WHAT DRIVES NEW PRODUCT SUCCESS? AN INVESTIGATION ACROSS PRODUCTS AND COUNTRIES

Katrijn GIELENS

Jan-Benedict E. M. STEENKAMP

Katrijn Gielens is Associate Professor of Marketing, Erasmus University, P.O Box 1738, 3000 DR Rotterdam, The Netherlands; ph. + 31-10-4088635/fax: +31-10-4089011/e-mail: K.Gielens@fbk.eur.nl. Jan-Benedict E. M. Steenkamp is CentER Research Professor of Marketing and GfK Professor of International Marketing Research, Tilburg University, P.O Box 90153, 5000 LE Tilburg, The Netherlands; ph. + 31-13-4662916/fax: +31-13-4668354/e-mail: J.B.Steenkamp@uvt.nl. We gratefully acknowledge the support of AiMark, which provided the data on which this study is based and the financial support of two U.S. CPG companies. The help of Peter Kempe (IRI) and Alfred Dijs (Europanel) is especially acknowledged. The project also benefited from support by the Flemish Science Foundation (F.W.O.) under grant No. G.0116.04. We thank Marnik Dekimpe, Christophe Van den Bulte, Harald van Heerde, and Delaine Hampton, as well as seminar participants at the 2003 MSI Conference on Global Marketing, the 2003 EMAC Conference, the Catholic University of Leuven, and the Tuck Business School for constructive comments.

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WHAT DRIVES NEW PRODUCT SUCCESS? AN INVESTIGATION ACROSS PRODUCTS AND COUNTRIES

ABSTRACT The introduction of new products is widely recognized as one of the most important marketing

activities of companies. Nevertheless, at least two caveats to an intensive new product strategy

exist. First, it is a risky strategy, as many new products fail in the market place. Second, it is a costly

strategy as R&D expenditures are rising sharply. To recoup R&D investments and meet ROI

requirements, it is often no longer sufficient to sell the product in a single country only.

Increasingly, firms launch and sell new products into their international markets. General rules for

market response are therefore increasingly needed. To that extent, our primary objective is to

systematically examine the cross-national generalizability of the impact of key product, competitive

environment, and consumer drivers on consumers’ first year purchase patterns of new product

across markets. Our data comprise the first year sales for 301 new consumer packaged goods

(CPGs) launched in the UK (74 CPG introductions), France (104), Germany (67), and Spain (56).

Individual-level purchases for each new product are obtained from the Europanel household panels

in each country, involving in total over 16,000 consumers. We derive cross-national empirical

generalizations, as well as differences regarding factors underlying new product success. We relate

the results to R&D recommendations, pan-European segmentation strategies, and local marketing

activities.

Keywords: New products, international generalizations, R&D, multilevel models

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INTRODUCTION

The introduction of new products is one of the most important marketing activities of companies.

Firms like Procter & Gamble, Sony, Microsoft, and Gillette have made the frequent introduction

of new products an essential part of their marketing strategy. Successful new product

introductions contribute substantially to long-term financial success (Bayus, Erickson, and

Jacobson 2003), are an effective strategy to increase primary demand (Nijs et al. 2001) and to

keep store brands at bay (Ailawadi, Neslin, and Gedenk 2001), and strengthen the competitive

position of the company (Shankar, Carpenter, and Krishnamurthi 1998). Drucker (1999) argued

that only companies with a systematic policy of innovation are likely to succeed. Consequently,

it is not surprising that the Marketing Science Institute (2002) has named new products a top tier

priority topic.

However, there are at least two caveats to a strategy of relying on innovation to

strengthen the company’s position. First, it is a very risky strategy, in that over 50% of new

products fail in the marketplace (Golder and Tellis 1993). Innovation is clearly not an isolated

activity. Rather it is interrelated with other business functions, especially marketing (Bayus,

Erickson, and Jacobson 2003). New products often fail because R&D has not yielded a product

that appeals to the marketplace, and/or because the marketing strategy associated with the new

product launch has been ineffective.

Second, it is a very costly strategy, and R&D expenditures are rising sharply. For

example, R&D for Gillette’s Mach 3 razor blade exceeded $700 million, while R&D costs for

major new drugs are typically between 500 million and 1 billion dollars, and new car platforms

cost over one billion dollars. To meet ROI requirements, it is often no longer sufficient to sell the

product in a single country only (Golder 2000). Increasingly, firms launch and sell new products

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into their international markets. General rules for market response are therefore increasingly

needed (Farley and Lehmann 1994). The challenge is to develop a new product strategy that is

sensitive to both supply side drivers (i.e., own company offering and resources and the

competitive setting) and demand drivers in an international context. Which factors have a similar

impact on new product success in different countries, and hence could be part of global new

product introduction strategies? Which ones work out differently in different countries, and

hence should be part of local adaptations to the introduction strategies? Does the context in

which the product is introduced have similar effects in different countries? An answer to these

questions requires detailed knowledge of the generalizability of factors underlying new product

success across countries.

Our study addresses these two issues. For a comprehensive set of key product,

competitive, and consumer factors we develop a systematic set of hypotheses as to their expected

effect on new product success. Collectively, these factors constitute our source model (Janssens,

Brett, and Smith 1995). We examine the cross-national generalizability of our source model in

four major European countries, viz., France, Germany, Spain, and the U.K.

The context of our study is the consumer packaged good (CPG) industry. It constitutes a

key industry, with consumer expenditure on packaged goods exceeding over 10% of total

consumer expenditure in most Western countries (Euromonitor 2003). The CPG industry is

characterized by heavy R&D spending. For example, L’Oreal employs 2,800 researchers, has

registered 500 patents in 2002, and annually spends about $500 million on R&D. Unilever

spends over $1 billion each year on R&D and P&G over $1.5 billion.1 Over half of the global

sales of Gillette and Colgate Palmolive are generated by products that were not on the market

five years ago. Analysts have noted that the future health and growth of the CPG industry will

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critically depend on its ability to expand sales through innovation and successful

commercialization of these innovations (Cook and Georgiadis 1997). However, it is worrisome

that new product failure is especially rampant in this industry with over 70% of new CPGs

failing within two years after introduction, with most failures occurring in the first year after

launch (Ernst&Young/ACNielsen 2000).

Our study can be positioned vis-à-vis previous research on new product success along

three dimensions: (1) the level of aggregation, namely market versus individual level, (2) the

sources of variation considered (product strategy, competitive environment, consumer, country),

and (3) the type of purchase behavior studied. Table 1 summarizes previous research on the

drivers of new product success along these three dimensions.2

---Insert Table 1 about here---

First, a large body of research at the aggregate (market) level has studied the diffusion of

new products (e.g., Gatignon, Eliashberg, and Robertson 1989) and success factors in the new

product diffusion development process (e.g. Calantone, Schmidt and Song 1996). Another

stream of research has examined new product success at the individual level (e.g.,

Chandrashekaran and Sinha 1995, Steenkamp and Gielens 2003).

Second, different sources of variation in new product success have been investigated.

Various product specific success drivers have been proposed mostly in connection to marketing

mix variables (e.g., Helsen and Schmittlein 1994, Steenkamp and Gielens 2003) and product

charcteristics (e.g., Calantone, Schmidt, and Song 1996). Individual-level studies have given

much attention to the role of consumer-related variables in new product success (e.g., Chatterjee

1 Information was obtained from official company reports, and refers to the year 2002. 2 Table 1 presents a number of key studies but due to space constraints, it does not provide an exhaustive overview of the literature. Most notably, aggregate-level studies without an international component are not included. See Mahajan, Muller, and Wind (2000) for an overview of that literature.

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and Eliashberg 1990, Manning, Bearden, and Madden 1995). This source of variation has not

been considered in aggregate studies. The impact of several competitive environment variables

has been studied in a few studies (Dekimpe, Parker, and Sarvary 1998, Ganesh and Kumar 1996,

Steenkamp and Gielens 2003). Variation across countries has only been investigated in aggregate

studies (e.g., Mahajan and Muller 1994). Table 1 reveals that most studies on new product

success incorporate only a subset of these potential sources of variation. Specifically, none of the

aggregate studies incorporates consumer variation, while none of the individual studies considers

the country dimension. This may lead to invalid conclusions on the relative impact of the drivers

(Chandrashekaran and Sinha 1995).

Third, as observed by Chandrashekaran and Sinha (1995), new product research has

focused almost exclusively on the first purchase and ignores subsequent purchases. This

generates limited insights concerning factors underlying new product success, especially for

CPGs where trial purchases constitute only a modest portion of total purchase volume and

subsequent purchases are the key to enduring success (Urban and Hauser 1993). Moreover, the

operationalization of trial behavior in individual-level studies is often based on self-reports (e.g.,

Manning, Bearden, and Madden 1995, Steenkamp and Burgess 2002).

In this study, we hope to overcome some of the gaps in the literature by examining the

intensity and trend of all first year purchases of new CPGs at the level of the individual

consumer. To study consumers’ first year purchase pattern, we trace how many items they

bought in each quarter of the first year. A multi-level Poisson regression model is used to model

differences in purchase intensity as well as differences in the purchase trend as a function of a

large set of product (marketing resources, roll-out strategy, brand reputation, product newness),

competitive environment (market concentration, price and non-price competition in the category,

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market power of national brands vis-à-vis private labels), and consumer (dispositional

innovativeness and socio-demographics) variables. To the best of our knowledge, we are the first

to add a cross-national dimension in an individual-level study by explicitly establishing

communalities and differences in the effects of the different constructs across four countries.

Managers often focus too much on the means of key variables, which may differ more across

countries than do their effects on response. As such, they feed the myth that international

differences are both large and unpredictable (Farley and Lehmann 1994). In contrast, being able

to generalize internationally about factors affecting market response should provide insight in

what elements of a new product strategy can be standardized and which elements really have

different effects across different countries.

The remainder of the paper is organized as follows. In the next section, we develop

hypotheses concerning the likely effect of various drivers of new product success. Next, we

describe the data set and the methodology, and report the results. The final section summarizes

the findings, draws managerial implications, and provides suggestions for future research.

DRIVERS OF NEW CPG SUCCESS

Our focal measures of new CPG success are a consumer’s purchase intensity and trend in

purchases in the first year after introduction. To gauge new product success, it is important to

both consider total purchases as well as the trend in purchases (cf. Gatignon and Robertson

1985). Industry studies (Ernst&Young/ACNielsen 2000) indicate that the first year is crucial for

the success of new CPGs. In this section, we develop hypotheses concerning the role of specific

product, competition, and consumer factors and their expected effects on consumers’ first-year

purchase intensity and trend. These hypotheses are tested separately in four major European

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countries to establish the extent to which the drivers of new product success are generalizable

across countries (Janssens, Brett, and Smith 1995).

Product Strategy Factors

Product strategy factors encompass both marketing resources available, as well as the strategic

factors associated with the new product introduction, including the international launch strategy

adopted for the product, the reputation of the brand, and the degree of newness of the product.

Marketing resources. A firm with larger marketing resources in a category is able to

provide more intensive marketing support (e.g., advertising, promotion) to back a new product

introduction (Calantone, Schmidt, and Song 1996) and to persuade retailers to carry the new product

and assign it the necessary shelf space (Rao and McLaughlin 1989). Intensity of marketing support

for the new product is expected to have a positive impact on a CPG’s first-year success (i.e., on first

year intensity and trend in purchases).

International rollout strategy. Several rollout strategies can be distinguished. We focus on

the potential advantages derived from a sequential strategy (Golder 2000). In this case, the firm can

collect experience and market intelligence in countries in which the product has already been

introduced and share this intelligence with other markets. Spillover effects or goodwill from present

lead markets to new lagging markets may prevent the firm from making the same mistakes in new

markets (Golder 2000). We therefore posit that a CPG’s first year success (i.e., intensity and trend in

purchases) will be greater if it has been introduced before in another country.

Brand reputation. A brand has a good reputation if consumers believe its products to be of

consistent high quality (Choi 1998, Shapiro 1983). When attributes of the new product are difficult

to observe prior to consumption, as is typically the case with CPGs (Moorthy and Zhao 2000), and

the high-reputation brand name is extended to a new product, consumers can plausibly believe that

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the new product is also of high quality (Choi 1998). The incentive to cheat by extending the

reputable brand name to a low-quality product is prevented by the loss of repeat sales of the new

product (Shapiro 1983), the loss of repeat sales of established products (Wernerfelt 1988), and the

loss of future sales due to the reduced extension potential of the brand (Choi 1998). Hence, we

expect that new products introduced by reputable brands exhibit greater first year success.

Newness. We follow several studies that suggest a U-shaped relation between product

newness and various measures of market success such as market share and ROI for industrial

products (Kleinschmidt and Cooper 1991), firm value in the automobile industry (Pauwels et al.

2004), and trial rate for CPGs (Steenkamp and Gielens 2003). This U-shaped relation can be

explained in terms of the two underlying factors of complexity and relative advantage, both of

which increase with newness, albeit not in a linear fashion (Steenkamp and Gielens 2003). We

extend previous research by studying this relationship with respect to both purchase intensity and

trend, and by testing this relation in a cross-national context using a consistent data and

measurement scheme.

Moreover, if a sequential rollout strategy is used to launch the new product, potential

adopters in lagging countries can witness the success of the new product in lead countries (Ganesh,

Kumar, and Subramaniam 1997). They will be more knowledgeable about the new product, the

perceived complexity of new products high on newness will be lower, while the advantages will be

more salient. We therefore expect the sequential rollout strategy to reinforce the positive impact on

a CPG’s first year success3 of products high in newness.

3 Here and elsewhere, we will focus on the effect of the interaction on purchase intensity. Although we expect a similar effect on the trend, we will not pursue the impact on the trend because of multicollinearity problems.

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Competitive Environment

The competitive context can act as a barrier to entry or can facilitate new entry. We examine the

role of the degree of concentration in the market, the extent of price and non-price competition, and

especially in the context of CPGs, the market power of national brands vis-à-vis private labels.

Concentration. A common hypothesis in industrial economics is that (tacit) collusion to

thwart new entrants is easier in markets characterized by high levels of concentration

(Lypczynski and Wilson 2001). As concentration increases, the importance of each brand to total

output will increase and firms are less likely to ignore the possible effect of any independent

action by a rival. Coordination of activities against new entrants is also easier in more

concentrated markets as the number of channels of coordination is smaller (Scherer and Ross

1990). Moreover, in concentrated markets, it is easier to monitor the competition. This makes it

more likely that new product introductions are noticed, which is a necessary requirement for

initiating a coordinated response (Chen, Smith, and Grimm 1992). We therefore expect the new

CPG’s success to be lower in categories characterized by high levels of concentration.

Price competition. In the CPG industry, price competition between brands is largely

conducted using promotions. Promotions typically contain a pricing component (Fader and

Lodish 1990) and are present at the point of purchase. Intense competitive price promotions in

the category signal a high degree of commitment of the incumbents to the category. It may even

be a deliberate strategy used by incumbents to render it more difficult for others to enter the

market (Lal 1990). Hence, we propose a negative impact of price promotion intensity in the

category on the first year success of the new CPG.

However, the effectiveness of this barrier is usually assessed against rivals possessing

less “skills” than the incumbent (Han, Kim, and Kim 2001). If the new product is able to

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differentiate itself from the existing offering on a non-price basis through innovation, tacit price

agreements will be hard to maintain (Lipczynski and Wilson 2001). We therefore expect the

newness of the new product to mitigate the negative effect of price promotion intensity.

Non-price competition. The two main forms of non-price competition are through new

product introductions and advertising (Lipczynski and Wilson 2001). Nijs et al. (2001) found that

new product introductions have an important primary demand effect, which in its turn may offer

better opportunities for subsequent new product introductions (Shankar, Carpenter, and

Krishnamurthi 1999). On the other hand, in categories characterized by frequent new product

introductions, most niches will be filled, so that a new product will find it more difficult to find

enough unmet demand (Schmalensee 1978). Hence, the effect of the extent of product-based

competition in a category on a new CPG’s first year success is not clear, a priori.

If the new product is able to provide consumers with substantial added value, it can reshape

preferences within the category and differentiate itself from other products. Moreover, by offering

substantial added value over its incumbents, the threat of collusive behavior becomes less likely.

Consequently, we expect that the effect of category new product introduction intensity on new

product success will be positively affected (i.e., less negative or more positive) by the degree of

newness of the new product.

Heavy advertising is a powerful weapon to increase market power of the existing products

via differentiation and loyalty building (Lipczynski and Wilson (2001). These authors call it (p. 197)

“arguably the most common method of differentiating products.” Consistent with this view,

Boulding, Lee, and Staelin (1994) and Mela, Gupta, and Jedidi (1998) found that brands might be

able to use advertising to ‘insulate’ themselves from direct competition. Heavy advertising by

incumbents will also increase the capital required to create awareness in the market (Robinson and

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Fornell 1985). Hence, it will be more difficult for new products to gain a foothold. Consequently,

we expect a new CPG’s success to be lower in categories characterized by intense advertising.

However, we expect that the negative effect of intense category advertising on a new product’s

first year success is attenuated when the new product is introduced under a strong brand name as

it has awareness and brand associations to build upon (Comanor and Wilson 1967).

Market power of national brands vis-à-vis private labels. Following Scherer and Ross

(1990), we define market power as the degree to which brands are able to command prices above

those implied by competition. Much of today’s power struggle – especially in CPG industry –

involves national brands versus private labels (Ailawadi, Neslin, and Gedenk 2001). Private

labels have increased their quality, prices, and market share in all Western countries to the extent

that in many categories, they have become a major threat to the market position of national

brands (Hoch 1996). However, the relative market power of national brands versus private labels

still varies considerably across categories. We expect that the success of a new CPGs introduced

by national brands will be higher the larger the market power of national brands vis-à-vis private

labels in the category.

Consumer Characteristics

Consumers’ willingness to purchase the new product is affected by product and competitive

strategies, but also by their own personal characteristics. If a systematic, generalizable effect of

consumer factors on their first-year purchase pattern is found across countries, this offers a basis

for international market segmentation. We examine personality and sociodemographic variables.

In the context of the wide array of personality characteristics, we focus on dispositional

innovativeness.

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Dispositional innovativeness. Dispositional innovativeness is defined as the predisposition

to buy new products and brands at an early stage, rather than to remain with previous choices and

consumption patterns across a variety of goods and services (Midgley and Dowling 1978,

Steenkamp and Gielens 2003). Steenkamp and Gielens (2003) reported a significant positive effect

of dispositional innovativeness on the trial rate of new CPGs. We extend this research by examining

its effect on consumers’ first-year purchase intensity and trend. We propose a positive effect of

dispositional innovativeness on a consumer’s first year purchase intensity. However, due to their

intrinsic need for change, consumers high on dispositional innovativeness have a decreased

tendency to stick to the same purchase response over time (Raju 1980). They tend to get bored more

easily with the new product and move forward to explore newer launches. Consequently, the effect

of innovativeness will diminish over time leading to a negative effect on the purchase trend.

Sociodemographics. Three sociodemographics– age, size of the household, and place of

residence - are included in this study. Younger people tend to be less risk averse, have a higher

optimal stimulation level than older people (Zuckerman 1994) and a lower stock of accumulated

experiences with the category (Assael 1995). Hence, younger consumers are more attracted to new

products but they will also switch more easily to new offerings. Thus, we expect that the first year

purchase intensity (trend) is negatively (positively) related to age.

In general, the larger the household, the higher the likelihood that significant within-

household preference heterogeneity exists, which can be addressed by purchasing multiple products

(Seetharaman and Chintagunta 1998). Moreover, larger households tend to be heavier users of the

category (through the sheer number of users). Hence, they have shorter interpurchase cycles and

more purchase occasions (Helsen and Schmittlein 1994). As such, we expect household size to be

positively associated with a new CPG’s first year success.

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Consumers living in the country’s major metropolis(es) tend to be more cosmopolitan

(Hannerz 1990), and diffusion research has documented a positive relationship between

cosmopolitanism and the tendency to innovate (Gatignon et al. 1989). Metropolises also tend to

have a denser retail infrastructure, thus making it easier for consumers to acquire the new product.

Hence, we propose that the first year success of the new CPG is higher among consumers living in

metropolises. Table 2 gives an overview of our predictions.

--- Insert Table 2 about here ---

METHOD

Sample Description

Data are gathered in four major European countries, i.e. France, Germany, Spain, and the U.K.,

in which we trace the first year purchases of respectively 104, 67, 56, and 74 new CPGs

launched in 1998-1999. These product introductions covered a wide range of food and

beverages, personal care, and household care products in 49 categories. Our data set includes the

introduction of well-known new products such as Kraft Lunchables in the food category, the fruit

drink Sunny Delight, the fabric refresher Febreze, the shampoo Fructis, and the razor blade

Mach3, besides a varying set of (local) products. About 30% of these new products were

introduced in several countries, however, not necessarily at the same time. Febreze, for instance,

was introduced in Spain about a year after its introduction in the U.K. The Mach 3, on the other

hand, was introduced within approximately one month in all markets.

Consumer purchases for each of the new products were monitored in panels of

respectively 3,582, 4,531, 3,388, and 4,869 households during a period of 12 months after the

new product was introduced. Industry analysts consider the first 12 months after introduction

critical for success or failure in the CPG industry (Ernst&Young / ACNielsen 2000). For every

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new product, the pan-European research agency Europanel delivered us the number of items

bought by each consumer in each quarter in the first year after introduction. The purchase

records for every consumer were provided by the pan-European market research agency

Europanel (GfK/Taylor Nelson Sofres).

The first year success of the new products in our data set varied considerably. Of the

Mach 3 razorblade 577 units were bought in the first year by the 4,869 consumers in the U.K.

panel, whereas the U.K. panel members only bought 210 units of the new Wilkinson razorblade,

which was introduced, in the same period. A similar pattern is found in France where 636 units

were purchased by the 3,582 consumers in the panel and only 164 units of the Wilkinson

razorblade. In contrast, the Dove bath cream, which was also was rated equally high on newness

in France and the U.K., performs substantially better in the U.K. where 1175 units were

purchased by the panel members of which 446 units in the first quarter. In France, however, only

487 units were bought in the first year of which only 48 units in the first quarter. Moreover,

purchases only picked up from the third quarter onwards. To explain this variation in both

purchase intensity and trend across products and countries the following measures are used.

Measures4

Product strategy factors. We used the country-specific market share of the firm in the

category in the year prior to introduction as proxy for the marketing resources the firm has at its

disposal to support the new product introduction. Marketing budgets are typically tied to market

share (e.g., Balasubramanian and Kumar 1990, 1997). To capture the effect of a sequential

launch strategy, we created a dummy, which equals one if the new product had been introduced

4 In 90% of all cases, the bivariate correlations between the different measures were below ⎪.35⎪. The highest correlation was recorded between advertising intensity and the market power of national brands vis-à-vis private labels in France and amounted to .54.

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in another country before5. If the dummy equals zero, no previous experience is available with

respect to the new product (cf. Golder 2000). Information on the reputation of the brand under

which the new product was introduced and the degree of newness of the new product were

provided by Europanel which collected this information among category management experts of

their local subsidiaries. Experts also have been used in other recent research for these purposes

(Gatignon and Xuereb 1997, Goldenberg, Lehmann, and Mazursky 2001, Pauwels et al. 2004,

Steenkamp and Gielens 2003). Each new product was rated independently by a varying group of

two to five experts. Brand reputation was measured by a three-point item pertaining to whether it

was a high quality brand (cf. Choi 1998). The degree of newness of a new product was measured

by a five-point item, referring to the extent to which the product was new and unique (Henard

and Szymanski 2001). Ratings for each item were discussed between Europanel experts until

consensus was obtained.

Competitive environment. Product categories were based on IRI’s classification.

Assignment of new products was typically straightforward, but was discussed in detail with

managers of Europanel. Computation of all competitive environment variables was based on

information collected in the year prior to introduction. Market concentration was measured by

the combined market share of the top three brands in the category (Lipczynski and Wilson 2001).

The extent of price competition in the category is operationalized as the percentage of volume

sold on promotion in the category (Cotterill, Putsis, and Dhar 2000). The degree of non-price

competition through innovation and advertising is operationalized as the number of new SKUs

introduced into a category relative to the total number of SKUs in the year previous to new

5 We explicitly checked whether a product was launched in any other country in the world prior to its launch in France Germany Spain or the U.K

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product entry and the advertising-to-sales ratio (Lipczynski and Wilson 2001), respectively, both

measured at the category level.

The market power of national brands vis-à-vis private labels was operationalized using

the quasi-Lerner index (Connor and Peterson 1992), L = (PNB-PPL)/PNB where PNB (PPL) is the

market share weighted average price of national brands (private labels). It reflects national

brands’ ability to raise prices above the prices of quality-equivalent private labels (Parker and

Kim 1997). A positive value of L indicates the presence of national-brand specific market power

versus private labels (Connor and Peterson 1992). In estimating the effect on first-year success of

the market power of national brands vis-à-vis private labels using the Lerner index, we control

for quality differences between national brands and private labels by adding the quality gap

between national brands and private labels in the category as a covariate6.

Consumer characteristics. Dispositional innovativeness was measured using an eight-

item instrument developed by Steenkamp and Gielens (2003). Items were rated on five-point

Likert scales. The items were administered to all 16,000 panel members (in France, Germany,

and Spain after back translation). Configural and metric invariance of the dispositional

innovativeness items across countries were supported.7 Thus, we can validly compare the effects

of dispositional innovativeness across countries (Steenkamp and Baumgartner 1998). Cronbach’s

alpha was .81, .83, .75, and .80, respectively in France, Germany, Spain, and the U.K. A

6 Information on the quality gap was collected in consumer questionnaires (cf. Narasimhan, Neslin, and Sen 1996). We administered two items: “In this category, the quality of brands is very high” and “In this category, the quality of shops’ own labels is very high,” to about 1,000 consumers in each country. The items were discussed with Europanel experts and were pretested. Each category was evaluated, on average, by 106 (France) to 136 (U.K) consumers. Respondents were users of the category. Item scores were averaged within categories, and the quality gap was computed as the difference between the two average scores. 7 Configural invariance of the one-factor model was supported. Although the χ2 is highly significant, which is not unexpected given the large sample size (Anderson and Gerbing 1988), the other indices indicated good model fit: χ2(76)=3530.87 (p < .001), CFI=.901, GFI=.932, RMSEA=.055. All factor loadings were significant at p < .001, and the average (within-group standardized) factor loading was .547. Equality of factor loadings was also supported:

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composite score was obtained by averaging the scale items. Household size was measured as the

number of members in the household and age of the respondent was measured in years.

Residence of the consumer was measured by a dummy, which equals one if the respondent lived

in the country’s major metropolis (London, Paris, Berlin, or Madrid).

Analytical Procedure

We recorded the number of items of product j bought by consumer i in quarter t (1,..,4) in

country k (k = France, Germany, Spain, U.K). To assess the impact of the explanatory variables

on consumers’ first-year CPG purchase patterns, we specify a Poisson regression model in which

our variables are aggregated at three levels (Raudenbush and Bryk 2002). Individual purchase

patterns, i.e. the number of units of the new product purchased in each quarter by a particular

consumer expressed as a function of time, comprise the level-1 model. The variation in purchase

intensity and trend among consumers is captured at level-2, while the variation in these two

parameters among new products is represented in level-3.

We assume that in country k the number of units of new product j (j = 1,…,Jk) purchased

by consumer i (i = 1,…,Ik) in quarter t follows a Poisson distribution with expected purchase

mean λtij. At level-1, consumer i’s (latent) purchase rate with respect to product j can be modeled

as a function of time:

(1) log(λtij,k) = π0ij,k + π1ij,kTimetij,k

where π1ij,k reflects the extent to which the log purchase rate will increase or decrease over time.

π1ij,k is thus the trend in log-purchases for consumer i and product j over the first year in country

k. The interpretation of the intercept π0ij,k depends on the coding of the variable Time

(Stoolmiller 1995). We adopt time-averaged coding, using orthogonal polynomial coefficients

χ2(97)=3936.10 (p < .001), CFI=.890, GFI=.923, RMSEA=.052. CFI and GFI decreased little while RMSEA, which

18

(i.e., Time was rescaled as -3, -1, 1 and 3; Ferguson 1981). In that case, the intercept π0ij reflects

the average log-purchase intensity over the first year of consumer i with respect to product j

(Stoolmiller 1995).

At level-2, we model the variation across consumers in average log-purchase intensity and

trend in log purchases as function of consumer characteristics:

π0ij,k = β00j,k + β01j,kINNOVi,k + β02j,kAGEi,k + β03j,kSIZE_HHi,k + β04j,kMETROPi,k + r0ij,k

π1ij,k = β10j,k + β11j,k INNOVi,k+ β12j,kAGEi,k + β13j,kSIZE_HHi,k + β14j,kMETROPi,k

(2.b)

(2.a)

where INNOV, SIZE_HH, AGE, and METROP refer, respectively, to dispositional

innovativeness, size of household, age of the consumer, and area of residence (1 = metropolis, 0

= elsewhere). All level-2 predictors were centered per country within products. This implies that

β00j,k represents the mean first-year log-purchases of product j in country k and β10j,k is the mean

trend in log-purchases over the first year for product j in country k (Raudenbush and Bryk 2002).

The error term r0ij,k is normally distributed with mean 0 and variance σk².

At level-3, the variation in first year log-purchase intensity and trend in log purchases

across products are modeled as a function of firm and competitive variables, associated with the

new CPG:

β00j,k = γ000,k + γ001,kMSHAREj,k + γ002,kSEQUENj,k + γ003,kBr_REPj,k + γ004,kNEWj,k +

(3.a)

γ005,kNEW2j,k + γ006,kSEQUENj,k*NEWj,k + γ007,kCONCENj,k + γ008,kPROMINTj,k +

γ009,kPROMINTj,k*NEWj,k + γ0010,kNPIj,k + γ0011,kNPIj,k*NEWj,k + γ0012,kADVINTj,k

+ γ0013,kADVINTj,k *Br_REPj,k + γ0014,kPOWERNBj,k + γ0015,kHOUSEHj,k +

γ0016,kPERSCAREj,k + γ0017,kQUALGAPj,k + u00j,k

takes both goodness of fit and model parsimony into account, improved.

19

β10j,k = γ100,k + γ101,kMSHAREj,k + γ102,kSEQUENj,k + γ103,kBr_REPj,k + γ104,kNEWj,k +

γ105,kNEW2j,k + γ107,kCONCENj,k + γ108,kPROMINTj,k + γ1010,kNPIj,k +

γ1012,kADVINTj,k + γ1014,kPOWERNBj,k + γ1015,kHOUSEHj,k + γ1016,kPERSCAREj,k

+ γ1017,kQUALGAPj,k

β0qj,k = γ0q0,k for q = 1,…,4

β1pj,k = γ1p0,k for p = 1,...,4

(3.d)

(3.b)

(3.c)

where MSHARE expresses the market share of the firm introducing the new product, SEQUEN

is a dummy variable indicating whether the product has been launched before in another country,

Br_REP is the reputation of the brand, and NEW refers to the degree of newness of the new

product. CONCEN, PROMINT, NPI, ADVINT, and POWERNB are the competitive

environment variables market concentration, intensity of promotion in the category, new product

activity, advertising intensity, and the market power of national brands vis-à-vis private labels

(quasi-Lerner index). Three covariates are included: HOUSEH, PERSCARE and QUALGAP.

HOUSEH and PERSCARE are two dummy variables indicating whether the new CPG belonged

to the broad domain of household or personal care products (baseline = foods). QUALGAP

represents the quality gap between national brands and private labels in the category. Predictors

were mean centered per country across new CPG introductions. γ000,k and γ100,k reflect the overall

log-purchase intensity and trend in log purchases over the first year of a new CPG product in

country k, respectively (Raudenbush and Bryk 2002). The random effect u00j,k is normally

distributed over products with an expected value of 0 and var(u00j,k) = τ00,k.

By including u00j,k we allow a random coefficient specification across products on the

intercept, i.e., for the log-purchase intensity. β10j,k is specified as a varying non-random

parameter and β01j,k, β02j,k, β03j,k, β04j,k, β11j,k, β12j,k, β13j,k, and β14j,k are fixed non-random

20

coefficients and are thus constrained to be constant across products. Although in principle, a

random specification could also be used for the trend term and the other coefficients,

Raudenbush and Bryk (2002) warn against such practice especially when the number of time

intervals is limited, because it negatively affects model convergence and the stability of the

parameter estimates. Substituting Equations (3.a-3.d) into Equations (2.a-2.b) and Equations

(2.a-2.b) in Equation (1) yields a multilevel model that was estimated for each country with

HLM 5 (Raudenbush, Bryk, and Congdon 2000).

RESULTS

Table 3 reports the marketing of the analysis of the impact of the firm, competitive environment

and consumer characteristics on a consumer’s first year purchase intensity and trend of a new

product. For each country, unstandardized coefficients and t-values are reported.8

---Insert Table 3 about here---

Product Strategy Factors

Consistent with our expectations, firm resources positively impacted purchase intensity and trend

in the first year. In all countries, the effect on purchase intensity was positive and significant

(γ001,France = .081, p < .10; γ001,Germany = .143, p < .05; γ001,Spain = .120, p < .05; γ001,UK = .469, p <

.10). The effect on purchase trend was significant in all countries but France (γ101,France = .019, p

> .10; γ101,Germany = .329, p < .01; γ101,Spain = .438, p < .01; γ101,UK = .197, p < .05). Further, as

expected, we report a significant positive impact of a sequential roll-out strategy on new product

purchase intensity in France, Spain, and Germany (γ002,France = .143, p < .10, γ002,Germany = .102, p

< .10, γ002,Spain = .411, p < .05), indicating that first-year purchase intensity in these countries was

on average between 7% (=(exp(.102)*100)-100) in Germany and 51% (=(exp(.411)*100)-100) in

21

Spain higher if the new product had been introduced previously in another country. No such

effect was observed in the U.K. (γ002,UK = .063, p > .10). The impact on purchase trend was also

positive, but did only reach statistical significance in France and Germany (γ102,France = .048, p <

.01, γ102,Germany = .069, p < .01, γ102,Spain = .016, p > .10, γ102,UK = .028, p > .10). These results

suggest that the impact of a sequential rollout strategy steadily grows over time in France and

Germany. For instance in Germany, in the first quarter the impact was about 21% below the

overall first-year average of 7% (= (exp(.102+.069*(-3))*100)-(exp(.102)*100)))9 and in the last

quarter 25% above the first-year average (= (exp(.102+.069*(3))*100)-(exp(.102)*100))).

Brand reputation had a positive impact on first year purchase intensity (γ003,France = .028, p <

.05, γ003,Germany = .369, p < .01; γ003,Spain = .142, p < .05; γ003,UK = .510, p < .05), and on the trend in

purchases (γ103,France= .008, p < .05; γ103,Germany = .067, p < .01; γ103,Spain = .034, p < .05; γ103,UK =

.041, p < .01).

The U-shaped relation between first-year purchase intensity and product newness was

supported. In all four countries, the quadratic terms were positive and significant: γ005,France =

.080 (p < .01), γ005,Germany = .205 (p < .01), γ005,Spain = .075 (p < .05), γ005,UK = .253 (p < .05).

Likewise, a U-shaped effect was found with respect to the trend (γ105,France = .011, p < .05;

γ105,Germany = .011, p < .01; γ105,Spain = .035, p < .01; γ105,UK = .026, p < .05). The results indicate

that products higher on newness provide an especially stronger platform for growth, as evidenced

by the significant positive quadratic effect of newness on the trend in purchases.10 Consider, for

example, two products, one high and one low on newness (operationalized as one standard

8 The p-values reported below are one-sided, given our directional hypotheses, the exception being the effect of category new product intensity for which no directional hypothesis was formulated (Ferguson 1981). 9 Please note that we rescaled time as –3, -1, 1, and 3. 10 This effect can be derived by rewriting the quadratic term as follows: (γ005, k + γ105, k* Time)*Novel2 whereby time is coded as –3, -1, 1, and 3.

22

deviation above and below the mean; Jaccard, Turrisi, and Wan 1990). Between the first and last

quarter, the product high (low) on newness experienced an increase on the purchase rate from

4% (4 %) to 49% (25%), 29% (36%) to 95% (61%), 4% (4%) to 31% (18%) and 26% (13%) to

63% (42%) in France, Germany, Spain and the U.K. compared to a product of average newness.

Using a sequential rollout strategy reinforces the impact of products high on newness on

first year purchase intensity in France, Germany, and Spain (γ006,France = .181, p < .01; γ006,Germany

= .491, p < .01; γ006,Spain = .092, p < .05). In the U.K., however, this effect was negative (γ006,UK =

-.060, p < .01). The use of a sequential rollout strategy in France, Germany, and Spain raises the

purchase intensity in the fourth quarter of products high on newness 38%, 74%, and 14%

respectively above the purchase intensity in case no rollout strategy was used.

Competitive Setting

In concentrated markets, consumers’ first-year purchase intensity of a new product was lower, as

proposed. We found this result in France, Spain and the U.K. (γ007,France = -.032, p < .05;, γ007,Spain = -

.049, p < .05; γ007 ,UK = -.012, p < .10). In contrast, a positive effect was reported in Germany

(γ007,Germany = .025, p < .01). The negative effect in France, Spain, and the U.K. becomes even more

pronounced over time, as we found a negative significant effect on the purchase trend (γ107,France= -

.007, p < .10; γ107,Spain = -.013, p < .05; γ107,UK = -.021, p < .05). In Germany, the impact was not

significant (γ007,Germany = -.004, p > .10).

Heavy competition using the price promotion weapon had its expected negative effect on

new product success in Germany (γ008,Germany = -.019, p < .01) and the U.K. (γ008,UK = -.025, p <

.10). However, in France the effect did not reach statistical significance (γ008,France = -.008, p > .10)

and in Spain the effect was in the opposite direction (γ008,Spain = .007, p < .05). In all four countries

the effect on the purchase trend was negative, as expected, but only in France and Germany this

23

effect was statistically significant (γ108,France = -.003, p < .01;, γ108,Germany = -.006, p < .01; γ108,Spain = -

.001, p > .10; γ108 ,UK = -.001, p > .10).

In line with expectations, the effect of price promotion intensity in the category was

moderated by the newness of the new product as witnessed by the positive significant effect in

Germany, Spain, and the U.K. (γ009,Germany = .021, p < .01; γ009,Spain = .045, p < .01; γ009,UK = .086,

p < .10). To illustrate this effect, in Germany and the U.K the purchase intensity in the fourth

quarter of products high on newness introduced in heavily promoted categories (both defined as

one standard deviation above the mean) was, respectively, 6%, and 10% below the average

purchase intensity, while purchase intensity of products low on newness introduced in heavily

promoted categories (defined as one standard deviation below the mean) was 16% and 20%

below the average purchase intensity. In Spain, fourth-quarter purchase intensity of relatively

novel (incremental) innovations introduced in heavily promoted categories was 19% above (16%

below) the average. In these countries, products high on newness are thus more “protected” from

the downside effects of high promotion intensity. In France, no significant moderating effect was

found.

Non-price competition through innovation had a positive effect on first year purchase

intensity in all four countries. All other things equal, in markets where new product activity is high,

new products achieve a higher first-year purchase level (γ0010,France = .169, p < .01; γ0010,Germany =

.075, p < .10, γ0010,Spain = .044, p < .01; γ0010,UK = .068, p < .10). This is consistent with the notion

that new product introductions may have an important primary demand effect which may create

opportunities for subsequent new product introductions (Shankar, Carpenter and Krishnamurthi

1998). However, this effect decreased over time as we found a negative impact on the first year

purchase trend (γ1010,France = -.007, p < .01; γ1010,Germany = -.070, p < .10, γ1010,Spain = -.024, p > .10;

24

γ1010,UK = -.103, p > .10), but the effect only reached significance in France and Germany. The

positive effect of new product introduction activity on purchase intensity was reinforced if the new

product scored high on newness (γ0011,France = .017, p < .01; γ0011,Germany = .022, p < .01, γ0011,Spain =

.045, p < .01; γ0011,UK = .075, p < .01). This confirms our expectations.

Heavy non-price competition through advertising acts as a barrier to entry in France, Spain,

and the U.K. (γ0012,France = -.079, p < .10; γ0012,Spain = -.100, p < .05; γ0012,UK = -.060, p < .10) whereas

in Germany it facilitates entry (γ0012,Germany = .060, p < .01). All the same, we find, as expected, a

negative effect on the trend in all four countries although it did not reach significance in France

(γ1012,France = -.001, p > .10; γ1012,Germany = -.026, p < .05; γ1012,Spain = -.102, p < .05; γ1012,UK = -.062, p

< .05). Consistent with expectations, in all four countries, we found the negative impact of category

advertising intensity to be moderated when the new product was introduced using a strong brand

name (γ0013,France = .014, p < .01; γ0013,Germany = .052, p < .05; γ013,Spain = .012, p < .10; γ0013,UK = .074,

p < .05). A new product introduced by a reputable brand (i.e., one standard deviation above the

mean) is therefore able to diminish the downside effects of heavy advertising intensity on the

purchase rate with 12%, 21%, 3%, and 17% in France, Germany, Spain, and the U.K., respectively.

Finally, consistent with our theorizing, in markets where the market power of national

brands vis-à-vis private labels is large, new products introduced by national brand manufacturers

were more successful: γ0014,France = .073 (p < .05), γ0014,Germany = .048 (p < .05), γ014,Spain = .061 (p <

.10), γ0014,UK = .058 (p < .05). This effect increased over time but the positive impact on the trend

was only significant in France and Spain (γ1014,France = .095, p < .01; γ1014,Germany = .020, p > .10;

γ1014,Spain = .039, p < .01; γ1014,UK = .046, p > .10).

25

Consumer Characteristics

We found support for our hypothesis that consumers higher on dispositional innovativeness buy

more of the new product (γ010,France = .222, p < .01; γ010,Germany = .276, p < .01; γ010,Spain = .129, p

< .01; γ010,UK = .089, p < .01). A one standard deviation increase in dispositional innovativeness

increased the first-year purchase volume between 7% in the U.K. and 25% in Germany. As

theorized, this positive effect of dispositional innovativeness diminished over time (γ110,France = -

.022, p < .01; γ110,Germany = -.019, p < .01; γ110,Spain = -.022, p < .01; γ110,UK = -.013, p < .01). In

France, for example, the effect of a one-standard deviation increase of dispositional

innovativeness on the purchase intensity in the first quarter is 26% but decreases to 13% in the

last quarter.

In all four countries, the new product purchase volume declined with age (γ020,France = -.007,

p < .01; γ020,Germany = -.015, p < .01; γ020,Spain = -.017, p < .05; γ020,UK = -.007, p < .01). However, as

expected, this negative effect was moderated over time, as we found a positive effect on the

purchase trend in France, Germany, and the U.K. (γ120,France = .001, p < .10; γ120,Germany = .001, p <

.01; γ120,UK = .001, p < .01). In Spain, no significant effect on the trend was found (γ020,Spain = -.000, p

> .10). With respect to household size we found a consistent positive significant effect across

countries on a consumer’s first year purchase intensity (γ030,France = .154, p < .01; γ030,Germany = .183,

p < .01; γ030,Spain = .047, p < .01; γ030,UK = .068, p < .01), which became more pronounced over time

(γ130,France = .006, p < .01; γ130,Germany = .006, p < .01; γ130,Spain = .009, p < .01; γ130,UK = .019, p < .01).

Finally, new product purchase intensity was higher for consumers that lived in the country’s

major metropolis (γ040,France = .151, p < .01; γ040,Germany = .216, p < .01; γ040,Spain = .291, p < .01;

γ040,UK = .019, p < .01). The impact on the purchase trend, on the other hand, was mixed. In France

and Spain, the expected positive effect was found, although it only reached significance in Spain

26

(γ140,France = .003, p > .10; γ140,Spain = .035, p < .01). In Germany and the U.K. a negative effect was

reported (γ140,Germany = -.022, p < .01; γ040,UK = -.001, p < .01).

DISCUSSION

This paper investigates the effect of product, competitive environment, and consumer drivers on

market success of new CPGs in a cross-national context. We structure our main conclusions and

implications around the two caveats to a strategy of relying on innovation to strengthen the

company’s position identified in the Introduction section: (i) it is a very risky strategy, in that the

overwhelming majority of new CPGs fails in the marketplace, requiring insight into the drivers

of new product success, and (ii) it is a very costly strategy, necessitating that firms increasingly

launch their products in international markets, which requires detailed knowledge of the extent of

generalizability of factors underlying new product success across countries.

Drivers of New Product Success

New products can fail because 1) R&D has not yielded a product that appeals to the marketplace

and/or because 2) the marketing strategy associated with the new product launch has been

ineffective (Cook and Georgiadis 1997). Concerning the first cause of new product failure, a key

parameter of the new product’s attractiveness is its degree of newness. We find a U-shaped

relation between newness and market success. Products of either incremental or major newness

are more successful than products of intermediate newness. This effect increases over time.

Products of intermediate newness appear to be stuck in the middle: too high on complexity

compared to products of incremental newness and too low on relative advantage compared to

products of major newness. Products that rate intermediate on newness may be identified before

launch and subject to closer scrutiny to assess whether certain features can be changed to modify

its newness.

27

An attractive innovation strategy that combines both ends of the U is a pulse strategy in

which really new innovations are introduced from time to time, followed by incremental product

improvements and line extensions, to fine-tune the product based on market feedback and to fill

additional niches. Such a strategy is likely to be more successful than continuous intermediate-

level innovations. P&G’s Swiffer cleaning system has followed this strategy to build a $1 billion

product in a relatively short time. The original Swiffer was a major innovation. Subsequent

incremental innovations introduced Swiffer Wet, Swiffer Dusters, Swiffer WetJet, Swiffer Mitts,

and Swiffer Max, new scents for the cloth, etc.

While incremental innovations are typically relatively easy to achieve in the R&D process,

this is less straightforward for major innovations. A study among 13 leading U.S. CPG companies

identified the generation of major new product ideas “as the critical bottleneck for growth” (Cook

and Georgiadis 1997, p. 96). It further found that consumers are the second most important source

of innovation (after competitors). However, not every consumer is equally useful in generating new

ideas and in evaluating really new products in the concept stage. Our work suggests that companies

might want to focus on the input of consumers that are relatively high on dispositional

innovativeness. These consumers have a higher tolerance for ambiguity, are more open to change,

curious, and creative, and have a lower need for clarity and structure (Foxall 1988). This personality

profile indicates that these people are less prone to reject really new ideas while being more likely to

come up with less conventional ideas themselves. Moreover, in this way, the company gets input

from those consumers who have a considerably higher purchase intensity with respect to the new

CPG in the crucial first year after launch. In sum, in order to increase the chances of coming up with

major new product ideas, we recommend that in the R&D process, companies listen selectively to

“the voice of the customer” and as such create a lead-user effect (cf. Morrison, Roberts, and von

28

Hippel 2000). One large U.S. CPG company has put this into practice, using a short-form of the

dispositional innovativeness scale, as screener for recruitment for their concept testing.

Concerning the second cause of new product failure identified by Cook and Georgiadis

(1997), even when the R&D process has produced a new CPG product that appeals to the

marketplace, it may still fail due to an ineffective marketing strategy. This is not unique to the

CPG industry. Indeed, there may be relatively few industries where Moore’s (1995) contention

applies that “the appropriate marketing strategy is to supply, and not to court, the customers”

(Bayus, Erickson and Jacobson 2003, p. 209). We identify three product-related variables that

affect new product success, viz., marketing resources, the launch strategy, and its branding

strategy. New product success is greater when the product is supported by more marketing

resources, when the product has been launched previously in another country (with the exception

of the U.K.; see below) - and this effect is further strengthened in the case of truly new products -

and when it is marketed as a brand extension, using a reputable brand name. Successful product

introductions contribute to the power in the category and to brand reputation (Choi 1998), which

will contribute to the success of future new product introductions, creating a virtuous cycle of

innovation success. Finally, we also find that new product success is affected by the competitive

environment, albeit the results differ somewhat between countries (see below).

Cross-National Generalizations

Our source model was tested in four major European countries. To what extent are there general

rules concerning the firm’s ability to expand sales through new product development and

marketing (Farley and Lehmann 1994)? Which factors have a similar impact on new product

success in different countries, and hence could be part of international new product introduction

29

strategies? Which ones work out differently in different countries, and hence should be part of

local adaptations of introduction strategies?

Many of our findings are consistent across countries. In all countries, the new product

profits from more intensive marketing support and the reputation of the brand. The U-shaped

relation between newness and sales is found in all countries. We consistently find that the

positive effects of really new products and brand reputation increase over time. The effect of the

competitive environment variables relative power of national brands versus private labels and

competition on new product introductions go in the same direction in all countries. In all four

countries, we find that product newness has a positive moderating effect on the effect of the

competitive environment variables category price promotion intensity and new product

introduction intensity. We also find in all four countries that brand reputation has a positive

moderating effect on the effect of the competitive environment category advertising intensity.

Perhaps the most important cross-national generalization is that the consumer variables work in the

same direction on purchase intensity in all four countries. Although the magnitude of the effects

differs somewhat across countries, in each country the first-year purchase intensity of new CPGs is

higher among consumers higher on dispositional innovativeness, larger households, younger

consumers, and consumers living in the country’s metropolis. This implies that a cross-national

‘ideal prospect’ segment exists that offers a basis for pan-European (or global, if the segment is also

found in other parts of the world) marketing strategies. For illustrative purposes, we define the ideal

prospect segment as those consumers who are in the top 25% on dispositional innovativeness and

household size, the bottom 25% on age, and who live in a metropolis. Compared to the

unsegmented market, this ideal prospect scheme has, in the first year after launch, a purchase

intensity which is, an average, 80%, 94%, 28% and 32% higher in respectively France, Germany,

30

Spain, and the U.K. Note that this ideal prospect scheme can be easily applied and rates high on

actionability. Not only is socio-demographic information readily available, dispositional

innovativeness can be measured a priori rather than ex post. Thus, consumers can be classified

according to their score on the innovativeness instrument before product introduction. This offers

the firm the opportunity to develop targeted strategies beforehand rather than after the critical first

months after introduction. One CPG firm uses the dispositional innovativeness items on their direct

marketing databases in several European countries and the U.S., e.g., for targeting coupons at the

more innovative consumers. This involves segmenting millions of addresses so that they can target

appropriately. Another large CPG firm uses the items on simulated test markets and has started to

build up purchase intent benchmarks for consumers who rate high on dispositional innovativeness

to decide on new product launches.

Notwithstanding these cross-national communalities, a number of interesting differences

between countries can also be observed. We find that a sequential launch strategy, in which the

product has been introduced before in another country, has a significant, positive impact on first

year success in France, Germany, and Spain. No significant effect on either intensity or trend is

found in the U.K. The difference in effectiveness of a sequential rollout strategy between the

U.K. and the other three countries is even more pronounced in case the new product scores high

on newness. The findings are consistent with the game-theoretic predictions of Kalish, Mahajan,

and Muller (1995). These authors derived analytically that a sequential strategy is favored for

countries whose consumers are relatively lower on innovativeness. Our results indicate that the

segment of consumers relatively high on dispositional innovativeness is indeed much larger in

the U.K. than in the other three countries. We computed the median of the distribution of

dispositional innovativeness scores pooled across the total pan-European sample of 16,000

31

consumers, weighted for population size and identified how many consumers in each country

rated above the pan-European median. This reveals that in a European context, 70.1% of the

British consumers are relatively innovative (rate above the pan-European median dispositional

innovativeness score), versus 48.7% of the Germans, 44.1% of the French, and 46.7% of the

Spanish.

Second, there are differences across countries in the effect of several competitive

environment variables. Concentration works in the expected direction in all countries but

Germany. This may be due to a unique feature of the German CPG industry, viz., the strong

position of the hard discounter Aldi (Bachl 2003). Aldi carries no national brands and its market

share has grown dramatically over the last decade. Consequently, its private label is often one of

the largest in the category. In fact, the correlation between market concentration in the category

and Aldi’s share in the category is .51. Since Aldi does not collude with national brands, this

implies that collusion opportunities may in fact be less in more concentrated markets.

In France, Germany, and the U.K. we find, as expected, that new product success is less

in heavily promoted categories. In Spain, the effect is less pronounced. The promotional

environment differs widely across Europe, due to different traditions and legal frameworks

specifying what types of promotion are allowed. Nevertheless, we find consistently that

relatively novel innovations suffer less from potential barriers erected by heavy price promoting.

Investing in newness thus helps to overcome competitive hurdles more easily.

Heavy advertising in the category acts as a barrier to entry in France, Spain, and the U.K.

In contrast, heavy category advertising seems to facilitate new product success in Germany

whereby we note that this positive effect decreases gradually over time. Scherer and Ross (1990,

p. 572) noted that advertising need not always act as barrier to entry and as weapon of “artificial”

32

product differentiation. They argued that “advertising can perfect competition by helping

consumers make better informed choices.” They posited that newspaper advertising is

“preponderantly informative” (p. 572). Informative advertising provides information about a

product’s price and its qualities, which increases market transparency. This may explain the

German results, as the share of print advertising is considerably higher in Germany (70%)

compared to France (50%), Spain (47%), and the U.K. (58%) (Euromonitor 2003).

Third, apart from these cross-national differences in the effect of competitive

environment, the level of a specific competitive environment variable in a specific category may

differ between countries, giving rise to differences in success of any given new product across

countries. After all, companies face global as well as local competitors in each market, the power

of private labels in a given category can differ between countries, and even global companies can

use different intensity of instruments across countries. For example, market concentration in

ready-to-eat cereals is 78% in Spain versus 52% in the U.K. Market power of national brands

(using the Lerner index) in the yogurt market is .57 in Germany, but only .20 in France and .09

in the U.K. Advertising intensity in the bath and shower market is 13% in Spain versus 4% in the

U.K., while promotion intensity in the fabric detergents category is 42% in Germany versus 19%

in France. Hence, for any given product introduction, the influence of the competitive

environment can differ substantially between countries. This underlines the importance of local

implementation, even for global strategies.

Limitations

Our study has various limitations, which offer avenues for future research. First, the empirical

part of our study focused on new packaged goods in France, Germany, Spain, and the U.K. The

question remains to what extent our findings may be generalizable to other countries. Including

33

more countries within and outside Europe would allow us to extend the scope of our

recommendations from a pan-regional to global scale. Moreover, a fourth country-level could

thus be integrated in our model, which would allow us to formally test the impact of cultural and

economic factors that may give rise to country differences.

Second, the measurement of some of our constructs could be further refined. Our

dependent variable was provided at a 3-monthly temporal level of aggregation. Future research

might employ monthly or even weekly data to increase the power of the trend analysis.

Following Balasubramanian and Kumar (1990, 1997) market share in the category was used as a

proxy for the marketing resources available to support the new product introduction.

Nevertheless, a direct measure of marketing support given to the new product is to be preferred.

As no information was available on the exact order of launch in different markets, the impact of

the sequential rollout strategy was measured using a dummy variable indicating whether the

product was introduced before in any other country. Differences in the effects across earlier and

later lagging countries and between specific pairs of countries could thus not be established. Our

overall measure of product newness is commonly used in new product research (Henard and

Szymanski 2001), but could be refined. Gatignon et al. (2002) identified a set of measures to

evaluate a new product’s locus, type and characteristics. Assessing the new product with greater

clarity on the units of analysis could lead to more insightful research on NPD.

Third, another source of variation that might be included in future research deals with the

store environment. Retailers are confronted with large numbers of CPG introductions in a wide

variety of categories. Desiraju (2001) reports that retailers have reacted to this onslaught of

introductions in variety of ways. Some retailers openly solicit new products indicating these new

items as the lifeblood of their business that have to be in their store before their competitors do.

34

Other retailers, in contrast, argue that introducing a new product is a service, which they provide to

the manufacturer. As a result, retailers have different attitudes towards new products. More

innovative retailers will provide a more nurturing environment for these new products, which will

positively impact the sales volume of these products. Future research could investigate the impact

and moderating effects of aspects of the retail environment such as retail pricing policy, private

label policy, etc. Further exploration of what aspects of the retail environment benefit the new

product in its infancy, may lead to improved predictions on what type of retail concepts may be

more interesting to build strategic relationships with and whether we can derive a general profile of

these ‘preferred’ retailers across different countries.

35

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44

Table 1:Overview Literature

Sources of difference Dependent variable First year purchases

Level aggregation Authors

Product Comp. setting Consumer Country Trial

Intensity Trend

Empirical basis

Calantone et al. 1996 √ √ √ 2 countries, 142 products Dekimpe et al. 1998 √ √ √ 74 countries, 1 D1

Dekimpe et al. 2000 √ √ 160 countries, 1 D Ganesh and Kumar 1996 √ √ √ 10 countries, 1 ind. product Ganesh et al. 1997 √ √ √ 16 countries, 4 Ds Gatignon et al. 1989 √ √ 14 countries, 6 Ds Helsen et al. 1993 √ √ 12 countries, 3 Ds Mahajan and Muller 1994 √ √ 16 countries, 1 Ds Putsis et al. 1997 √ √ 10 countries, 4 Ds Takada and Jain 1991 √ √ 4 countries, 8 Ds Talukdar et al. 2002 √ √ 31 countries, 6 Ds

Market level

Tellis et al. 2003 √ √ √ 16 countries, 137 Ds Chandrashekaran and Sinha 1995 √ √ √3 1 country, 1 CPG2, 3236 cons. Chatterjee and Eliashberg 1990 √ √ 1 country, 65 consumers Gauvin and Sinha 1997 √ √ 1 country, 9742 consumers, 8 Ds Helsen and Schmittlein 1994 √ √ √3 1 country, 4 CPG, 2261 cons. Im et al. 2003 √ √ 1 country, 10CD, 296 cons. Manning et al. 1995 √ √ 1 country, 74 consumers Sinha and Chandrashekaran 1992 √ √ 1 country, 3689 consumers, 1 D Steenkamp and Burgess 2002 √ √ 1 country, 3328 cons. Steenkamp and Gielens 2003 √ √ √ √ 1 country, 239 CPGs, 3658 cons.

Individual level

This study √ √ √ √ √ √ 4 countries, 301 CPGs, 16370 consumers

Remarks: 1: D refers to durable; 2: CPG refers to consumer packaged good; 3: These studies look at the timing of repeat purchases rather than a trend.

Table 2: Overview Expected Effects of Key Determinants of First Year New Product Success

Drivers of CPG success First year purchase intensity

First year purchase trend

Product strategy factors Marketing resources Sequential rollout strategy Brand reputation Product newness * Sequential rollout strategy

+ + + ∪ +

+ + + ∪

Competitive environment Concentration Price competition

Price promotion intensity * Product newness

Non-price competition New product introduction intensity * Product newness Advertising intensity * Brand reputation

Market power national brands viz. private labels

- - + ? + - + +

- - ? -

+ Consumer characteristics

Dispositional innovativeness Age Size household Living in a country’s metropolis

+ - + +

- + + +

Table 3: Results Expectation France Germany Spain U.K.

int. trend intensity trend intensity trend intensity trend intensity trend

Coef. t Coef. t Coef. t Coef. tCoef

. t Coef. t Coef. t Coef. t

Product factors Mark. resources (γ001, γ101 )Sequential rollout (γ002, γ102) Brand reputation (γ003, γ103) Newness (γ004, γ104) Newness2 (γ005, γ105) *Rollout (γ006, γ106)

+ + +

+ +

+ + +

+

.081 .143.028

-.041 .080 .181

1.65 1.63 1.70 3.28 3.68 2.66

.019 .048 .008

-.006 .011

.273 3.64 1.80 2.67 2.19

.143 .102 .369

-.101 .205 .491

2.02 1.65 4.47 3.24 5.00 5.88

.329 .069 .067 .013 .011

3.71 2.32 2.62 2.67 3.96

.120 .411 .142

-.048 .075 .092

2.05 2.31 1.96

-1.88 1.79 1.73

.438 .016 .034

-.022 .035

4.07 .060 1.69 3.05 2.71

.469 .063 .510

-.103 .253

-.060

1.58 1.15 2.09 1.72 1.98 2.31

.197 .028 .041

-.019 .026

1.91 1.02 2.34 2.58 2.04

Competitive environment Concentration (γ007, γ107) Price competition

Price prom. Int. (γ008, γ108) * Newness (γ009)

Non price competition NP intensity (γ0010, γ1010) * Newness (γ0011) Adv. Intensity (γ0012, γ1012) * Brand reputation (γ0013)

Power NB (γ0014, γ1014)

- - + ? + - + +

- - ? -

+

-.032

-.008 .009

.169 .017

-.079 .014 .073

1.79

.59 .21

3.15 4.65 1.43 2.32 2.14

-.007

-.003

-.007

-.001

.095

1.33

6.32

8.51

.99

2.47

.025

-.019 .021

.075 .022 .060 .052 .048

7.04

3.03 4.15

1.81 3.37 2.49 2.24 1.99

-.004

-.006

-.070

-.026

.020

.068

5.14

1.71

1.99

.291

-.049

.007 .045

.044 .045

-.100 .012 .061

1.81

1.74 5.63

2.38 3.92 2.03 1.48 1.67

-.013

-.001

-.024

-.102

.039

2.23

1.01

1.31

1.73

3.56

-.012

-.025 .086

.068 .075

-.060 .074 .058

1.58

1.53 1.40

1.92 2.75 1.48 1.67 1.83

-.021

-.001

-.103

-.062

.046

1.81

.812

1.39

1.84

.354 Consumer Dis. innovativ. (γ010, γ110) Age (γ020, γ120) Size household (γ030, γ130) Metropolis (γ040, γ140)

+ - + +

- + + +

.222

-.007 .154 .151

20.38 4.43

17.82 5.18

-.022 .001 .006 .003

8.12 1.56 3.45 .059

.276

-.015 .183 .216

21.36 10.17 19.29 6.20

-.019 .001 .006

-.022

4.62 2.95 3.95 2.59

.129

-.017 .047 .291

7.39

13.07 5.25 7.04

-.022 -.000 .009 .035

3.58 .117 4.35 3.47

.089

-.007 .068 .019

8.32

-4.18 6.11 2.61

-.013 .001 .019

-.001

10.10 6.11

11.97 3.48