SATHEESH SEENIVASAN K. SUDHIR January 2012 First Draft: … · 2019. 12. 30. · Ph: +61-399034184...

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: [email protected]). 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: [email protected]). 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: [email protected]). 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.

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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.

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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

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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

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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

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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.

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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,

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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.

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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.

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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

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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

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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.


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

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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.


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.

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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

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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.


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.

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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.


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

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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.


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%.


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.


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

REFERENCES

Ailawadi, Kusum and Bari Harlam (2004), “An Empirical Analysis of the Determinants of Retail

Margins: The Role of Store Brand Share”, Journal of Marketing, 68 (1), 147-66.

______________, Koen Pauwels and Jan-Benedict E.M. Steenkamp (2008), “Private-Label Use

and Store Loyalty,” Journal of Marketing, 72 (6), 19-30.

Batra, Rajeev and Indrajit Sinha (2000), “Consumer-Level Factors Moderating the Success of

Store brand Brands,” Journal of Retailing, 76(2), 175-191.

Corstjens, Marcel and Rajiv Lal (2000) "Building Store Loyalty through Store Brands," Journal

of Marketing Research, 37 (3), 281-292.

Deloitte (2010), "A Walk down the Grocery Aisle: Executive Survey Results Exploring Private

Label and National Brands", (accessed Aug 2011), [available at http://www.deloitte.com]

Dhar, Sanjay K., S.J. Hoch, and N. Kumar (2001), “Effective Category Management Depends on

the Role of the Category,” Journal of Retailing, 77, 165-184.

Forbes (2010), "Wal-Mart Turns to Private-Label Products", (accessed Aug 2011), [available at

http://www.forbes.com]

Fournier Susan and Julie L. Yao (1997), “Reviving Brand Loyalty: A re-conceptualization within

the framework of consumer-brand relationships”, International Journal of Research in

Marketing, 14, 451-472

Gauri, Dinesh K., K. Sudhir and Debabrata Talukdar (2008), “The Temporal and Spatial

Dimensions of Price Search: Insights from Matching Household Survey and Purchase

Data”, Journal of Marketing Research, 45 (2), 226-240.

IRI (2008), “Times and Trends Special Report: U.S. & Europe Private Label 2008”.

29

Meza, Sergio and K. Sudhir (2008), “Do Private Labels Increase Retailer Bargaining Power”,

Quantitative Marketing and Economics, 8 (3), 333-63.

Narasimhan, Chakravarthi and Ronald Wilcox (1998), “Store brands and the Channel

Relationship: A Cross-Category Analysis,” Journal of Business, 71 (4), 573-600.

Odin Yorick, Nathalie Odin, Pierre Valette-Florence (2001), “Conceptual and Operational

Aspects of Brand Loyalty: An Empirical Investigation”, Journal of Business Research,

53, 75-84.

Scott-Morton Fiona and Florian Zettelmeyer (2004), “The Strategic Positioning of Store Brands

in Retailer-Manufacturer Negotiations”, Review of Industrial Organization,24(2), 161-94.

PLMA (2007), Private Label Manufacturers Association Website, (accessed Aug 2009)

[available at http://plma.com/storeBrands/sbt07.html].

PLMA (2011), Private Label Manufacturers Association Website, (accessed Aug 2011)

[available at http://plma.com/storeBrands/sbt11.html].

Richardson, Paul S., Arun K. Jain and Alan Dick (1996), “Household Store Brand Proneness: A

Framework”, Journal of Retailing, 72 (2), 59-185.

Sainsbury (2010), “Annual Report and Financial Statements 2010”, (accessed Aug 2011),

[available at www.j-sainsbury.co.uk/files/reports/ar2010_report.pdf]

Sethuraman, Raj and Catherine Cole (1999), “Factors Influencing the price premiums that

consumers pay for national brands over store brands,” Journal of Product and Brand

Management, 8 (4), 340-351.

Storebrandsdecisions (2011), "Carrefour Seeks Dramatic PL Expansion", (accessed Aug 2011),

[available at www.storebranddecisions.com]

30

Sudhir, K. and Debabrata Talukdar (2004), “Does Store Brand Patronage Improve Store

Patronage? “, Review of Industrial Organization, 24 (2), 143-160.

Wooldridge, Jeffrey M. (2002), Econometric Analysis of Cross Section and Panel Data.

Cambridge MA: The MIT Press.

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)


-.022 (.089)

Distance to Store .0001 (.0001)

-.0001 (.001)


-.006** (.003)

.001 (.002)

.001 (.005)

Income .0001 (.001)

-.0001 (.001)

.0002 (.0003)

-.001 (.001)


.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)


SB Share 2.066*** (.053)

SB Share2 -1.021*** (.093)

Sales Area .0005**(.0002)


Counters per unit area .002 (.078)




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)


19.220** (9.439)


.138*** (.044)



.0001 (.004)


Education -.004 (.011)

.003 (.005)

Income .021** (.009)

-.008** (.004)


.020* (.012)

Age -.017*** (.005)

-.001 (.002)

CBG loyalty .240*** (.050)

Manuf. Coupon share

-1.145*** (.126)

Year dummy -.001 (.015)

.265** (.120)


***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




-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)


4.976 (6.340)


.050* (.030)



-.0005 (.003)

Distance to Store -.004 (.003)


.003 (.003)

Income .018***(.006)

-.004* (.003)


.012 (.008)

Age -.002 (.004)

-.003* (.002)

CBG loyalty .112***(.034)

Manufacturer coupon share

-.095 (.085)

Year dummy .001***(.009)

.062 (.080)


***p<0.01; **p<0.05; *p<0.10

2

Appendix B

Moderating Role of Store Brand Type and Product Category Characteristics



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)


4.435 (5.858)


.026 (.026)



-.001 (.003)



.0002 (.003)

Income .010** (.005)

-.002 (.002)


.009 (.007)

Age -.0002 (.003)

-.004***(.001)

CBG loyalty .077** (.032)

Manufacturer coupon share

-.093 (.078)

Year dummy -.001 (.009)

.054 (.074)


***p<0.01; **p<0.05; *p<0.10

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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)


10.192 (10.487)

6.111**(2.638)

11.238***(3.998)


.131** (.065)

-.038 (.023)

.005 (.009)

-.017 (.024)

-.004 (.014)


-.021 (.070)

-.167** (.082)


.0003 (.005)

-.002 (.003)

.001 (.001)

.002 (.004)

-.005** (.002)

Distance to Store -.007 (.005)

.001 (.008)

-.001 (.001)


.005 (.005)

-.008* (.005)

.001 (.001)

-.004 (.004)

.003 (.002)

Income .014* (.008)

-.005 (.004)

-.002 (.003)

.001 (.001)

.002 (.004)

-.004* (.002)


.016 (.014)

-.018* (.010)

.002 (.003)

-.023** (.011)

.010* (.006)

Age -.006 (.007)

-.005 (.003)

-.001 (.002)

-.001 (.001)

.005*** (.002)

-.003*** (.001)

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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

SATHEESH SEENIVASAN K. SUDHIR January 2012 First Draft: … · 2019. 12. 30. · Ph: +61-399034184...

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Transcript of SATHEESH SEENIVASAN K. SUDHIR January 2012 First Draft: … · 2019. 12. 30. · Ph: +61-399034184...