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POLYGAMOUS STORE LOYALTIES:

AN EMPIRICAL INVESTIGATION

Qin Zhang†

Manish Gangwar

P.B. Seetharaman

August 31, 2017

Forthcoming in Journal of Retailing

†Qin Zhang is Assistant Professor at School of Business, Pacific Lutheran University.

Manish Gangwar is Assistant Professor of Marketing at Indian School of Business, Hyderabad, India.

P. B. Seetharaman is W. Patrick McGinnis Professor of Marketing at Olin Business School, Washington University in

St. Louis. Corresponding author: Qin Zhang, e-mail: zhangqc@plu.edu, Ph: 253-535-7253.

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POLYGAMOUS STORE LOYALTIES:

AN EMPIRICAL INVESTIGATION

Abstract

Grocery store loyalty has been traditionally viewed as a trait of consumers

toward a particular store for their overall shopping needs. In this study, we argue

that store loyalty shall be regarded as category specific trait, i.e., a consumer could

be loyal to store A in category one while at the same time be loyal to store B in

category two. We name this consumer behavior polygamous store loyalties.

We use an in-home scanning panel dataset that tracks purchases of 1321

households in 284 grocery categories across 14 retail chains over a 53-week period

in a large US market. First, we provide model free evidence of polygamous store

loyalties in the data, even though the overall store loyalty based on the traditional

view is low. Next, we propose a model to separate category specific effects from

overall store level effects. Finally, we discuss how retailers can use the results to

gain a new perspective on store attractiveness to improve overall store patronage.

Keywords: Store Loyalty, Polygamous Store Loyalties, Store Attractiveness, Store-

Category Attractiveness, Multi-Category Analysis

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1. INTRODUCTION

The grocery industry in the US is highly competitive, though there has been an increase in

market concentration due to the continuous consolidation of the supermarket segment, where the

market share of the top four players – Wal-mart Stores, The Kroger Co., Safeway and Publix

Super Markets – rose from 17% in 1992 to 36% in 2013 (Bells 2015). Consumers search across

stores and across time to take advantage of the substantial savings available to them (Gauri, Sudhir

and Talukdar 2008). According to Deloitte’s 2013 American Pantry report, on average, consumers

shop at five different stores to fulfill their grocery needs. Retailers strive to woo consumers to their

stores and often focus on consumers’ overall store preferences or their entire shopping baskets.

This focus is also reflected in the rich empirical literature in marketing that studies consumers’

store switching behavior in grocery shopping (e.g., Bucklin and Lattin 1992; Bell and Lattin 1998;

Bell, Ho and Tang 1998; Broniarczyk, Hoyer and McAlister 1998; Bodapati and Srinivasan 2006;

and Briesch and, Chintagunta and Fox 2009, to name a few)1, where a consumer is largely viewed

as someone who is either loyal to a store or not loyal to any store for his/her overall grocery

shopping needs. However, recent survey studies by practitioners regarding the trends in US

grocery shopping reveal that the idea of loyalty to a single “primary store” is giving way to a

diversity of stores, as consumers are dividing their shopping across retailers and choosing a

different favorite in each category (The Hartman Group Inc. 2014). Consumers seem to have much

to gain from not being loyal to a single store (Talukdar, Gauri and Grewal 2010). If store loyalty is

fractured at the entire shopping basket level, could store loyalty still be present at the category

level?

1 Here, the term “grocery” refers to not only food products, but also non-food products, such as general household

products, health and beauty aids (HBA) products, etc., which are carried by a typical US grocery store.

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In this study, we extend the traditional view of store loyalty for overall shopping needs by

bringing in the dimension of categories, a concept we call store-category loyalty. We elucidate this

concept with the two following examples.

Jane Smith does her shopping regularly at three different stores – Albertsons, Safeway, and

Whole Foods Market – visiting them fairly equally over a period of time. Under the traditional

view of store loyalty, such a consumer is labeled as a store switcher. However, unlike a typical

store switcher, who is usually assumed to switch among stores either to redeem the lowest

available price in each product category (also called a ‘cherry picker’ see Fox and Hoch 2005, and

Gauri, Sudhir and Talukdar 2008) or because of travel exigencies that take the consumer closer to

one store or another at any given point of time, Jane always purchases some categories (e.g., soft

drinks) from Albertsons, some categories (e.g., produce) from Safeway, and other categories (e.g.,

wine) from Whole Foods Market. In other words, Jane is, in fact, loyal to different stores for

different product categories. Understanding this aspect of Jane’s shopping behavior may help

retailers avoid erroneously concluding that her lack of overall store loyalty implies her lack of

store loyalty to any of the stores for any product category; this, in turn, would help these retailers

capitalize on the fact that their stores are, in fact, highly attractive to Jane in certain categories.

Alternatively, consider John Doe, who does most of his grocery shopping at Costco, yet

tends to buy cheese at Trader Joe’s as it offers an extensive and diverse product assortment in the

cheese category. In such a scenario, by focusing solely on the overall store loyalty of John Doe to

Costco, one may miss out on the opportunity to learn from John’s strong preference for Trader

Joe’s in the cheese category. Deeper exploration of such traits may help retailers identify and

understand such preferences, thus improving store patronage.

Our goal for this paper, therefore, is to examine consumers’ store loyalty as a category-

specific trait in comparison to overall store loyalty. In other words, we want to examine the

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polygamous store loyalties of consumers that vary across categories. Dowling and Uncles (1997)

used the concept of “polygamous loyalty” in the context of brand loyalty. They argue that it is a

better description of consumers’ seemingly non-loyal behavior than either once-and-for-all brand

switching to another brand or a tendency to flit from one brand to another without any fixed

allegiance. In this paper, we use the term “polygamous store loyalties” in the context of store

loyalty to describe consumers’ behavior that is seemingly non-loyal at the store level but still loyal

at the store-category level.

We conceptualize store-category loyalty as a construct that represents a consumer’s long-

term propensity to choose a store in a category, which is determined by the attractiveness of a store

in a given category relative to that of other stores. We argue that by adding the category dimension

to the concept of store loyalty, retailers can gain new insights into consumers’ relationships with

stores and generate actionable retail strategies at the category level to strengthen these

relationships. The category perspective also provides retailers an alternative view of the

competitive landscape, enabling them to better understand their competitive positions and respond

with appropriate strategies. By leveraging and focusing on categories, particularly the top

performing categories, retailers will be able to prioritize and better allocate their limited marketing

resources across categories to maximize their effects.

We use an in-home scanning panel dataset that tracks purchases of 1321 households in 284

grocery categories across 14 retail chains over a 53-week period in a large US market. At the

aggregate level, the data shows little overall store loyalty; however, once the category dimension is

added, households exhibit strong store loyalty at the category level. By examining household

purchases in multiple stores and multiple categories simultaneously, we are able to separate store-

category level effects from the overall store level effects after controlling for household

heterogeneity.

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Our analysis, which uncovers intrinsic store-category attractiveness, confirms our

conjecture that viewing store loyalty as a category specific trait can provide new insights about

consumer store choice behaviors. Our analysis provides a unique view of stores’ relative standing

in terms of their intrinsic category attractiveness, which can help retailers develop appropriate

strategies to defend their unique positioning and improve overall store patronage.

For the effects of retailers’ merchandising strategies, our analysis shows that on average,

store-category attractiveness is positively affected by the number of brands and average number of

SKUs per brand in a category and negatively affected by relative prices and temporal price

variation. However, as expected, we do see considerable variation across categories and

households. We notice great heterogeneity in the effects of the merchandising programs across

categories. It is larger than the heterogeneity across households, except in the case of price

sensitivity, suggesting that customization of merchandising programs should be based on

categories. We also show how retailers can use our estimates to rank-order and, therefore,

prioritize categories in terms of each merchandising variable, so that they can better allocate their

limited marketing resources across categories and across merchandising programs.

The paper is organized as follows. In section 2, we review the relevant literature and

discuss the positioning and contribution of this paper. We describe our unique panel dataset

involving 244 product categories and 14 retail chains in section 3. Section 4 provides empirical

evidence in support of polygamous store loyalties. Section 5 describes the proposed model and key

variable constructs that influence store-category attractiveness, which, in turn, determines store-

category loyalty. We also discuss the model estimation methodology in this section. The empirical

findings are presented and discussed along with managerial implications in section 6. In section 7,

we summarize the paper with conclusions. Finally, we discuss the limitations of the paper and

provide directions for future research in section 8.

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2. LITERATURE REVIEW

We review three streams of literature. The first deals with studies of consumers’

longitudinal store choice decisions. The second deals with studies of consumers’ store loyalties.

The third deals with studies of consumers’ category incidence decisions and multi-category

shopping behaviors. Finally, we position our work relative to these streams of literature.

2.1 Store Choices

There is extant literature that studies consumers’ store choice decisions and has sought

answers to the question of what factors drive consumers’ store choice decisions in a trip (e.g., Bell

and Lattin 1998; Bell, Ho and Tang 1998; and Briesch, Chintagunta and Fox 2009, to name a few).

We discuss below a few representative studies in which store choice models have been developed

or applied at the category level.

Gijsbrechts, Campo and Nisol (2008) find that category-specific store preferences play a

role in consumers’ store choice behavior. They group the typical retailer defined categories into

three types of products: convenience, specialty and fresh products. They observe that category-

preference complementarities could be one of the reasons why consumers shop at multiple stores

despite the lack of the stimuli of temporary sales promotions. Briesch, Dillon and Fox (2013) also

take a store-category perspective. They formulate a logit model to position categories and stores in

multi-attribute space and identify “destination categories” that influence consumers’ store choice

decisions.

2.2 Store Loyalty

The focus of store choice papers is modeling what factors impact temporal store choice

decisions of consumers, and one has to make indirect inferences about consumers’ store loyalties

using such models. Other researchers explicitly study consumers’ store loyalties and have sought

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answers to research questions such as whether consumers are loyal to stores and what store and

consumer level factors drive consumers’ store loyalty.

One of the earliest academic studies dealing directly with store loyalty was undertaken by

Tate (1961) in the context of supermarkets. He found that 10% of US households were exclusively

store loyal to a single store, while at the other end of the scale, 33% of households were highly

store disloyal, visiting five or more stores. He also found that customers commonly purchased

staples at their primary store and fill-in products at the secondary store.

Using personal in-home interviews, Stephenson (1969) identified the drivers of customers’

store loyalties to be intrinsic store attributes such as physical store characteristics, store personnel

and location convenience, as well as store merchandising strategies such as merchandise selection

and prices charged. Arnold, Oum and Tigert (1983) had similar findings in their analysis of

international survey data collected from six major markets in four countries.

Analyzing consumers’ shopping data across multiple stores, Rhee and Bell (2002) found

that a consumer’s loyalty to their favorite store was determined by the store’s geographical

proximity, as well as the consumer’s knowledge of the store’s assortment, layout and prices.

2.3 Category Purchase Decisions and Multi-category Shopping

Consumers make category purchase decisions during their store visits. Since different

categories serve different consumption needs of consumers, earlier research that study the

interaction between category incidence decisions and brand-level decisions assumes that incidence

decisions are independent across categories (e.g., Bucklin and Lattin 1991, Chiang 1991, and

Chintagunta 1993). This assumption is later relaxed as researchers recognize that allowing

correlations between categories helps in gaining a better understanding of consumer choices in

individual categories (Seetharaman et al 2005).

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For category incidence decisions, two types of correlations are modeled. One type assumes

that in consumers’ brand preferences or responses to marketing mix variables, there is a common

component across categories (typically associated with a consumer or a brand) and each category

has its own category specific responses (Ainslie and Rossi 1998; Seetharaman, Ainslie and

Chintagunta 1999; Singh, Hansen and Gupta 2004; and Prasad, Strijnev, and Zhang 2008). The

second type of correlations arises from demand complementarity between categories such as cake

mix and cake frosting (e.g., Manchanda, Ansari and Gupta 1999; Chib, Seetharaman, and Strijnev

2002; Mehta 2007; Ma, Seetharaman and Narasimhan 2012, etc.). To model this type of

correlations, researchers typically allow marketing variables of one category to affect the

incidence outcomes of other categories in a shopping basket. Such models involve high dimension

computation.

2.4 Positioning of This Paper

The focus of this study is not on modeling consumers’ temporal store choice decisions, as

in the literature reviewed in section 2.1 where consumers’ store loyalty is indirectly inferred.

Rather, we are interested in understanding consumers’ long-term propensity to purchase from a

store, as in the literature reviewed in section 2.2. However, unlike the papers discussed in section

2.2, which focus on overall store loyalty of consumers, we view store loyalty as a category-

specific trait.

In motivating this focus of category on store loyalty in our study, the literature reviewed in

section 2.1 becomes relevant because those store choice models have been developed at the

category level. Comparatively, store loyalty models in section 2.2 have not been adequately

developed or applied at the category level. Towards addressing this deficiency, we propose and a

store loyalty model at the category level. In doing this, we are able to demonstrate how a more

nuanced understanding of store loyalty at the category level can help retailers gain new insights.

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To the best of our knowledge, there are two papers that allude to the idea of store loyalty as

a category specific household trait. Bell, Ho, and Tang (1998) use category-specific store loyalty

as a weighting factor to construct the variable cost of a store for a consumer across categories.

They argue that category-specific store loyalty reduces prices for consumers implicitly because it

reduces the time and cost needed for them to search for the category in the store. Dreze and Hoch

(1998) classify grocery products into two types: (1) Type I products, for which consumers are

loyal to a specific retailer and, as far as possible, always shop at that retailer for those products,

and (2) Type II products, which are not associated with any retailer and are bought at whichever

retailer consumers happen to be shopping at when they plan or remember to buy the product.

Using a controlled store experiment, the authors show that a store can successfully transform Type

II products into Type I products using cross-merchandising programs. Although Dreze and Hoch

(1998) distinguish between product categories for which a consumer is store loyal and product

categories for which the consumer is not loyal, the authors however did not investigate whether a

consumer could be simultaneously loyal to different stores for different product categories, and

more importantly, what factors influence such polygamous store loyalties. Our study primarily

focuses on addressing these issues.

Another study that is conceptually relevant to ours is the one by Inman, Shankar, and

Ferraro (2004) in which the authors investigate the role of the association of categories to channels

on channel share of volume. The authors first ask survey respondents to name categories

associated with corresponding channels to obtain a perceptual distance measure and perform a

correspondence analysis to measure the relative strength of the association. Next, the authors use

the channel-category association as an input to study its role on channel share of volume.

In our study, we accommodate the correlations across categories that arise due to

households’ common response across categories as discussed in section 2.3. We account for the

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common responses for both category intercept and the responses to merchandising programs of the

categories. We are able to separate the store-level effects from store-category level effects after

accounting for household heterogeneity.

In summary, this study contributes to the existing marketing literature by providing an

alternative view of store loyalty, which is that a consumer’s loyalty to a store can be category-

specific. Our study also helps understand to what extent consumers are attracted to different stores

in different categories, and further demonstrates how understanding stores’ attractiveness at the

category level can help retailers customize their marketing resources and strategies at the category

level to improve overall store attractiveness to consumers.

3. DATA DESCRIPTION

We use in-home scanning data on longitudinal purchases of 1321 metropolitan households

in a large southwestern city. The data contains detailed purchase information (e.g., the transaction

date, the retail chain visited, the category and SKU purchased, and price paid, etc.) of these

households in 284 grocery categories across 14 retail chains, over a 53-week period from

September 2002 to September 2003. The 14 retail chains belong to three types of retail formats:

traditional supermarkets, supercenters, and warehouse club stores. There are nine traditional

supermarket chains ─ Albertsons, Bashas’, Food 4 Less, Food City, Fry Food Store, IGA,

Safeway, Trader Joe’s, and Wild Oats Market, two supercenters ─ Super Kmart and Wal-mart

Supercenter, and three warehouse club chains ─ Costco, Sam’s Club, and Smart & Final.

4. THE MODEL FREE EVIDENCE FOR POLYGAMOUS STORE LOYALTIES

First, we demonstrate the presence of overall store loyalties in our dataset. In Figure 1, we

report the histogram of a number of different stores at which all 1321 households shop.

Throughout the 53-week study period, we see that only 12 out of the 1321 households shop at a

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single store, the modal value is six, and there are three households that shop at as many as 13

different stores.2 Next, for each household we identify its favorite store, i.e., the store at which the

household makes the largest number of shopping trips over the study period. Subsequently, we

calculate the proportion of shopping trips made by each household at its favorite store over its total

number of shopping trips, and report the probability mass histogram for this proportion across all

1321 households in the dataset in Figure 2. We observe that 50.2% of the households do not visit

their favorite store on 50% or more of their shopping trips. The two figures indicate that

households typically divide their grocery shopping among many different stores and that there

appears to be little overall store loyalty for these households based on the traditional view of store

loyalty.

[INSERT FIGURE 1 and 2 HERE]

Next, we add the category dimension into the analysis, and something interesting emerges.

We observe that each of the 1321 households, including those who shop at multiple stores, makes

all of its category purchases exclusively at the same store for at least one category. Figure 3

displays the frequency distribution of households across the number of categories in which a

household is observed to make all its purchases from a single store. The figure shows that many

households seem to purchase a large number of categories exclusively from one store.3 Since

different households may purchase a different number of categories, we also display the frequency

distribution of households in terms of the percentage of categories that each of these households

buys exclusively from one store in Figure 4. We find that, on average, the percentage of categories

that a household buys exclusively from one store is 38%. We also conduct similar calculations

2 For expositional convenience, we use “store” and “retail chain” interchangeably. 3 We also draw a figure similar to Figure 3 but only consider household-category combinations where at least 10

category purchases are made by corresponding households. As in Figure 3, the figure also shows that many

households seem to purchase a large number of categories exclusively from one store. However, low frequency

categories are shown to be more likely to be purchased exclusively at a single store. This figure is available from the

authors upon request.

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from the category perspective. In Figure 5 we plot the frequency distribution of categories in terms

of the percentage of households that buy these categories exclusively at one store. We find that, on

average, among the households that make purchases in a category, 49% of them buy the category

exclusively from one store.

[INSERT FIGURE 3, 4 and 5 HERE]

One may argue that the findings in Figures 3, 4 and 5 could be attributed to the fact that

many households make most of their single-store category purchases exclusively at their favorite

stores while the rest of their purchases are scattered across the other stores. To evaluate whether

this is the case, for each of the households that makes single-store category purchases in at least

one category (in this case, all 1321 households), we first count the number of stores at which the

household makes single-store category purchases across all categories. We then plot a probability

mass of this count across all households in Figure 6. We observe that only about 10.2% of the

households make all of their single-store category purchases exclusively at one store. This

provides convincing evidence that many households do not make all of their single-store category

purchases exclusively at their favorite stores. Instead, households make single-store category

purchases at many different stores; in other words, households seem loyal to different stores for

different product categories.

[INSERT FIGURE 6 HERE]

Based on Figures 1-6, we can conclude that, despite the lack of appearance of overall store

loyalty for their grocery shopping, households do exhibit polygamous store loyalties, that is, the

consumers are attracted to different stores for different categories. Next, we further explore this

phenomenon to understand the key influencers of store-category loyalty from a long-term

perspective, particularly those that relate to the strategic merchandising programs of retailers as

opposed to tactical promotions. To achieve this goal, we propose a model that decomposes store-

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category attractiveness, which determines store-category loyalty, into store-specific, store-

category-specific and store-household-specific effects.

5. EMPIRICAL ANALYSIS

5.1 The Proposed Model

In this study, we focus on a household’s long-term relationship with a store at the category

level. We conceptualize that store-category loyalty represents a household’s long-term propensity

to choose a store in a category. Specifically, we consider a market where H households

(h=1,2,…,H) make purchases in C categories (c=1,2,…,C) among S stores (s=1,2,…S). The

observed category purchases of household h in category c across S stores is represented by

1 2( , ,..., )h h h h

c c c ScN n n n , where h

scn denotes the total number of purchases made by household h in

category c at store s during a period. We assume that each observed store choice outcome vector in

a category, h

cN , is generated by a multinomial process determined by the underlying latent store-

category loyalty vector h

cSCL of a household h in category c across S stores, where

1 2, ,..., ,h h h h

c c c ScSCL scl scl scl i.e., ~ ( )h h

c cN Multinomial SCL , and h

cSCL is normalized to one such

that 1

1S

h

sc

s

scl

.4

We assume that h

cSCL follows the axioms of Bell, Keeney and Little (1975), which provide

a theoretical foundation for the attraction models. Following their conceptual framework, we also

assume that the household h’s loyalty for store s in category c, h

scscl , is proportional to the

attractiveness of store s to household h in category c , h

scA ; i.e. h h

sc scscl A . Cooper and Nakanishi

4 Alternatively, one can conceptualize a household’s SCL based on the household’s category expenditures at the store.

Since the correlation between shares of category expenditure and shares of category purchase incidences across stores

in our data is 0.977, we do not expect meaningful differences in results to emerge from using the alternative

conceptualization of SCL in our model.

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(1988) propose a multinomial logit (MNL) specification for the attraction models for its logical

consistency. Accordingly, we also choose this widely accepted MNL specification for our

proposed model, which is written as follows:

1

exp

exp

h

sch

sc Sh

rcr

Ascl

A

(1)

Next, we discuss the factors that influence store-category attractiveness, h

scA , which in turn,

affects latent store-category loyalty, h

scscl .

5.2 Store-Category Attractiveness

The attractiveness of a store in a given category to a household can arise from multiple

factors that can be attributed to store characteristics, category characteristics, household

characteristics, and various combinations of these three types of characteristics. Thus, we

decompose store s’s attractiveness in category c to household h, h

scA , into various corresponding

components as follows:

,h h h h

sc s sc s c sc scA X (2)

where s represents the mean intrinsic attractiveness of store s; sc represents the intrinsic

attractiveness of store s in category c; s and sc together s sc represent the intrinsic store-

category attractiveness; h

s denotes household heterogeneity in store attractiveness.5 The term

h

c scX represents the household store-category attractiveness attributed to store s’s merchandising

effort in category c; scX is a vector of K variables representing store s’ merchandising strategies in

5 We also control for observed heterogeneity in store category attractiveness both in category and household

dimensions, but for ease of exposition we discuss it separately in section 6.2.3. See equation (5) for full specification

of the model.

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category c (such as product assortments and pricing);h

c denotes household h’s response specific

to category c, which we call merchandising effectiveness (we will discuss h

c and scX in detail in

the next two subsections); and finally, the last term,h

sc , accounts for the household’s store-

category level idiosyncrasies in store category attractiveness (Cooper and Nakanishi 1988).

5.3 Merchandising Effectiveness

A household’s response to merchandising strategies may differ across categories. To better

understand these differences, we decompose merchandising effectiveness, h

c , into the following

components: (1) the mean effect common across categories and households, denoted by ; (2) the

effect specific to category but common to all households, c ; and (3) the effect unique to

household h but common across all categories, denoted by h (Ainslie and Rossi 1998,

Seetharaman; Ainslie and Chintagunta 1999; Singh, Hansen and Gupta 2004; and Prasad, Strijnev,

and Zhang 2008). Mathematically, the decomposition can be written as follows: 6

,h h h

c c c (3)

where h

c captures the residual unobserved deviation that is specific to both household h and

category c. Next, we discuss each merchandising variable in the vector of scX that represents store s’

merchandising strategies in category c.

5.4 Key Merchandising Variables

We are particularly interested in how merchandising strategies, which are under the control

of retailers, influence a store’s attractiveness in a category to households. The typical

merchandising variables can be constructed to represent a store’s product assortment, pricing and

6 Similar to intrinsic store attractiveness, we also control for observed heterogeneity in merchandising effectiveness in

both category and household dimensions. Again for ease of exposition, we discuss it separately in section 6.2.3. See

equation (6) for full specification.

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promotional strategies (e.g., features, display and coupons, etc.). Empirical researchers have

shown that although temporary promotional activities are effective in achieving short-term goals

such as store traffic and sales, their effects on long-term measures such as market shares (Nijs,

Dekimpe, Steenkamp and Hanssens 2001) are minimal. Since store category attractiveness is

relatively stable over time and governed by consumers’ perceptions, in this study, we focus on

retailers’ product assortment and pricing strategies which are long-term in nature. In our model,

the effects of retailers’ promotional strategies are essentially subsumed in intrinsic store-category

attractiveness.

One of the challenges of modeling purchases in multiple categories in a single united

framework is that we need measures that are comparable across categories, across stores and

across different strategies. To achieve this objective, we follow Briesch, Chintagunta and Fox

(2009) to construct index variables that are normalized by corresponding average values and thus

operationalized to be unit-free and scale-free to ensure appropriate comparison.

5.4.1 Product Assortment Variables

Several studies, based on surveys and lab experiments, have revealed that product

assortments play an important role in consumers’ store evaluations and/or store choice decisions

(Meyer and Eagle 1982; Arnold, Oum and Tigert 1983; Craig, Ghosh and McLafferty 1984; and

Louviere and Gaeth 1987). It has further been shown that consumers’ perceptions of product

assortments are multi-dimensional (Broniarczyk, Hoyer and McAlister 1998; and Chernev and

Hamilton 2009). Therefore, we construct product assortment variables that capture the two most

important dimensions, namely, breadth and exclusiveness, of the category assortments.

First, we measure the assortment breadth in category c at the store s from two aspects –

brand breadth and SKU breadth – within a brand. We explain the two variables below:

Number of Brands in the Category at the Store (BRANDsc).

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This variable is defined as

1

scsc S

rc

r

BRANDSBRAND

BRANDS S

, where BRANDS sc stands for

the total number of brands in category c available at store s.

Average Number of SKUs per Brand in the Category at the Store (SKUsc).

This variable is defined as

1

/

/

sc scsc S

rc rc

r

SKUS BRANDSSKU

SKUS BRANDS S

, where SKUSsc stands

for the total number of SKUs in category c available at store s.

Next, we construct variables to measure assortment exclusiveness. To the extent that a

private label is exclusive to the store, this measure can serve as a proxy for the exclusiveness of

the store’s product assortments.7 We construct the following assortment exclusiveness variable:

Number of Private Labels in the Category at the Store (PVTLABELsc).

This variable is defined as

1

scsc S

rc

r

PVTLABELSPVTLABEL

PVTLABELS S

, where, PVTLABELSsc

stands for the number of private label SKUs in category c available at store s.

5.4.2 Price Variables

One of the robust findings in research on store choice decisions is that the perception of

low prices is an important factor in driving positive consumer evaluations of stores (Baumol and

Ide 1956; Brown 1978; Meyer and Eagle 1982; Arnold, Oum and Tigert 1983; Bell and Lattin

1998; and Bell, Ho and Tang 1998). We construct the following variable to measure the

attractiveness of the store’s pricing in the category:

Price Index of the Store in the Category (PRICEsc).

7 We do recognize that retailers’ decisions of providing shoppers different private label options go beyond the purpose of just being

exclusive; for example, retailers offer private label products to gain higher profit margins or to have better negotiating leverage with

manufacturers of national brands (Ailawadi, Pauwels and Steenkamp 2008).

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This store-category level variable is operationalized as the average of normalized SKU

prices (i.e., price of an SKU divided by the average SKU price in the dataset) and defined

as 1

scN

scu

u cu

sc

sc

P

AvgPPRICE

N

where Pscu stands for the average price of SKU u in category c

in store s over time, AvgPcu stands for the average price of SKU u in category c across all

stores over time, and is the total number of SKUs in category c at store s. When

constructing this price variable, we aim at eliminating the effects of non-pricing factors,

such as differences in package sizes (thus, potential quantity discounts) and quality (e.g.,

organic products may be priced higher than non-organic products), on price levels by

normalizing the SKU prices ─ dividing the SKU prices by average prices.

Given the same level of average category prices at two stores, the store with lower price

variability over time may be interpreted as a consistent and dependable provider of good value in

the category from a long-term perspective, which may result in greater store attractiveness to

consumers in the category. Conversely, a store with greater price variability over time may

encourage consumers to shop at that store only when low prices are offered in the category and

drive consumers, during periods of high prices, to search for lower prices at other stores. In other

words, greater price variability in a category at a store may reduce the overall attractiveness of the

store to households in the category from a long-term perspective. For this reason, we include the

following variable to measure the price variability of the store in the category:

scN

20

Price Variability of the Store in the Category (PRICEVARsc).

This variable is defined as

1

,scsc S

rc

r

CVPRICEVAR

CV S

where CVsc stands for the

coefficient of (temporal) variation over time of category prices in category c at store s at

time t, 1

Cat_Price

scN

scut

u cu

sct

sc

P

AvgP

N

. Notice that Cat_Pricesct is defined similarly to

scPRICE but with a time subscript, and it is also normalized to eliminate the effects of non-

pricing factors on prices as in PRICEsc.

In summary, we include three variables to represent a store’s assortment strategies in a

category, among which BRANDsc and SKUsc measure the relative breadth of the assortment, and

PVTLABELsc measures the relative exclusiveness of the assortment. We also include two variables

to measure two aspects of the pricing strategies of the store in the category PRICEsc measures

the relative price level and PRICEVARsc measures the relative price variability over time. In Table

1, we provide descriptive statistics pertaining to these variables in our dataset, which indicate that

all variables have comparable scales in the data.

[INSERT TABLE 1 HERE]

5.5 Estimation

Given the total number of purchases made by household h in category c across different

stores during the study period, 1 2( , ,..., )h h h h

c c c ScN n n n , we can write the likelihood function for

household h in c and then the total likelihood across all categories and households as follows:

1 1 1

1

exp

exp

h

sc

hH C S

sc

Shh c src

r

n

AL

A

(4)

21

Since our data contains individual household level purchases across multiple stores in

multiple categories, this enables us to separate store-category level effects from overall store level

effects and household heterogeneity. We employ a hierarchical Bayes technique to estimate the

two-way random effect model and make standard parametric assumptions. Specifically, we

assume that sc ,h

s and h

sc follow S-1 dimension independent multivariate normal distribution

with respective zero mean vectors and (S-1) by (S-1) full variance covariance matrix sc ,

hs

and

hsc

.8 For c , h , and

h

c , we assume they follow K-dimension independent multivariate normal

distribution, where K is the number of variables representing the merchandising strategies, with

respective zero mean vectors and K by K full variance covariance matrix c

, h

and .hc

After

augmenting a latent step to estimateh

scA , via a Metropolis Hastings step, the rest of the Gibbs

sampler is fairly straightforward under conjugate priors.

6. ESTIMATION RESULTS AND MANAGERIAL IMPLICATIONS

We estimate the proposed model using the data described in section 3. To attenuate the

concern about the impact of low purchase frequency categories on store-category attractiveness,

we drop categories and households that have fewer than 100 purchase observations from the

original dataset. This results in 925,153 category purchase incidences generated by 1,280

households purchasing across 244 categories. For the proposed model, we estimate 13 mean

intrinsic store attractiveness terms s , 2719 category-specific intrinsic store attractiveness terms

sc , 16,640 household-specific intrinsic store attractiveness terms h

s , five merchandising

effectiveness terms , 244 category-specific merchandising effectiveness terms c , and 1,280

8 For identification purpose, we use one store as the base store and restrict its elements in

h

scA to 1; therefore, the

dimension is S-1.

22

household-specific merchandising effectiveness terms h .9 In addition to the proposed model,

we also estimate three benchmark models:

1) Benchmark Model 1: No category-level variations and no household heterogeneity in intrinsic

store attractiveness and in merchandising effectiveness. Mathematically, the model can be

written as:

;h h h

sc s c sc scA X where h h

c c

2) Benchmark Model 2: No category-level variations but allow for household heterogeneity in

intrinsic store attractiveness and in merchandising effectiveness. The model can be written as:

;h h h h

sc s s c sc scA X where h h h

c c

3) Benchmark Model 3: Allow for category-level variations but no household heterogeneity in

intrinsic store attractiveness and in merchandising effectiveness. The model can be written as:

;h h h

sc s sc c sc scA X where h h

c c c

Table 2 lists the log-likelihood and the deviance information criterion (DIC) of the

proposed model and the three benchmark models. The comparison shows that the proposed model

outperforms all benchmark models, implying that the attractiveness of a store varies substantially

by category as well as by household. Besides addressing household heterogeneity, it is also

important to pay attention to category differences in store attractiveness to households.

Next, we discuss the parameter estimates of the proposed model, which are most pertinent

to our main research questions.

[INSERT TABLE 2 HERE]

9 In addition, to account for observed category heterogeneity in category purchase frequency and budget share and

household heterogeneity in family size and family income, we also estimate 13*4 = 52 terms for intrinsic

attractiveness across 13 stores and 5*4 = 20 terms for 5 merchandising effectiveness variables. For ease of exposition,

we discuss the details in section 6.2.3.

23

c

6.1 Intrinsic Attractiveness

6.1.1 Intrinsic Overall Store Attractiveness

We first look at the estimates for intrinsic store attractiveness, s , which represents the

overall attractiveness of the stores (such as overall perceptions of store quality, store images,

convenience of store locations, etc.) common to all households and across all categories after

accounting for the impact of retailers’ assortment and pricing strategies. This is reported in Table

3.10 We find that Fry Food Store has the highest intrinsic store attractiveness (10.18), suggesting

that after accounting for the impact of retailers’ assortment and pricing strategies, Fry Food Store

is intrinsically the most attractive store to households. The intrinsic attractiveness of Albertsons,

Bashas’, Safeway and Wal-mart Supercenter is greater than that of the Trader Joe’s while the

intrinsic store attractiveness of Food 4 Less and IGA is slightly less. For the rest of the six stores,

this estimate turns out to be insignificant, suggesting that after accounting for the impact of

retailers’ assortment and pricing strategies, these stores have similar overall attractiveness as

Trader Joe’s. Next, we look at store attractiveness from the category perspective.

[INSERT TABLE 3 HERE]

6.1.2 Intrinsic Store-Category Attractiveness

The extent to which intrinsic store attractiveness varies across categories is measured by

the variance of intrinsic store category attractiveness, sc . Relatively low diagonal values in

sc imply that intrinsic store-category attractiveness is perhaps only a function of overall store

characteristics (e.g., store-level customer services, store-level check-out services and store

location, etc.), while a large value implies that the intrinsic store attractiveness of a category

depends on characteristics that are specific to the category (we will further discuss the issue

10 For identification purposes, we set the intrinsic attractiveness for Trader Joe’s at zero.

24

related to observed category characteristics in section 6.2.3). We observe large variations in store

attractiveness across categories and substantial heterogeneity across households (which is

measured by hs

); both standard deviations are reported in Table 3. Specifically, we find that

53.84% of the intrinsic store-category attractiveness estimates sc are significantly different

from zero, indicating that 53.84% of the categories have their own category specific store

attractiveness that is different from mean store attractiveness s . This underscores our conjecture

that store loyalty is best viewed as a category specific trait.

Next, we discuss how a retailer can use our estimates of intrinsic store-category

attractiveness to help manage categories within the store and compete with other stores.

6.1.3 Competitive Positions of Stores based on Intrinsic Store Category Attractiveness

We provide a unique perspective of stores’ competitive positions based on stores’

intrinsic category specific attractiveness, sc . The correlation of sc between two stores (derived

from sc ) across categories indicates how similar (or dissimilar) two stores are in consumers’

minds in terms of their categories’ standing of intrinsic category attractiveness. For example, a

positive correlation indicates that two stores have a similar rank ordering of categories in terms of

intrinsic category attractiveness and hence compete closely with each other at the category level. A

correlation matrix can help a retailer understand its position against other stores at the category

level in the competitive environment and thus formulate appropriate marketing strategies. In Table

4, we report the correlation matrix of sc across stores. From the table, we see that the correlations

among Albertsons, Bashas’, Fry Food Store and Safeway are more than 0.9, suggesting that these

stores compete closely with each other at the category level.

[INSERT TABLE 4 HERE]

25

We use a factor analysis to analyze the correlation matrix and find that the first two factors

explain 89.37% of cumulative variance. We plot the stores on a map using the two factors. The

perceptual map is reported in Figure 7. The map shows that this market can be best described by

four clusters. The first cluster consists of Albertsons, Bashas’, Fry Food Store and Safeway, which

are supermarkets generally implementing HiLo pricing strategies. They have high correlations (0.9

and above) among themselves on intrinsic store category attractiveness and therefore compete

head to head in consumers’ minds, after accounting for the stores’ assortment and pricing

strategies. Their higher overall store attractiveness also confirms that they are major players in this

market. The second cluster consists of Food 4 Less, Food City and IGA. These stores have limited

assortments and generally implement everyday low price (EDLP) strategies (Gauri, Trivedi and

Grewal 2008). They have high correlations (0.8 and above) among themselves and therefore are

direct competitors. The two club stores Costco and Sam’s Club are in cluster three, which can be

characterized as supercenters with EDLP strategies but only accessible to club members. The two

stores have a positive correlation between themselves (0.6) but have negative correlations with the

rest of the stores. This suggests that the two club stores compete with each other (but not head to

head) and are complementary to most of the other stores. Super Kmart, Smart & Final and Wild

Oats Market can be grouped into one cluster. They exhibit relatively small correlations with other

stores, implying that they are perceived to have niche positions and indirectly compete with other

stores. Finally, Wal-Mart Supercenter can be categorized as a group of its own. It has moderate

positive correlations with the stores in cluster one (e.g., Albertson’s and Safeway, etc.) and Super

Kmart, and relatively small correlations with the rest of the stores. This implies that it competes

(though not head to head) with HiLo stores.

[INSERT FIGURE 7 HERE]

26

6.1.4 Rank Ordering Most Intrinsically Attractive Categories

Retailers can rank order categories based on intrinsic store category attractiveness. This

can help them identify top categories to effectively allocate merchandising and marketing

resources across categories and leverage their stores’ competitive advantages to improve overall

store patronage. For each of the 13 stores, we rank the categories based on their intrinsic store-

category attractiveness and list the top 10 categories that have the highest values in Table 5.11

Given our conjecture about store-category attractiveness, it is not surprising to see that different

stores are strong in different categories but exhibit similar intrinsic store-category attractiveness

patterns within a cluster as mentioned in section 6.1.3.

[INSERT TABLE 5 HERE]

Retailers can further investigate categories that have high intrinsic store-category

attractiveness to understand what makes those categories intrinsically attractive in their stores and

cultivate their attractiveness further. For example, in Albertsons, frozen fruits, croutons and frozen

pizza are ranked as the top three categories. Albertsons can investigate what additional factors

unrelated to assortment and pricing strategies, such as salient aisle placement of the categories,

may contribute to their high attractiveness. Furthermore, our analysis can also help a retailer

estimate the attractiveness of a category that is not currently present in the store. Leveraging the

correlation of attractiveness across stores sc , a retailer can derive the attractiveness of the new

category that was not present in the focal store based on information extrapolated from the same

category in other stores. For example, though Food City currently doesn’t carry skin care products,

our model can predict, on average, how attractive skin care products would be to consumers if

introduced in Food City.

11 Note that these categories are ranked only based on intrinsic store-category attractiveness. The overall attractiveness

of a category at a store depends on both merchandising effectiveness of retailers as well as the category’s intrinsic

store attractiveness.

27

6.2 Effects of Merchandising Programs on Store-Category Attractiveness

6.2.1 Effects Common across Categories and Households

Retail managers are interested in understanding the main effects of their merchandising

strategies on store-category attractiveness. We report the estimated mean effects that are common

across categories and households, , for each of the assortment and price variables in the first

column of Table 6. We notice that the estimated mean effect ( ) for the number of brands

(BRANDsc) is positive (1.47), suggesting that offering more brands in an average category

increases the category’s store attractiveness. The estimated mean ( ) for the average number of

SKUs per brand (SKUsc) is positive (0.60), implying that, on average, the number of SKUs within

a category also has a positive effect on its store-category attractiveness. The results also show

substantial variation across categories (discussed further in section 6.2.2). Although intuitively it

would seem that increasing assortment breadth may help store-category attractiveness, the existing

empirical research on the effect of assortment breadth on category revenues has shown mixed

results. For example, Dreze, Hoch and Purk (1994) find that sales go up after assortment

reduction, while Broniarczyk, Hoyer and McAlister (1998) and Boatwright and Nunes (2001) find

no effect. Borle et al (2005) find that assortment reduction has a negative effect on both shopping

frequency and purchase quantity but observe that the impact varies widely by category. Our

research supports the findings of Borle et al (2005), as we find that increasing assortment breadth

increases store-category attractiveness and also that the effect varies greatly by category.

[INSERT TABLE 6 HERE]

Table 6 also shows that the estimated mean is insignificant for the number of private

labels (PVTLABELsc). Corstjens and Lal (2000) analytically demonstrated that only when the

quality of store brands exceeds a threshold level does carrying store brands increase the store’s

attractiveness. Our finding is consistent with theirs, particularly in that, on average, merely

28

increasing the breadth of private label assortments within a category has no impact on store-

category attractiveness.12

The estimated effects of both price variables are consistent with our a priori expectations.

Specifically, the estimated mean for PRICEsc is negative (-0.49), while that of PRICEVARsc is

negative (-0.09). These findings suggest that, on average, a retailer who adopts an EDLP strategy

within a category enjoys higher store-category attractiveness. However, we find that there is

substantial variation across categories, which we discuss in the next section.

6.2.2 Category-Specific Merchandising Effects

The proposed model enables us to identify the merchandising effects of a specific category

on store-category attractiveness to consumers. This can help retailers improve their stores’

attractiveness to consumers through improved category management. The magnitude of the

estimated c represents the degree of deviation in category c from the mean effect of the kth

merchandising (e.g., product assortments or pricing) program, , on store-category attractiveness.

Between two categories, the store-category attractiveness of the category with a higher absolute

value of c is more responsive to changes in the kth merchandising program. Therefore, rank-

ordering the categories based on the magnitudes of the estimated values of c would help retailers

prioritize among categories particularly for merchandising program k; this, in turn, would enable

retailers to appropriately allocate their limited marketing resources across categories. As an

illustration, in Table 7, we list the top 10 categories with positive deviations ( 0c ) that differ

significantly from the mean effect ( ) and top 10 categories with negative deviations ( 0c )

12 As our data does not contain the information on the quality of the private label brands, in this empirical application we cannot

distinguish private label brands based on quality differences.

29

that differ significantly from the mean effect ( ).13 Depending on the direction of the mean effect

(i.e., the sign of ) of a specific merchandising variable, these categories are respectively the

most responsive and the least responsive to changes in the merchandising program. For example,

since the mean effect of the number of brands within the category is positive BRANDS( 1.47) , the

store-category attractiveness of motor oil is the most responsive and that of dish detergent is the

least responsive to changes in the number of brands within the category. On the other hand, since

the mean effect of price is negative PRICE( 0.49) , the store-category attractiveness of non-fruit

drinks is the most price sensitive while that of frozen seafood is the least price sensitive.

[INSERT TABLE 7 HERE]

Our study allows retailers to make informed decisions on how different types of

merchandising programs (e.g., assortment versus price), or even different levels in a given

merchandising program (e.g., few versus many brands), can be customized for different categories

to improve overall store attractiveness to consumers. Careful analysis of categories with high

intrinsic category attractiveness (Table 5) in a store combined with how these categories respond

to various merchandising efforts (Table 7) can help retailers craft customized strategies to improve

store patronage.

6.2.3 Category Heterogeneity

We uncover substantial variation in the estimated effects of product assortments and

pricing strategies across categories. The variation is captured by the standard deviations of the

estimated category-specific effects, c , across categories. Similarly, the heterogeneity among

households is captured by the standard deviations of the estimated household-specific effects,

13 For ease of exposition, we report the rank-ordering for three merchandising variables only. The results for rest of the

merchandising variables are available from authors upon request.

30

h , across households. We report these two standard deviations in the second and third columns

of Table 6, respectively. A comparison between these two columns shows that except for the

price variable, there are large variations in the effects of retailers’ assortment and pricing

programs across categories. Though the variation across categories in the effect of the number of

private labels is relatively small in terms of absolute value, the variation is directional, i.e., for

some categories, increasing the number of private label SKUs will increase their store-category

attractiveness, while for other categories, it will decrease their store-category attractiveness. These

results suggest that when planning merchandising programs, retailers should pay close

attention to the differences across categories. Our results are also consistent with studies where

opposite effects of various merchandising strategies are found due to choices of different

categories (e.g., Dreze, Hoch and Purk 1994; Broniarczyk, Hoyer and McAlister 1998; Boatwright

and Nunes 2001; and Borle et al 2005, to name a few).

We next investigate how specific observed category characteristics potentially explain the

heterogeneity. First, we explain the estimation procedure before discussing the results. To analyze

the effects of observed category characteristics, we had decomposed the category specific effect in

merchandising effectiveness, c , see equation (6), into two components: (i) cZ , where cZ is a

vector of variables that represent observed category characteristics (i.e., purchase frequency and

budget share) and is the vector of corresponding responses; and (ii) c , the remaining effect

specific to category c that is not explained by the observed category characteristics. To maintain

consistency, we also decompose intrinsic store-category attractiveness, sc , see equation (5), into

two components: (i) s cZ , where s is a vector of corresponding responses to cZ ; and (ii) sc , the

remaining intrinsic store-category attractiveness that is not explained by the observed category

characteristics. Additionally, we also estimate the effects of household characteristics by including

31

a vector of demographic variables (i.e., family size and family income), hD . Similar to category

heterogeneity decomposition, household heterogeneity in merchandising effectiveness, h , is

decomposed into observed heterogeneity,hD , where is a vector of coefficients for the

observed household demographic variables, and unobserved heterogeneity, h . Similarly, we also

decompose household heterogeneity in intrinsic household store attractiveness, ,h

s into observed

household heterogeneity,h

sD , and unobserved household heterogeneity h

s . To ensure consistent

estimates, we incorporate the above decomposition into equation (2) and (3) via a hierarchical

structure and ran a one-step Bayesian hierarchical estimation. Specifically, equation (2) and (3)

were estimated as the following two equations respectively:

;h h hh hsc s s c sc c sc scs s

A Z XD (5)

h hh hc c c cZ D (6)

We report the estimates for , in Table 8a and , in Table 8b, respectively. In Table

8a, we can see that category purchase frequency has positive effects on intrinsic store-category

attractiveness for most stores. Category budget share has positive effects on intrinsic store-

category attractiveness only for the two club stores. For most stores, store attractiveness is higher

for larger families. Costco is more attractive for households with higher family income, which is

consistent with the positioning of Costco. Table 8b shows that only one observed category

characteristic – category purchase frequency – positively affects how the store-category

attractiveness of a category responds to the number of brands in the category. Other observed

category and household characteristics have no impact. With more data on observed category

characteristics, retailers can use our model to comprehend how the store-category attractiveness of

different categories will respond differently to merchandising strategies.

32

[INSERT TABLE 8a and 8b HERE]

7. SUMMARY AND CONCLUSIONS

Most of the marketing literature views store loyalty as a behavioral trait of consumers that

relates to consumers’ store choice decisions, particularly for explaining which stores are the most

frequently visited by those consumers for their overall grocery shopping needs. However, if a

consumer is observed to shop at multiple grocery stores over time, thus appearing to not be store

loyal, she/he may still purchase some product categories consistently from one store, exhibiting

store loyalty at the category level. We call this consumer behavior “store-category loyalty”. Our

empirical findings indicate that capturing pertinent information on category-specific consumer

shopping behavior can shed new light on store loyalty, which would be of interest to the broader

community of researchers as well as retailers.

Using purchase data from 1321 households for 284 grocery categories across 14 retail

chains in a large southwestern city in the US, we demonstrate strong empirical evidence of store

category loyalty in the data, even though the overall store loyalty based on the traditional view is

low. We propose a model to examine the effects of key factors influencing store-category

attractiveness, which determines store category loyalty. By simultaneously studying households’

purchases in multiple categories at multiple stores on a large scale, we are able to decompose such

effects into those that are common across categories and across households and those that are

specific to a particular category or household. Furthermore, we demonstrate how the estimation

results from the proposed model can assist retailers in designing appropriate retail strategies at the

category level with the overall aim of improving overall store patronage.

Our paper augments prior literature on the effect of category attractiveness on store loyalty.

Our integrated approach of incorporating category-specific store attractiveness provides deeper

33

insights into consumers’ store choice behavior. In particular, our approach can provide a different

perspective of the relative positioning of stores in consumers’ minds based on intrinsic store-

category attractiveness after controlling for retailers’ merchandising programs. Our study also

enables retailers to make informed decisions on how to employ merchandising programs of

different types (e.g., assortment versus price), and different levels (e.g., few versus many brands),

which can be customized at the category level to improve overall store patronage. In a nutshell, we

believe that viewing store loyalty as a category-specific trait and understanding category-specific

store attractiveness can help managers boost overall store patronage.

8. Limitations and Directions for Future Research

There are some possible areas for future research. First, our analysis aggregates the time

dimension since we do not have weekly promotional information (e.g. features, displays, coupons,

etc.) in the data. Moreover, the weekly data for product assortments and prices is sparse for a large

number of categories. We are unable to accommodate the time dimension in this study. It would be

useful to analyze a dataset that contains weekly store environment data with comprehensive

information in these areas. That analysis can help parse out the influence of short-term marketing

activities on long-term store-category attractiveness. Second, although we model correlations

among stores and control for household-level preferences that are common across categories, due

to the curse of dimensionality, we are unable to explicitly model cross-category correlations that

may arise due to demand complementarity (e.g., cake mix and cake frosting). Accounting for such

correlations may be useful to uncover how a household’s store-category loyalty may be related

across categories. Third, although we do not expect meaningful differences in results to emerge

from using purchase quantity or expenditure in the model due to the high correlation between

households’ shares of category expenditure and shares of category purchase incidences across

34

stores in our data, it would be of interest for future studies to use purchase quantity or expenditure

to compare alternative approaches for conceptualizing store-category loyalty. Last but not least, it

would be interesting for future research to link households’ store-category loyalty with

households’ overall store loyalty, as understood in the store loyalty literature. Such a study will

assist retailers in better understanding how piecemeal management of category loyalty can

eventually lead to an overall advantageous position for their stores in the market.

We hope that our work prompts future research on modeling and understanding the

influence of categories on the relationship between a consumer and a store and its implications on

retail practice.

35

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39

Figure 1. Frequency Distribution of Households in Terms of Number of

Different Stores at Which a Household Shops

Figure 2. Probability Mass of Households in Terms of Proportion of Shopping Trips Made

at a Household's Favorite Store

12

47

82

167

209

268

198

144

106

59

197 3

0

100

200

300

1 2 3 4 5 6 7 8 9 10 11 12 13

Nu

mb

er o

f H

ou

seh

old

s

Number of Stores at which a Household Shops

0.2

3.6

5.5

8.9

11.210.4 10.4

8.68.2

6.15.6 5.3

4.13.6 3.6

2.5 2.3

0

2

4

6

8

10

12

20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Per

cen

tag

e o

f H

ou

seh

old

s

Proportion of Shopping Trips Made at a Household's Favorite Store

40

Figure 3. Frequency Distribution of Households in Terms of Number of Categories in

Which a Household Makes all Category Purchases Exclusively at One Store

Figure 4. Frequency Distribution of Households in Terms of the Percentage of

Categories that a Household Buys Exclusively at One Store

75

372354

212

117

191

0

50

100

150

200

250

300

350

400

< 10 10 to 20 20 to 30 30 to 40 40 to 50 >=50

Nu

mb

er o

f H

ou

seh

old

s

Number of Categories in Which a Household Makes all Category Purchases

Exclusively at One Store

25

241

306

255

174

106

73 66 75

0

50

100

150

200

250

300

350

10% 20% 30% 40% 50% 60% 70% 80% > 80%

Nu

mb

er o

f H

ou

seh

old

s

Percentage of Categories that a Household Buys Exclusively at One Store

41

Figure 5. Frequency Distribution of Categories in Terms of Percentage of

Households that Buy Categories Exclusively at One Store

Figure 6. Probability Mass of Households in Terms of Number of Stores that a

Household has Exclusive Store-Category Loyalty (SCL)

1

8

25

54

72

59

37

1612

0

10

20

30

40

50

60

70

80

10% 20% 30% 40% 50% 60% 70% 80% > 80%

Nu

mb

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f C

ate

go

ries

Percentage of Households that Buy a Category Exclusively at One Store

10.2

24.5

29.3

22.4

9.6

3.30.6 0.1

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8

Per

cen

tag

e o

f S

CL

Ho

use

ho

lds

Number of Stores to Which a Household has Exclusive SCL

42

Figure 7. Perceptual Map of Stores Based on Correlation Matrix of Intrinsic Store-

Category Attractiveness sc

Dimension 1

43

Table 1. Descriptive Statistics of Assortment and Price Variables

Min Max Mean Std. Dev. VIF

PRICEADV 0.759 1.526 0.997 0.066 1.023

PRVLABEL 0.000 5.780 0.648 0.771 2.439

PRICEVAR 0.002 4.224 1.033 0.487 1.046

SKUs 0.158 4.391 1.045 0.456 2.381

BRANDS 0.033 4.857 0.988 0.628 2.245

Table 2. Comparison of Model Performance

Model Log-likelihood DIC

Benchmark Model 1: No category-

level variations and no household

heterogeneity -725495 1451383

Benchmark Model 2: No category-

level variations but with household

heterogeneity -714953

1430332

Benchmark Model 3: With category-

level variations but no household

heterogeneity -675103 1350389

Proposed model: With category-

level variations and with household

heterogeneity -670355 1340866

44

Table 3. Intrinsic Mean Store Attractiveness and Standard Deviation of

Unobserved Category and Household Heterogeneity a

Market Share

Mean Attractiveness

Category Heterogeneity sc

Std. Dev

Household Heterogeneity h

s Std. Dev.

Albertsons 8.72% 4.68 1.97 4.87

Bashas’ 10.82% 4.28 2.08 5.32

Costco 3.78% 0.11 2.58 3.50

Food 4 Less 0.77% -0.16 1.11 2.49

Food City 1.29% -0.06 0.98 2.16

Fry Food Store 39.10% 10.18 2.56 4.79

IGA 1.31% -0.25 1.45 3.25

Safeway 18.46% 5.49 2.11 5.25

Sam's Club 1.85% 0.01 1.05 2.86

Smart & Final 0.06% 0.00 0.68 0.00

Super Kmart 1.56% 0.06 1.21 3.78

Wal-Mart Supercenter 11.75% 3.41 1.54 6.14

Wild Oats Market 0.12% 0.00 0.35 0.02 a Estimates in grey are insignificant at the 95% confidence level.

45

Table 4. Correlations across Stores based on Intrinsic Store-Category Attractiveness

Albertsons Bashas’

Fry Food Store

Safeway Food 4 Less Food City IGA Sam's Club Costco Super Kmart Wild Oats

Market Smart &

Final Wal-Mart

Supercenter

Albertsons 1.00

Bashas’ 0.95 1.00

Fry Food Store 0.94 0.94 1.00

Safeway 0.94 0.95 0.93 1.00

Food 4 Less 0.59 0.64 0.60 0.61 1.00

Food City 0.50 0.57 0.49 0.52 0.88 1.00

IGA 0.62 0.64 0.57 0.62 0.86 0.79 1.00

Sam's Club -0.30 -0.23 -0.18 -0.21 -0.35 -0.38 -0.42 1.00

Costco -0.60 -0.57 -0.57 -0.51 -0.63 -0.58 -0.71 0.62 1.00

Super Kmart 0.25 0.28 0.19 0.31 0.37 0.30 0.36 -0.12 -0.19 1.00

Wild Oats

Market 0.12 0.17 0.16 0.15 0.45 0.48 0.42 -0.07 -0.19 0.43 1.00

Smart & Final 0.26 0.30 0.26 0.26 0.46 0.51 0.38 -0.02 -0.23 0.43 0.64 1.00

Wal-Mart

Supercenter 0.61 0.62 0.68 0.61 0.42 0.30 0.34 -0.15 -0.40 0.63 0.38 0.42 1.00

46

Table 5. Top 10 Categories with the Highest Intrinsic Store-Category Attractiveness (after controlling for marketing variables)

1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Albertsons FZ FRUIT CROUTONS FZ PIZZA ICE CREAM/

SHERBET MILK

RFG SIDE

DISHES FLOUR/ MEAL PIZZA - RFG FZ PASTA

RFG MEAT/

POULTRY

PRODUCTS

Bashas’ CROUTONS FZ FRUIT MILK FLOUR/ MEAL

RFG MEAT/

POULTRY

PRODUCTS

PIZZA - RFG RFG SIDE

DISHES JUICES - FROZEN FZ PIZZA FZ PASTA

Fry Food

Store

RFG MEAT/

POULTRY

PRODUCTS

FZ FRUIT RFG SIDE

DISHES CROUTONS

FZ PLAIN

VEGETABLES

ICE CREAM

CONES/MIXES FLOUR/MEAL MILK FZ PIZZA

ICE CREAM/

SHERBET

Safeway FZ FRUIT CROUTONS FLOUR/MEAL FZ PLAIN

VEGETABLES PIZZA - RFG

RFG SIDE

DISHES MILK

ICE CREAM/

SHERBET FZ PASTA

RFG MEAT/

POULTRY

PRODUCTS

Food 4 Less FLOUR/ MEAL FZ PLAIN

VEGETABLES FZ PIES STUFFING MIXES

EVAPORATED/

CONDENSED

MILK

JUICES - FROZEN MILK CROUTONS ALL OTHER

SAUCES

CREAMS/

CREAMERS

Food City FLOUR/MEAL DRY BEANS/

VEGETABLES FZ PIES

FZ PLAIN

VEGETABLES

EVAPORATED/

CONDENSED

MILK

FOILS & WRAPS PIZZA - RFG JUICES - FROZEN ALL OTHER

SAUCES

FZ DESSERTS/

TOPPING

IGA FLOUR/ MEAL MILK FZ PLAIN

VEGETABLES FZ PIES FZ FRUIT

EVAPORATED/

CONDENSED

MILK

CROUTONS GELATIN/

PUDDING MIXES

FZ DESSERTS/

TOPPING PIZZA - RFG

Sam's Club FZ SEAFOOD

FABRIC

SOFTENER

LIQUID

BAKING NUTS LAUNDRY

DETERGENT

FOOD & TRASH

BAGS

MOIST

TOWELETTES MOTOR OIL BUTTER VITAMINS

SANITARY

NAPKINS/

TAMPONS

Costco MOUTHWASH ENGLISH

MUFFINS

MOIST

TOWELETTES

SANITARY

NAPKINS/

TAMPONS

SNACK NUTS/

SEEDS/ CORN

NUTS

VITAMINS MOTOR OIL

FABRIC

SOFTENER

LIQUID

FZ POULTRY RAZORS

Super Kmart OFFICE

PRODUCTS LIGHT BULBS FLOUR/ MEAL CANDLES

WRITING

INSTRUMENTS SKIN CARE COUGH DROPS HAIR COLORING

COSMETICS -

NAIL

COSMETICS -

EYE

Wild Oats

Market RICE/ POPCORN

CAKES

FZ PLAIN

VEGETABLES

EVAPORATED/

CONDENSED

MILK

COLD/

ALLERGY/ SINUS

LIQUIDS

CANDLES FLOUR/MEAL CREAMS/

CREAMERS COUGH DROPS SEAFOOD - RFG SHAMPOO

Smart &

Final FZ DESSERTS/

TOPPING FZ PASTA FZ PIZZA BLEACH

ALL OTHER

SAUCES ASEPTIC JUICES

DISPOSABLE

TABLEWARE

LAUNDRY

DETERGENT

CREAMS/

CREAMERS

HOUSEHOLD

CLEANER

CLOTH

Wal-Mart

Supercenter PIES & CAKES

RFG MEAT/

POULTRY

PRODUCTS

FOILS & WRAPS FZ FRUIT FLOUR/ MEAL BABY FOOD BLEACH VINEGAR FZ BREAD/ FZ

DOUGH HAIR COLORING

47

Table 6. Mean Merchandising Effectiveness and Standard Deviation of Unobserved

Category and Household Heterogeneity a

Variable Mean Response

Category Heterogeneity

Std. Dev.

Household

Heterogeneity Std. Dev.

BRANDS 1.47 0.95 0.54

SKUs 0.60 0.72 0.44

PVTLABEL 0.01 0.16 0.15

PRICE -0.49 0.19 1.40

PRICEVAR -0.09 0.44 0.41 a

Estimates in grey are insignificant at the 95% confidence level.

48

Table 7. Top 10 Categories Whose Response ( c ) Positively Differs from Mean Response and Top

10 Categories Whose Response Negatively Differs from Mean Response across All Categories

BRANDS BRANDS

c PRICE PRICE

c PRICEVAR PRICEVAR

c

MOTOR OIL 3.821 FZ SEAFOOD 0.418 MOTOR OIL 1.918

CANDLES 2.943 FZ NOVELTIES 0.395 MOIST TOWELETTES 1.183

LIGHTERS 2.262

RFG TORTLLA/

EGGRLL/ WONTN WRAP

0.340 CANDLES 1.147

RFG DIPS 2.250 DISH DETERGENT 0.326 RFG DIPS 0.943

BAKED GOODS -

RFG 2.140 BOTTLED WATER 0.307

DRY BEANS/

VEGETABLES 0.927

DRY BEANS/

VEGETABLES 1.779 SOUR CREAM 0.302 SKIN CARE 0.904

MOIST

TOWELETTES 1.662

SALAD DRESSINGS -

SS 0.301 FZ PIES 0.807

HAIR SPRAY/

SPRITZ 1.642

MILK FLAVORING/

COCOA MIXES 0.290 ENGLISH MUFFINS 0.798

HAIR

ACCESSORIES 1.598

BEER/ALE/

ALCOHOLIC CIDER 0.283

COLD/ ALLERGY/ SINUS

TABLETS 0.658

AUTOMOBILE

FLUIDS/ ANTIFREEZE

1.549 DISPOSABLE

TABLEWARE 0.279 RFG ENTREES 0.645

DISH

DETERGENT -1.292

NON-FRUIT DRINKS -

SS -0.280

SPAGHETTI/ ITALIAN

SAUCE -0.574

BOTTLED

WATER -1.340

FIRST AID

TREATMENT -0.282

SNACK BARS/ GRANOLA

BARS -0.583

MILK FLAVORING/

COCOA MIXES

-1.402 SPIRITS/ LIQUOR -0.289 RFG MEAT/ POULTRY

PRODUCTS -0.627

WINE -1.438 HAIR ACCESSORIES -0.328 CAT/DOG LITTER -0.654

FZ NOVELTIES -1.439 LIGHTERS -0.341 TOILET TISSUE -0.683

RFG SIDE DISHES -1.482 PICKLES/RELISH/

OLIVES -0.354

TOOTHBRUSH/ DENTAL

ACCESORIES -0.715

DISPOSABLE

TABLEWARE -1.558

RFG MEAT/ POULTRY

PRODUCTS -0.360 PEANUT BUTTER -0.718

SALAD

DRESSINGS - SS -1.634 BAKED GOODS - RFG -0.363

PICKLES/RELISH/

OLIVES -0.771

FZ SEAFOOD -1.888 CANDLES -0.365 RFG TORTLLA/ EGGRLL/

WONTN WRAP -0.796

RFG TORTLLA/ EGGRLL/ WONTN

WRAP

-2.480 MOTOR OIL -0.371 DESSERT TOPPINGS -0.805

49

Table 8a.Observed Category and Household Heterogeneity in Intrinsic Store Attractiveness a

Purchase_Freq Budget_Share Family_Size Family_Income

Albertsons 0.011 -0.088 0.166 0.013

Bashas’ 0.008 -0.142 0.103 -0.040

Costco -0.015 0.205 0.125 0.086

Food 4 Less 0.006 -0.067 0.145 -0.078

Food City 0.003 -0.041 0.086 -0.086

Fry Food Store 0.008 -0.184 0.358 -0.147

IGA 0.009 -0.055 0.042 -0.111

Safeway 0.007 -0.074 -0.210 0.079

Sam's Club -0.007 0.105 0.148 0.018

Smart & Final 0.000 0.000 0.000 0.000

Super Kmart 0.010 -0.103 0.142 -0.178

Wal-Mart Supercenter 0.009 -0.121 0.482 -0.244

Wild Oats Market 0.000 0.000 0.001 -0.001 a Estimates in grey are insignificant at the 95% confidence level.

Table 8b. Observed Category and Household Heterogeneity in Merchandising Effectivenessa

Purchase_Freq Budget_Share Family_Size Family_Income

BRANDS 0.003 -0.056 -0.011 -0.009

SKUs -0.001 0.010 -0.018 0.016

PVTLABEL -0.001 -0.010 -0.008 -0.012

PRICE -0.013 0.051 -0.109 0.037

PRICEVAR 0.001 -0.024 0.019 -0.007 a Estimates in grey are insignificant at the 95% confidence level.