering local historical information, benchmarks,
learnings, success stories, performing validations, developing evaluation
criteria relevant to the Russian FMCG market;
Simplification of reporting and modeling approaches;
Localized media models, gauging effectiveness of various marketing
instruments in the local market;
Reducing project timing;
Openness in explaining modeling principles, client-friendly guidelines on
terms of use, inputs , outputs, research implications, limitations, targeting /
market definition, required pre-studies, requirements to study materials, etc.;
Flexibility in forecasting and modeling – forecasting various scenarios etc;
Reasonable pricing;
Thoughtful and careful application of global standardized STM techniques to
local business needs;
Training local consultants;
Increasing forecast accuracy;
Development of user-friendly simulators and other STM-related software;
Improving fieldwork quality;
Productive work following the recommendations above could help in establishing
better cooperation between clients and agencies, but more importantly, it would
boost STM market development in Russia. A majority of clients will appreciate
flexible, proactive, “client-oriented” approach as opposed to conservative, “model-
centered” services based on “western” execution standards. This would allow to
accumulate sufficient experience and to customize existing models for the needs of
the local market. Moreover, this would provide great opportunity to bring high quality
service to more clients today, not years later, i.e. when the markets are ready for
“western” models. Emerging markets offer considerable potential for growth not only
154
for FMCG manufacturers, but for their business partners as well. Therefore, it is
highly advisable for STM vendors to pay particular attention to developing business
in emerging countries, such as Russia.
155
REFERENCES
AB InBev (2009) Annual Report 2009 [online] Available at: http://www.ab-inbev.com/pdf/AB_InBev_AR09.pdf [Accessed 12.06.2010] ACNielsen (2006) ‘The Profile of Russian Shopper’, Moscow: ACNielsen Annual Conference Adrien, C. and Mooth, R. (2009) Five Secrets to Bringing Stronger Products to Market – Game changing metrics [online] Available at: http://blog.nielsen.com/nielsenwire/consumer/secrets-to-bringing-stronger-products-to-market/ [Accessed 11.07.2010] Agaeva, M. (2008) ‘Marketing guesstimates: does the manufacturer need test marketing?’, Sales Business, No. 05/11, [online] Available at: http://www.sostav.ru/articles/2008/11/05/ko1/# [Accessed 12.11.2009] Aivazian, S. and Mkhitarian, V. (1998) Applied Statistics and Essentials of Econometrics, Moscow: Unity Press Aliaga, M. and Gunderson, B. (1999) Interactive Statistics, New Jersey: Prentice Hall Alam,I. and Perry,C. (2002) ‘A customer-oriented new service development process’, Journal of Services Marketing, Vol. 16, Issue 6, pp.515 - 534 Ansoff, I. (2007) Strategic Management (Classic Edition), London: Palgrave Macmillan Armstrong, G. and Kotler, P. (2009) Marketing an Introduction, Harlow: Pearson Education Limited Armstrong, G. and Scott, J. (1987) ’Pretest market models: A critical evaluation’, International Journal of Forecasting, Vol. 3, Issue 3/4, pp. 542-543 Bagozzi, R. (1997) Advanced Methods of Marketing Research, Oxford: Blackwell Publishers Baker, M. (1999) ‘Sales Forecasting’, The IEBM Encyclopedia of Marketing, London: International Thompson Business Press, p. 278-290 Balogun, J. and Hailey, V. (2008) Exploring Strategic Change (3rd Edition), Harlow: Pearson Education Baldinger, A. (1988) ‘Trends and Issues in STMs: Results of an ARF pilot project’, Journal of Advertising Research, Vol. 28 Issue 5, pp. 50-56 Baldinger, A. and Haley, R. (1991) ‘The ARF copy research validity project’, Journal of Advertising Research, Vol. 31, Issue 2, p. 11-32 Barker, J. (1992) Paradigms: The Business of Discovering the Future, New York: Harper Business Bell, D., Bonfrer, A. and Chintagunta, P. (2005) ‘Recovering SKU-Level Preferences and Response Sensitivities from Market Share Models Estimated on Item Aggregates’, Journal of Marketing Research, Vol. 42, pp. 169-182
156
Belinson M. (1986) ‘Gap widens in testing approaches for different consumer products’, Marketing News, Vol. 20 Issue 22, p. 18 Belotserkovskaya, O. and Shuranov, A. (2005) ‘Is marketing research required for launching a new product in the Russian market?’, Company – A weekly business magazine, No. 11/04 [online] Available at: http://www.ko.ru/document.php?id=11660 [Accessed 12.11.2009] Bemmaor, A. (1995) ‘Predicting Behavior from Intention-to-Buy Measures’, Journal of Marketing Research, Vol. 32, No. 2, pp. 176-191 Berenson, M. and Levine, D. (1986) Basic Business Statistics (7th Edition), London: Prentice-Hall International Bilgram, V., Brem, A. and Voigt, K. (2008) ‘User-centric innovations in new product development', International Journal of Innovation Management, Vol. 12, No. 3, pp. 419-458 Belliveau, P., Griffin, A. and Somermeyer, S. (2002) The PDMA toolbook for New Product Development, New York: John Wiley and Sons Bockenholt, U. and Dillon, W. (1997) ‘Some New Methods for an Old Problem: Modeling Preference Changes and Competitive Market Structures in Pretest Market Data’, Journal of Marketing Research, Vol. XXXIV, pp. 130-142 Booz, Allen & Hamilton (1988), ‘New Products Management for the 1980s’, New York: Booz, Allen & Hamilton Borovikov, V. (2003) STATISTICA for Professionals (2nd Edition), Moscow: Piter press Boughton, P., Nowak, L., and Washburn J. (1996) ‘A decision model for marketing research relationship choices’, Journal of Services Marketing, Vol. 10 Issue 1, pp.56 - 69 Brody, A. and Lord, J. (2000) Developing new food products for a changing marketplace, Boca Raton: CRC Press Buhl, A. and Zofel, P. (2002) SPSS: Einfuhrung in die modern Datenanalyse unter Windows, Munchen: Pearson Education Deutschland Gmbh Burdey K. and Troyan N. (1999) ‘New product launches in the Russian market: stages and marketing support’, Advertising Ideas, No. 1/99 [online] Available at: http://www.advi.ru/archive/article.php3?pid=163 [Accessed 08.07.2010] Burdey K. (2010) How not to embarrass yourself when ordering a market research study [online] Available at: http://www.okresearch.ru/article5.html [Accessed 08.07.2010] Burgess, S. and Steenkamp J. (2006) ‘Marketing renaissance: How research in emerging markets advances marketing science and practice’, International Journal of Research in Marketing, Vol. 23, Issue 4, Pages 337-356
157
Burton, R., Chandler, J. and Holzer, H. (1986) Quantitative Approaches to Business Decision Making, New York: Harper & Row Publishers Buur, J., and Matthews, B. (2008) ‘Participatory innovation’, International Journal of Innovation Management, Vol. 12, No. 3, pp. 255–273 Byrne, B. (2010) Structural Equation Modeling with AMOS (2nd Edition), New York: Taylor & Francis Group Carlberg, C. (2006) Excel Sales Forecasting, New York: Wiley Publishing Charnes, A., Cooper, W., DeVoe, J. and Learner, D. (1966) ‘Demon: Decision Mapping via Optimum Go-No Networks-a Model for Marketing New Products’, Management Science, Vol. 12, No. 11, pp. 865-887 Chambers, J., Mullick, S. and Smith, D.(1971) ‘How to choose the Right Forecasting Technique’, Harvard Business Review, Vol. 49, No. 4, pp. 45-74 Christensen, C. (1997) The innovator’s dilemma: when new technologies cause great firms to fail, Boston: Harvard Business School Press Clancy, K. and Krieg, P. (2003) ‘Surviving Innovation’, Marketing Management, No. 2, March-April, p.112-121 Clancy, K., Krieg, P. and Wolf M. (2006) Market new products successfully, Oxford: Lexington Books
Clancy, K., Shulman, R. and Wolf, M. (1994) Simulated Test Marketing: Technology for Launching Successful New Products, Oxford: Lexington Books Clemente, M. (2002) The Marketing Glossary: Key terms, Concepts and Applications, New Jersey: Clemente Communications Group Colgate (2009) Annual Report 2009 [online] Available at: http://investor.colgate.com [Accessed 12.06.2010] Cooper, R. and Edgett, S. (2010) Stage-Gate® - Your Roadmap for New Product Development [online] Available at: http://www.prod-dev.com/stage-gate.php [Accessed 01.07.2010] Crawford, C. (1989) ‘How Product Innovators Can Foreclose the Options of Adaptive Followers', Journal of Marketing Management, Vol. 4, No. 3, pp. 277-287 Danone (2009) Annual Report 2009 [online] Available at: http://danone09. danone.com/en/fb/data/catalogue.pdf [Accessed 12.06.2010] Darden, W. and Reynolds, F. (1974) ‘Backward Profiling of Male Innovators’, Journal of Marketing Research, Vol. XI, pp. 79-85 Datamonitor (2006) ‘Targeting Profitable Consumer Trends In Brazil, Russia, India and China Insights into Emerging Groups & Behaviors in BRIC’, London: Datamonitor
Datamonitor (2005) ‘Evolution of Global Consumer Trends’, London: Datamonitor Datamonitor (2010) ‘Savory snacks industry in Russia’, London: Datamonitor
158
Datamonitor (2010) ‘Savory snacks industry in the US’, London: Datamonitor Datamonitor (2010) ‘Savory snacks industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Savory snacks industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Savory snacks industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Make up industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Make up industry in the US’, London: Datamonitor Datamonitor (2010) ‘Make up industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Make up industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Make up industry in Japan’, London: Datamonitor Datamonitor (2010) ‘OTC Pharmaceutical industry in Russia’, London: Datamonitor Datamonitor (2010) ‘OTC Pharmaceutical industry in the US’, London: Datamonitor Datamonitor (2010) ‘OTC Pharmaceutical industry in the UK’, London: Datamonitor Datamonitor (2010) ‘OTC Pharmaceutical industry in Germany’, London: Datamonitor Datamonitor (2010) ‘OTC Pharmaceutical industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Dairy industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Dairy industry in the US’, London: Datamonitor Datamonitor (2010) ‘Dairy industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Dairy industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Dairy industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Soft Drinks industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Soft Drinks industry in the US’, London: Datamonitor Datamonitor (2010) ‘Soft Drinks industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Soft Drinks industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Soft Drinks industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Hair care industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Hair care industry in the US’, London: Datamonitor Datamonitor (2010) ‘Hair care industry in the UK’, London: Datamonitor
159
Datamonitor (2010) ‘Hair care industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Hair care industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Household products industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Household products industry in the US’, London: Datamonitor Datamonitor (2010) ‘Household products industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Household products industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Household products industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Confectionery industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Confectionery industry in the US’, London: Datamonitor Datamonitor (2010) ‘Confectionery industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Confectionery industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Confectionery industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Alcoholic Drinks in Russia’, London: Datamonitor Datamonitor (2010) ‘Alcoholic Drinks in the US’, London: Datamonitor Datamonitor (2010) ‘Alcoholic Drinks in the UK’, London: Datamonitor Datamonitor (2010) ‘Alcoholic Drinks in Germany’, London: Datamonitor Datamonitor (2010) ‘Alcoholic Drinks in Japan’, London: Datamonitor Datamonitor (2010) ‘Hot Drinks in Russia’, London: Datamonitor Datamonitor (2010) ‘Hot Drinks in the US’, London: Datamonitor Datamonitor (2010) ‘Hot Drinks in the UK’, London: Datamonitor Datamonitor (2010) ‘Hot Drinks in Germany’, London: Datamonitor Datamonitor (2010) ‘Hot Drinks in Japan’, London: Datamonitor Datamonitor (2010) ‘Tobacco industry in Russia’, London: Datamonitor Datamonitor (2010) ‘Tobacco industry in the US’, London: Datamonitor Datamonitor (2010) ‘Tobacco industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Tobacco industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Tobacco industry in Japan’, London: Datamonitor Datamonitor (2010) ‘Beer industry in Russia’, London: Datamonitor
160
Datamonitor (2010) ‘Beer industry in the US’, London: Datamonitor Datamonitor (2010) ‘Beer industry in the UK’, London: Datamonitor Datamonitor (2010) ‘Beer industry in Germany’, London: Datamonitor Datamonitor (2010) ‘Beer industry in Japan’, London: Datamonitor Davila, T., Epstein, M. and Shelton R. (2006) Making Innovations Work (3rd Edition), New Jersey: Wharton School Publishing Davis, J. (2007) Measuring Marketing: 103 Key Metrics every marketer needs, Singapore: John Wiley and Sons Davis, J. (1997) Advertising Research Theory and practice (Russian Edition, 2003), New Jersey: Prentice Hall, Moscow: Williams De Kare-Silver, M. (1998) E-shock: The electronic shopping revolution: strategies for retailers and manufacturers, London: Macmillan Business Dibb, S. and Simkin, L. (2008) Marketing Planning: A Workbook for Marketing Managers, London: South-Western Cengage Learning Dibb, S., Simkin, L., Pride, W. and Ferrell, O. (1997) Marketing: Concepts + Strategies (3rd European Edition), Boston: Houghton Mifflin Company Dorsey, P. (2004) The Five Rules for Successful Stock Investing, New Jersey: John Wiley and Sons, Inc Draper, N. and Smith, H. (1998) Applied Regression Analysis (3rd Edition), New York: John Wiley & Sons Drummond, G., Ensor, J. and Ashford R. (2008) Strategic Marketing: Planning and Control (3rd Edition), Oxford: Elsevier Duboff, R. and Spaeth, J. (2000) Market Research Matters: Tools and Techniques for Aligning Your Business, New York: John Wiley & Sons Dubrov, A., Mkhitaryan, V. and Troshin, L. (1998) Multi-dimensional statistical methods, Moscow: Finance and Statistics Economist Intelligence Unit (2010) ‘Industry Report: Consumer goods and retail January 2010 - Russia’ , London: EUI Economist Intelligence Unit (2010) ‘Consumer goods and retail January 2010 – The United States’ , London: EUI Economist Intelligence Unit (2010) ‘Consumer goods and retail January 2010 – The United Kingdom’ , London: EUI Economist Intelligence Unit (2010) ‘Consumer goods and retail January 2010 - Germany’ , London: EUI
161
Economist Intelligence Unit (2010) ‘Consumer goods and retail January 2010 - Japan’ , London: EUI Ehrenberg, A. (1988) Repeat-Buying (2nd Edition), Oxford: Oxford University Press Enrick, N. (1970) Decision-Oriented Statistics, London: Brandon Systems Press Inc Eskin, G. and Malec, J. (1976) ’A model for estimating sales potential prior to the test market’, American Marketing Association Educators’ Conference, Series No. 39 Ethiraj, S. and Zhu, D. (2006) ‘Performance effects of imitative entry’, Strategic Management Journal, No. 29, pp. 797–817 ESOMAR (2008) ICC/esomar international code on market and social research [online] Available at: http://www.esomar.org/index.php/codes-guidelines.html [Accessed 20.11.2009] ESOMAR (2004) Global Market Research 2004: ESOMAR Industry Report [online] Available at: http://www.esomar.org/web/publication [Accessed 24.07.2010] ESOMAR (2008) Global Market Research 2008: ESOMAR Industry Report [online] Available at: http://www.esomar.org/web/publication [Accessed 24.07.2010]
ESOMAR (2009) Global Market Research 2009: ESOMAR Industry Report [online] Available at: http://www.esomar.org/web/publication [Accessed 24.07.2010] ESOMAR (2009) Global Market Research 2009: ESOMAR Industry Report [online] Available at: http://www.esomar.org/web/publication [Accessed 24.07.2010] ESOMAR (2010) Global Prices Study 2010 [online] Available at: http://www.esomar.org/web/publication [Accessed 24.07.2010] ESOMAR (2010) ‘Prices study 2010 Special’, Research World, No. 21, pp.16-27 Evard, B. and Gipple, C. (2001) Managing Business Change, Indianopolis: Wiley Publishing Fader, P., Hardie, B., and Zeithammer, R. (2003) ‘Forecasting New Product Trial in a Controlled Test Market Environment’, Journal of Forecasting, Vol.4, p. 391–410 Fader, P. and Hardie, B. (2005) ‘The value of simple models in new product forecasting and customer-base analysis’, Applied stochastic models in business and industry, Vol.21, p. 461–473 Farris, P., Bendle, N., Pfeifer, P. and Reibstein, D. (2006) Marketing Metrics: 50+ Metrics Every Executive Should Master, NJ: Wharton School Publishing
Farris, P. and Albion, M. (1981) The Advertising Controversy: Evidence on the Economic Effects of Advertising, Boston: Auburn House Publishing Company Fink, A. (2010) Conducting Research Literature Reviews (3rd Edition), London: SAGE Publications Fourt, L. and Woodlock, J. (1960) 'Early prediction of market success for new grocery products.' Journal of Marketing, 25, pp. 31–38
162
Foxall, G. (1990) Consumer Psychology in Behavioral Perspective, London: Routledge Franses, P. and Paap, R. (2007) Quantitative Models in Marketing Research, Cambridge: Cambridge University Press Gill, J. and Johnson, P. (1991) Research Methods for Managers, London: Paul Chapman Publishing Gill, J. and Johnson, P. (2010) Research Methods for Managers (4th Edition), London: SAGE Publications Griffin A. (1997) ‘PDMA research on new product development practices: updating trends and benchmarking best practices’, Journal of Product Innovation Management, Vol. 14, p.429 Groucutt, J. (2007) Business Degree Success, New York: Palgrave Macmillan Groucutt, J. (2005) Foundations of Marketing, New York: Palgrave Macmillan Gujarati, D. and Porter, D. (2009) Basic Econometrics (5th Edition), Boston: McGraw Hill International Edition Gundee, H. (1982) ‘Points to check when planning simulated test marketing study’, Marketing News, Vol. 15, Issue 17, p. 3 Hanke, J., Reitsch, A. and Wichern, D. (2001) Business Forecasting (7th edition), New Jersey: Prentice Hall Hart, S., Tzokas, N. and Saren, M. (1999) ‘The effectiveness of market information in enhancing new product success rates’, European Journal of Innovation Management, Vol. 2, No. 1, pp. 20–35 Harvard Business School (2003) Harvard Business Essentials: Managing change and transition, Boston: Harvard Business School Press Hassan S. (2008) ‘Bringing Lead-User Innovations to the Market: Research and Management Implications’, SAM advanced management journal, Vol. 3 Hayes, J. (2010) The Theory and Practice of Change Management (3rd Edition), New York: Palgrave Macmillan Henkel (2009) Annual Report 2009 [online] Available at: http://www.henkel.com/com/content_data/165334_2010.02.25_FY_2009_annualreport_EN.pdf [Accessed 12.06.2010] James, B. (1985) Business Wargames, Cambridge: Abacus Press Johnson, G., Scholes, K. and Whittington R. (2008) Exploring corporate strategy (8th Edition), Harlow: Financial Times Prentice Hall Juster, F. (1966). Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design, Cambridge: National Bureau of Economic Research
163
Kachalov, I. (2008) Sales planning with accuracy exceeding 90%, Moscow: Piter Press Kassarjian, H., Robertson, T. (1973) Perspectives in Consumer Behavior, Glenview: Scott, Foresman and Company Kim, J. and Bae, Z. (2008) ‘The role of online brand community in New Product Development’, International Journal of Innovation Management, Vol. 12, No.3, p. 357–376 Koop, G. (2003), Bayesian econometrics, Chichester: John Wiley and Sons Koop, G. (2008), Introduction to econometrics, Chichester: John Wiley and Sons Kondratiev, N. (1993), Selected texts, Moscow: Russian Academy of Sciences Kotler, P. (1998) Marketing Management: Analysis, planning, implementation and control (9th Edition), New Jersery: Prentice Hall Kraft Foods (2009) Annual Report 2009 [online] Available at: http://thomson.mobular.net/thomson/7/2956/3950/document_2/KFT_FactSheet10.pdf [Accessed 12.06.2010] Kratt, M. (2009) A History of the Supermarket in America [online] Available at http://www.groceteria.com/about/history.html [Accessed 20.11.2009] Klimin, A. (2007) Stimulating sales, Moscow: Vershina Lawrence, K., Geurts, M. and Guerard J. (2002) Advances in Business and Management Forecasting (Volume 3), Oxford: Elsevier Lilien, G., Kotler, P. and Moorthy K. (1992) Marketing Models, London: Prentice-Hall Inc. Lilien, G., Rangaswamy, A. and De Bruyn, A. (2007) Principles of Marketing Engineering, Bloomington: Trafford Publishing L’Oreal (2009) Annual Report 2009 [online] Available at: http://www.loreal-finance.com/eng/world-presence [Accessed 12.06.2010] Lin, L. (1986), ‘Estimating sales volume potential for new innovative products with case histories’, Proceedings of the 36th ESOMAR Conference, Monte Carlo: ESOMAR, pp. 159 – 174 Lin, L. (1997), BASES New Product Sales Forecasting Model – A Collection of Earlier BASES & BASES Related Papers, Taichung: National Chung-Hsing University Mahajan, V. and Muller, E. (1998) ‘When Is It Worthwhile Targeting the Majority Instead of the Innovators in a New Product Launch?’, Journal of Marketing Research, Vol. XXXV, p. 488-495 Mahajan, V. and Wind, Y. (1988) ‘New Product Forecasting Models: Directions for Research and Implementation’, International Journal of Forecasting, Vol. 4, p. 341-358
164
Malhotra, N. (2007) Marketing Research: An Applied Orientation (5th Edition), New Jersey: Pearson Education Limited
Markowitz, L. (2007) Concept Screening: Why Purchase Intent Is Losing Its Appeal: Reliance on Purchase Intent Can Lead to Lower Success Rates for Consumer Packaged Goods Products [online] Available at: http://www.ipsos.fr /InsideIpsos/attachment/2449-0-Ipsos_PI_LosingAppeal_POV-145.pdf [Accessed 12.10.2008] McDonald, M. and Dunbar, I. (2008) Market segmentation, Oxford: Elsevier McDonald, M. (2008) Marketing plans (6th Edition), Oxford: Elsevier McNeil, R. (2005) Business-to-Business Market Research: Understanding and measuring business markets, London: Kogan Page Limited Mick, D. and Fournier, S. (1998) ‘Paradoxes of technology’, Journal of Consumer Research, Vol. 25, No. 2, pp.123-144. Mintel (2007) Press Release 2007 [online] Available at: http://money.cnn.com /2007/02/02/news/companies/newproducts_survey/index.htm [Accessed 20.10.2009] Mentzer, J. and Moon, M. (2005) Sales Forecasting Management: A Demand Management Approach (2nd Edition), London: SAGE Publications Mondy, R., Sharplin, A., Holmes, R. and Flippo, E. (1986) Management: Concepts and Practices (3rd Edition), Boston: Allyn and Bacon Inc. Moore, G. (2002) Crossing the Chasm, New York: HarperCollins Punishers Inc Morwitz, W., Steckel, J. and Gupta, A. (2007) ‘When do purchase intentions predict sales?’, International Journal of Forecasting, 23,pp. 347–364
Nestle (2009) Annual Report 2009 [online] Available at: http://www.nestle.com/ InvestorRelations/Reports/ManagementReports/2009.htm [Accessed 12.06.2010] Noelle, E. (1971) Umfragen in der Massengesellschaft: Einfuhrung in die Methoden der Demoskopie (Russian Edition, 1993), Munchen: Rowohlt Taschenbuch Verlag GmbH / Moscow: ABA Estra Oxford University (2010) Oxford Dictionaries Online [online] Available at: http://oxforddictionaries.com [Accessed 12.06.2010] Parameswaran, M. (2005) Building powerful brands using consumer research, Delhi: McGraw Hill Publishing Parfitt, J. and Collins, B. (1968) ‘Use of Consumer Panels for Brand Share Prediction’, Journal of Marketing Research, 5, pp.13-46.
Parmerlee, D. (2000) Analyzing Markets, Products, and Marketing Plans (AMA Toolbox), New York: McGraw-Hill Publishing Parmerlee, D. (1993) Developing Successful Marketing Strategies (AMA Marketing Toolbox) (AMA Toolbox), New York: McGraw-Hill Publishing
165
Penenberg, A. (2009) Viral loop: the Power of Pass-it-on, London: Hodder & Stoughton Ltd Peng, L. and Finn, A. (2008) ‘Concept testing: the state of contemporary practice’, Marketing Intelligence & Planning, Vol. 26, No. 6, pp. 649-674 Pepsico (2009) Annual Report 2009 [online] Available at: http://www.pepsico.com/Investors/Annual-Reports.html [Accessed 12.06.2010] Pickton, D. and Broderick, A. (2005) Integrated Marketing Communications (2nd Edition), Harlow: Pearson Education Ltd Porter, M. (2006) Competitive Strategy: Techniques for Analyzing Industries and Competitors, New York: Free press PriceWaterhouseCoopers (2006) 'From Beijing to Budapest (4th edition) – Russia’, London: PriceWaterhouseCoopers
PriceWaterhouseCoopers (2010) 'Innovation by Large Companies in Russia: Mechanisms, barriers, perspectives’, Moscow: PriceWaterhouseCoopers
Prince, M. (1992) ‘Choosing simulated test marketing systems’, Marketing Research, Vol. 4, Issue 3, p. 14-16 P&G (2009) Annual Report 2009 [online] Available at: www.annualreport.pg.com/annualreport2009 [Accessed 12.06.2010] Reckitt Benckiser (2009) Annual Report 2009 [online] Available at: http://www.rb.com/Investors-media/Investor-information [Accessed 12.06.2010] Rossiter, J. and Percy, L. (1987) Advertising and promotion management, New York: McGraw-Hill Rossiter, J. and Danaher, P. (1998) Advanced Media Planning, Norwell: Kluwer Academic Publishers Rossi, P., Allenby, G. and McCulloch, G. (2009) Bayesian Statistics and Marketing, Chichester: John Wiley & Sons Rosstat,Federal State Statistics Service (2010) Russia in figures [online] Available at: http://www.gks.ru/wps/PA_1_0_S5/Documents/jsp/Detail_default.jsp?category=1112178611292&elementId=1135075100641 [Accessed 04.07.2010] Ruvinsky J. (2007) ‘Map planes science’, Discover , No. 10, [online] Available at: http://discovermagazine.com/2007/oct/planet-science [Accessed 01.07.2010] Schneider, J. (2004) New product launch: 10 proven strategies, Deerfield: Stagnito Communications Inc. Schorsch, M. (2009) Market Entry Strategies for Russia: A comprehensive survey based on expert interviews, Hamburg: Diplomica Verlag Schwartz, B. (2004) The Paradox of Choice: Why More is Less, New York: HarperCollins
166
Shocker, A. and Hall, W. (1986) ‘Pretest Market Models: A Critical Evaluation’, Journal of Product Innovation Management, Vol. 3 ,Issue 2, pp86-107 Sekaran, U. (2003) Research Methods for Business: A Skill-Building Approach (4th Edition), NJ: John Wiley & Sons Inc. Shim, J. (2000) Strategic business forecasting (2d Edition), London: CRC Press Schlossberg, H. (1989) ‘Simulated vs. Traditional Test Marketing: the first one’s improving while the second one is hardly dead’, Marketing News, Vol. 23, Issue 22, p. 1-11 Slutskin, L. (2006) MBA course on business forecasting, Moscow: Alpina Business Books Snedecor, G. and Cohran, W. (1968) Statistical Methods (7th Edition), Ames: Iowa State University Press Solomon, M. (2002) Consumer Behavior (5th Edition), New Jersey: Prentice Hall Souder, W. and Sherman, D. (1994) Managing new technology development, New York: McGraw-Hill SPSS (2001) SPSS Base 11.0 Users’ Guide, Chicago: SPSS Inc SPSS (1993) SPSS Base 6.0 Users’ Guide, Chicago: SPSS Inc SPSS (1993) SPSS Base 6.0 Syntax Reference Guide, Chicago: SPSS Inc SPSS (2010) Survey Analysis Using PASW® Statistics, Chicago: SPSS Inc Stierand, M. and Lynch, P. (2008) ‘The art of creating culinary innovations’, Tourism and Hospitality Research, No. 8, pp. 337 – 350. Stutely, R. (2007) The Definitive business plan (2d Edition), Harlow: Financial Times Prentice Hall The Coca-Cola Company (2009) Annual Report 2009 [online] Available at: http://www.thecoca-colacompany.com/ourcompany/ar/downloads.html [Accessed 12.06.2010] The Economist Intelligence Unit (2006) ‘Country Profile – Russia’, London: The Economist Intelligence Unit
Thomas R. (1993) New Product Developmet: Managing and Forecasting For Strategic Success, New York: John Wiley and Sons Tidd, J. and Bessant, J. (2009) Managing Innovation (4th Edition) , Chichester: John Wiley & Sons TNS (2009) Russia Ad Index 2009 [online] Available at http://www.adindex.ru/ rating/2009/marketing/company/ [Accessed 20.10.2009] Trott, P. (2008) Innovation management and new product development (4th Edition) , Harlow: Pearson Education
167
Trott, P. (2001) ‘The role of market research in the development of discontinuous new products’, European Journal of Innovation Management, Vol. 4, No 3, pp. 117-125 Tull, D. and Hawkins, D. (1993) Marketing Research: Measurement & Method (6th Edition), New York: Macmillan Publishing Unilever (2009) Annual Report 2009 [online] Available at: http://annualreport09.unilever.com/ [Accessed 12.06.2010] Urban, G. and Hauser, J. (1993) Design and Marketing of New products (2d Edition), London: Prentice-Hall Urban, G., Hauser, J. and Dholakia, N. (1987) Essentials of New Product Management, NJ: Prentice Hall Urban, G. (1975) ‘Perceptor: A Model for Product Positioning’,Management Science, Vol. 21, No. 8, pp. 858-871.
Urban, G. (1970) ‘Sprinter Mod III: A Model for the Analysis of New Frequently Purchased Consumer Products’, Operations Research, Vol. 18, No. 5., pp. 805-854.
Urban, G. and Silk, A. (1978) ‘Pre-Test-Market Evaluation of New Packaged Goods: A Model and Measurement Methodology’, Journal of Marketing Research, Vol. 15, No. 2, pp. 171-191.
Usunier, J. (2000) Marketing across cultures (3rd Edition), Harlow: Pearson Education Van Assen, M., Van den Berg, G. and Pietersma, P. (2009) Key management models: The 60+ models every manager needs to know (2nd Edition), Harlow: Pearson Education Limited Von Hippel, E. (2005) Democratizing innovation, Cambridge: MIT Press Von Hippel, E. (1988) The sources of innovation, Oxford: Oxford University Press Von Hippel, E., Thomke, S., and Sonnak, M. (1999) ‘Creating breakthroughs at 3M’, Harvard Business Review, September-October, pp. 47-56. Von Hippel, E. and Thomke, S. (2002) ‘Customers as innovators: A New Way to Create Value’, Harvard Business Review, April, pp. 74-81. Von Hippel, E. and Katz, R. (2002) ‘Shifting Innovation to Users via Toolkits’, Management Science, Vol. 48, No. 7, pp. 821–833 Watkins, T. (1984) ‘Do STM models work?’, Journal Of The Market Research Society, Vol. 26, Issue 3, pp. 255-256 Webb, J. (1992) Understanding & Designing Marketing Research, London: Academic Press Limited Wetzels, M., Ruyter, K. and van Birgelen M. (1998) ‘Marketing service relationships: the role of commitment’, Journal of Business & Industrial Marketing, Vol. 13, Issue 4/5, pp.406 - 423
168
Wherry, J. (2006) ‘Simulated Test Marketing: It's Evolution and Current State in The Industry’ , MIT Sloan Business School [Online] Available at http://mit.dspace.org/bitstream/handle/1721.1/37225/85813336.pdf?sequence=1 [Accessed 20.01.2007]
Willke, J. (2002) ‘The future of Simulated Test Markets: Obsolescence of current models and the characteristics of models of the future’. Amserdam: ESOMAR Conference Proceedings Wilson G. (1990) ‘Sometimes it's a sin to use a concept test model’, Marketing News, Vol. 24, Issue 14, p.20 Wind, Y. and Green, P. (2004) Market Research and Modeling: Progress and Prospects, Boston: Kluwer Academic Publishers Woodruff, R. and Flint, D. (2003) ‘Research on business-to-business customer value and satisfaction’, Advances in Business Marketing and Purchasing, Volume 12, pp.515-547 Wuyts, S., Verhoef, P. and Prins, R. (2009) ’Partner selection in B2B information service markets’, International Journal of Research in Marketing, Vol. 26, Issue 1, pp. 41-51
169
APPENDICES
SIMULATED TEST MARKETING RESEARCH BRIEF
This document outlines key information that might be needed for Simulated Test Marketing. However, please always check with your STM vendor if they have appropriate brief template as well as market data and marketing plan input forms
1. CONTACT INFORMATION / REQUESTED BY:
Specify the following: • Company: • Name: •Position: • Brand: • Date: • Contact details:
2. BUSINESS BACKGROUND
This section should present an overview of the current business situation. Contents of this section should provide enough information to ensure a consistent and cooperative effort between Agency and Client. In particular, consider to include some of the following: • Business need (Strategic situation review):
1) Historical background on business development 2) Corporate market positioning: Market leader, challenger, follower, niche player 3) Strategic direction: Market penetration, New products for existing market, Expansion; Diversification; 4) Actual market strategy: Attack or defense etc, Differentiation, Price leadership etc
• Target market definition: 1) Consumer / Shopper: • Core need; • Occasions; • Profile; • Projected number; • Product experience 2) Products : • Key features, pack sizes etc; • Differentiation; Substitute categories; • Average quality level 3) Market size and dynamics: • Size; • Life stage; • Trends / Growth rate; • Structure; • Predictability; • Seasonality 4) Competitors: • Shares / Domination / Fragmentation • Quality of competition • Order of entry 5) Promotion: • Dominant strategies, tools and channels (TV etc) • Branding policies often used 6) Pricing: • Price levels and variation • Price sensitivity 7) Infrastructure / Environment: • Distribution (levels, channels, geography etc) • Retailers • Suppliers • Regulators 8) Finance: • Investments • Rewards – profitability • Risks
Share with the agency relevant info from all sources ‐ retail audit, consumer qualitative and quantitative research, industry reports. Excessive information must be moved to appendix. Try to keep it short and concise.
3. DESCRIPTION OF NEW INITIATIVE This section should give sufficient details about new product offer, in particular:
1) Product newness: • product formula and characteristics • brand positioning / concept / USP • pack • price 2) Differences as compared to market definition: • Specific target (e.g. category non‐buyers) • Unique features 3) New product type: • New‐to‐the‐Country/Firm • Line Extensions • Brand Stretching • Re‐positioning
• Product/Price change 4) Marketing support: • Advertising / Promotional activities (ATL / BTL) • Distribution (channels etc) • Retail
(merchandizing standards etc) 5) Stage of NPD / Readiness of initiative: • Final test marketing or Development • What exactly is final 6) Time‐to‐market • Launch date
4. BUSINESS OBJECTIVE / ACTION STANDARDS
This section should state the marketing problem, outline decision options, and suggest action standards upon research results. For example,
1) Marketing problem: “achieve $X mln incremental sales with the new product”, “gain X% share from competitor Y” , “capture X positioning niche”, “prevent competitive attack with X new product” etc
2) Actions/Action Standard: if X criteria is met then • Go national • Re‐work / Recycle • Postpone until some market conditions are met • Proceed with traditional test market • Discontinue / Drop … etc
3) Decision makers: • C‐level, Regional management etc • Functional area (marketing, sales, finance etc)
5. RESEARCH OBJECTIVES / INFORMATION NEEDS
This section should outline key questions that need to be addressed in the study. Specify your main information needs, for example: • Estimate potential sales volume; • Measure cannibalization; • Assess price sensitivity; • Define optimum range of SKUs; • Evaluate new product advertising • identify strengths and weaknesses of positioning, packaging and product against key competitors; • project awareness, trial and repeat given the certain marketing support etc
6. SCENARIOS TO SIMULATE
This section should summarize marketing scenarios to simulate. In particular: Marketing mix options: • Number of concepts / commercials; • Number of packaging designs; • Price levels;
• Range composition; • Number of product formulas etc
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5
Concept / TVC <Specify> <Specify> <Specify> <Specify> <Specify>
Pack format <Specify> <Specify> <Specify> <Specify> <Specify>
Price <Specify> <Specify> <Specify> <Specify> <Specify>
Product formula <Specify> <Specify> <Specify> <Specify> <Specify>
Marketing support options: • Optimistic • Pessimistic • Realistic
TV (30" GRP) Press Outdoor Weighted
Distribution% Samplings / Promotion
Other
Optimistic <Specify> <Specify> <Specify> <Specify> <Specify> <Specify>
Realistic <Specify> <Specify> <Specify> <Specify> <Specify> <Specify>
Pessimistic <Specify> <Specify> <Specify> <Specify> <Specify> <Specify>
As research has shown that it is important to consider competitive response, please indicate what might be likely competitors’ actions to your new product launch (time, format etc)
7. METHODOLOGY If you are familiar with STM technique please indicate modeling options that you would like to include (e.g. BASES Price Advisor, Ipsos Perceptor etc). However, it is up to the agency to recommend the most effective approach.
8. SAMPLE DESIGN You might want to specify exact requirements to sample composition, i.e. description of the target group. Sample and screening criteria: •Age; • Gender; • City/Region; •Income; •Category consumption etc
9. DELIVERABLES / TIMING
Outline in this section, what would you like to be reported (i.e. live presentation, report, tables, simulator etc.) and when do you want it. For example: Forecast options: • Horizon (1, 2, 3 years) • Acceptable accuracy • Interval (year, quarter, month) • Detail (Brand, SKU) Marketing mix diagnostics: • Top‐line key performance indicators • Tables • Report etc
10. BUDGETARY CONSTRAINTS
Indicate if any.
11. MATERIALS / STIMULI Which materials and what quantity of them are available for the study? When they will be made available to the agency and where? Anticipated availability of materials: • Advertising (concepts / animatics / tvcs); • Packaging (finished, mock‐ups, images); • Product samples • Market data and marketing plans
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 1
Dear colleagues,
We are pleased to invite you to participate in a survey on sales forecasting services for the new FMCG products on the Russian market. The research is being
conducted by Oxford Brookes University Business School within the MSc Business Management program. The main objectives of the research are to : (1) examine the current state of Russian business‐forecasting services market (2) evaluate market perspectives
(3) develop a set of actionable recommendations on performing sales forecasting for a new product launches in the Russian market. This unique study is aimed to deliver highly relevant findings based on vast experience of its participants, who are the major business and consumer insights experts in Russia. The research is conducted in accordance with the ethical principles of ESOMAR (ICC/ESOMAR international code on market and social research, 2008) and Oxford Brookes University Business School. By doing so, we guarantee accuracy and independence of assessments, confidentiality of personal information, publication of results only in a “depersonalized” summary form.
The final report will be available for you in September, 2010. We really do hope that you will find it very useful in your daily job, during the process of sales forecasting for new products, business planning and agency selection. Thank you very much in advance for your participation in the study!
Nikolay Korotkov, Research Executive, MSc BM student
0. Dear colleagues, we are looking for specialists who have certain professional competences and responsibilities for performing particular professional tasks in
FMCG companies. Are you in charge of any of the following? / SINGLE ANSWER /
1. Business forecasting 2. Marketing research 3. Strategic planning 4. Innovations / New Product Development 5. Marketing (Category or Brand management) / CODES 1‐5 = CONTINUE/
6. Financial planning / CODES 6‐12 = CLOSE THE INTERVIEW/ 7. Logistics 8. Public relations 9. Technology / Production management 10. Relationships with suppliers 11. Sales / Relationships with clients / Account management 12. None of the above
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 2
1. Would you please tell us the name of your company: ______________________________________ 2. In which department do you currently work? / SINGLE ANSWER /
1. Marketing ( Category of brand management ) 2. Marketing ( NPD / Innovations) 3. Marketing research 4. Strategic planning and forecasting 5. Trade marketing 6. Finance 7. Other (Please specify) __________________________________
3. What is your role (position) in the department? / SINGLE ANSWER / 1. Department director 2. Team/ Working group manager 3. Specialist / Expert 4. Другое (УТОЧНИТЕ) __________________________________
3a. How long do you work in the FMCG industry? 1. Less than 2 years 2. 2‐5 years 3. 6‐10 years 4. More than 10 years
4. What categories (or brands) are you responsible for? /OPEN‐ENDED/ __________________________________________________________________________________
5. Concerning these categories (or brands), how often did the task of sales forecasting arise in the following situations in your department in 2009 ? /PLEASE SPECIFY IN THE TABLE BELOW/ /SINGLE ANSWER PER SITUATION/
Didn’t arise Once a year 2‐3 times per year 4‐6 times per year 7‐10 times per year
Once a month or more often
DK/NA
Launch of a new brand in a new category (“New‐to‐the country” or “New‐to‐the world”)
1 2 3 4 5 6 7
Launch of a new brand in a existing category (“New‐to‐the firm”)
1 2 3 4 5 6 7
Line extention in the existing category 1 2 3 4 5 6 7 Brand Stretching, i.e. extending existing brand into aanother category
1 2 3 4 5 6 7
Brand re‐launch, re‐positioning 1 2 3 4 5 6 7 Brand/Product Improvements and Cost Reductions, i.e.Changes in one or several components of marketing mix ‐ i.e. new pack, new product formula, new communication, new pricing)
1 2 3 4 5 6 7
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 3
7. Which research techniques or information sources did you or your colleagues use while forecasting in each of the following business situations in 2009? /PLEASE SPECIFY IN THE TABLE BELOW/
New‐To‐The‐
Country or World
New‐To‐The‐Firm
Line Extention
Brand Stretching Brand re‐launch /
Repositioning
Brand/Product Improvements and Cost Reductions
Changes in marketing mix / 4Ps
Qualitative studies (focus‐groups, brainstorming, ideation sessions etc.) 1 1 1 1 1 1
Quantitative diagnostic tests of certain components of marketing mix without sales forecasting option (concept tests, advertising tests, product tests, price tests, pack tests)
2 2 2 2 2 2
Quantitative tests of certain components of marketing mix with elements of sales forecasting(for example CONJOINT, STEP etc.)
3 3 3 3 3 3
Simulated Test Marketing, Volumetric Bundle Test – quantitative test of ALL components ofmarketing mix that simulates realistic market situation for a representative sample of potential buyers and provides sales forecast with declared accuracy of 20% or more
4 4 4 4 4 4
Traditional test market – new product placement in a sample of real stores, followed by monitoring of sales during 3‐4 months
5 5 5 5 5 5
Economentric sales forecasting based on historical market data, trends and accumulated periodical business statistics (retail audit, consumer tracking, household panel, industry statistics, media etc)
6 6 6 6 6 6
Consumer & Shopper Segmentations (Ad hoc or syndicated TGI, MMI etc.) 7 7 7 7 7 7
External expert analysis and assessments/ analytical works (PLEASE SPECIFY): _____________________________________________________________________________
8 8 8 8 8 8
Internal in‐house expert analysis and assessments/ analytical works (PLEASE SPECIFY): _____________________________________________________________________________
9 9 9 9 9 9
Other (PLEASE SPECIFY) _______________________________________________________________________________________________________________________________________________________________________________________________________________________________________
10 10 10 10 10 10
DK / NA /Didn’t face this situation 11 11 11 11 11 11
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 4
7a. From your personal experience, what are the key factors affecting accuracy of the forecast for a new product and making this process a success? /OPEN‐ENDED/
________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ 7b. What are the difficulties that you usually face when forecasting sales for a new product? What issues require further exploration? /OPEN‐ENDED/ ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ 7c. From your personal experience, what is the average forecasting error, in a majority of cases of sales forecasting for innovations? /SINGLE ANSWER/
1. below 5% 2. 5%‐10% 3. 10%‐20% 4. 20%‐30% 5. 40%‐50% 6. Above 50% 7. DK/NA
7d. Speaking about all new product launches, that you have participated, would you be able to estimate how many of them were successful (i.e. reached targets set in the business plan)? /SINGLE ANSWER/
1. Less than 10% 2. 10‐25% 3. 25‐50% 4. 50‐75% 5. More than 75% 6. DK/NA
/ASK Q8 IF NO CODES 4 CIRCLED IN Q7/ 8. You’ve mentioned that you didn’t conduct or participated in “simulated test marketing” studies in 2009. What are the main reasons of that? /OPEN‐ENDED/
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________________________
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 5
9. Which of the following “simulated test marketing” techniques have you ever heard of, at least by the name? /MULTIPLE ANSWERS/ 10. Which of the following “simulated test marketing” techniques have you or your colleagues have ever used? /MULTIPLE ANSWERS/ 11. Which of the following “simulated test marketing” studies did you participate in 2009? / FOR EACH TECHNIQUE MENTIONED IN Q10/ 12. How many of these studies did you participate in 2009? / FOR EACH TECHNIQUE MENTIONED IN Q11/
9. Aware
10. Used / Have experience
11. Conducted in 2009 12. Quantity of STM studies in
2009
ACNielsen BASES ‐ BASES I или BASES II 1 1 1 _____
ACNielsen BASES ‐ Snapshot или Pre‐BASES 2 2 2 _____
A/R/M/I Marketing ‐ Simulated Test Market 3 3 3 _____
Aegis Copernicus ‐ Discovery 4 4 4 _____
Comcon Sales Vision 5 5 5 _____
GfK ‐ MarketingLab / Volumteric TESI 6 6 6 _____
InVivo ‐ MarketMind 7 7 7 _____
Ipsos Novaction ‐ Designor Shelf, Desighor D'Light, Designor Concept 8 8 8 _____
Ipsos Novaction ‐ NextGen, Innoscreen Forecast 9 9 9 _____
M/A/R/C ‐ Assessor 10 10 10 _____
MASMI ‐ Simulated Test Market 11 11 11 _____
Synovate ‐ MarketQuest MVP 12 12 12 _____
TNS / RI ‐ Microtest 13 13 13 _____
TNS / RI ‐ eValuate 14 14 14 _____
TNS / RI ‐ FYI (Foresight, InSight, Repurchase) 15 15 15 _____
OTHER (PLEASE SPECIFY)________________________________________________________ 16 16 16 _____
OTHER (PLEASE SPECIFY)________________________________________________________ 17 17 17 _____
OTHER (PLEASE SPECIFY)________________________________________________________ 18 18 18 _____
OTHER (PLEASE SPECIFY)________________________________________________________ 19 19 19 _____
OTHER (PLEASE SPECIFY)________________________________________________________ 20 20 20 _____
DK/NA/None 21 21 21 21
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 6
/ASK Q14 IF CODE 20 IS NOT CIRCLED IN Q10/
14. Concerning the last few STM studies, that you have participated recently, would you please estimate the average budget per study ?
1. Below 300 thousand rubles / Below $10 thousand 2. 300 ‐ 600 thousand rubles / $10‐$20 thousand 3. 600 ‐ 900 thousand rubles / $20‐$30 thousand 4. 900 ‐ 1200 thousand rubles / $30‐$40 thousand 5. 1200 ‐ 1500 thousand rubles / $40‐$50 thousand 6. 1500 ‐ 1800 thousand rubles / $50‐$60 thousand 7. Above 1800 thousand rubles / Above $60 thousand 8. DK/NA
15. Speaking about mid‐term perspective (3‐5 years), can you please estimate the change in number of “simulated test marketing” studies conducted per year with your participation?
1. The numbers of such studies will decline 2. The number of such studies will remain on the same level /GO TO Q17/ 3. The numbers of such studies will increase
4. We’re not going to conduct STMs or participate in such studies /GO TO Q16/
5. DK/NA /GO TO Q17/
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 7
/ IF ANSWERS 2, 3, 4 IN Q15 GO TO Q17 /
16. Would you please list the main reasons why you are not going to conduct “simulated test marketing” studies in the next 3‐5 years? /OPEN‐ENDED/ ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________________________
17. /IF NO EXPERIENCE WITH STMs, I.E. CODE 20 IS CIRCLED IN Q10 – CLOSE THE INTERVIEW/ How, in your opinion, the service provided by the local agencies in the area of “simulated test marketing” can be improved? /OPEN‐ENDED/ ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________________________
18. Which “simulated test marketing” techniques or agencies providing that service would you recommend to your other colleagues? / OPEN‐ENDED/
__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
19. Why? Please list the key reasons.
___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________________________
20. Which “simulated test marketing” techniques or agencies providing that service would you NOT recommend to your other colleagues? / OPEN‐ENDED/ __________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
21. Why? Please list the key reasons. ___________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ _________________________________________________________________________________________________________________________________
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 8
/IF ANSWERS 1‐3 IN Q15 , GO TO Q23/ 22. You have mentioned that you are going to conduct a “simulated test marketing” study in the next 3‐5 years. In the table below you will find the list of factors
which could, to a certain extent, affect your choice of technique or agency. ‐ Which 5 (five) factors are more important for you, when you are making your decision on technique or agency? /5 ANSWERS/ ‐ Which single factor out of these 5 is of a top importance? /SINGLE ANSWER/
TOP 5 IMPORTANT
MOST IMPORTANT
Service quality:
1 Highly accurate forecast 1 1
2 Professional presentation of findings and recommendations 2 2
Service supplier:
3 Recommended by management or company protocols 3 3
4 Positive previous experience 4 4
5 Well‐known agency 5 5
Technique:
6 Simple and easy‐to‐understand 6 6
7 Suitable for Russian market 7 7
8 A lot of diagnostic information 8 8
9 Publications in professional literature 9 9
Communication quality:
10 High quality service and project management 10 10
11 Individual approach for every project 11 11
Price:
12 Affordable price 12 12
13 Transparent and flexible pricing 13 13
Technology:
14 High speed of research 14 14
15 High quality of data collection 15 15
16 Use of modern technologies 16 16
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 9
/ FOR EACH TECHNOQUE MENTIONED IN Q10/ 23. You have mentioned that you or your colleagues have experience in conducting “simulated test marketing" studies. We would like to know your opinion
about practical use of these techniques and cooperation with the service providers. Would you please evaluate to what extend you agree or disagree with each of the following statements regarding <NAME OF THE TECHNIQUE > . For your answers please use 5‐point scale below, where 1 means “COMPLETELY DISAGREE” , 5 means “COMPLETELY AGREE”. There are no right or wrong answers, we are interested only in your personal opinion.
STM model 1 STM model 2 STM model 3
Service quality:
1 Highly accurate forecast 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
2 Professional presentation of findings and recommendations 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Service supplier:
3 Recommended by management or company protocols 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
4 Positive previous experience 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
5 Well‐known agency 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Technique:
6 Simple and easy‐to‐understand 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
7 Suitable for Russian market 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
8 A lot of diagnostic information 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
9 Publications in professional literature 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Communication quality:
10 High quality service and project management 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
11 Individual approach for every project 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Price:
12 Affordable price 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
13 Transparent and flexible pricing 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Technology:
14 High speed of research 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
15 High quality of data collection 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
16 Use of modern technologies 1..2..3..4..5 1..2..3..4..5 1..2..3..4..5
Questionnaire Version 2.0 © 2010 Oxford Brookes University Business School, p. 10
24. For each “simulated test marketing" that you have ever used, would you please tell us, how satisfied were you with the quality of service provided by the agency. For your answers, please use 10‐point scale, where 1 means “COMPLETELY DISSATISFIED” and 10 means “COMPLETELY SATISFIED”.
1 = ” COMPLETELY DISSATISFIED”, 10 = ” COMPLETELY SATISFIED”.
STM technique 1 1 2 3 4 5 6 7 8 9 10
STM technique 2 1 2 3 4 5 6 7 8 9 10
STM technique 3 1 2 3 4 5 6 7 8 9 10
STM technique 4 1 2 3 4 5 6 7 8 9 10
STM technique 5 1 2 3 4 5 6 7 8 9 10
25. From your personal experience, or from experience of your colleagues, would you please evaluate relative accuracy of the sales forecast observed in the
Russian market?
Accuracy
Above GLOBAL AVERAGE declared by
the agency
Meets GLOBAL AVERAGE declared by the agency
Below GLOBAL AVERAGE declared by the agency
Far below GLOBAL AVERAGE declared by
the agency
DK/NA
STM technique 1 1 2 3 4 0
STM technique 2 1 2 3 4 0
STM technique 3 1 2 3 4 0
STM technique 4 1 2 3 4 0
Thank you for participation in our study! If you wish to receive a copy of the final report, please leave your contact information: ___________________________________________________________________________________________________________________
Appendix 3
Dear Sirs,I am writing this letter to officially apply for a grant to use Sawtooth web-interviewing software for my MSc dissertation project at Oxford Brookes University, UK. As requested, this is to confirm the following information:
Name of applicant: Korotkov Nikolay, Nationality: Russian, Passport: 63#0398596
CV: 34 y.o., MA in applied math, over 10 years experience in marketing research (Ipsos, Coca-Cola), currently obtaining Master’s degree in Business Management from Oxford Brookes University Business School
Address in Russia: 117638 Moscow, Fruktovaya street, 16, flat 133, +7-(916)-696-17-32
Address in the UK: Cheney Student Village Room L1C, Cheney Lane, Oxford, OX3 OBD, UK
Email: [email protected], [email protected]
Name of Institution and Department: Oxford Brookes University Business School Wheatley Campus WheatleyName of Institution and Department: Oxford Brookes University, Business School , Wheatley Campus, Wheatley, Oxford OX33 1HX, UK, +44 (0) 1865 485771, www.brookes.ac.uk, www.business.brookes.ac.uk
Student Course and Ref#: 09022263, Master of Science in Business Management course
Research topic: Simulated Test Marketing (STM) and its practical application in the Russian FMCG market
Research Supervisors: Dr. Nicoletta Occhiocupo, http://www.business.brookes.ac.uk/bs/profile.asp?id=p0038727
Research Module Leader: Dr. Yuksel Ekinci, http://www.business.brookes.ac.uk/bs/profile.asp?id=p0075228
MSc Programme Director: Jonathan Groucutt, http://www.business.brookes.ac.uk/bs/profile.asp?id=p0073453MSc Programme Director: Jonathan Groucutt, http://www.business.brookes.ac.uk/bs/profile.asp?id p0073453
Research issue: Many marketing research practitioners as well as academics argue that a majority of complex traditional sales forecasting techniques for new FMCG products, originally developed for mature western markets, are of limited practical use in the rapidly growing emerging markets, such as Russia. Literature review has shown the lack of research on this issue.
Research objective: Explore the needs and current forecasting practices for innovations (including STMs) on the Russian FMCG market and deliver actionable recommendations on the most appropriate forecasting approaches.
Sampling: n=30 marketing research and forecasting experts from the top100 biggest FMCG companies in RussiaSampling: n 30 marketing research and forecasting experts from the top100 biggest FMCG companies in Russia
Question flow (briefly): what products/services/methodologies are currently used to forecast sales for the new products in the Russian market, frequency of use, recommendations, advantages/disadvantages etc. (approximately 30 questions)
Methodological approach: Quantitative B2B U&A, web-interviewing, 30 minutes
Ethical code of conduct: ESOMAR, Oxford Brookes University
Timing: June-July 2010
Justification for usage of Sawtooth Software: Web-interviewing is the only way to reach study participants. Sawtoothis the only powerful tool for advanced web-data collection
Sawtooth components required: SSI Web only (no conjoint)
Hereby I confirm that the software will be utilized only for the purposes of academic dissertation described above. Also, this is to confirm that no commercial organization will directly benefit from this project.y p j
Yours sincerely,
Nikolay Korotkov, MSc BM student, Oxford Brookes University Business SchoolyDate: 24/05/2010