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Are Loyal Store Brand Users Less Store Loyal?
SATHEESH SEENIVASAN K. SUDHIR
DEBABRATA TALUKDAR*
January 2012
First Draft: August 25, 2009
______________________ * Satheesh Seenivasan is Lecturer, Department of Marketing, Monash University, Caulfield, Vic 3145, Australia. Ph: +61-399034184 (e-mail: satheesh.seenivasan@monash.edu). K. Sudhir is James L. Frank Professor of Private Enterprise and Management, Yale School of Management, New Haven, CT 06520. Ph: 203-432-3289 (e-mail: k.sudhir@yale.edu). Debabrata Talukdar is Professor of Marketing, School of Management, State University of New York at Buffalo, Buffalo, NY 14260. Ph: 716-645-3243 (e-mail: dtalukda@buffalo.edu). The authors are in alphabetical order and all contributed equally. The authors thank Marcel Corstjens, Vithala Rao and Jiwoong Shin for their comments on the paper.
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Are Loyal Store Brand Users Less Store Loyal?
Abstract
Do store brands help differentiate a store to attract store loyal consumers? Or do they
attract price sensitive cherry pickers who are not store loyal? To answer these questions
empirically, the authors construct appropriate metrics of store brand loyalty and store loyalty,
that do not impose mathematical relationships between the two variables—a problem with recent
works in this area. Using data from multiple sources, multiple retailers, and controlling for
potential spurious correlations due to differences in levels of grocery spending and household-
store spatial configurations, the authors demonstrate a strong and robust positive monotonic
relationship between loyalty to store brands and store loyalty, providing support for the store
differentiation rationale for store brands. Further, they demonstrate a link between store brand
quality and store loyalty-- premium store brand patrons are more loyal than regular store brand
patrons—an incentive for retailers to invest in store brand quality. Finally, loyalty to store brands
in highperceivedrisk,stapleandnon‐hedoniccategoriesleadstogreaterstoreloyalty
relativetolowrisk,non‐stapleandhedoniccategories.
Keywords: Store brands; store loyalty; store differentiation; retail competition; premium private
labels.
2
Store or private label (PL) brands have successfully evolved from being a just another
low priced alternative to a widely accepted brand class of their own. They have traditionally been
very successful in Europe with shares of over 25% in major European markets (IRI 2008). In the
United States, store brand share has traditionally lagged behind Europe, but has caught up with
Europe during the recent recession. With sales of $88.5 billion in 2010, store brands now account
for almost 25% of unit sales (PLMA 2011). Further, store brands have also gained in consumer
esteem with almost 77% of American consumers considering them to be as good as or better than
national brands (PLMA 2011).
Retailers continue to invest in growing store brands. According to a recent Deloitte study,
85% of retail executives are paying more attention to building their store brands and 70% of
them are investing in innovation of store brand products (Deloitte 2010). For example, Sainsbury
in the UK, launched 1,300 new store brand products and improved a further 3,500 in 2010
(Sainsbury 2010); the French retailer Carrefour plans to increase its store brand market share
from 25% to 40% by adding more than 1500 new products and redesigning its store brand
packaging (Store Brands Decisions 2010). In the US, Wal-Mart and Kroger (with 35% of sales
from store brands) revamped their store brand lines to increase market share (Forbes 2010).
There are many reasons for retailers to invest in store brands. For instance, store brands
provide greater margins to retailer (e.g., Ailawadi and Harlam 2004; Meza and Sudhir 2010) and
improve retailers’ bargaining power with respect to manufacturers to help negotiate lower
wholesale prices (Scott-Morton and Zettelmeyer 2004; Meza and Sudhir 2009). In this paper we
explore a third reason for why retailers vigorously support store brands: their potential ability to
ameliorate retail competition. According to the Private Label Marketing Association (PLMA),
“retailers use store brands to … win the loyalty of its customers” (PLMA 2007). The argument is
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that store brands serve to differentiate a store and create store loyalty because of their exclusivity
to specific retail chains (Richardson, Jain and Dick 1996). Based on game theoretic analysis,
Corstjens and Lal (2000) show that store brands can generate store differentiation and loyalty as
long as their quality is high enough to satisfy a significant proportion of consumers, inducing
them to purchase again. This store differentiation ability is attributed to the store exclusivity of
store brands and/or consumers’ inherent brand choice inertia.
Sudhir and Talukdar (2004) find support for the store differentiation argument by
estimating a positive linear relationship between store brand loyalty in terms of store or PL brand
share of store spend and store loyalty in terms of store’s “share of wallet” (SOW). Ailawadi,
Pauwels and Steenkamp (2008) however show an inverted U shaped relationship between PL
share of store spend and SOW, suggesting that the store differentiation motivation may only go
so far; beyond a threshold share for store brands, continued investments in private label brands
by retailers may be counterproductive. The authors note: “Retailers are making a concerted effort
to grow their PL, but the inverted-U shaped relationship between PL share and SOW shows that
even for a high quality PL program, one can overdo it.”
But retailers around the globe continue to invest in store brands, even after having
attained high levels of store brand share. Is their continued faith in the store differentiation role
of store brands misguided? Or do they make investments in store brands to enjoy the superior
margins and increased bargaining power with respect to manufacturers, even if it reduces the
loyalty to the store? In this paper we revisit the “store brand as a differentiator” rationale by
investigating the relationship between store brand loyalty and store loyalty, allowing for
potential nonlinear effects.
4
The paper addresses three substantive questions related to the store differentiation role of
store brands: First, are households who are loyal to store brands more store loyal? We allow for
the possibility of a nonlinear inverted U shaped relationship, but find the effect to be
monotonically positive. Given the conflicting past findings on this important managerial issue,
we assess the robustness of this result along multiple dimensions. We demonstrate that the result
is robust to data from multiple sources and multiple retailers. We also control for a number of
variables that might lead to potential spurious correlations between store brand loyalty and store
loyalty. For example, we control for the level of total grocery spend, because a household who
spends a lot on groceries will also generally spend more (thus, exhibit higher loyalty) on store
brands; but such correlation is spurious for our purposes. Similarly, another possible common
factor driving the store brand loyalty - store loyalty relationship may be the geographic
configurations of supermarkets and households. If a store location is convenient to a household,
this alone may lead to higher store loyalty and store brand loyalty for that household even if
there is no direct relationship between the two variables. Controlling for such variables, we still
find a robust positive monotonic relationship. Finally, we disaggregate data to a finer quarterly
frequency and find that even lagged store brand loyalty of a household has a strong positive
monotonic relationship to current levels of store loyalty, providing further evidence of a causal
relationship.
Second, we address the impact of store brand quality on store loyalty. Retailers continue
to invest substantially in improving store brand quality; in a recent survey, two-thirds of retailers
stated that they are increasing their offerings of premium store brands (Deloitte 2010). Do these
investments translate to greater store loyalty? While theoretical researchers (e.g., Corstjens and
Lal 2000) and practitioners (Deloitte 2010) suggest that greater store brand quality will lead to
5
greater store loyalty, there is little empirical work addressing the question. We find support for
such a link.
Third, we address the question of how the link between store brand loyalty and store
loyalty varies by category. We develop and test conjectures for three category characteristics
(high perceived risk, hedonic, staple categories) based on past studies on store brands at the
category level. Batra and Sinha (2000) find that store brand purchases are higher in categories
where consumers perceive lower risk of making a mistake. Similarly, Narasimhan and Wilcox
(1998) find that in categories with high perceived risk, it will be difficult to convince consumers
to purchase store brands and hence manufacturers will be less inclined to reduce their wholesale
prices in the event of a store brand entry. Sethuraman and Cole (1999) find that consumers will
be more willing to pay premium prices for national brands in hedonic categories. Thus store
brands have a natural disadvantage in high perceived risk and hedonic categories. Our
conjecture is that a household that is loyal to store brands in such high perceived risk and
hedonic categories, despite natural disadvantages for store brands is likely to be more store loyal
overall. Staple categories are those where consumers purchase frequently and routinely and
spend a large portion of their shopping budget (Dhar, Hoch and Kumar 2001). We conjecture
that households loyal to store brands (which are only exclusively available at the store) in staple
categories, are likely to be more store loyal.
A challenge in measuring the empirical relationship between store brand loyalty and store
loyalty is that one needs metrics that are not mathematically related by definition. There are two
issues in the extant literature. First, store loyalty is based on store spend, while store brand
loyalty is based on store brand spend in both Sudhir and Talukdar (2004) and Ailawadi, Pauwels
and Steenkamp (2008). But since store spend = store brand spend + national brand spend by
6
definition, there is an in-built positive mathematical relationship between the two metrics, which
renders the empirical interpretation of a positive relationship between the two metrics suspect.
Further, Ailawadi, Pauwels and Steenkamp (2008) define store brand loyalty = store brand
spend / store spend, while defining store loyalty= store spend/total spend across stores. With
these metrics, the dependent variable store loyalty is mathematically negatively related to store
brand loyalty, because store spend is in the numerator of the dependent variable and in the
denominator of the independent variable. Thus the metrics used in Ailawadi, Pauwels and
Steenkamp (2008) have both a positive and negative mathematical relationship built-in,
potentially leading to an inverted-U shaped relationship between store brand loyalty and store
loyalty. In this paper, based on the conceptual foundations underlying the definitions of
behavioral loyalty, we argue for a new metric of store brand loyalty that does not mathematically
induce a negative correlation with store loyalty, and also for a new metric for store loyalty that
does not include store brand spend, to avoid the positive mathematical relationship. Using our
revised metrics, we are able to demonstrate that there is indeed a strong and robust empirical
monotonic relationship between store brand loyalty and store loyalty, providing support for the
differentiation role of store brands.
The rest of the paper is as follows. We next describe the data and the variable
operationalization. We follow this with our empirical analyses and discussion of the results.
Data and Variable Operationalization
The focal retailer in our study is a large supermarket chain in northeastern United States
which carries store brands in 125 of the total 299 categories it offers. The average store brand
7
share of households at the focal retailer is about 19%, which is in line with the U.S. national
average. We employ two different data sources in our study. First, we use scanner data provided
by the focal retailer which covers transactions in all the categories carried by the retailer for a
period of two years (2006 and 2007). In this dataset, we focus on 517 households for whom we
also have attitudinal variables from our household survey. Our second data source is a Nielsen
panel dataset comprising of transactions made by 569 households in food categories across all
stores in the same market for the year 2006.1 With this Nielsen panel data, we test the store brand
loyalty - store loyalty relationship for the same focal retailer as in the first data set, as well as for
another leading retail chain in the same market. Additionally, we use retail competition and store
information from Nielsen Spectra database to control for store characteristics.
As discussed in the introduction, we need to construct appropriate metrics of store brand
loyalty and store loyalty that do not have mathematically inbuilt relationship between the two
variables. Conceptually, we want to test if there is an empirical relationship between the extent
of store brand spend and store spend. There are two key issues in directly testing this
relationship. First, there is a potential spurious correlation between the two variables that is
moderated by the total level of grocery spend. We can control for household level differences in
the level of grocery spend, by normalizing both the store brand spend and store spend of
households by their respective total grocery spend giving rise to store brand share (a proxy for
store brand loyalty) and store loyalty respectively. However, since store spend = store brand
spend + national brand spend, there is still an in-built positive relationship between the two
variables. To avoid this correlation of store spend with store brand spend, we consider store
spend only in the 174 out of 299 categories that do not have store brands, in measuring store
loyalty. Thus our metrics of store brand loyalty and store loyalty do not have any built-in 1 We thank Tom Pirovano and Phil McGrath of Nielsen for providing us access to this data.
8
mathematical relationships; further by normalizing the store brand and store spends by total
grocery spend, we also remove any spurious correlations.
For the scanner data from the focal retailer, we only have households’ purchase data at
the retailer and not at competing stores. For this dataset, we use two different estimates of
households’ total grocery spend. First, we use estimated grocery spend of households at the level
of each census block group (CBG), conditional on observable demographic characteristics,
supplied by an independent marketing firm. Second, we use stated weekly grocery spending of
households from our survey. For the Nielsen panel data set, we have the complete purchase
history of households across all retailers; therefore we have the true spend data of households
instead of estimated spend. We test whether our results are robust and consistent across both
datasets and across different metrics of total spend. In this context, it is relevant to note that
retailers typically do not have the purchase information of their customers at competing stores
and thus rely on such third party estimates to determine the store loyalty of their customers. By
testing whether our results using the more widely available third party information is consistent
with the results from the stated spend and true spend data, we seek to provide practitioners and
researchers guidance on whether the third party estimates of spend are likely to be of practical
use when studying issues relating to retail “share of wallet” for households.
We control for several store characteristics and demographic factors that can influence a
household’s store choice and store brand choice decisions. Nielsen’s Spectra data provides us
with store characteristics like sales area and number of checkout counters. For each sample
household, the retail chain provides us with information about distances of the household from
the nearest own and competing stores and their respective inter-store distance. We use revealed
measures from scanner data for household specific deal proneness, manufacturer coupon share,
9
and price differential between national brand and store brands. Further, we also use attitudinal
variables like households’ store brand perception, shopping enjoyment and stated brand loyalty
from survey data for our empirical analysis.
Besides objective factors like distance and competition, attractiveness of a store to a
household also depends on hedonic attributes like service quality, in-store environment etc.,
which are difficult to quantify even with proxy variables. To capture how attractive a store is to
the neighborhood where a household resides, we use average store loyalty of all households in
the focal household’s census block group as an additional control variable. This variable also
accounts for neighborhood influence in a household’s store choice decision.
To understand the role of store brand quality in accentuating a retailer’s store
differentiation ability, we study whether premium store brand patrons exhibit more loyalty to the
retailer for the same level of store brand loyalty. We identify premium store brand patrons using
the proportion of store brand spending on premium store brands. The retailer under consideration
carries store brands under four different “premium” brand names, besides the regular store
brands under the retailer’s name. These premium brands are priced significantly higher (p<0.05)
compared to regular store brands and have a smaller price differential relative to national brands.
They also have better packaging and are available in categories like organics, health and beauty
care, etc. where quality is of paramount importance,2 indicating that the focal retailer uses these
brands to signal higher quality and differentiate them from the regular store brands. We
operationalize premium store brand patrons as the top quartile of customers in terms of spending
on premium store brands as a proportion of their total store brand spending at the focal retailer.
For studying the moderating role of category characteristics, we classified product
categories at the focal retailer as hedonic/non-hedonic and risky/non-risky using hedonicity and 2 The focal retailer offers either regular or premium store brand in a category, but not both.
10
perceived risk scores obtained from a survey of 60 undergraduate students. Top quartile of
categories in terms of their hedonicity and perceived risk scores are identified as hedonic and
risky categories respectively. We then calculated the share of store brands in hedonic and risky
categories for each sample household and classified the top quartile of households with high
store brand share in the respective categories as hedonic and risky store brand patrons
respectively.
For classifying product categories as staple or non-staple, we combine household level
category purchase frequency information with Nielsen’s national level category penetration data
to identify household specific staple categories. Using a median split of purchase frequency and
penetration, we group categories with high purchase frequency and high penetration as staple and
the rest as non-staple for each household (Dhar, Hoch and Kumar 2001). Then we compute the
households’ store brand share in staple categories at the focal retailer and, classify the top
quartile of households in terms of store brand spend in staple categories as staple store brand
patrons. Details of operationalization of the variables are provided in Table 1.
(“Insert Table 1 about here”)
Empirical Analysis
We use the following structure for our empirical analysis. First, we begin by analyzing
the store brand loyalty-store loyalty relationship. We begin with simple descriptive analysis
using scatter plots and follow it up with a simultaneous equations analysis with a large number of
robustness checks. We follow that analysis by testing the hypothesis about how store brand
quality and category characteristics moderate the store brand loyalty-store loyalty relationship.
11
The Store Brand Loyalty-Store Loyalty Relationship
We begin with an exploratory analysis of the relationship between consumers’ store
brand spend and their store spend. The scatter plot of store brand spend versus store spend in
figure 1a indicates that consumers who spend more on the focal retailer’s store brand also tend to
spend more at the retailer. Figure 1b shows a similar plot as figure 1a, but with only store spend
in categories without store brands (i.e., national brand spend). Both plots show a clear
monotonic relationship, suggesting evidence for a link between store brand loyalty and store
loyalty. To check the possibility that this monotonic relationship is due to a common third
variable (total spend in groceries), we normalize store brand spend and store spend by total
grocery spend, to obtain store brand share (loyalty) and store loyalty metrics. The scatter plot of
the two variables in figure 1c shows that this relationship is also positive and monotonic.
(“Insert Figures 1a, 1b and 1c about here”)
Following this bi-variate analysis, we estimate a system of two simultaneous equations
for store brand share and store loyalty that allows for potential reverse causality in their
relationship. Further, we also allow for potential non-monotonic relationship by including both
the linear as well as quadratic terms of the focal variables. The complete specification of our
base model with attitudinal variables is presented below.
1 StoreLoyalty
α α SBShare α SBShare α SalesArea α Dealproneness
α Counters α ShoppingEnjoy α StoreDistance α Education
α Income α HHsize α Age α CBGLoyalty α Year ε
12
2 SBShare
β β StoreLoyalty β StoreLoyalty β NBLoyalty β SBImage
β NB_SBDiff β Dealprone β ShoppingEnjoy β Education
β Income β HHsize β Age β ManufCpnShr β Year ε
In this specification, identification is achieved through exclusion restrictions, i.e., store
loyalty equation has four variables excluded in the store brand share equation - distance to store
(StoreDistance), sales area (SalesArea), number of checkout counters (Counters) and CBG
loyalty (CBGLoyalty). These four variables influence a household’s store loyalty by affecting
the attractiveness of the store overall, without any direct impact on household’s preferences for
its store brands Similarly, the store brand share equation is identified by four variables excluded
in the store loyalty equation - national brand-store brand price differential (NB_SBDiff),
manufacturer coupon share (ManufCpnShr), stated national brand loyalty (NBLoyalty) and
retailer-independent perception of store brand image (SBImage). In addition, we also include
squares and cross products of exogenous variables as additional instruments (Wooldridge 2002).
The two-stage least squares estimates for the simultaneous equations are reported in
Table 2. Consistent with our exploratory analysis findings, only the linear store brand share term
is positive and significant and therefore, households with high store brand share are also store
loyal. In the store brand share equation, only the effect of linear store loyalty term is significant
indicating that store brand share also increases monotonically with store loyalty.
One concern here is that the dependent variables being shares, lie between 0 and 1 and
hence the standard regression assumptions may not hold. We repeat the analysis with logistic
transformation of dependent variables and the results are qualitatively invariant for this analysis.
As shares are more interpretable (and capture the intuition in the scatter plots better), we report
13
regressions with shares directly rather than results with logistic transformation. The scatter plots
in figure 1 should reassure us further, that the linear relationship is consistent with the data.
(“Insert Table 2 about here”)
For completeness, we note that the other control variables in the regressions have
expected signs. Distance from the household to store has a negative impact on store loyalty
suggesting that households patronize their nearest retailer. Also, the deal proneness of a
household has significant negative impact on store loyalty as deal prone households are likely to
price search across multiple retailers. Sales area of a store positively influences store loyalty.
Also, we find that high income households with high opportunity costs of time tend to be loyal to
one retailer. As expected, average store loyalty of all households in the neighborhood (CBG) is
positively related to the store loyalty of the household. Among the control variables in the store
brand share equation, we find that deal prone households and those with positive attitude towards
store brands are likely to have high store brand share. Finally, household age and income are
negatively related to its store brand share.
Robustness Checks
We check the robustness of the monotonic positive relationship between store brand
share and store loyalty in four ways. First we address the potential concern that the estimate of
total grocery spend from the third-party firm may not be accurate. We therefore test the
relationship with two alternative estimates of total grocery spend and from multiple data sources.
Second, we test whether the relationship generalizes to a second major competing retailer in the
same market, so as to provide reassurance that both retailers benefit from store brands through
greater store differentiation. Third, we control for potential spurious correlation due to the
household-store spatial configuration. Finally, we test the causal nature of the relationship by
14
disaggregating the data into quarterly periods and testing whether lagged quarter store brand
share positively impacts current quarter store loyalty.
Alternative metrics of total grocery spend. First, we repeated our analyses with store loyalty and
store brand share values calculated using stated total grocery spend of households instead of the
third-party estimated grocery spend. The results based on the stated grocery spending are
consistent with the main results described earlier.
We further check the robustness of our results with Nielsen panel dataset comprising of
transactions made by 569 households in food categories across all the stores during the year
2006. The complete purchase history of households in this dataset allows us to also use actual
total grocery spend of households instead of estimated spend thereby yielding greater faith in our
metric of store brand share and store loyalty. The results for this analysis are provided in the
second column of Table 3.
(“Insert Table 3 about here”)
Generalizability of the relationship to a competing retailer. We use the Nielsen dataset to assess
the robustness of the store brand loyalty-store loyalty relationship at the second major competing
retailer in the same market.3 The estimates in Table 3 shows that only the linear store brand share
term is significant in the store loyalty equation, indicating a positive monotonic relationship
between store brand share and store loyalty, consistent with the finding for the first retailer. The
fact that we find that their respective store brands serve to differentiate the two biggest
competing retailers in the same market gives us greater faith in the store differentiation role of
store brands.
3 The caveat is that the Nielsen panel dataset covers only food categories unlike our main data that is based on all grocery categories. Also, we do not have attitudinal information for the Nielsen data; so the variables NB-SB price differential, store brand perception, shopping enjoyment and CBG loyalty are not used for this analysis.
15
Control for household-store spatial configuration. As discussed in the introduction, the spatial
configuration of the store and household could potentially induce a spurious correlation between
store brand share and store loyalty. For example, if a household is price sensitive and therefore
wants to buy store brands, but only one store is proximate to the household, the correlation
between store brand share and store loyalty might be induced by the spatial configuration. To
address this concern, we now control for spatial configuration effects.
We draw on the literature on the role of spatial configuration in household’s search
behavior and characterize a household’s spatial configuration using a three dimensional vector
(D12, D1, D2), which captures the distance of the household from its two closest competing stores
and the inter-store distance between these stores (Gauri, Sudhir and Talukdar 2008). Here D12
refers to the distance between the competing stores; D1 is the distance of the household from
focal store while D2 refers to its distance from the competitor. Following Gauri, Sudhir and
Talukdar (2008), we classify the inter-store distance as small (D12 < .3 miles) and large (D12 >
2 miles). Similarly, the distance of households from the two stores are classified as small (<= 1.8
miles) or large (> 1.8 miles) resulting in five different spatial configurations specified as LLL,
LSL, LLS, SLL and SSS. Under this specification, a household type of LSL implies that the
household is located closer to the focal store, away from competing store and the inter-store
distance is large.
Among these households, those of type SSS and SLL can easily engage in cross-store
shopping because of the smaller inter-store distance between the focal retailer and its competitor.
Similarly, LLS households, for whom the competitor is closer are likely to have lower loyalty to
the focal retailer. Yet if these households have high store brand share, this reflects relatively
strong preference for the store brand and thus store brands perform a differentiation role. If store
16
brand induce store differentiation, we should see a more positive relationship between store
brand share and store loyalty for these households relative to households in other spatial
configurations. Conversely, if the positive store brand loyalty – store loyalty relationship is
driven by spatial configuration, then we would expect a lower store brand loyalty – store loyalty
relationship for these households. The results are presented in the middle column in Table 4.
(“Insert Table 4 about here”)
As expected, the households of type SSS, SLL and LLS have lower average store loyalty
compared to other households who do not have as much shopping options. But consistent with
the store brand’s differentiation role, the relationship between store brand share and store loyalty
is stronger for these households indicating that those who patronize focal retailers’ store brand
when other shopping options geographically close are even more loyal to the retailer for the
same level of store brand share. This result further supports the store differentiation role of store
brands and rules out the possibility that the positive relationship between store brand share and
store loyalty is driven by household’s spatial configuration with respect to stores.
Lagged store brand share – store loyalty relationship. Next, we explore the dynamics of the
causal relationship between store brand loyalty and store loyalty by directly testing for Corjstens
and Lal’s (2000) proposition that positive consumer experience with a retailer’s store brand
would lead to increased store loyalty in the next period. A consumer who purchase a retailer’s
brand and is satisfied with its quality will have to visit that retailer to purchase these brands in
the subsequent period; given that store brands are exclusive to a retail chain. Therefore,
household’s store brand purchases in a time period would be predictive of their future store
loyalty. For this, the issue of simultaneity doesn’t arise as store brand share and store loyalty of a
household are measured at two different time windows.
17
To allow such a dynamic analysis, we disaggregate the annual measurements we used in
our main analysis based to quarterly level measurements, providing us 8 quarterly panel
observations (over the 2 year period) per household. We use the primary scanner panel dataset
from the focal retailer for this regression; the results are presented in Table 5. Again, we find a
monotonic positive relationship between store brand share and store loyalty, consistent with our
findings from the simultaneous equation model. It should be noted that though both linear and
quadratic store brand share terms are significant, the relationship between store brand share and
store loyalty is monotonic for feasible range of store brand share values (0 to 1). Thus, store
brand purchases are also predictive of the future store loyalty of households, thereby
substantiating the store differentiation role of store brands.
(“Insert Table 5 about here”)
In summary, all of the robustness checks are consistent with the store differentiation role
of store brands identified in our primary analysis.
Effect of Store Brand Quality and Category Characteristics
Having established the primary store differentiation role of store brands, we test how
store brand quality and category characteristics moderate the link between store brand share and
store loyalty. To test the effect of store brand quality, we test whether for a given level of store
brand share, a premium store brand patron has greater store loyalty. Similarly, for categories, we
test whether for a given level of store brand share, households that disproportionately purchase
store brands in hedonic, high perceived risk and staple categories exhibit greater store loyalty.
The model specification including the interaction effect for store brand quality and category
characteristics is shown below.4 The results are reported in the rightmost column in Table 4.
4 We omit the quadratic SB Share term as it is not significant.
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3 StoreLoyalty
α α SBshare α SBshare ∗ Premiumpatron α SBshare
∗ Hedonicpatron α SBshare ∗ Riskpatron α SBshare
∗ Staplepatron α SalesArea α Dealproneness α Counters
α ShoppingEnjoy α StoreDistance α Education α Income
α HHsize α Age α CBGLoyalty α Year ε
4 SBShare
β β StoreLoyalty β StoreLoyalty β NBLoyalty β SBImage
β NB_SBDiff β Dealprone β ShoppingEnjoy β Education
β Income β HHsize β Age β ManufCpnShr β Year ε
The interaction between store brand share and premium store brand patron dummy is
significantly positive indicating that for the same level of store brand share, customers who
predominantly buy premium store brands have higher store loyalty than those buying regular
store brands. However, the focal retailer in our study offers premium store brands only in a few
categories. To rule out the possibility that premium store brands are offered only in categories
conducive to these brands thereby leading to a stronger relationship with store loyalty, we
compared the store brand shares in premium and regular categories. We find that the mean store
brand share in premium store brand categories (mean=15%, SD=.078, n=14) is less than that in
regular store brand categories (mean=25.98%, SD=.208, n=92), indicating that premium store
brands are not just offered in categories conducive to these brands.
In addition, we also examined whether premium store brand patrons are simply heavy
users of the categories where these brands are offered rather than those interested in quality. An
examination of category wise store brand shares shows that premium store brand patrons have
19
lower store brand share in regular categories (18.56%) compared to premium store brand
categories (25%) whereas their counterparts have higher store brand share in regular categories
(20.68%) versus premium store brand categories (5.57%). Together, these findings indicate that
premium store brand patrons are interested in high quality store brands as they purchase higher
proportion of store brands in premium store brand categories. On the other hand, their non-
premium counterparts have lower store brand share in premium store brand categories where the
price advantage of store brands is lower. Overall, we conclude that high quality store brands lead
to greater store differentiation and store loyalty.
In terms of category characteristics, as conjectured, we find that the impact of store brand
share on store loyalty is higher for households who patronize store brands in staple and high risk
categories. This implies that for the same level of store brand share, households who buy store
brands primarily in high risk categories are likely to be more store loyal. Similarly, as
conjectured, higher store brand purchase in staple categories which are purchased more often and
therefore likely to drive store visits also have a greater reinforcing effect on store loyalty.
The interaction between store brand share and hedonic store brand patron dummy is
negative and significant. This negative interaction coefficient however is smaller than the main
effect; implying that even for hedonic store brand patrons, the overall relationship between store
brand share and store loyalty is positive. But contrary to our conjecture, households who
patronize store brands in hedonic categories are less loyal to the store than households loyal in
non-hedonic categories. Perhaps this could be because households who seek value through store
brands in "fun or lifestyle" product categories could be more price sensitive. Comparing the
profiles of store brand patrons in different categories (see Table 6), we find evidence for this
20
conjecture. Store brand patrons in “hedonic” categories have higher deal proneness and
manufacturer coupon shares, but also lower profit contribution margins.
(“Insert Table 6 about here”)
Revisiting the Inverted U-shaped Relationship
Thus far we have argued that that the inverted U-shaped relationship in Ailawadi,
Pauwels and Steenkamp (2008) is driven by their metric of store brand loyalty, which is
normalized by within-chain store spend. We therefore suggested our alternative metric of store
brand loyalty as most appropriate for studying the store brand loyalty – store loyalty relationship.
We now address two questions: First, which of the two metrics should be the preferred empirical
construct of store brand loyalty from a conceptual perspective—independent of empirical
context? Second, is the inverted-U shaped relationship really driven by the store brand loyalty
metric used in Ailawadi, Pauwels and Steenkamp (2008)?
A preferred metric of store brand loyalty5
We begin by considering how brand loyalty has been operationalized in the literature.
Broadly, there are two approaches: stochastic and deterministic loyalty (Odin, Odin and
Florence, 2001). Stochastic or behavioral loyalty is based on observed purchase behavior which
is assumed to reveal the underlying brand preferences. Deterministic loyalty, on the other hand,
is based on attitudinal constructs and seeks to offer theoretical explanations for loyalty (Fournier
and Yao, 1997).
The behavioral definition, more relevant in our context, is typically built on share of
brand purchases among all available alternatives. Thus brand loyalty in a category is often
defined as the share of spend on a brand relative to total spend in the category. Since data is
5 We thank the review team for encouraging us to elaborate on the conceptual underpinnings and relative advantages of the two metrics.
21
typically available only for one retailer (and not across competing retailers), researchers have
worked with an approximation of loyalty using data from the retailer on whom they have data. It
is important to note that this metric is a valid approximation of brand loyalty, only if a brand’s
share is roughly equal across all competing retailers. While the assumption is plausible (but
often not satisfied) for national brands, it will not be met for store brands by definition, because
store brands are exclusive to a particular retailer. Therefore, to capture the underlying principle
of “share of brand purchases among all available alternatives,” it is important that the store brand
loyalty metric captures not merely store brand share within a retailer, but the share of the store
brands among all available alternatives; and that should include alternatives available at
competing retailers (their store brands and national brands). Our metric of store brand loyalty
meets this principle and we therefore conclude that our metric is conceptually more appealing for
future work measuring store brand loyalty.
Is the Inverted-U relationship driven by the store brand loyalty metric?
To assess this, we compare our earlier empirical results using the across-chain spend
normalization for store brand share with the Ailawadi, Pauwels and Steenkamp (2008) results
using the within-chain spend normalization. The scatter plot of within-chain spend normalized
store brand share and store loyalty in Figure 2 indicates an inverted U-shaped relationship.6 We
also replicate the inverted U shaped relationship with simultaneous equations regression reported
in Table 7. Note that both the linear and quadratic store brand share terms are significant in the
store loyalty equation with the peak of the inverted U occurring when store brand share is 0.23.
In conjunction, with our earlier results based on our across-chain spend normalized store brand
6 To replicate the results in Ailawadi, Pauwels and Steenkamp (2008), we follow that paper in computing a household’s total spend within the focal retail chain based on only categories with store brands.
22
share metric in Table 2, we conclude that the difference between our result and the Ailawadi,
Pauwels and Steenkamp (2008) result is due to the differences in the store brand loyalty metric. 7
(“Insert Table 7 and Figure 2 about here”)
To clarify the intuition for the inverted U relationship, we provide a hypothetical
example. Consider two shoppers A and B who both spend a total of $100 on groceries every
week as illustrated in Table 8. Shopper A is a primary shopper at retail Chain 1 and spends $80
there, while Shopper B is a secondary shopper at retail Chain 1 and only spends $20 there. Now
suppose Shopper A buys $40 worth of store brands at Chain 1, while Shopper B buys $15 worth
of store brands at Chain 1. Intuitively, primary Shopper A who spends 40% of her entire grocery
purchases on Chain 1's store brand is more loyal to that retailer’s brand than secondary Shopper
B who only spends 15% of her grocery purchases on Chain 1's store brand. With across-chain
store brand share, for Chain A, Shopper A’s store brand loyalty is 40%, while Shopper B’s store
brand loyalty is 15%.
(“Insert Table 8 about here”)
But, with within-chain store brand spend normalization, the cherry picking Shopper B’s
store brand loyalty is 75% and the primary Shopper A’s loyalty falls to 50%. Thus, secondary
shoppers who cherry pick store brands and are not store loyal end up by definition being
measured as highly store brand loyal. We suggest that this inflated store brand share of cherry
picking secondary shoppers drives the observed inverted – U relationship.
To test this intuition, we classify each household in our sample into “primary shopper”
and “secondary shopper” based on the household’s self-report as to whether the focal chain is its
primary grocery store. Among the secondary shoppers, a subset who engage in both cross-store
7 We also estimated simultaneous equations regressions with the same store loyalty metric as in Ailawadi, Pauwels and Steenkamp (2008) and our across-store spend normalized store loyalty metric, and find the monotonic positive relationship. So the difference is not driven by the change in our store loyalty metric.
23
(spatial) and over-time (temporal) intensive price search are classified as “cherry pickers”
following the price search propensity scales described in Gauri, Sudhir and Talukdar (2008).
Based on this classification, the 517 households in our sample fall into three distinct customer
segments for the focal retailer: (1) primary shoppers (281 households); (2) cherry picking
secondary shoppers (99 households); (3) other secondary shoppers (132 households). The
distribution of these three shopper segments on the two-dimensional “Store Loyalty” versus
“Store Brand Share” matrix are shown in figure 3, for the two different metrics of store brand
loyalty.
(“Insert Figure 3 about here”)
As evident from figure 3, with within-chain spend normalized store brand share, the
segment with high store brand share and low store loyalty is primarily comprised of “cherry
picking secondary shoppers” segment. However, with across-chain spend normalization, store
brand share of cherry picking shoppers reduce to much lower level than that of primary shoppers.
This indicates that high store brand share of cherry picking secondary shoppers with within-
chain normalization is due to their low spend in the chain rather than high spend in the chain’s
store brands. When store brand share is normalized by across-chain spend, the highest store
brand share shoppers are primary shoppers, who are actually store loyal. This interpretation of
the behavior of the segments is corroborated by the descriptive statistics on the shopping
characteristics of the three segments of shoppers presented in Table 9. As we conjecture, the
“cherry picking secondary shoppers” segment indeed has high store brand share of within-chain
spend, but have much smaller total spend on the focal retailer’s store brands than the “primary
shoppers” segment.
(“Insert Table 9 about here”)
24
In addition, we also examine the nature of store brand share-store loyalty relationship
within each of these three consumer segments. The scatter plots of the segment wise relationship
between store brand share and store loyalty are presented in figure 4. The scatter plots show that
the relationship is inverted – U shaped with within-chain spend normalized store brand share and
is monotonic with across-chain spend normalized store brand share for all three shopper
segments.8 Our finding that the relationship between store brand loyalty and store loyalty is
monotonically positive even for cherry pickers further reassures the store differentiation role of
store brands.
(“Insert Figure 4 about here”)
Conclusion
Store brands are widely acknowledged as effective tools for retailers to increase profit
margins and gain bargaining power with respect to manufacturers. Further, the conventional
wisdom is that store brands can create a point of differentiation for the retailer that can enhance
store loyalty (Richardson, Jain and Dick 1996). This wisdom is also supported by analytical
research (Corstjens and Lal 2000). From a managerial perspective, the existence of such store
differentiation role of store brands has significant implications for the efficacy of retailers’ store
brand expansion strategy and thus, on their competitive performance and bargaining power with
respect to manufacturers.
An important question in this context then becomes whether we find empirical evidence
in support of the store differentiation role of store brands. Unfortunately, there are not only very
few existing studies on this important issue, but their findings also remain ambiguous. Early
8 A simultaneous equations regression confirms the monotonic relationship for each segment and is available in an appendix from the authors.
25
empirical research does in fact find evidence in support of the store differentiation argument for
store brands (Sudhir and Talukdar 2004). However, some recent studies question this argument
by finding that heavy store brand buyers are less store loyal (Ailawadi, Pauwels and Steenkamp,
2008) and store brand patrons are more vulnerable to Wal-Mart supercenters (Hansen and Singh,
2008). Given the enormous strategic implications of the store differentiation role of store brands
to retailers, these apparent conflicting empirical findings about the role among the existing few
studies underscore the need for additional studies to deepen our understanding on the issue and
to potentially reconcile the existing findings. The goal of our current study was to address that
need by undertaking an in depth investigation of whether store brands users are also store loyal.
Our findings from multiple datasets and retailers demonstrate that store loyalty of
consumers increases with their store brand purchases. Further, we also demonstrate that the
inverted-U shaped relationship observed in a recent study is due to cherry picking secondary
shoppers getting measured as store brand loyal consumers when store brand share is computed
with respect to their spending with a specific retail chain. Thus, we are able to reconcile the
apparent conflicting empirical findings in the past studies regarding the nature of the relationship
between store brand share and store loyalty, and to rehabilitate the conventional wisdom and
analytical research based belief that the relationship is positive and monotonic. In addition, we
demonstrate that this positive monotonic relationship is not driven by lack of shopping
opportunities due to a household’s spatial configuration with respect to competing stores.
We further find that households who purchase premium store brands are likely to be more
loyal to the store, thereby providing empirical support for the stronger differentiation role of high
quality store brands in fostering store loyalty (Corstjens and Lal 2000). Finally, we find that for
the same level of store brand share, household’s patronizing store brands in staple and high risk
26
categories are likely to be more store loyal. Thoughtherelationshipbetweenstorebrand
loyaltyandstoreloyaltyisalsopositiveinhedoniccategories,therelationshipismore
positiveinnon‐hedoniccategories. To summarize, we re-establish the notion that higher store
brand purchases by customers help retailers in creating higher store loyalty through positive store
differentiation, and document for the first time the role of store brand quality and product
category characteristics in moderating that positive store differentiation.
We conclude with some suggestions for future research. While our research rehabilitates
the conventional wisdom about the monotonic relationship between store brand share and store
loyalty on the demand side at the customer level, there are supply side issues that need further
research. As store brands gain substantial market share, retailers may be tempted to reduce the
assortment of national brands offered within the store. However, this may lead to a backlash with
national brand customers whose store loyalty may decline in response to this change in
assortment. Thus an increase in store brand share could indirectly reduce store loyalty through its
adverse impact on store assortment. This mechanism might explain the difficulties faced by
Sainsbury, Sears and A&P reported in Ailawadi, Pauwels and Steenkamp (2008). They state:
"[Sainsbury] needed to scale back its emphasis on PL because SOW began to decline as
consumers believed that the dominant presence of the Sainsbury PL constrained their choice. In
the United States, Sears and A&P are examples of retailers that pushed PL too far in the past;
found that store traffic, revenue, and profitability suffered; and needed to retract." This effect due
to the supply side actions on assortment represents an entirely different mechanism by which
store brand share can affect customer store loyalty.
It is important to recognize that supply side effects need not always reduce store loyalty.
For example, increased bargaining power due to increased store brand share might lead a retailer
27
to negotiate lower wholesale prices for national brands. That will give the retailer a greater
competitive advantage relative to competing retailers and therefore will help it to gain in store
traffic and loyalty among national brand enthusiasts. How these opposing effects net out in terms
of the impact on store loyalty is an empirical question. Perhaps a structural model of demand and
supply may be needed to tease out the net long-term effects of rising store brand share on store
loyalty. We leave that as a challenging, but interesting topic for future research.
28
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31
TABLE 1
Variable Operationalization
Variable Operationalization
Store Loyalty Ratio of spend in categories where store brands are not offered at the focal retail chain to total grocery
spend of the household across all retail chains
Store Brand Share Ratio of store brand spend at focal retail chain to total grocery spend of the household across all retail
chains
Household shopper type* Each sample household is first grouped as either “primary shopper” or “secondary shopper” based on its
stated information in the survey as to whether or not its primary grocery store belongs to the focal retail
chain. Among secondary shoppers, a subset is classified as “cherry pickers”. The “cherry picking”
secondary shoppers are identified as those engaging in both cross-store and inter-temporal price search
behaviors, based on five-item spatial and temporal price search propensity scales described in Gauri,
Sudhir and Talukdar (2008).
Household spatial configuration
(LLL, LSL, LLS, SLL and SSS)
Household’s spatial configuration is characterized using 3 dimensional vectors (D12, D1, D2) where D12 is
the distance between the competing stores (small if < .3 mi and large if > 2 mi); D1 is the distance of the
household from focal store and D2 is its distance from the competitor (small if <= 1.8 mi and large if >
1.8 mi).
Premium SB patrons Top quartile of households ranked with respect to ratio of spending in premium store brands to total store
brand spending
Staple SB patrons Top quartile of households with high ratio of spending in store brands in staple categories to total store
brand spending
Hedonic SB patrons Top quartile of households with high ratio of spending in store brands in hedonic categories to total store
brand spending. Hedonic categories are identified from survey of students with 1 item measure on a 3
32
point scale (“The following product is fun to have”). Top 25% of categories with high hedonic scores are
considered as hedonic categories.
Risky SB patrons Top quartile of households with high ratio of spending in store brands in high risk categories to total
store brand spending. High risk categories are identified from survey of students with 1 item measure on
a 5 point scale (“If a product in the following category fails to meet your performance expectations that
would be a - Minor inconvenience to Major problem”). Top 25% of categories with high risk score is
considered as risky categories.
Sales area Sales area of the store from Nielsen spectra database.
Distance to store Distance of the household from the store of the focal retail chain where the household spends most of its
grocery budget
Counters per unit area Number of checkout counters per unit area in the store (obtained from the Spectra data)
NB-SB price differential Average unit price of national brands minus average unit price of store brands as a percentage of national
brand unit price. Price differential for each household is the weighted average across 31 departments
with weights equal to share of that department in that household’s total spending at focal retailer.
Deal proneness Ratio of total price savings at the focal retailer to total spending at the focal retailer
Manufacturer coupon share Ratio of manufacturer coupon savings at the focal retailer to the total spending at the focal retailer
National Brand Loyalty* 2 item measure on a 5 point scale:
I have my “favorite” brand in various product categories like detergent, cereal that I regularly buy.
I usually buy my favorite brand in a product category on a shopping trip even if other competing brands.
are on price deals.
Store Brand Image* 3 item measures on a 5 point scale:
I think the quality of store brands is as good as the national brands for most products.
I think the grocery store brands provide good value for the price paid.
33
I usually buy grocery store brands if they are available.
Shopping Enjoyment* 3 item measures on a 5 point scale:
I enjoy grocery shopping.
Grocery shopping is boring (reverse coded).
I look forward to my grocery shopping trips.
Age Median age from Census data
Income Average income from Census data
Education Average number of years spent at school from Census data
Household size Median household size information from Census data
CBG loyalty Average store loyalty (to the focal retailer) of all households in the Census Block Group where a
household resides
* Based on our primary survey data.
34
TABLE 2
Store Brand Share - Store Loyalty Relationship
Variable Coefficient estimates (SE)
Store Loyalty SB Share
Intercept .084 (.171) -2.022 (2.216)
SB Share 2.136*** (.487)
SB Share2 -1.550 (1.034)
Store Loyalty
.589*** (.132)
Store Loyalty2
-.218 (.163)
Sales Area .001** (.0004)
NB loyalty
-.0003 (.003)
SB Image
.011*** (.003)
NB-SB price differential
5.220 (6.027)
Deal proneness -.210*** (.050)
.059** (.029)
Counters per unit area -.006 (.132)
Shopping Enjoyment -.001 (.006)
.0001 (.003)
Distance to Store -.006** (.003)
Education .0001 (.007)
.003 (.003)
Income .013** (.006)
-.005* (.002)
Household size -.020 (.017)
.011 (.008)
Age -.002 (.004)
-.003* (.002)
CBG loyalty .119*** (.035)
Manuf. Coupon share
-.090 (.081)
Year dummy .003 (.009)
.065 (.776)
Adjusted R2 .323 .267
***p<0.01; **p<0.05; *p<0.10
35
TABLE 3
Store Brand Share - Store Loyalty Relationship Using Nielsen Panel Data
Variable
Coefficient estimates (SE)
Retailer 1 Retailer 2 Store Loyalty SB Share Store Loyalty SB Share
Intercept -.135 (.127) -.008 (.065) .020 (.037) -.087* (.050)
SB Share 3. 288** (1.842)
.538** (.250)
SB Share2 -8.241 (5.085)
-.781 (.599)
Store Loyalty
1.295 (.987)
1.329 (.462)
Store Loyalty2
-1.593 (3.332)
6.051 (13.928)
Sales Area .0002 (.0004)
- .0001 (.0001)
NB loyalty
.104** (.053)
.132* (.079)
Deal proneness -.048 (.068)
.005 (.035)
-.028** (.012)
.009 (.394)
Counters per unit area -.055 (.089)
-.022 (.089)
Distance to Store .0001 (.0001)
-.0001 (.001)
Education .007 (.005)
-.006** (.003)
.001 (.002)
.001 (.005)
Income .0001 (.001)
-.0001 (.001)
.0002 (.0003)
-.001 (.001)
Household size -.004 (.005)
.007 (.005)
-.004** (.002)
.013** (.005)
Age .003 (.005)
-.001 (.002)
.002 (.001)
-.001 (.004)
Manuf. Coupon share
.076 (.241)
.009 (.394)
Adjusted R2 .480 .141 .489 .036
***p<0.01; **p<0.05; *p<0.10
36
TABLE 4
Moderators of Store Brand Share – Store Loyalty Relationship9
Variable Household spatial configuration Product category characteristics
Store Loyalty SB Share Store Loyalty SB Share
Intercept .123 (.166)
-1.933 (2.331) .250* (.132) -1.653 (2.153)
SB Share 1.474*** (.490)
1.461***(.167)
SB Share2 -1.167 (1.004)
Shopper type (SLL, SSS, LLS) -.043* (.024)
SB Share * Shopper type (SLL, SSS, LLS) .924*** (.271)
SB Share * Premium patron
.425***(.121)
SB Share * Hedonic patron
-.473** (.192)
SB Share * Risk patron .454***(.175)
SB Share * Staple patron .508***(.125)
Store Loyalty
.670*** (.134)
.462*** (.075)
Store Loyalty2
-.346* (.163)
-.151* (.089)
Adjusted R2 .355 .250 .414 .330
***p<0.01; **p<0.05; *p<0.10
9 Demographic, store and attitudinal control variables are included in the analysis and are available upon request from authors in an appendix.
37
TABLE 5
Relationship between Store Loyalty and Store Brand Share in Previous Quarter
Variable Coefficient estimates
(SE)
Intercept .071 (.080)
SB Share 2.066*** (.053)
SB Share2 -1.021*** (.093)
Sales Area .0005**(.0002)
Deal proneness -.186*** (.027)
Counters per unit area .002 (.078)
Shopping Enjoyment -.004 (.003)
Distance to Store -.004** (.002)
Education .002 (.004)
Income .016*** (.003)
Household size -.032*** (.010)
Age .002 (.002)
CBG loyalty .104*** (.019)
R2 .534
***p<0.01; **p<0.05; *p<0.10
38
TABLE 6
Shopping Characteristics by Types of Store Brand Patrons
Variable
Sample mean values for the focal retailer
Premium SB
patrons
Hedonic SB
patrons
Risky SB
patrons
Staple SB
patrons
Store Loyalty (%) 26.40 17.63 20.47 28.38
NB profitability (%) 19.43 17.91 20.12 19.49
SB profitability (%) 32.43 30.20 30.98 31.12
Profitability (%) 21.15 19.52 21.45 21.13
Deal proneness (%) 26.01 31.15 28.62 29.38
Manufacturer coupon
share (%) 2.67 3.7 2.75 2.99
Items bought on deal per
trip (%) 62.42 66.91 63.43 64
39
TABLE 7
Store Brand Share – Store Loyalty Relationship for Within-Chain Spend Normalized Store Brand
Share Metric
Variable
Coefficient estimates (SE)
SB Share Within-Chain Spend Normalization Store Loyalty SB Share
Intercept .235 (.241) -6.997** (3.471)
SB Share 2.004*** (.739)
SB Share2 -4.280***(1.449)
Store Loyalty
.337* (.059)
Store Loyalty2
-.428* (.233)
Sales Area .001 (.001)
NB loyalty
.001 (.005)
SB Image
.023*** (.005)
NB-SB price differential
19.220** (9.439)
Deal proneness -.422*** (.073)
.138*** (.044)
Counters per unit area -.157 (.206)
Shopping Enjoyment -.006 (.009)
.0001 (.004)
Distance to Store -.011** (.004)
Education -.004 (.011)
.003 (.005)
Income .021** (.009)
-.008** (.004)
Household size -.023 (.027)
.020* (.012)
Age -.017*** (.005)
-.001 (.002)
CBG loyalty .240*** (.050)
Manuf. Coupon share
-1.145*** (.126)
Year dummy -.001 (.015)
.265** (.120)
Adjusted R2 .134 .112
***p<0.01; **p<0.05; *p<0.10
40
TABLE 8 Properties of Different Store Brand Share Metrics: Illustrative Example
Shopper characteristics Shopper A Shopper B
Total spend ($) 100 100
Chain A spend ($) 80 20
Chain B spend ($) 20 80
Chain A loyalty (%) 80 20
Store Brand A spend ($) 40 15
Share of Store Brand A (%)
relative to Chain A spend 50 75
Share of Store Brand A (%)
relative to total spend 40 15
41
TABLE 9
Shopping Characteristics by Shopper Types
Variable
Sample mean values for the focal retailer
Primary
shoppers
Cherry picking
secondary shoppers
Other secondary
shoppers
Store Loyalty (%) 34.89 6.76 6.67
SB Share (% Within-chain Spend) 20.94 29.56 14.40
SB Share (% Across-chain Spend) 13.11 3.30 1.79
Weekly basket size ($) 60.08 11.97 13.77
Weekly SB sales ($) 8.49 2.18 1.17
Weekly gross profits ($) 14.47 2.68 2.51
a
b. Sca
c.
a. Scatter P
atter Plot of S
Scatter Plo
F
Plot of Store
Store Brand
ot of Store Br
42
IGURE 1
Brand Spen
Spend and S
rand Share-S
nd and Total
Spending in
Store Loyalt
Store Spend
Non-SB cat
ty Relationsh
d
tegories
hip
43
FIGURE 2
Scatter Plot of Store Brand Share (Within-chain spend) and Store Loyalty
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
0 0.1 0.2 0.3 0.4 0.5
Sto
re L
oyal
ty
Store Brand Share (within-chain spend)
PS
Distributi
S – Primary Sh
ion of Shopp
hoppers; CPSS
F
per Segment
S – Cherry Pick
44
IGURE 3
s by Store B
king Secondary
Brand Share a
y Shoppers; OS
and Store Lo
SS – Other Sec
oyalty
condary Shopp
ers
45
FIGURE 4
Store Brand Share - Store Loyalty Relationship by Shopper Segment
1
Appendix A
Moderating Effect of Household’s Spatial Configuration
Variable Coefficient estimates
Store Loyalty SB Share
Intercept .123 (.166)
-1.933 (2.331)
SB Share 1.474*** (.490)
SB Share2 -1.167 (1.004)
Shopper type (SLL, SSS, LLS) -.043* (.024)
SB Share * Shopper type (SLL, SSS, LLS) .924*** (.271)
Store Loyalty
.670*** (.134)
Store Loyalty2
-.346* (.163)
Sales Area .0004 (.0004)
NB loyalty
-.0002 (.003)
SB Image
.011*** (.003)
NB-SB price differential
4.976 (6.340)
Deal proneness -.171*** (.048)
.050* (.030)
Counters per unit area .030 (.128)
Shopping Enjoyment -.003 (.006)
-.0005 (.003)
Distance to Store -.004 (.003)
Education -.004 (.007)
.003 (.003)
Income .018***(.006)
-.004* (.003)
Household size -.010 (.017)
.012 (.008)
Age -.002 (.004)
-.003* (.002)
CBG loyalty .112***(.034)
Manufacturer coupon share
-.095 (.085)
Year dummy .001***(.009)
.062 (.080)
Adjusted R2 .355 .250
***p<0.01; **p<0.05; *p<0.10
2
Appendix B
Moderating Role of Store Brand Type and Product Category Characteristics
Variable Coefficient estimates
Store Loyalty SB Share
Intercept .250* (.132) -1.653 (2.153)
SB Share 1.461***(.167)
SB Share * Premium patron .425***(.121)
SB Share * Hedonic patron -.473** (.192)
SB Share * Risk patron .454***(.175)
SB Share * Staple patron .508***(.125)
Store Loyalty
.462*** (.075)
Store Loyalty2
-.151* (.089)
Sales Area .0001 (.0004)
NB loyalty
.0004 (.003)
SBImage
.010*** (.003)
NB-SB price differential
4.435 (5.858)
Deal proneness -.170*** (.047)
.026 (.026)
Counters per unit area -.084 (.127)
Shopping Enjoyment -.002 (.005)
-.001 (.003)
Distance to Store -.006** (.003)
Education -.006 (.006)
.0002 (.003)
Income .010** (.005)
-.002 (.002)
Household size -.024 (.017)
.009 (.007)
Age -.0002 (.003)
-.004***(.001)
CBG loyalty .077** (.032)
Manufacturer coupon share
-.093 (.078)
Year dummy -.001 (.009)
.054 (.074)
Adjusted R2 .414 .330
***p<0.01; **p<0.05; *p<0.10
3
Appendix C
Segment wise Store Brand Share – Store Loyalty Relationship
Variable Primary Shoppers Secondary shoppers Cherry pickers
Store Loyalty SB Share Store Loyalty SB Share Store Loyalty SB Share
Intercept .148 (.270) -3.872 (3.869) -.022 (.083) -2.256*** (.964) .204*** (.078) -4.124*** (1.474)
SB Share 1.185** (.524)
2.901*** (1.057)
1.773*** (.508)
SB Share2 -.357 (.807)
-27.793 (18.865)
-6.352 (4.217)
Store Loyalty
.385* (.232)
.387** (.178)
.369* (.175)
Store Loyalty2
-.004 (.207)
-.551 (.938)
.177 (.134)
Sales Area .001* (.0007)
.0001 (.0001)
-.001***(.0003)
NB loyalty
.002 (.005)
-.001 (.001)
.001 (.002)
SB Image
.017*** (.005)
.001 (.001)
.002 (.002)
NB-SB price differential
10.192 (10.487)
6.111**(2.638)
11.238***(3.998)
Deal proneness -.413*** (.101)
.131** (.065)
-.038 (.023)
.005 (.009)
-.017 (.024)
-.004 (.014)
Counters per unit area .063 (.233)
-.021 (.070)
-.167** (.082)
Shopping Enjoyment -.007 (.010)
.0003 (.005)
-.002 (.003)
.001 (.001)
.002 (.004)
-.005** (.002)
Distance to Store -.007 (.005)
.001 (.008)
-.001 (.001)
Education .002 (.012)
.005 (.005)
-.008* (.005)
.001 (.001)
-.004 (.004)
.003 (.002)
Income .014* (.008)
-.005 (.004)
-.002 (.003)
.001 (.001)
.002 (.004)
-.004* (.002)
Household size -.002 (.029)
.016 (.014)
-.018* (.010)
.002 (.003)
-.023** (.011)
.010* (.006)
Age -.006 (.007)
-.005 (.003)
-.001 (.002)
-.001 (.001)
.005*** (.002)
-.003*** (.001)
4
CBG loyalty .178*** (.057)
. 038* (.022)
-.023 (.019)
Manuf. Coupon share
-.282 (.196)
.010 (.025)
.146** (.071)
Year dummy -.002 (.015)
.128 (.133)
.004 (.006)
.079** (.033)
.013** (.006)
.138*** (.051)
Adjusted R2 .218 .180 .085 .104 .198 .248
***p<0.01; **p<0.05; *p<0.10
Recommended