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Working Paper Series
Analysing the Impact of Supermarket Promotions: A Case Study Using Tesco Clubcard Data in the UK Melanie Felgate Kent Business School Andrew Fearne Kent Business School Salvatore Di Falco London School of Economics and Political Science
Working Paper No. 234
January 2011
ISSN 1748‐7595 (Online)
ANALYSING THE IMPACT OF SUPERMARKET PROMOTIONS: A CASE STUDY USING TESCO CLUBCARD DATA IN THE UK
Dr Melanie Felgate
Research Associate
Centre for Value Chain Research, Kent Business School, University of Kent, Canterbury,
Kent, CT2 7PE, UK
Tel: +44(0)1227 824766
Email: [email protected]
Professor Andrew Fearne
Professor of Food Marketing and Supply Chain Management
Director of the Centre for Value Chain Research, Kent Business School, University of Kent,
Canterbury, Kent, CT2 7PE, UK
Tel: +44(0)1227 824840
Email: [email protected]
Dr Salvatore DiFalco
Lecturer in Environment and Development
London School of Economics and Political Science, Houghton Street, London, WC2A 2AE,
UK
Tel: +44(0)20 7852 3778
Email: [email protected]
ABSTRACT
The aim of this paper is to show how supermarket loyalty card data from a sample of over 1.4
million UK households can be used to analyse the effect of promotions on the sales of specific
products and across different shopper segments. Regression analysis is used to test four hypotheses:
H1: Positive promotional impacts at the product level do not systematically result
in category growth.
H2: Promotional impacts are sensitive to the specific mechanic used
H3: Promotional impacts are sensitive to specific product characteristics
H4: Promotional impacts are sensitive to the demographic characteristics of
specific shopper segments
The analysis compares the effects of different promotional mechanics upon different cuts and
tiers of product across the fresh beef category in Tesco – the largest supermarket chain in the UK. The
results show that promotions do not always lead to category growth, there is considerable variability
in the impact of different promotions mechanics across different products, and households respond
differently depending upon their life-stage. The paper demonstrates the potential benefits of the
analysis of loyalty card data for own-label products in destination categories to ensure that retailers
and their suppliers are implementing the most effective promotional strategies. The evidence from the
data analysis suggests this may not always be the case.
INTRODUCTION
This paper reports the use of loyalty card data from one of the biggest retailers in the world –
Tesco - to analyse the impact of promotions. While the case study is based on data from the UK, it
demonstrates how other major retailers with similar loyalty card schemes (e.g. Kroger in the USA,
and Casino in France) could make use of this powerful source of behaviour data to gain a better
understanding of the effectiveness of promotions in key (destination) categories.
The Tesco ClubCard is used regularly by 17 million households in the UK (approximately 40
percent of all UK households) and provides data not only on what is being sold on a national basis but
also who is buying it, with shoppers segmented by television advertising region, lifestage, lifestyle
(Tesco have their own lifestyle segmentation based on the distribution of products in a shopper’s
basket over a twelve week period) and geo-demographics. Panel data of this type or on this scale has
not previously been used in any prior academic studies on the impact of promotions. Thus, this paper
provides a contribution to the promotions literature by enhancing the empirical evidence base and
demonstrating the value of analysis that is highly disaggregated – by product type and shopper profile.
The example used in this paper is fresh meat – a key destination category for most supermarket
chains, with a particular emphasis on fresh beef, which in the UK accounts for 56%of fresh meat
sales. However, the model used could be applied to any product category in order to help retailers and
food manufacturers make better (more informed) decisions with regard to promotional planning. In
particular, by identifying how different types of shoppers respond differently to different types of
promotions, retailers and manufacturers will also be able to target their promotional activity more
effectively.
The use of promotions in retailing has increased rapidly in recent times (Nielsen Wire 2009),
yet more often than not promotions are being implemented with an inadequate understanding of
which mechanisms are most effective, for which products and for which shopper segments. Despite
this growth in the use of promotions, particularly for fast moving consumer goods, consideration of
their impact and effectiveness amongst academics has been limited and identified as an important
area for future research, as has greater use of supermarket panel data to provide insights into how
different shoppers respond to price changes, including promotions (Grewal and Levy, 2009). .
Moreau et al (2001) argued that a lack of understanding by retailers about consumer perceptions of
promotions can lead to weaknesses in their marketing strategies. Thus, as the use of promotions
continues to grow, it is increasingly important to gain a more complete understanding of how
consumers actually behave in response to different promotional activities.
Previous studies have focused on the potential effects of promotions, including brand
switching, category expansion and purchase acceleration (E.g. Chiang 1991, Chintagunta 1993;
Foubert and Gijsbrechts 2007; Manning and Sprott 2007; Neslin and Shoemaker 1983; Putsis and
Dhar 2001). However, the vast majority of these studies have relied upon small scale scanner datasets
and small-scale experiments which many would argue cannot be considered representative of
supermarket shoppers as a whole. This paper is unique in that it is the first to use loyalty card data
from a large scale panel of over 17 million households to analyse promotions, and hence can be
considered as more robust than previous academic research.
The use of promotions and their impact on retail performance
Drawing upon the definitions put forward from previous researchers (e.g. Blattberg and
Neslin 1990; Kotler 1988; Webster 1971) promotions are defined as marketing events limited in
duration, implemented to directly influence the purchasing actions of customers with the underlying
intention of achieving the objectives set out in the marketing strategy for the retailer and/or
manufacturer. These objectives may include improving competitive position, brand extension,
category expansion or increasing profitability.
The use of sales promotions in the UK has increased significantly over the last decade,
particularly in grocery retailing where competition between retailers has intensified. Nielsen Wire
(2009) reported that in May 2009 a record 32 percent of all grocery sales in the UK were made up of
products on promotion. In the US the figure is even higher, with a reported 42.8 percent of grocery
sales made on promotion in September 2009, up from 40.8 percent in 2008 (Nielsen Wire 2009).
Yeshin (2006) stated several reasons thought to have contributed to the increase in the use of
promotions in the UK in recent years:
• The rising strength of retailers, with the top four supermarkets (Tesco, Asda,
Sainsburys and Morrisons) now controlling almost 70 percent of grocery sales,
resulting in fierce competition between own-label and branded products as well as
for overall market share.
• A reduction in product differentiation
• A reductions in the level of brand loyalty
This has resulted in both UK supermarkets and their (branded) suppliers becoming
increasingly dependent on promotional activity to drive sales growth. It is our contention that much (if
not most) of this activity has occurred with limited analysis (and thus understanding) of the impact of
promotional activity beyond the uplift in sales of the promoted products.
Over the last few decades substantial inroads have been made in empirical research into the
effects of retail promotions and what influences their effectiveness. For a promotion to be profitable
for a retailer it must increase overall category sales, not just switch sales between brands.
The type of mechanic used can influence how effective a promotion is. It is likely that there is
not one type of promotion which is most effective for all products within a category, but rather that
different promotions are more effective on different brands and products.
Research by Wansink et al (1998) found that on average the sales volume increased by
125% with single-unit promotions, compared to 165% with multiple unit promotions. Research by
Manning and Sprott (2007) studied specifically the effects of multiple unit price promotions on
purchasing behaviour. The uplift in quantity purchased as a result of multiple unit price promotions
was found to be dependent upon the magnitude of the quantity specified in the offer and the rate of
product consumption for the specific product category.
Both the length and the frequency of promotions can affect how responsive the consumer
will be. The longer a promotion lasts, the less effective it will be, because over time the effect of the
promotion upon sales will be reduced (e.g. Rao and Thomas 1973, Blattberg R. C. and Wisniewski
1987). The explanation for this may be that after a promotion has run for a certain length of time,
consumers will come to expect that they can buy the product at the offer price and so will stockpile
less and increase their inter-purchase time. Martínez-Ruiz et al (2006a) advised that promotions for
non-perishable storable products should not exceed ten days, otherwise profitability will be reduced.
The frequency of promotions will affect the consumer’s reference price (Kalwani and Yim 1992,
Mayhew and Winer 1992) and hence can lower the height of the promotional spike in sales (Raju
1992). If a brand is discounted often, consumers will come to anticipate the promotion and will
expect to always pay a lower price for the product.
Factors related specifically to individual product categories such as brand share (Bemmaor
and Mouchoux 1991), perishability and bulkiness (Bell et al 1999; Manning and Sprott 2007;
Wansink and Deshpandé 1994), and the occasion for which the product will be used (Meat and
Livestock Commission 2002) can all influence the shoppers’ response to promotions. For example
Bemmaor and Mouchoux (1991) found that as a result of price cuts, smaller brands experience a
larger relative increase in sales, compared with larger more established brands. It was found that
consumers will increase their usage for products which are perishable, as a result of promotions (Bell
et al 1999). However, in categories of more staple items such as toilet paper and detergent, stockpiling
takes place, increasing the inter-purchase time, meaning that the consumers move their purchasing
forward, but do not increase their overall consumption (Wansink and Deshpandé 1994). Most, if not
all, product categories have a variety of brands and products suited to different uses or occasions and
this can influence how the shopper responds to promotions within a category.
Characteristics related specifically to the shopper and their household can also influence the
effectiveness of promotions such as the household size, presence of children, age and income. The
larger the family, the further the budget needs to stretch and the more likely they will be to buy into
promotions (Bawa and Gosh 1999; Urbany et al 1996). There is conflict in the literature as to the
effect age has on promotional response. Some believe younger shoppers are more likely to purchase
products on promotion, either because they are more price sensitive than older shoppers (Ainslie and
Rossi 1998) or because they are more likely to be influenced by stimuli such as displays and
advertising and hence make decisions on impulse at the point of purchase than older shoppers
(Inman and Winer 1998). However, it has also been suggested that older shoppers have more time to
shop and therefore may be more likely to take advantage of promotions (Raju 1992). In reality it is
quite likely that the response by different age groups will depend upon other factors such as the
product being promoted and the mechanism used.
Hypotheses
Our understanding of how promotional response varies within and between product categories
and across shopper segments is crucial as it enables better targeting of promotions to ensure they
yield the best results possible. Despite the amount of data now available to retailers through loyalty
card schemes and point of sale transaction data, very little research has been undertaken to assess
which promotions are most successful for which products and the impacts of promotions beyond the
sales uplift of the promoted product. On the basis of our review of the existing promotional literature,
four hypotheses were formulated for the purpose of this paper:
H1: Positive promotional impacts at the product level do not systematically result
in category growth.
H2: Promotional impacts are sensitive to the specific mechanic used
H3: Promotional impacts are sensitive to specific product characteristics
H4: Promotional impacts are sensitive to the demographic characteristics of
specific shopper segments
These hypotheses will be tested using the methodology outlined below.
METHOD
Multiple regression was used to estimate the impact of different promotional mechanics
within the fresh meat category. Multiple regression enables us to evaluate the separate contributions
of one or more variables acting jointly on a single dependant variable. Moreover, it is easily
applicable to the analysis of promotions as a given product may use several types of promotion, each
of which may have a different effect on sales of both the promoted product and substitute products.
Regression analysis techniques have been widely used by other researchers to estimate the effects of
promotions (see e.g. Bolton 1989; Macé and Neslin 2004; Sethuraman and Tellis 2002; Van Heerde et
al 2004). Regression analysis is well suited to the analysis of large samples of data and for measuring
the effects efficiently across different product sub-groups, identifying switching and substitution
effects between products and differences across segments of shoppers. The method enables several
independent variables to be modelled at the same time, taking into account interactions, and is a
logical choice given the available dataset.
Data
There are two main types of data used in the promotions literature to analyse the impact of
promotions on purchasing behaviour: panel data and retailer scanner data. Panel data provides
information at an individual household (or segment) level; for example by household size or by age.
Popular sources of panel data include A. C. Nielsen and TNS Worldpanel. Examples of studies which
have utilised panel data include Ailawadi et al (2007), Bell et al.(1999), Chintagunta (1993), Foubert
and Gijsbrechts (2007) and Vilcassim and Jain (1991). All these studies suffer from the same
limitation in that the panel data used is relatively small scale and therefore less representative of the
whole population when trying to make inferences about the way people respond to promotions.
Store-level scanner data pools all sales in a given store, or chain of stores over a period of
time but does not contain information on specifically which type of household these sales relate to.
Examples of studies which have incorporated store-level scanner data include Macé and Neslin
(2004), Martínez-Ruiz et al. (2006b) and Raju (1992). Panel data is more detailed than store-level
scanner data and can be more useful in many circumstances, for example to compare the effects of
promotions on different categories of shoppers or on brand loyalty. However, panel data can be
‘noisier’ than aggregate store-level data because in panel data sales will typically be generated from a
smaller sample size of consumers, which is likely to result in more week to week variation. The lower
noise in aggregate store sales data is a strong factor in favour of using it for many types of analysis.
Store-level scanner data also has the advantage of containing a much larger sample than panel data,
meaning there will be less variation in the sales data which should yield more robust results.
For this study weekly supermarket purchase data was used from all Tesco supermarkets
across the UK, collected via the Clubcard loyalty scheme, which covers approximately 80 percent of
total sales. The dataset combines the benefits of both store-level scanner data and panel data, and is
collected on a much larger scale than those datasets previously used for promotion research.
At the time of data collection for this research approximately 40 percent of UK households,
owned a Tesco Clubcard. Tesco is the largest grocery retailer in the UK, with a segmented retail
strategy, serving the entire spectrum of shoppers from price sensitive to up-market, and through
different retail formats such as on-line, convenience and supermarkets. Recent figures indicate
Tesco’s market share to be at 30.9 percent of total grocery retailing in the UK (TNS, 2009, cited in
Wall Street Journal [online], 2009). Thus, the sample of shoppers is considered to be as closely
representative of UK supermarket shoppers as possible from a single dataset.
The power of using retailer loyalty card data is the ability to generate purchasing data at a
segmented level on a large scale. For the purposes of this research the database was used to create a
cross-sectional panel data set, with sales data sorted by life-stage and TV advertising region. Within
the dunnhumby database there are five life-stage segments and ten TV advertising regions, which
made it possible to create a panel dataset based on fifty individual segments in total. The different
life-stage segments are young families (all children under 10yrs), older families (at least one child
over 10yrs), young adults (aged 20-39yrs), older adults (aged 40-59yrs) and pensioners. The different
regions of the UK, used within the panel dataset, were Scotland, Wales and the West, the South West,
the South East, the Midlands, East England, London, Yorkshire, the North East and the North West.
To create the panel dataset, sales data for each life-stage segment for each region was collated
together into a large dataset comprising fifty panels in total.
The dataset comprised of weekly sales over an 86 week period from 29th May 2006 to 21st
January 2008. In order to offset the affect caused by increases in distribution (due to the continued
growth in Tesco stores) the sales value was divided by the number of stores selling the product each
week. This created the dependant variable; sales value per store.
Example - Fresh Beef Category
In the UK, the majority of fresh meat sold through supermarkets is private label with little or
no branding, making it unique from most non-commodity product categories. In other grocery
categories where branding is more prevalent, consumer attitudes towards private label promotions
may differ from the fresh meat sector. Garretson et al (2002) argued that consumers may have
different attitudes towards private label products (which are generally priced below national brands)
and national branded products on promotion.
While there is no branding, as such, within the fresh meat sector in the UK, products are
positioned differently with a range of price tiers, from value products through to premium and
organic. These different tiers of products can be considered in essence to be separate ‘brands’ and the
model enabled us to identify switching effects between these different ‘brands’ of beef. As well as
different tiers there are also different cuts such as roasting joints, ground beef, and steaks. For the data
analysis individual beef stock keeping units (SKUs) were grouped into eleven subgroups categorised
by cut, and by tier. The products were first sorted by cut: roasting joints, frying and grilling meats
such as steaks and chops, and ground beef. These groups were then further disaggregated by ‘tier’:
standard label (e.g. Tesco private label brand), premium (e.g. Tesco Finest brand), organic, value (e.g.
Tesco Value) and healthy label (e.g. lean or Tesco Healthy Living range).
Substitution is most likely between the different tiers (e.g. Organic, Standard) of the same cut
(e.g. Roasting joints), rather than between different cuts, therefore the analysis was carried out within
the different cuts of meat. Roasting joints were analysed together, as were fry/grilling beef and ground
beef. This made it possible to observe switching effects of promotions between tiers of products,
within each cut sub-category. For example, a promotion on standard roasting beef joints may
influence sales of value roasting beef joints. Substitution between cuts is considered less likely since
different cuts are used for different meal occasions.
The Model
A multiple regression model estimated using the fixed effects method was used. Fixed effects
estimation was chosen as this method is most appropriate for the analysis of cross-sectional panel data
because it controls for heterogeneity across different types of shopper segments or ‘panels’. The
following equation represents the model used for the regression analysis:
SALESit = β0 + β1SPCit1 + β2MPCit2 + β3LPCit3+ β4MULTIit4+ eit
In the model, SALESit represents the dependant variable sales value per store for a given product sub-
group, i, in a given time period, t. The parameters of the model are β0, which represents a fixed
unknown parameter, and a series of 0-1 dummy variables representing the different types of price
promotion for product sub-group i in the time period t. Single unit price reductions were by far the
most commonly used form of promotion in the fresh beef category, and these were split into three
different levels of price reduction. Small price reductions of less than fifteen per cent off (SPC),
medium price reductions of between fifteen and thirty per cent off (MPC), and large price reductions
(LPC) of more than thirty per cent off the original price. The final independent variable represented
all types of multi-buy promotions (MULTI). The error term, e, incorporates all the immeasurable
factors which may also be influencing sales aside from promotions. The beta coefficients are reported
in the results to show which promotions have the largest effects on which products, and amongst
which consumers, segmented by life-stage. The coefficients represents the value by which sales will
increase for the retailer during the promotion, which will help to inform the retailer about how much
additional stock is required when planning promotions.
RESULTS AND DISCUSSION
In total 24.4 percent of fresh beef sales occurred while promotions were taking place during
the time period analysed. Table 1 shows the percentage of sales within the different beef sub-groups
occurring while on promotion. . These percentages were calculated by summing the sales of each
individual product within each sub-group during the weeks when a promotion was taking place on the
given product, and then diving this by the total sales for each product.
Table 1: Percentage of Sales occurring on Promotion by Beef Sub-group over the 86 Week period
from 29th May 2006 to 21st January 2008
Sub-group % Of Sales Occurring On Promotion
Roasting Beef Ground Beef Fry/Grilling Beef
Total 39.96% 39.0% 16.7%
Standard 50.31% 54.67% 17.61%
Premium 27.88% 9.88% 23.32%
Organic 31.71% 3.47% 12.44%
Healthy 19.91%
Value 3.92%
In total almost 40 percent of roasting beef sales occurred while products where on promotion,
compared with 39 percent for ground beef and a much lower value of 16.7 percent for fry/grilling
beef. Within the roasting and ground categories the highest percentage of sales on promotion occurred
within Standard sub-group, yet for the fry/grilling beef category the highest percentage of sales on
promotion occurred within the premium sub-group. We will now present the results of the model,
testing each hypothesis in turn. The tables report the results from the regression analysis, with only
those results which are significant at least at the 5 percent significance level being reported.
Hypothesis 1: Positive promotional impacts at the product level do not systematically result in
category growth
The first hypothesis states that positive promotional impacts at the product level do not
systematically result in category growth. For promotions to benefit the retailer they must ultimately
result in category growth, as profit is not generated through shoppers merely switching existing
consumption between brands or products within a category. Table 2 reports the results of the
regression analysis at the category level, showing the impact beef promotions had on the value of the
beef category overall.
Table 2: Regression results at the total category level with respect to different Promotional
Mechanisms
Coefficient (£/week)Fresh Beef Category
Small Price CutsMedium Price Cuts -187524.5*Large Price CutsMulti-Buys 229238.9*
R-Sq 0.1437
Promotion
*p<0.05
Promotions account for just 14% of the variance in sales of beef at the category level, which
tells us that promotions are just one of several factors likely to be influencing sales. At the total
category level the impact of promotions overall was found to be insignificant, however when divided
into specific promotional mechanics, it can be seen that multi-buys added value to the fresh beef
category while medium price cuts de-valued the fresh beef category. This result is important as it
shows that not all promotions add value to the retailer, and indicates that it would be highly beneficial
for retailers to make informed decisions when deciding promotional plans. However, this result does
not necessarily mean multi-buys are the best promotion to use for all fresh beef products.
Hypothesis 2: Promotional impacts are sensitive to the specific mechanic used
The second hypothesis states that the impact of promotions will depend upon the specific
mechanic used. This has already been touched upon above, where it was seen that multi-buys had a
positive effect on the fresh beef category overall, while medium price cuts de-value the category.
However it is likely that the effectiveness of different promotional mechanics will vary when
analysing the category at a more disaggregated level. Table 3 reports the coefficients from the
regression analysis, showing the overall effect on sales at the cut level as a result of the different
promotional mechanics.
Table 3: Regression Results for the Roasting Beef, Ground Beef and Fry/Grilling categories with
respect to different Promotional Mechanisms
Promotion
Coefficient (£/week)
Roasting
Beef
Ground
Beef
Fry/Grilling
Beef
Small Price Cut (<15%)
Medium Price Cut (15-30%) 89.26**
Large Price Cut (>30%) 120.44**
Multi-buy 9.78** 270.65**
R-Sq 0.132 0.106 0.380
**p<0.01
*p<0.05
Promotions have the biggest influence on sales within the fry/grilling beef category, since the
R-Squared value tells us 38 percent of the variance in sales is attributable to promotions. The R-
squared values are much lower within the ground and roasting Beef categories. Within the roasting
category, large price cuts were found to have the greatest significant positive impact on sales value,
indicating that sales value per store will increase by £120 during the promotion. Within the ground
beef category multi-buys were the only promotion to have a significant impact overall, while medium
price cuts were the only promotion to have a significant impact on fry/grilling beef sales. Small price
cuts did not have a significant impact on sales across any of the beef cuts.
These results support the findings of a qualitative study by the Meat and Livestock
Commission (2002), which indicated that price discounts would be more effective than multi-buys at
driving sales of key occasion meats such as roasting joints and fry/grilling meats. Multi-buys were
found to be the only effective form of promotion within the ground beef category, which adds further
backing to the research carried out by the MLC, which found that multi-buys would be most effective
on ‘core everyday proteins’ such as ground beef which shoppers buy weekly out of habit for everyday
meals during the week.
Here we have identified that promotional response is sensitive to the mechanic used, with
different cuts responding better to different mechanics. The results here show that promotions are not
only sensitive to the mechanic used but also to the cut of beef. Hypothesis three tests in further detail
how promotional response varies by specific product tier such as value or premium.
Hypothesis 3: Promotional impacts are sensitive to specific product characteristics
The third hypothesis states that the impact of promotions will vary depending upon
characteristics specific to the product. The results, when testing hypothesis two, have already
demonstrated that different promotional mechanics work better on different cuts of beef. Here we
explore this in more detail, further disaggregating the data within the specific cuts to how promotional
response varies between different tiers of product.
Table 4 reports the regression results for the roasting beef sub-groups with respect to different
price promotions within the roasting category
Table 4: Regression Results for the Roasting Beef sub-groups with respect to different Price
Promotions within the Roasting category
Standard Roasting Beef
Premium Roasting Beef
Organic Roasting Beef
Value Roasting Beef
Premium ‐ Medium Price Cut ‐130.1661** 9.872005** 4.495259** ‐4.430451**Organic ‐ Medium Price Cut 94.39361** 14.29107** 2.342127**Standard ‐ Medium Price Cut 2.265968**Standard ‐ Large Price Cut 138.0552** ‐5.520764** 10.01471**Standard ‐ Multi‐Buy ‐8.998592** 17.43943** ‐1.529242*
R‐Sq 0.174 0.061 0.381 0.119
Promotion
Coeficient (£/week)
* p< .05* * p< .01
Within the roasting sub-group, promotions had the greatest impact on sales of organic
roasting beef, as promotions were found to account for 38 percent of the variance in sales of organic
roasting beef. The r-squared values were much lower for the other sub-groups.
There were found to be differences across the tiers in terms of which promotions had the
greatest impact. Large price cuts on standard roasting beef had the greatest positive impact on sales of
standard beef. Conversely medium price cuts on premium roasting beef were found to have a
significant negative impact on sales of standard roasting beef, as well as value roasting beef. This
suggests shoppers are trading up to premium tier products away from lower tier products such as
standard and value when they are on promotion, which adds weight to theories of asymmetric brand
switching (see e.g. Krishnamurthi and Raj 1991). However, there is evidence of consumers trading
down from premium tier products as a result of large price cuts and multi-buy offers on standard
roasting beef, which goes against the theory of asymmetrical brand switching, whereby shoppers will
trade up a tier but not down.
There are some results which are hard to interpret, for example any kind of promotion within
the roasting beef fixture appears to have a positive impact on sales of organic roasting beef. One
theory behind this may be that the promotions attracted customers to the fixture initially, but some
decided to buy organic rather than the product on promotion perhaps because the product looked more
appealing.
Table 5 reports the regression results for the ground beef sub-groups with respect to different price
promotions within the ground category
Table 5: Regression Results for the Ground Beef sub-groups with respect to different Price
Promotions within the Ground beef category
Standard Ground Beef
Premium Ground Beef
Organic Ground Beef
Healthy Ground Beef
Premium - Medium Price Cut -335.089* 18.16696* -25.0329*Healthy - Multi-buy 170.707* 18.83326* 16.26799* 401.6603**Organic - Multi-buy 28.81646** -105.194**Standard - Multi-buy 378.365** 16.3145** -17.19021*
R-Sq 0.164 0.26 0.113 0.544**p<0.01*p<0.05
Promotion
Coeficient (£/week)
Within the ground beef sub-group promotions have the biggest impact in the healthy
subgroup, where promotions account for 54 percent of the variance in sales. Multi-buy promotions on
healthy ground beef have a significant positive effect on sales of healthy ground beef, but consumers
will switch to organic when on promotion. Within the standard subgroup, multi-buy offers on have a
large positive impact on sales, but consumers will switch from standard to premium when premium
ground beef is on offer. Sales of organic ground beef are negatively affected by promotions on
standard and premium products.
Harder to explain is the positive impact on premium ground beef of multi-buy promotions on
healthy and standard ground beef. However it may be that consumers, who only want one unit of the
product, switch to premium because they do not want to partake in the multi-buy promotions on
healthy or standard ground beef that they otherwise would buy. This would therefore suggest that
multi-buy offers have a negative impact on some shoppers, who will actively switch to a substitute
product as a result. Similar affects are observed elsewhere in the sub-group, for example standard
multi-buy promotions apparently increase sales of premium ground beef.
Table 6 shows the regression results for the fry/grilling beef sub-groups with respect to different Price
Promotions within the fry/grilling category
Table 6: Regression Results for the Fry/Grilling Beef sub-groups with respect to different Price
Promotions within the Fry/Grilling category
Standard Fry/Grilling Beef
Premium Fry/Griling Beef
Organic Fry/Grilling Beef
Premium ‐ Small Price Cut 165.1634** ‐12.06273**Premium ‐ Medium Price Cut 78.9555** 24.25732**Premium ‐ Large Price Cut 140.2161** 26.59584** ‐6.521285*Organic ‐ Small Price Cut ‐91.78317** ‐11.73351** 54.66264**Organic ‐ Medium Price Cut 82.41607** ‐27.19562** 36.62011**Standard ‐ Small Price Cut 84.8706** 14.97713**Standard ‐ Medium Price Cut 141.8864** 6.438946** 17.52011**
R‐Sq 0.567 0.465 0.341
Promotion
Coeficient (£/week)
* p< .05* * p< .01
Of all the beef sub-groups, promotions had the biggest impact on sales for fry/grilling beef.
The R-squared values are generally much higher for fry/grilling sub-groups than within the ground
and roasting beef categories. Promotions accounted for about 57 percent of the variance in sales of
standard fry/grilling beef. Interestingly the results imply that it was promotions within other
fry/grilling sub-groups which had as much of an impact on sales of standard products, as promotions
within the standard sub-group itself. The promotions having the greatest positive impact on sales of
standard fry/grilling products were small and large price cuts on premium beef, and medium price
cuts on standard beef. Small price cuts on organic beef were the only promotion to negatively impact
upon sales of standard beef. The reason so many promotions within other tiers of fry/grilling beef had
such a positive impact on sales of standard beef may be due to the heavy presence of promotions
within the fry/grilling category overall which led to several promotional clashes across tiers.
Sales of premium fry/grilling beef were negatively affected by promotions on organic
fry/grilling beef, and vice versa sales of organic beef were negatively affected by premium
promotions. This indicates organic and premium products are close substitutes and shoppers will swap
one for the other when a promotion occurs.
The results provide evidence to support hypothesis three. There were found to be differences
in promotional response across cuts and price tiers of product. The results also support hypothesis
two, in that there were differences in response depending upon the promotional mechanic used.
Hypothesis 4: Promotional impacts are sensitive to the demographic characteristics of
specific shopper segments
One of the benefits of panel data over and above scanner data is the ability to observe how
behaviour varies between consumer segments. Loyalty card data particularly leans itself towards such
analysis as retailers can create detailed segmentations of their shoppers. The literature review revealed
that it was thought characteristics such as age and household size can impact upon promotional
response. Therefore the dataset was also used to determine how different shoppers responded to the
promotions based upon their lifestage. Tables’ 7A-C report the results of the regression analysis by
life-stage segment for the standard roasting beef, standard ground beef and standard fry/grilling beef
sub-groups with respect to the various promotions. The
Table 7A: Regression Results by Consumer Life-stage for the Standard Roasting Beef sub-group
Older Adults Young Adults Older Families Young Families PensionersPremium ‐ Medium Price Cut ‐147.9276** ‐119.2251** ‐174.8232** ‐161.3345** ‐47.51983**Organic ‐ Medium Price Cut 82.17182** 90.74004** 140.5603** 134.0492** 24.44671**Standard ‐ Large Price Cut 191.0175** 116.3995** 189.5294** 120.0747** 73.2548**
R‐Sq 0.1582 0.1637 0.1969 0.1739 0.1089
Promotion
Coeficient (£/week)
* * p< .01* p< .05
Of the five life-stage segments, sales value per store for standard roasting beef in response to a
large price cut increases the most for older adults and older families, and the least for pensioners.
Sales of standard roasting beef fell in response to medium price cuts on premium roasting beef across
all segments. Sales value increased the most in response to the price cuts on premium roasting beef
for Families, both older and younger.
Table 7B: Regression Results by Consumer Life-stage for the Standard Ground Beef sub-group
Older Adults Young Adults Older Families Young Families PensionersPremium ‐ Medium Price Cut ‐221.1436** ‐439.846** ‐670.0788** ‐95.0449**Healthy ‐ Multi‐buy 248.365** 122.1197**Standard ‐ Multi‐buy 246.5703** 363.3129** 556.3392** 666.7191**
R‐Sq 0.1419 0.1282 0.1263 0.1332 0.1021* * p< .01* p< .05
PromotionCoeficient (£/week)
Standard multi-buy promotions had a large and positive effect on sales of standard ground beef within
all segments except Pensioners. The segment for which sales value increased the most in response to
the standard ground beef multi-buy promotion was young families. Similarly the young families were
the segment for which sales of standard ground beef fell the furthest in response to the price
promotions on premium ground beef. Sales value of standard ground beef amongst both older adults
and pensioners actually increased in response to multi-buy promotions on healthy ground beef. This
indicates that shoppers within these segments were put off by the multi-buy offer on healthy ground
beef, which perhaps they normally purchase, and instead switched to buying standard ground beef.
Older adults and pensioners perhaps do not want to purchase multiple packs of ground beef since they
do not have families to feed, so switch their spend to a product not on offer.
Table 7C: Regression Results by Consumer Life-stage for the Standard Fry/Grilling Beef sub-group
Older Adults Young Adults Older Families Young Families PensionersPremium ‐ Small Price Cut 150.0513** 194.8505** 212.9796** 231.7815** 36.15398**Premium ‐ Medium Price Cut 73.65662** 67.02216** 113.3383** 109.122** 31.63837*Premium ‐ Large Price Cut 138.6866** 122.4337** 207.6007** 174.8731** 57.48661**Organic ‐ Small Price Cut ‐72.80364** ‐100.5544** ‐135.1511** ‐137.3172**Organic ‐ Medium Price Cut 54.00508** 106.0432** 124.3196** 124.1518**Standard ‐ Small Price Cut 78.66549** 78.75664** 144.0243** 108.3398**Standard ‐ Medium Price Cut 114.4792** 115.811** 222.2258** 223.0028** 33.91312*
R‐Sq 0.3162 0.3071 0.3365 0.3031 0.2923
PromotionCoeficient (£/week)
* * p< .01* p< .05
Looking at the promotions specifically on standard fry/grilling beef, sales value increased the
most amongst Older families in response to small price cuts, while spend amongst young families
increased the most in response to medium price cuts. Spend by families also increased by the most in
response to small price cuts on organic beef; with sales of standard fry/grilling beef declining the most
for both young and older families. Promotions in the fry/grilling category had the smallest effect on
sales amongst pensioners.
CONCLUSIONS
The main purpose of this paper was to demonstrate that supermarket loyalty card data can be
used to generate unique empirical insights into the effectiveness of promotions. Retailers and
manufacturers need to move away from the notion that one promotion fits all and instead focus their
efforts on targeting promotions effectively and using those mechanisms which will yield the best
results for the particular product or category in question. The use of loyalty card schemes amongst
retailers worldwide is increasing, which means more detailed analysis of promotional impacts, of the
kind reported here is becoming increasingly possible.
One of the main points arising from the results is the considerable variability in the impact of
different promotion mechanics between and within the different sub-groups. This illustrates the point
that one promotion does not fit all and promotional strategies should take greater notice of the
effectiveness at the individual product level, to avoid devaluing the product category. In the example
used for this paper, there were found to be differences in which promotions work best within each cut
and tier within the beef category, which could not be captured by analysis of highly aggregated data.
Indeed the r-squared values in the examples used in this paper were generally higher for the analysis
carried out at the disaggregated level, than at the total category level. Much of the promotional
research, not just in the meat category, has used highly aggregated data on product categories to
measure promotional impacts where in fact very different results may have been observed if the data
used had been at a more disaggregated level.
The research has also demonstrated how promotional response varies across life-stage
segments. Generally spend amongst families increased the most in response to promotions, and for
pensioners the least. This result is perhaps unsurprising, but some interesting points have arisen. For
example pensioners and older adults appear to be put off multi-buy promotions within the ground beef
category, to the extent that they switch their purchases to standard ground beef in response to multi-
buy offers on healthy ground beef. Further research across other grocery categories using panel data is
necessary for retailers to have a broad understanding as to how different segments respond to enable
them to plan and target promotions more effectively. There is a huge gap in the literature at present
for such research, particularly using large scale panel datasets.
The findings in this paper have also contributed further to the literature on brand switching.
Existing literature is unanimous in the belief that if a lower tier brand is promoted it does not attract
customers from high-tier brands, but the promotion of higher quality, premium priced brands impacts
significantly upon weaker brands (see e.g. Kumar and Leone 1988; Krishnamurthi and Raj 1991;
Mulhern and Leone 1991; and Martínez-Ruiz et al. 2006a). While the results from the ground beef
sub-group backed up this theory with sales of standard ground beef falling considerably in response to
promotions on premium ground beef, some other results were conflicting. For example, while
medium price cuts on premium roasting beef were found to have a significant negative impact on
sales of standard and value beef, there was evidence of consumers trading down from premium tier
products as a result of large price cuts and multi-buy offers on standard roasting beef. These findings
suggest that while the theory of asymmetric can be applied in some circumstances, it does not apply
for all products categories.
This paper significantly contributes to previous literature in the subject of promotions through
analysing the impacts using a data set unique in both its size and scope, and which has not previously
been used for such a purpose. Loyalty card panel data is now more accessible than ever to many of the
major retailers, but so far it is not being used to the best of its potential to inform decision making as
to which promotions will be most effective. As can be seen from the results, some promotions have a
greater impact than others, but retailers need to understand these differences in order to implement
promotions effectively. There is much scope to expand upon this research through adding additional
variables to the model, such as seasonality and merchandising, as well as using other product
categories.
LIMITATIONS AND AREAS FOR FURTHER RESEARCH
There were several limitations to this study which can be built upon and developed with
future research. The r-squared values were relatively low, which tells us that there are many other
factors influencing sales at the same time as promotions. For example point of sale displays,
advertising and merchandising taking place at the time of promotion. If some products on promotion
are being positioned on aisle ends or being heavily advertised, then this is likely to increase the impact
of the promotions since more people will be aware of the promotion. Research by Sethuraman and
Tellis (2002) found there to be a positive relationship between the advertising and the response to
promotions, but other research has contradicted this (Nijs et al 2001). Ailawadi et al (2009) stress the
need for further research in this area to enable retailers to effectively account for manufacturer
advertising in their promotion budgeting decisions. Since this information was unavailable, it was not
possible to consider how the positioning or advertising of promoted products influenced the impact on
sales in this paper; however this is a variable which can be included in the model in the future and
could yield very insightful results using such a large scale panel dataset.
A further limitation to the research carried out is that it does not consider any additional
benefits that promotions within the red meat category brought to the retailer. Even if a promotion does
not increase the value of the category or a particular promoted sub-group, it is still possible that the
retailer is benefiting from increased footfall as a result the promotions and shoppers are spending
money elsewhere in the store. However, these possible indirect effects of promotions to the retailer do
not benefit the primary producers who inevitably lose out, particularly if promotions are de-valuing
products within the overall category.
The next step following this research is to further develop the model through adding
additional variables to attempt to capture more of the variables influencing sales in addition to
promotions, such as seasonality and in-store merchandising. It is hoped this will strengthen the model
and further demonstrate the value in such analysis by retailers worldwide using their own loyalty card
data to create more sustainable and profitable promotional strategies. The model used here is just one
of several which could be used to analyse the impact of promotions with supermarket loyalty card
data. Further research will enable us to test other models and compare the results to ultimately identify
which model is most appropriate to analyse the promotional impacts.
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