Are Loyal Store Brand Users Less Store Loyal? SATHEESH SEENIVASAN K. SUDHIR DEBABRATA TALUKDAR* January First Draft: August 25, 2009

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1 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: ( satheesh.seenivasan@monash.edu). K. Sudhir is James L. Frank Professor of Private Enterprise and Management, Yale School of Management, New Haven, CT Ph: ( k.sudhir@yale.edu). Debabrata Talukdar is Professor of Marketing, School of Management, State University of New York at Buffalo, Buffalo, NY Ph: ( 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.

2 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 householdstore 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 high perceived risk, staple and non hedonic categories leads to greater store loyalty relative to low risk, non staple and hedonic categories. Keywords: Store brands; store loyalty; store differentiation; retail competition; premium private labels. 1

3 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 2

4 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. 3

5 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 4

6 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 5

7 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 6

8 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 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. 7

9 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, 8

10 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. 9

11 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. 10

12 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 11

13 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 12

14 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 13

15 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 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. 14

16 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 (D 12, D 1, D 2 ), 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 D 12 refers to the distance between the competing stores; D 1 is the distance of the household from focal store while D 2 refers to its distance from the competitor. Following Gauri, Sudhir and Talukdar (2008), we classify the inter-store distance as small (D 12 <.3 miles) and large (D 12 > 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 15

17 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. 16

18 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. 17

19 3 StoreLoyalty α α SBshare α SBshare Premium patron α SBshare Hedonic patron α SBshare Risk patron α SBshare Staple patron 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 18

20 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 nonpremium 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 19

21 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 loyalty 5 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. 20

22 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 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. 21

23 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. 22

24 (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 withinchain 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 ) 23

25 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. 24

26 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 25

27 categories are likely to be more store loyal. Though the relationship between store brand loyalty and store loyalty is also positive in hedonic categories, the relationship is more positive in non hedonic categories. 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 26