EXAMINING POLYGAMOUS STORE LOYALTY
Qin Zhang
Manish Gangwar
P.B. Seetharaman†
March 13th, 2013
†
Qin Zhang is Assistant Professor of Marketing, Henry B. Tippie College of Business, University of Iowa. Manish
Gangwar is Assistant Professor of Marketing, Indian School of Business, Hyderabad, India. P. B. Seetharaman is W.
Patrick McGinnis Professor of Marketing, Olin Business School, Washington University in St. Louis.
Corresponding author: Qin Zhang, e-mail: [email protected], Ph: 319-335-3125.
0
EXAMINING POLYGAMOUS STORE LOYALTY
Abstract
The literature on grocery store loyalty views a consumer as possessing
store loyalty toward a particular store for her or his overall shopping needs. In this
study, we argue that store loyalty can be viewed as a category-specific trait, i.e., a
consumer could be loyal to Store A in category 1, but loyal to Store B in category
2. We call this store-category loyalty. With the presence of store-category loyalty,
we further argue that retailers should focus on consumers’ store preferences in
individual categories to improve overall store loyalty and revenues. We use an inhome scanning panel dataset that tracks 1321 households in 284 grocery
categories across 16 retail chains over a 53-week period in a large US market. The
data first suggest little overall store loyalty, based on the traditional view;
however, once the category dimension is added, extensive store loyalty at the
category level is uncovered. In order to more carefully examine consumers’ storecategory loyalty behavior, we propose a modified market share attraction model
(Cooper and Nakanishi 1988). By examining household purchases in multiple
stores and multiple categories simultaneously, we are able to decompose the
effects of non-merchandising and merchandising programs of retailers on
household store-category loyalty into effects that are common across stores,
categories and households, and effects that are store-specific, category-specific
and household-specific. We illustrate how the estimation results from the
proposed model can be used to help retailers improve overall store loyalty and
store revenues.
Keywords: Store Loyalty, Store-Category Loyalty, Market Share Attraction Model,
Multi-Category Analysis, Hierarchical Bayes.
1
1. INTRODUCTION
The grocery industry in the US is highly fragmented, with even the top retailers (such as
Wal-Mart, Kroger, Safeway etc.) accounting for only 5 - 10% of industry sales (TNS Global
Market Research 2009). Retailers have been focusing on attracting consumers to their stores for
their entire shopping baskets. This focus has also been reflected in the rich empirical literature in
marketing that studies consumers’ store switching behavior in grocery shopping.1 This literature
has sought answers to research questions such as the following:
1. What factors drive consumers’ grocery store choices over time? (e.g., Bucklin and Lattin
1992, Bell and Lattin 1998, Bell, Ho and Tang 1998, Broniarczyk, Hoyer and McAlister
1998, Bodapati and Srinivasan 2006, Briesch, Chintagunta and Fox 2009)
2. Are most consumers store loyal or do they switch among grocery stores? And what
stochastic patterns characterize such switching behavior among stores? (e.g., , Schapker
1966, Keng and Ehrenberg 1984, Uncles and Ehrenberg 1990)
3. What factors – store- and consumer-level – drive consumers’ store loyalties? (e.g., Tate
1961, Corstjens and Lal 2000, Rhee and Bell 2002)
The above-mentioned literature largely views a consumer as possessing store loyalty
toward a particular store for her or his overall grocery shopping needs. For example, depending
on the percentage of shopping trips that are accounted for by a particular store in the market,
consumers are considered as either store-loyals or store-switchers (Prasad 1972, Fox and Hoch
2005, Gauri, Sudhir and Talukdar 2008). We contend in this study that such a view of overall
store loyalty, which focuses on the bilateral relationship between a store and a consumer, may be
limiting from the perspectives of both research and retail practice. We provide examples to
explain this point next.
Jane Smith does her shopping at three different stores – Albertson’s, Safeway, and WalMart Supercenter – visiting each store about equally over time so that none can claim to be her
favorite store. Such a consumer is labeled under the traditional view of store loyalty 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
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 U.S. grocery store.
2
picker,” see Fox and Hoch 2005, Gauri, Sudhir and Talukdar 2008), or because of travel
exigencies that take the consumer closer to one store or another on a given week, Jane always
purchases some categories (e.g., soft drinks) in Albertson’s, some categories (e.g., produce) in
Safeway, and other categories (e.g., meat) in Wal-Mart Supercenter. In other words, Jane is, in
fact, loyal to different stores in different product categories. Not understanding this aspect of
Jane’s shopping behavior may lead one to spuriously conclude that her lack of overall store
loyalty implies her lack of store loyalty to each of the stores for any product category; this, in
turn, would lead a retailer to not capitalizing on the empirical fact that their store is, in fact,
highly attractive to Jane in some categories.
Alternatively, consider John Doe who does his grocery shopping most of the time at
Costco, but always buys cheese at Safeway because it offers a larger and more diverse product
assortment in the cheese category. In such a case, only focusing on the overall store loyalty of
John Doe to Costco will miss out on the opportunity to learn from John’s strong preference for
Safeway in the cheese category, which may help improve John’s overall store loyalty to
Safeway.
Our focus in this study, therefore, is to examine store loyalty as a category-specific trait,
instead of the overall store loyalties of consumers. In other words, we examine polygamous store
loyalties of consumers across categories. 2 We argue that, instead of focusing on the entire
shopping baskets of consumers, retailers can effectively improve overall store loyalty and
revenues by customizing their marketing resources at the category level. For example, if it is
shown that baby food is the top category that is the most responsive to the assortment breadth
and assortment quality in choosing a store, retailers should then prioritize their marketing
resources for assortments to this category (e.g., assign more shelf spaces, allocate more resources
to obtain better selections of baby food products) to improve consumer loyalty to their stores.
Alternatively, if in another category, say carbonated beverages, it is found that store revenues
would increase the most by reducing the prices, rather than increasing the assortments, retailers
should then direct their attention to the pricing strategy in this category to effectively increase
store revenues. As a third possibility, if it is shown that consumers have high intrinsic store
preferences for a store, say Safeway, but are not responsive to the store’s merchandising
programs in most categories, then Safeway should divert marketing resources from those
2
Similar “polygamous loyalty” findings have been documented in the literature on consumer loyalty programs (see,
for example, Dowling and Uncles 1997).
3
merchandising programs toward non-merchandising programs such as strategies that can make
these categories more likely to be noticed or more easily to be located by consumers in the store
(e.g., display these categories in a prominent location). By bringing the category dimension into
the relationship between a store and a consumer, we can provide more actionable knowledge for
retailers to plan on retail strategies to strengthen that relationship.
To the best of our knowledge, there are only a few studies in the marketing literature that
discuss the roles of categories on consumer store choice behavior. Gijsbrechts, Campo and Nisol
(2008) propose a consumer shopping behavior model in which consumers make decisions of
store visit frequency, category allocation and shopping patterns simultaneously by minimizing
the total costs of fixed shopping cost, variable shopping costs and handling and holding cost. In
their empirical application, the authors group the typical retailer-defined categories into three
product category types: convenience, specialty and fresh products. They find that categorypreference complementarities can be one of the reasons why consumers shop at multiple stores
even without the stimulation of temporary sales promotions. Though the authors show that the
category-specific store preference plays a role in consumers’ store choice behavior, the main
focus of their study is still on the bilateral relationship between a consumer and a store.
Dreze and Hoch (1998) explicitly study consumers’ category preferences in a store. They
classify grocery products into two types: (1) Type I, for which consumers are loyal to a specific
retailer and, and as much as possible, always shop at that retailer for those products, and (2) Type
II, which are not associated with any retailer and are bought at whichever retailer consumers
happen to visit when they plan or remember to buy the products. 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. This finding is very important for retail practice.
Though Dreze and Hoch (1998) distinguish between categories where a consumer is store loyal
and categories where the consumer is not, the authors do not study whether a consumer could be
loyal to different stores in different product categories, and more importantly, what factors
influence such loyalty. This is the focus of our study.
A number of statistical models of households’ store choices (Aaker and Jones 1971,
Blattberg, Peacock and Sen 1976, Keng and Ehrenberg 1984, Uncles and Ehrenberg 1990,
Bucklin and Lattin 1992) have been developed at the category level; however, a category-level
view of, and basis for, store loyalty has not been provided so far. In this study, as we are more
interested in how consumers divide their grocery shopping among different stores from a long
4
term perspective, we focus on consumers’ store loyalty instead of their temporal store choice
behavior for each shopping trip. In doing this, we contribute to the marketing literature on
understanding to what extend a consumer is loyal to different stores in different categories and
how understanding such polygamous loyalties of consumers can help managers customize their
marketing resources and strategies at the category level in order to improve overall store loyalty
and store revenues.
We use an in-home scanning panel dataset that tracks 1321 households in 284 grocery
categories across 16 retail chains over a 53-week period in a large US market. The data first
show little overall store loyalty, based on the traditional view; however, once the category
dimension is added, extensive store loyalty at the category level is uncovered. In order to more
carefully examine households’ store-category loyalty, we propose a modified market share
attraction model (Cooper and Nakanishi 1988). By examining household purchases in multiple
stores and multiple categories simultaneously, we are able to decompose the effects of nonmerchandising and merchandising programs of retailers on household store-category loyalty into
effects that are common across stores, categories and households, and effects that are storespecific, category-specific and household-specific.
We find that consumer store-category loyalty decreases if increasing the category
assortment breadth or carrying popular products (instead of the same number of niche products).
However, carrying SKUs that better match the preferences of the shoppers in the store,
improving the relative price advantage over other stores, or lowering the temporal variation in
prices, improves store-category loyalty. We also notice great heterogeneity in the effects of the
merchandising programs across categories and further uncover some category characteristics,
such as category purchase frequency, category bulkiness and refrigeration, have a moderating
impact on such effects. On the basis of these results pertaining to category characteristics, the
retailer can predict the effect of a given assortment or price variable on store-category loyalty for
a particular category even when its category-level purchase data is not available.
We show how a retailer 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. Our study informs retailers how different types of
merchandising programs (e.g., assortments versus prices), or even different levels of a given
merchandising program (e.g., few versus many brands), can be customized in different categories
in order to improve the overall store loyalty of their consumers. In other words, viewing store
5
loyalty as a category-specific trait and estimating the category-specific effects (as well as storespecific, household-specific and interaction effects) of merchandising programs can articulate
clearer ways of boosting the overall store loyalty for the retailers than focusing on overall store
loyalty directly (as done traditionally in the literature and retail practice).
We estimate intrinsic store attractiveness, which capture the effects of factors other than
retailers’ merchandising programs on store-category loyalty. For stores that show little variation
in intrinsic store attractiveness across categories, we suggest that the retailers focus on improving
overall store characteristics (e.g., store-level customer services). For stores that show great
variation in intrinsic store attractiveness across categories, we suggest that those retailers focus
on the categories with the highest store attractiveness, e.g., the top 25 categories. These
categories represent flagship categories in those stores and can be systematically analyzed to
boost store loyalty in other categories. A retailer opening a new store in a local neighborhood
with intensive retail competition may find it worthwhile to initially focus disproportionately on
these top categories, in terms of making them more salient, in order to better build store loyalty
for the new store.
Lastly, we identify categories whose product assortments and pricing strategies have the
highest effects on store revenues. These categories do not necessarily perfectly align with those
with the highest impact on store-category loyalty (as discussed above). For each store-category
combination, our empirical results can be used to identify the relative efficacy of various
category-level merchandising programs in boosting total store revenues.
The rest of the paper is organized as follows. In section 2, we describe our unique panel
dataset involving 284 product categories and 16 retail chains. This section also provides strong
empirical evidence for the presence of store-category loyalty. In section 3 we describe the
proposed model and the proposed variables influencing consumer store-category loyalty in
detail. We also briefly discuss the model estimation methodology in this section. The empirical
findings are presented and discussed, along with an associated managerial exercise, in section 4.
Section 5 summarizes and concludes the paper.
6
2. DATA DESCRIPTION AND MOTIVATING EMPIRICAL EVIDENCE FOR STORECATEGORY LOYALTY
We use in-home scanning data on longitudinal purchases of 1321 metropolitan
households in a large southwestern city. The data contain detailed purchase information (e.g.,
transaction date, retail chain visited, category, SKU, price paid, quantity purchased etc.)3 of these
households in 284 grocery categories across 16 retail chains, over a 53-week period from
September 2002 to September 2003. The 16 retail chains belong to 3 types of retail formats:
traditional supermarkets, supercenters, and warehouse club stores. There are 10 traditional
supermarket chains, 2 supercenters and 4 warehouse club chains. 4 Moreover, two of the
traditional supermarket chains – Supermarket 8 and 10 – are boutique retail chains that specialize
in organic and exotic foods. To get a feel about the relative position of the 16 retail chains, we
present a price-assortment map of the 16 retail chains in Appendix A.
Next, we demonstrate the presence of store-category loyalty using our data. In Figure 1
we report the histogram of number of different stores at which a household shops during the 53week period, over all 1321 households in the sample. We see that only 12 out of the 1321
households shop at a single store 5 throughout the study period. Among the remaining 1309
households, the modal value is 6, and there are 3 households that shop at as many as 13 different
stores over the study period. 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. We
then calculate the proportion of shopping trips made by each household at its favorite store over
its total number of shopping trips. In Figure 2, we report the probability mass histogram for this
proportion across all 1321 households in the sample. We see that about 50.2% of the households
do not visit their favorite store on 50% or more of their shopping trips. These two figures show
that households typically divide their grocery shopping among many different stores, and there
appears to be little store loyalty, based on the traditional view of store loyalty, in this market.
[INSERT FIGURE 1 and 2 HERE]
3
The data do not contain information on promotions (e.g., feature, display and coupons) at the retail chains.
For reasons of confidentiality, we cannot disclose the geographic identity of the market or the names of the 16
retail chains. Therefore, we identify the retail chains as Supermarket 1, Supermarket 2,…, Supermarket 10,
Supercenter 1, Supercenter 2, Club 1, Club 2, Club 3, and Club 4. It is fair to say that the majority of these retail
chains have national presence in the US.
5
For expositional convenience, we use “store” to refer to “retail chain” hereafter.
4
7
Next, we add the category dimension into the picture, and something interesting emerges.
We find that each of the 1321 households, including those that shop at multiple stores, makes all
of its category purchases exclusively at the same store for at least one category in its shopping
basket. In Figure 3, we report the histogram of the number of categories in which a household is
observed to make all of its category purchases in one single store during the 53-week period,
across all 1321 households. It is clear that many households make all category purchases
exclusively in one store for a large number of categories.
[INSERT FIGURE 3 HERE]
One may argue that the finding in Figure 3 could be attributed to the fact that many
households make most of its single-store category purchases exclusively at one store -- its
favorite store. In order to test 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 to 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 4. We see that only about 10.2% of the households make all of their single-store category
purchases exclusively at one store over the study period. This strongly underscores the fact that
households do not make all of their single-store categories purchases exclusively at their favorite
store. Instead, households make single-store category purchases in many different stores; in other
words, households seem loyal to different stores in different categories.
[INSERT FIGURE 4 HERE]
Taking Figures 1-4 together, we can conclude that (at least for this dataset), despite the
seeming lack of overall store loyalty for their grocery shopping, households show extensive
“polygamous store loyalty” in the sense of being loyal to different stores in different categories.
The purpose of this research is to obtain an understanding of the key influencing factors
of such store-category loyalty from a long term perspective, particularly those that relate to the
controllable merchandising programs of retailers. Additionally, we decompose such effects to
store-specific, category-specific and household-specific effects, as well as effects that are
common across stores, categories and households. We also examine the extent of variation in the
estimated category-specific effects across categories, so that appropriate managerial implications
8
can be derived at the category level in order to help retailers improve overall store loyalty and
store revenues.
3. EMPIRICAL ANALYSIS
3.1 The Proposed Model
In this study, we focus on the three-way relationship between a household, a category and
a store in a long term perspective. Consider a market where there are H households (h=1,2,…,H)
that make purchases in C categories (c=1,2,…,C) among S stores (s=1,2,…S). We conceptualize
a household’s store-category loyalty as the household’s share of category purchase incidence
attracted by a store, which is a long term propensity stable over time. Alternatively, one can also
conceptualize a household’s store-category loyalty based on the household’s share of category
purchase expenditure attracted by a store.6 We denote household h’s store-category loyalty in
category c by SCLch  (SCL1ch , SCL2ch ,..., SCLSch ) ; and
S
 SCL
s 1
sch
 1 . As in Cooper (1993), we
assume that SCLsch is proportional to the store’s category attractiveness, attraction of store s to
household h in category c, Asch , i.e., SCLsch  k  Asch , where k is a constant of proportionality.
According to the fundamental theorem of market share (Kotler and Keller 2011, Cooper 1993),
the following relationship between store category loyalty and store category attraction holds.
SCLsch 
Asch
S
A
.
(1)
rch
r 1
Equation (1) is also called Market Share Attraction Model (Cooper 1993, Fok, Franses, and Paap
2002). It is built on the premise that a household’s store category loyalties depend on the
household’s relative attractions for different stores.
6
For our empirical application, as the correlation between category purchase incidences and category expenditure in
our data is as high as 0.95, we find no meaningful differences in results emerge from using the conceptualization of
store-category loyalty based on share of category purchase expenditure.
9
3.2 Key Factors Influencing Store-Category Loyalty
One would expect three types of characteristics to influence the attraction of a category in
a store to a household, thus the household’s store-category loyalty: (1) store characteristics, (2)
category characteristics, (3) household characteristics, and (4) different combinations of these
three types of characteristics. Next, we multiplicatively decompose the attraction of store s to
household h in category c into components that capture effects of non-merchandising programs
and merchandising programs as shown below.
Asch   sch  ch X sch 
(2)
where sch captures the effects of non-merchandising programs and represent the Intrinsic
Attractiveness of store s in category c to household h. X sch is a vector of store-categoryhousehold variables that represent the store s’ merchandising strategies in category c that are
relevant to h (we will discuss the variables in detail in the next subsection).  ch is the
corresponding coefficients and it represents the effects of merchandising programs, such as
product assortments, pricing and promotional strategies, on the attraction of store s to household
h in category c.  sch captures the residual unobserved effects of store s in category c to
household h. Note that the specification in equation (2) make equation (1) be a Market Attraction
Model with a Multinomial Logit specification as below:
SCLsch 
exp  sch   ch X sch 
S
 exp 
r 1
rch
  ch X rch 
(3)
.
Next, we decompose the Intrinsic Attractiveness of store s in category c to household h,
sch，into three components that are store-specific, store-category specific and store-household
specific as below:
 sch   s   sc   sh   sch
(4)
where, s is mean attraction of store s to all households over all categories and represents the
Intrinsic Store Attractiveness of store s; sc is the deviation of category c from the mean
10
attraction, and the sum of s and sc represents the Intrinsic Store-Category Attractiveness of
store s in category c. sh is the deviation of household h from the mean attraction, and the sum of
s and sh measures household h’s preference to store s.Moreover, we also decompose the
effects of merchandising programs,  ch , into three components: (1) the effect common across
stores, categories and households, denoted as  ; (2) the effect specific to category c, denoted as
 c ; (3) the effect specific to household h, denoted as h (Ainslie and Rossi 1998, Seetharaman,
Ainslie and Chintagunta 1999, Singh, Hansen and Gupta 2004, Prasad, Strijnev, and Zhang
2008). Specifically, we write the kth element of  ch as follows:
chk   k   ck  hk  chk
(5)
where chk captures the residual unobserved deviation that is specific to household h in category c.
3.3 Key Merchandising Variables Influencing Store-Category Loyalty ( X sch )
We are particularly interested in how merchandising programs – which are under the
control of retailers – influence store-category loyalty. The typical merchandising variables can be
constructed from the aspects of product assortment, pricing and promotional (e.g., features,
display and coupons etc.) strategies. As our data does not contain the information on retailers’
promotional strategies, in this study we specifically focus on product assortments and pricing
strategies.
3.3.1 Product Assortment Variables
Several studies, based on surveys and lab experiments, have revealed that product
assortments are very important factors in consumers’ store evaluations and/or store choice
decisions (Meyer and Eagle 1982, Arnold, Oum and Tigert 1983, Craig, Ghosh and McLafferty
1984, Louviere and Gaeth 1987). It has further been shown that consumers’ perceptions of
product assortments are multi-dimensional in that consumers pay attention to both assortment
breadth and assortment quality (Broniarczyk, Hoyer and McAlister 1998, Chernev and Hamilton
11
2009). Therefore, we construct product assortment variables that relate to both the quality, as
well as the breadth, of the category assortments.
First, we measure the assortment breadth in category c at store s from three aspects –
brand breadth, SKU breadth within a brand and size breadth as explained as below:7

Number of Brands in the Category at the Store (BRANDsc).
BRANDS sc
This variable is defined as BRANDsc 


  BRANDS rc  S
 r 1

S
, where BRANDSsc 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 SKU sc 
SKUS sc / BRANDS sc


  SKUS rc / BRANDS rc  S
 r 1

S
, where SKUSsc stands
for the total number of SKUs in category c available at store s.

Average Number of Sizes per Brand in the Category at the Store (SIZEsc).
This variable is defined as SIZEsc 
SIZES sc / BRANDS sc


  SIZES rc / BRANDS rc  S
 r 1

S
, where, SIZESsc
stands for the total number of different product sizes in category c available at store s.
Next, we construct variables to measure the assortment quality. Laboratory studies have
shown that a consumer’s evaluation of an alternative depends on the valence of the alternative’s
unique features and that the consumer focuses disproportionally more on unique, as opposed to
common, features (Houston and Sherman 1995, Dhar and Sherman 1996). Stores vary on the
options of private labels that they provide to their shoppers (i.e., whether and how many private
SKUs to carry). To the extent that a private label is unique to a store, this measure can serve as a
proxy for the uniqueness of the store’s product assortments.8 We construct the following variable
7
Another potential assortment breadth variable is “Average Number of Flavors in the Category at the Store”. Since
not all categories in our data can be characterized using flavors, we do not use this variable in our analysis.
8
We do recognize that retailers’ decisions of providing shoppers different options of private label go beyond the
purpose of just being unique, 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).
12
to represent the assortment uniqueness of a store (and, therefore, assortment quality) in the
category:

Number of Private Labels in the Category at the Store (PVTLABELsc).
This
variable
is
defined
as PVTLABELsc 
PVTLABELS sc


  PVTLABELS rc  S
 r 1

S
,
where,
PVTLABELSsc stands for the number of private label SKUs in category c available at
store s.
The assortment quality that consumers perceive not only includes the uniqueness of the
product assortments that a store carries in a category, but also the attractiveness of the items that
the store carries. For example, assortments containing best-selling SKUs, which are likely to
appeal to a majority of buyers, may be perceived as more attractive than assortments containing
less popular items (Chernev and Hamilton 2009). In addition, assortments that better match the
individual preferences of the store’s shoppers may be perceived as more attractive (Broniarczyk,
Hoyer and McAlister 1998). Toward this end, we construct two following assortment quality
variables that capture the attractiveness of the category assortments:

Total Market Shares of Available SKUs in the Category at the Store (SKUMKTSHAREsc).
This variable is defined as SKUMKTSHAREsc 
N sc
 MS
u 1
scu
where MSscu stands for the
market share (based on the purchase quantities across all households) of SKU u, which is
available at store s, in category c, and Nsc stands for the total number of SKUs carried by
store s in category c. This variable represents the collective market share of the SKUs that
are available at store s in category c.

Household-Preference-Matching of SKUs in the Category at the Store (PREFMATCHsch).
13
N sc
This variable is defined as PREFMATCH sch 
 AVAIL
u 1
scu
 Whcu
 S N kc

    AVAILkcu  Whcu   S
 k 1 u 1

, where
AVAILscu is an indicator variable that takes the value 1 if SKU u is available in category c
at store s, and 0 otherwise, Whcu is household h’s purchase quantity share for SKU u in
category c across all stores over the study period. The numerator of this variable
represents the weighted average availability of SKUs in the category across stores, where
the weights are household-specific preferences.
3.3.2 Price Variables
One of the robust findings in store patronage research is that low prices are 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, Bell, Ho and Tang
1998). We construct the following variable to measure the attraction of the store’s pricing level
in the category:

Price Advantage of the Store in the Category (PRICEADVsc).
 T  AvgPcu  
  
 T 

u 1  t 1  Pscut
 
This variable is defined as PRICEADVsc 
where Pscut stands for
N sc
N sc
the price of SKU u in category c in store s at time t, AvgPcu is the average price of SKU u
in category c over time across all stores, T is the total number of weeks and N sc is the total
number of SKUs in the category at the store. When constructing this price variable, we
consider the following issues: (1) we eliminate the effects of different magnitudes of
prices across different SKUs within the category due to non-pricing factors such as
heterogeneous package sizes (thus, potential quantity discounts) and qualities (e.g.,
national brands may charge higher prices than private label brands do) through the SKU
price normalization by dividing the SKU prices by average prices; (2) we ensure that
when a SKU is not available, which can be operationalized as the price being positive
14
infinity, in a store at any point of time during the study period its contribution to the
category price advantage at that time is 0.
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, which may result in greater consumer loyalties to that store 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 among other stores. In other words, greater price
variability in a category at a store would reduce the overall attraction of the store to households
in the category. For this reason, we include the following price variable to measure the price
variability of the store in the category:

Price Variability of the Store in the Category (PRICEVARsc).
This variable is defined as PRICEVARsc 
CVsc


  CVrc  S
 r 1

S
, where CVsc stands for the
coefficient of (temporal) variation of category prices in category c at store s over time,
where the category price in category c at store s at time t, Cat_Price sct , is defined as
 Pscut 

u 1 
cu 
. Notice that the category price is also normalized to

N sc
N sc
Cat_Price sct
  AvgP
eliminate the effects of non-pricing factors on prices.
In summary, we include six variables to represent a store’s assortment strategies in a
category, among which BRANDsc, SKUsc, and SIZEsc measure the relative breadth of the
assortment, and PVTLABELsc, SKUMKTSHARE sc, and PREFMATCHsch measure the relative
quality of the assortment. We also include two variables to measure the two aspects of the
pricing strategies of the store in the category - PRICEADVsc measures the relative price level and
PRICEVARsc measures the relative price variability over time. Notice that all the variables are
normalized by the corresponding average values across stores and thus operationalized to be
unit-free and scale-free indices. This makes these measures comparable across categories, across
15
stores and across different strategies, helping the interpretations and discussions of parameters.
In Table 1, we provide descriptive statistics pertaining to these variables in our dataset, which
show that all variables have comparable scales in the data.
[INSERT TABLE 1 HERE]
3.4 Estimation
In the data, we observe the purchase incidences that household h makes in category c
across S stores over time, Nch  (n1ch , n2ch ,..., nSch ) . To estimate the model in (3), one temptation
is to directly calculate SCLsch from the data by dividing the observed number of the purchase
incidences by h at store s in category c, nsch , by the sum of h’s total purchase incidences in the
category over all S stores,
S
n
r 1
rch
, during the studying period. One problem from this
operationalization is that shares for some stores for a particular household and category may be
zero, which is not allowed in the Market Share Attraction Model. Moreover, the values of SCLsch
calculated in this way will be sensitive to the length of the study period used in the data. As we
consider store-category loyalty represents the relative long term preferences of a household to a
store-category, we assume that the purchase incidences observed during the study period are the
outcome of a multinomial process determined by the store category loyalty, i.e.,
Nch  Multinomial (SCLch )
(6)
Thus, we can write the household level likelihood function as follows.
L    SCLsch nsch
cC hH sS


 exp      X    
sc
sh
ch sch
sch

   S

cC hH sS 
  exp  rc   rh   ch X rch   rch  
 r 1

16
nsch
(7)
We assume that sc , sh and  sch in equation (3) follow multivariate normal distributions
with zero mean and covariance matrix sc , sh and  respectively. Similarly,  ck , hk and chk
in equation (5) are also assumed to follow multivariate normal distributions with zero mean and
covariance matrix  k ,   k and  k respectively. We use Hierarchical Bayes estimation
methodology, specifically, Markov Chain Monte Carlo (MCMC) sampling, to estimate the
model. More details about the estimation approach are provided in the Appendix B.
4. RESULTS AND DISCUSSION
We estimate our proposed model using the data described in section 2. The log-marginallikelihood of our proposed model is -882868.5. We also estimate a benchmark model that has
intercepts only, and its log-marginal-likelihood is -2118610.1.9 Compared with the benchmark
model, our proposed model fits the data much better. To test the robustness of our model results
in terms of inclusion of all available categories with different purchase frequencies, we also
estimate the proposed model using a subset of the dataset that only includes household purchases
in the top 100 most frequently purchased categories. The estimation results are similar to those
that we obtain from using the full dataset.10 Thus, we argue that our estimation results are not
merely driven by non-frequent category purchases.
There are a large number of parameters estimated from our proposed model. Next, we
only discuss those that are most relevant to our research purposes.
4.1 Effects of Merchandising Programs on Store-Category Loyalty
4.1.1
Effects common across Stores, Categories and Households
Retail managers are interested in understanding the main effects of their merchandising
programs on store-category loyalty. In this regard, we report the estimated effect common across
9
The likelihood function of the benchmark model can be written as
n

 sch
 exp      X    
s
sh
h sch
sch
 .
L    S
cC hH sS 







X
exp
 r rh h rch rch  

 r 1

10
The results are available from the authors upon request.
17
stores, categories and households,  0k , for each of the assortment and price variables, in Table 2.
We can see that the estimated  0 for the assortment breadth variables – the number of brands
(BRAND), average number of SKUs per brand (SKU), and average number of sizes per brand
(SIZE), in the category at the store – on store-category loyalty are all negative (-0.039, -0.034
and -0.035, respectively). This suggests that a greater category assortment breadth lowers storecategory loyalty. Although intuitively increasing assortment breadth may appear help the
category and the store, existing empirical research on the effect of increasing the assortment
breadth on category revenues has shown otherwise. Dreze, Hoch and Purk (1994), and
Broniarczyk, Hoyer and McAlister (1998) find no effect, while Boatwright and Nunes (2001),
and Borle et al. (2005) find a negative effect. Our results are consistent with the latter set of
findings.
[INSERT TABLE 2 HERE]
The results pertaining to the effects of assortment quality on store-category loyalty are
mixed. In Table 2, we can see that the estimated  0 for the number of private labels
(PVTLABEL), which represents the store’s assortment uniqueness in the category, on storecategory loyalty is insignificant. In their analytical study of the effects of store brands on store
loyalty, Corstjens and Lal (2000) show that when the quality of store brands exceed a threshold
level, carrying store brands can increase store loyalty.11 Our finding is consistent with theirs in
the sense that merely increasing the breadth of private label assortment within a category has no
impact on store-category loyalty, on average.
Contrary to our expectation, the mean effect of the total market shares of available SKUs
(SKUMKTSHARE) on store-category loyalty is negative (-0.44), implying that if two stores have
the same number of SKUs in a category, the store that carries more popular (i.e., higher market
shares) SKUs will have lower store-category loyalty than the store that carries more niche (i.e.,
smaller market shares) SKUs. One explanation for this result could be that popular products are
generally carried by a lot of stores, which makes it more difficult for a store with more popular
products to differentiate itself in that category from other stores (given other conditions such as
assortment breadth are the same), which, in turn, results in lower store-category loyalty.
11
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 by their different levels of quality.
18
The estimated  0 for the household preference matching of SKUs (PREFMATCH) on
store-category loyalty is positive (0.96), which implies that as the available SKUs in a category
at the store better match the preferences of consumers, consumers’ store-category loyalty for that
store is higher. This finding, which is consistent with the finding in Briesch, Chintagunta and
Fox (2009), suggests that retailers stand to benefit from investing in data warehouses (e.g.,
constructed using loyalty card programs), as well as data mining infrastructures, in order to
uncover SKU-level preferences of their consumers in different product categories.
The estimated effects of the two price variables are both consistent with our a priori
expectations. Specifically, the estimated  0 for PRICEADV is positive (0.03), while that of
PRICEVAR is negative (-0.04). These findings suggest that a retailer who adopts an EDLP
pricing strategy (which involves setting low average prices and little price variability over time)
within a category would enjoy higher store-category loyalty.
4.1.2
Category-Specific Effects
We uncover substantial heterogeneity in the estimated effects of product assortments and
pricing strategies across categories. The heterogeneity is captured by the cross-category standard
deviations of the estimated category-specific effects,  ck , which represent the category-specific
deviations from the common effects. These cross-category standard deviations are reported in
the second column of Table 2. We can see, for example, that the cross-category standard
deviations in the estimated effects of assortment breadth – BRAND, SKU, and SIZE - on storecategory loyalty are 0.083, 0.063, and 0.078 respectively, which are about twice as large as their
corresponding estimates of  0 (-0.039, -0.034 and -0.035, respectively). This implies that even
though the estimated effects of assortment breadth common across all stores, categories and
households, are negative, there are still a large number of categories for which assortment
breadth has a positive impact.
Given the estimates of  ck for each category c and each variable k,12 we next investigate
whether some observed category characteristics may explain the uncovered heterogeneity of  ck
across different categories. For this purpose, we construct four category characteristics, i.e.,
12
Given the large number of categories in our dataset, we have not reported the full set of category-specific
estimates in the paper. These are available from the authors upon request.
19
category purchase frequency, category expenditure, whether a refrigerator is needed for the
category, and bulkiness of product packages in the category. Taking one variable (k) at a time,
we linearly regress the estimated  ck ’s, across all categories, on these category characteristics.
This yields eight linear regressions, one for each of assortment and price variables. We report the
results of these linear regressions in Table 3. First, we see that category expenditure cannot
explain the variations in any of the estimated  ck ’s across categories. Second, we see that as
category purchase frequency increases, the estimated effect of PREFMATCH on store-category
loyalty becomes less positive and the estimated effect of PRICEVAR becomes less negative.
Third, we see that bulkier categories are associated with (1) more negative effects of SIZE and
PVTLABEL, (2) less positive effects of PREFMATCH, (3) less negative effects of
SKUMKTSHARE, and (4) less negative effects of PRICEVAR on store-category loyalty. Fourth,
we see that refrigerated categories are associated with (1) less negative effects of SKU and
SKUMKTSHARE, (2) more negative effects of, and (3) less positive effects of PREFMATCH
and PRICEADV on store-category loyalty. On the basis of these second-stage regression results,
a retailer can predict the effect of a given assortment or price variable on store-category loyalty
for a category even when its category-level purchase data is not available.
[INSERT TABLE 3 HERE]
The magnitude of the estimated  ck represents the degree of deviation in category c from
the effect of the kth merchandising (i.e., product assortment or price) program that is common
across stores, categories and households on store-category loyalty. Between two categories, the
category with a higher absolute value of  ck is more responsive to the kth merchandising program.
Therefore, rank-ordering the categories based on the magnitudes of the estimated values of  ck
would help retailers prioritize among categories in terms of merchandising program k; this, in
turn would enable retailers to better allocate their limited marketing resources across categories.
In Table 4, we list the top 25 categories whose store-category loyalties are estimated to be the
most responsive to changes in each of four product assortment variables (BRAND, SKU, SIZE,
SKUMKTSHARE), as well as a price variable (PRICEADV).13 Table 4 shows that, as far as
13
For expositional convenience, we report the rank-ordering for these five merchandising variables only. The rankorders for the other three variables are available from the authors upon request.
20
assortment breadth is concerned, baby food is the most responsive category to a change in the
number of brands; hair coloring is the most responsive category to a change in the average
number of SKUs per brand; and skin care is the most responsive category to a change in the
number of package sizes per brand. As far as assortment quality is concerned, we find that baby
food is the most responsive to a shift to niche product positioning (i.e., carrying low market share,
instead of high market share, SKUs). We also see that hair coloring is the most responsive to
price changes.
[INSERT TABLE 4 HERE]
In retail practice, carbonated beverages and sugar are frequently used as loss leader
products in order to attract consumers to visit a store. Our study lends faith to this practice since
these two categories figure in our list of top 25 categories whose store-category loyalty is the
most responsive to price changes. More importantly, our study informs retailers how different
types of merchandising programs (e.g., assortments versus prices), or even different levels of a
given merchandising program (e.g., few versus many brands), can be customized in different
categories in order to improve the overall store loyalties of their customers. In other words,
viewing store loyalty as a category-specific trait and estimating the category-specific effects of
merchandising programs (as shown in this study) can articulate clearer ways of boosting the
overall store loyalty for the retailers than focusing on overall store loyalty directly (as done
traditionally in retail practice and in the literature).
4.2 Effects of Merchandising Programs on Store Revenues
Retailers often have an objective of improving overall store revenues. For this purpose,
we identify categories whose product assortments and pricing strategies have the largest effects
on store revenues. For each category-store combination, we calculate the change in store
revenues that results from a 1% change in the value of each assortment breadth variable
(BRAND, SKU, and SIZE), assortment quality variable (SKUMKTSHARE, PVTLABEL,
PREFMATCH), and price variable (PRICEADV, PRICEVAR) used in the analysis.14 We then
average this change in store revenues across all stores. The top 25 categories, which yield the
14
As we do not have cost information on various merchandising programs in the data, we are only able to calculate
the effect of a given percentage change in each merchandising variable on store revenues, as opposed to the effect of
a given dollar investment on each merchandising variable on store profits, i.e., return on investment.
21
largest change in store revenues, are reported correspondingly for each merchandising variable in
Table 5. 15 We suggest that retailers prioritize these categories in terms of allocating limited
marketing resources across categories for a given merchandising strategy. Further, we notice that
when comparing these categories to their counterparts in Table 4, which represent the categories
with the highest impact on store-category loyalty, there is very little overlap in the identified
categories between Tables 4 and 5. This suggests that different resource allocation (across
categories) strategies should be implemented depending on the managerial objectives that a
retailer hopes to achieve.
[INSERT TABLE 5 HERE]
For each category, a retailer will find it worthwhile to know which category-level
merchandising variable will be most effective in terms of increasing store revenues. Our
empirical results can be used to shed light on this question. We demonstrate this in the
carbonated beverages category, which has the largest expenditure share, and in Supermarket 5.
We compare the changes in store revenues that result from 1% changes in the various
assortments and price variables. We find that increasing SKU relevance is the most effective,
followed by the replacement of popular SKUs by an equivalent number of niche SKUs, with
increasing the total number of brands in the category coming at third best.
4.2 Intrinsic Store-Category Attractiveness
The intrinsic store-category attractiveness captures the category-specific store effects on
store-category loyalty that are due to factors other than a store’s merchandising programs. We
first report the estimation results for the intrinsic attractiveness of the 16 stores,  s , in Table 6.
For identification purpose, we use Club 4, which has the least observed market share, as the
baseline store. Not surprisingly, because retailers’ merchandising programs significantly impact
their market shares, the ranking of the intrinsic store attractiveness does not closely correspond
to the ranking of observed store market shares. Notice that only three out of 15 stores have
significantly greater intrinsic store attractiveness than Club 4. Supercenter 2, which has the
fourth largest observed market share, has, in fact, lower intrinsic store attractiveness (-0.272)
15
For expositional convenience, we only report the results corresponding to the five merchandising variables, i.e.,
BRAND, SKU, SIZE, SKUMKTSHARE) and PRICEADV. The complete results for the remaining merchandising
variables are available from the authors upon request.
22
than Club 4. Supermarket 9, which has the sixth largest observed market share, has the lowest
intrinsic store attractiveness (-0.338).
[INSERT TABLE 6 HERE]
The sum of  s and  sc represents the intrinsic store-category attractiveness of store s in
category c. We calculate the average value of this sum, separately for each category, across the
16 stores. In Table 7, we list the top 25 categories which have the highest intrinsic store-category
attractiveness. A retailer opening a new store in a local neighborhood with intensive retail
competition may find it worthwhile to initially focus disproportionately on these 25 categories, in
terms of making them more salient and attractive, in order to better build store loyalty for the
new store.
[INSERT TABLE 7 HERE]
Next, we look at the extent to which intrinsic store-category attractiveness varies across
categories, as measured by the standard deviation of  sc , which are also reported in Table 6. A
small value of the standard deviation implies that the intrinsic store-category attractiveness in a
category is perhaps a function of overall store characteristics (e.g., store-level customer services,
store locations etc.), while a large value implies that the intrinsic store-category attractiveness is
perhaps a function of category characteristics that are not related to the category assortment and
pricing programs (e.g., store aisle in which the category is physically located). Interestingly, the
store with the highest intrinsic store attractiveness, Supermarket 5, is also observed to have the
second highest value of the standard deviation of  sc . It is incumbent on this store to figure out
what category characteristics drive its intrinsic store-category attractiveness to be high in some
categories, but not in others, so that these characteristics can be systematically exploited to boost
its store attractiveness in all categories. Conversely, two stores, i.e., Supermarket 8 and Club 3,
which have low value of intrinsic store attractiveness, also have the lowest values of the standard
deviation across categories. For these stores, which are consistently perceived by consumers to
be the least attractive intrinsically across categories, a focus on store characteristics can help
boost consumer store loyalty across all categories. In Table 8, we list the top 5 categories with
the highest intrinsic store-category attractiveness in Supermarket 5, Supercenter 1 and Club 1,
which are the top stores with the largest observed market shares for each store format. The table
23
demonstrates how the estimates of  sc can help retailers identify their flagship categories in their
respective stores.16 A systematic category analysis, which may include the uses of qualitative
methods such as in-depth interviews with some store shoppers, to understand factors that make
these categories highly attractive to shoppers, can then inform the retailer on how to boost store
loyalty in other categories.
[INSERT TABLE 8 HERE]
5. SUMMARY AND CONCLUSIONS
The marketing literature views store loyalty as a behavioral trait that applies at the level
of consumers’ store choice decisions, in terms of explaining which stores are the most frequently
visited by consumers for their overall grocery shopping needs. However, a consumer who is
observed to shop at many grocery stores over time, thus appearing to be not store loyal overall,
may still purchase different product categories from different stores in a loyal manner over time.
We call this store-category loyalty. We argue that useful information on consumer shopping
behavior can be obtained by studying this phenomenon.
Using purchase data from 1321 households in 284 grocery categories across 16 retail
chains in a large southwestern city, we show that there is strong empirical evidence of storecategory loyalty in the data, although overall store loyalty based on the traditional view is low.
We then use a modified market share attraction model (Cooper and Nakanishi 1988) to examine
the effects of key factors influencing store-category loyalty, particularly those related to retailers’
merchandising strategies. By simultaneously studying each household’s purchases in multiple
categories at multiple stores on a large scale, we are able to decompose such effects into storespecific, category-specific, and household-specific effects, as well as the interaction effects
between the stores, categories and households. Further, we illustrate and conduct managerial
exercises to show how the estimation results from the proposed model can be used to help
retailers better plan retail strategies at the category level in order to improve overall store loyalty
and/or store revenues.
Our paper adds to the few studies in the marketing literature that argues that storespecific category preferences may play an important role on consumers’ store choice behavior.
We empirically demonstrate the presence of the store loyalty at the category level when the
16
The complete estimates of  sc for individual categories are available from the authors upon request.
24
overall store loyalty is lacking. We further examine the effects of key influencing factors on
store-category loyalty in order to generate usable information to help retailers improve overall
store loyalty and store revenues. Using our approach, retailers can identify the position within
their stores, as well as the positions of the stores relative to their competitors in that category.
Due to the larger number of categories and also to keep the model tractable, in this study we do
not model the potential correlations between categories that may due to factors such as
complementarity (e.g., Chib, Seetharaman, and Strijnev 2002). Other areas that may be of
interest to future research is explicitly examining the relationship between store-category loyalty
and overall store loyalty as well as between the store-category loyalty and brand loyalty. We
hope that our work spurs more future research on modeling and understanding consumers’ storecategory loyalty behavior and its implications on retail practice.
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Schapker, B. L. (1966). Behavior Patterns of Supermarket Shoppers, Journal of Marketing, 30,
4, 46-49.
Seetharaman, P. B., Ainslie, A., Chintagunta, P. K. (1999). Investigating Household State
Dependence Effects across Categories, Journal of Marketing Research, 36, 4, 488-500.
27
Singh, V. P., Hansen, K., Gupta, S. (2005). Modeling Preferences for Common Attributes in
Multi-category Brand Choice, Journal of Marketing Research, 42, 2, 195−209.
Tate, R. S. (1961). The Supermarket Battle for Store Loyalty, Journal of Marketing, 25, 6, 8-13.
Train, K. E. (2003). Discrete Choice Methods with Simulation, Cambridge University Press.
Uncles, M. D., Ehrenberg, A. S. C. (1990). The Buying of Packaged Goods at US Retail Chains,
Journal of Retailing, 66, 3, 278-296.
Figure 1. Histogram of Number of Different Stores at Which a Household Shops within 53
weeks (Across All Households)
300
Number of Households
268
209
198
200
167
144
106
100
82
59
47
19
12
7
3
12
13
0
1
2
3
4
5
6
7
8
9
10
11
Number of Different Stores at which a Household Shops
28
Figure 2. Probability Mass of Proportion of Shopping Trips Made at a Household's
Favorite Store (Across All Households)
12
11.2
10.4 10.4
10
8.9
8.6
8.2
Percentage of Households
8
6.1
6
5.6
5.5
5.3
4.1
3.6
4
3.6
3.6
2.5
2.3
2
0.2
0
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95 100
Proportion of Shopping Trips Made at a Household's Favorite Store
Figure 3: Histogram of Number of Categories in Which a Household Make All of Its
Category Purchases Exclusively in One Store (Across All Households)
Number of Households
60
50
40
30
20
10
0
1
8
15
22
29
36
43
50
57
64
71
78
85
92
Number of Categories in Which a Household Makes All of Its Category
Purchases in One Store
29
99
106
Figure 4: Probability Mass of Number of Stores to Which a Household Makes SingleCategory Purchases (Across All Households)
Percentage of SCL
Households
35
29.3
30
24.5
25
22.4
20
15
10.2
9.6
10
5
3.3
0.6
0.1
7
8
0
1
2
3
4
5
6
Number of Stores to Which a Household has Exclusive SCL
Table 1. Descriptive Statistics of Assortment and Price Variables
BRAND
MIN
0.018
MAX
4.857
MEAN
1
STD DEVIATION
0.759
SKU
0.106
4.391
1
0.448
SIZE
0.269
2.612
1
0.274
PVTLABEL
0
8.640
0.625
1.150
SKUMKTSHARE
2.7e-08
1
0.378
0.338
PREFMATCH
0.001
9.155
1
0.511
PRICEADV
0.293
98.944
1.032
0.294
PRICEVAR
0
4.815
0.890
0.584
30
Table 2. Estimated Effects of Assortment and Price Variables a
Variable
Mean Response
BRAND
-0.039
(0.007)
SKU
SIZE
PVTLABEL
Category Std Dev.
0.083
Household Std Dev.
0.088
-0.034
(0.006)
0.063
0.056
-0.035
(0.008)
0.078
0.081
-0.008 b
0.084
0.098
(0.008)
a
SKUMKTSHARE
-0.440
(0.025)
0.309
0.138
PREFMATCH
0.960
(0.047)
0.644
0.259
PRICEADV
0.031
(0.005)
0.057
0.048
PRICEVAR
-0.044
(0.005)
0.062
0.023
Posterior means of model parameters are reported in the table. The standard errors are reported within parentheses.
b
Estimate is not significant at the 0.05 level.
Table 3.Explaining Cross-Category Heterogeneity in Estimated Effects of Assortment and
Price Variables Using Category Characteristics
BRAND
SKU
SIZE
-4.3E-03
-1.1E-03
0.023
Purchase
-2.6E-04
Frequency
9.2E-04
Category
3.8E-03
Expenditure
3.8E-04
Intercept
Refrigerator
0.011
0.012
**
Bulkiness
-7.9E-04
-1.3E-03
PVTLABEL PREFMATCH SKUMKTSHARE PRICEADV
*
0.051
*** 0.429
***
-0.191
-2.9E-04
-0.008
-0.108
**
0.034
-2.6E-03
0.010
-1.2E-03
3.2E-03
0.041
-0.031
3.5E-03
-0.003
0.007
-0.016
-0.228
***
0.091
**
-0.011
-0.007 *
-0.020 *** -0.146
***
0.080
***
3.3E-03
*
* Parameters are significant at the 0.10 level.
** Parameters are significant at the 0.05 level
*** Parameters are significant at the 0.01 level
31
***
-0.006
PRICEVAR
-0.027 ***
**
** 0.005
0.009
*
Table 4. Top 25 Categories Whose Store Category Loyalty is the Most Responsive to
Assortment and Pricing Strategies
BRAND
SKU
SIZE
SKUMKTSHARE
PRICEADV
BABY FOOD
HAIR COLORING
SKIN CARE
BABY FOOD
HAIR COLORING
CARBONATED
BEVERAGES
TIGHTS/SOCKS
DESSERTS - RFG
BLANK AUDIO/VIDEO
MEDIA
FIRST AID TREATMENT
CUPS & PLATES
MILK
NON-FRUIT DRINKS - SS
HAIR ACCESSORIES
COFFEE FILTERS
DESSERTS - RFG
RFG SIDE DISHES
COFFEE FILTERS
HOUSEHOLD LUBRICANTS
PASTA - RFG
DENTURE PRODUCTS
FIRST AID TREATMENT
CAT FOOD
VITAMINS
FZ BREAKFAST FOOD
PIZZA PRODUCTS
SNACK BARS/GRANOLA
BARS
RFG MEAT/POULTRY
PRODUCTS
NON-FRUIT DRINKS - SS
CAT FOOD
RFG ENTREES
PEANUT BUTTER
PHOTOGRAPHY
SUPPLIES
COSMETICS - LIP
CARBONATED BEVERAGES
SHAMPOO
RFG ENTREES
FABRIC SOFTENER
LIQUID
OUTDOOR/LAWN
FRTLZR/WDKLLR
GELATIN/PUDDING MIXES
SPAGHETTI/ITALIAN
SAUCE
SEAFOOD -SS
PASTA
BATH/BODY
SCRUBBERS/MASSAGERS
DRY BEANS/VEGETABLES
DINNER SAUSAGE
RFG JUICES/DRINKS
TOOTHPASTE
OTHER FROZEN FOODS
GRAVY/SAUCE MIXES
DOG FOOD
SOAP
HAIR COLORING
SKIN CARE
CLEANING
TOOLS/MOPS/BROOMS
FZ BREAKFAST FOOD
CREAMS/CREAMERS
VEGETABLES
FZ SEAFOOD
FABRIC SOFTENER LIQUID
LIGHTERS
JUICE/DRINK
CONCENTRATE - SS
BABY FOOD
FZ PREPARED VEGETABLES
MAYONNAISE
ADULT INCONTINENCE
PHOTOGRAPHY
SUPPLIES
STUFFING MIXES
FABRIC SOFTENER LIQUID
BABY NEEDS
TEA - BAGS/LOOSE
WRITING INSTRUMENTS
MAYONNAISE
ICE CREAM/SHERBET
MILK FLAVORING/COCOA
MIXES
RFG MEAT/POULTRY
PRODUCTS
DIAPERS
RFG JUICES/DRINKS
EXTERNAL ANALGESIC
RUBS
SUGAR
BLANK AUDIO/VIDEO
MEDIA
PREMIXED
COCKTAILS/COOLERS
FZ PLAIN VEGETABLES
TIGHTS/SOCKS
DEODORANT
BAKED GOODS - RFG
SOUP
LAUNDRY DETERGENT
ADULT INCONTINENCE
NATURAL CHEESE
MOIST TOWELETTES
PASTRY/DOUGHNUTS
MEXICAN FOODS
PANTYHOSE/NYLONS
ENGLISH MUFFINS
HAIR ACCESSORIES
SHORTENING & OIL
FLOUR/MEAL
AUTOMOBILE
WAXES/POLISHES
RUG/UPHOLSTERY/FABRIC
TREATMT
RFG JUICES/DRINKS
SPAGHETTI/ITALIAN
SAUCE
SPAGHETTI/ITALIAN
SAUCE
RFG SIDE DISHES
VEGETABLES
YOGURT
TEA/COFFEE READY-TODRINK
GRAVY/SAUCE MIXES
RFG MEAT/POULTRY
PRODUCTS
FIRST AID ACCESSORIES
OUTDOOR/LAWN
FRTLZR/WDKLLR
DESSERTS - RFG
CUPS & PLATES
GASTROINTESTINAL LIQUID
HAIR STYLING GEL/MOUSSE
OFFICE PRODUCTS
GASTROINTESTINAL TABLETS
SOUR CREAM
DESSERTS - RFG
DIAPERS
a
Categories in black respond positively while categories in red respond negatively to assortment and pricing strategies.
32
Table 5. Top 25 Categories That Affect Store Revenues the Most with Changes in
Assortment and Pricing Strategies a
BRAND
SKU
SIZE
SKUMKTSHARE
PRICEADV
CARBONATED
BEVERAGES
MILK
CARBONATED
BEVERAGES
CARBONATED BEVERAGES
CARBONATED
BEVERAGES
FZ DINNERS/ENTREES
CARBONATED
BEVERAGES
MILK
FZ DINNERS/ENTREES
FRESH BREAD & ROLLS
LAUNDRY DETERGENT
COLD CEREAL
NATURAL CHEESE
FRESH BREAD & ROLLS ICE CREAM/SHERBET
SPIRITS/LIQUOR
NATURAL CHEESE
CAT FOOD
SALTY SNACKS
WINE
DOG FOOD
SOUP
FRESH BREAD & ROLLS
SPIRITS/LIQUOR
MILK
ICE CREAM/SHERBET
RFG JUICES/DRINKS
WINE
FRESH BREAD & ROLLS
BEER/ALE/ALCOHOLIC
CIDER
TOTAL CHOCOLATE
CANDY
DIAPERS
BEER/ALE/ALCOHOLIC
CIDER
MILK
LAUNDRY DETERGENT
YOGURT
YOGURT
RFG JUICES/DRINKS
LUNCHEON MEATS
CAT FOOD
COLD CEREAL
CIGARETTES
VEGETABLES
RFG SALAD/COLESLAW
CIGARETTES
VITAMINS
RFG SALAD/COLESLAW
DOG FOOD
BEER/ALE/ALCOHOLIC CIDER
SOUP
SALTY SNACKS
SNACK BARS/GRANOLA
BARS
CIGARETTES
CRACKERS
DOG FOOD
RFG ENTREES
FRESH BREAD & ROLLS
TOTAL CHOCOLATE
CANDY
FZ NOVELTIES
ICE CREAM/SHERBET
SEAFOOD -SS
LAUNDRY DETERGENT
DIAPERS
TOILET TISSUE
VEGETABLES
SOUP
VITAMINS
MAYONNAISE
MEXICAN FOODS
DIAPERS
SHAMPOO
SOAP
SNACK BARS/GRANOLA
BARS
COOKIES
VITAMINS
WINE
CAT FOOD
SPIRITS/LIQUOR
TOTAL CHOCOLATE CANDY
YOGURT
NATURAL CHEESE
CRACKERS
YOGURT
FZ SEAFOOD
FZ BREAKFAST FOOD
FZ APPETIZERS/SNACK
ROLLS
BAKING MIXES
DISH DETERGENT
RFG JUICES/DRINKS
DISH DETERGENT
FZ SEAFOOD
FOOD & TRASH BAGS
COOKIES
CIGARETTES
MAYONNAISE
BABY FOOD
FZ POULTRY
FZ BREAKFAST FOOD
BOTTLED JUICES - SS
PAPER TOWELS
CUPS & PLATES
SPIRITS/LIQUOR
SPAGHETTI/ITALIAN
SAUCE
FZ PIZZA
MEXICAN SAUCE
RFG JUICES/DRINKS
TOILET TISSUE
RFG MEAT/POULTRY
PRODUCTS
SPORTS DRINKS
CREAMS/CREAMERS
SOUP
BABY FOOD
FZ DINNERS/ENTREES
DRY PACKAGED
DINNERS
HAIR COLORING
MEXICAN SAUCE
DRY PACKAGED DINNERS
BREAKFAST MEATS
CRACKERS
SALAD DRESSINGS - SS
FABRIC SOFTENER
LIQUID
SPAGHETTI/ITALIAN
PICKLES/RELISH/OLIVES
SAUCE
a
MARGARINE/SPREADS/BUTTER
TOBACCO PRODUCTS
BLEN
Categories in black respond positively while categories in red respond negatively to assortment and pricing strategies.
33
Table 6. Estimated Intrinsic Store Attractions a
STORE
Supermarket 1
Mean Attraction
0.086
b
Category Std. Dev.
0.867
(0.079)
Supermarket 2
-0.057
0.989
(0.057)
Supermarket 3
9.21e-8
0.176
(0.000)
Supermarket 4
1.39e-6
0.124
(0.000)
Supermarket 5
0.788
1.101
(0.203)
Supermarket 6
-8.63e-6
0.277
(0.000)
Supermarket 7
0.127
1.152
(0.061)
Supermarket 8
5.75e-8
0.022
(0.000)
Supermarket 9
-0.338
0.628
(0.063)
Supermarket 10
3.56e-8
0.019
(0.000)
Supercenter 1
-5.21e-6
Supercenter 2
-0.272
0.298
(0.000)
0.533
(0.137)
Club 1
-1.82e-6
0.482
(0.000)
Club 2
-3.47e-6
0.162
(0.000)
Club 3
-8.90e-8
0.024
(0.000)
a
Posterior means of model parameters are reported in the table. The standard errors are reported within parentheses.
b
Estimates in bold are significant at the 0.05 level.
34
Table 7. Top 25 Categories across Stores that Have the
Highest Intrinsic Store-Category Attraction
Category
Need to Be Refrigerated
Need to Be Frozen
FZ PASTA
Yes
Yes
RFG MEAT/POULTRY PRODUCTS
Yes
FZ PIES
Yes
Yes
FZ PIZZA
Yes
Yes
FZ DESSERTS/TOPPING
Yes
Yes
RFG TORTLLA/EGGRLL/WONTN WRAP
Yes
FZ PREPARED VEGETABLES
Yes
ICE CREAM CONES/MIXES
GELATIN/PUDDING MIXES
CARBONATED BEVERAGES
Yes
CROUTONS
MILK
Yes
BREADCRUMBS/BATTERS
CREAMS/CREAMERS
Yes
COTTAGE CHEESE
Yes
YOGURT
Yes
LUNCHES - RFG
Yes
FROSTING
Yes
CREAM CHEESE/CR CHS SPREAD
Yes
RFG DIPS
Yes
DIP/DIP MIXES - SS
ORIENTAL FOOD
FZ FRUIT
Yes
SPORTS DRINKS
INSTANT POTATOES
35
Yes
Table 8. Top 5 categories with the Highest Intrinsic Store-Category Attraction
Supermarket 5
Supercenter 1
Club 1
RFG MEAT/POULTRY
PRODUCTS
HAIR SPRAY/SPRITZ
VITAMINS
FZ PIES
AIR FRESHENERS
MOUTHWASH
FZ PIZZA
PIES & CAKES
FZ POULTRY
CREAMS/CREAMERS
CHILDREN'S ART SUPPLIES
SNACK NUTS/SEEDS/CORN NUTS
YOGURT
AUTOMOBILE FLUIDS/ANTIFREEZE
TOBACCO PRODUCTS
36
Appendix A: Relative Positions of the 16 Retail Chains in a Price-Assortment Map
To get a feel about the relative position of the 16 retail chains, we present a priceassortment map of the 16 retail chains below.
1.3
Retail Chain Price Adv Index
1.2
Supercenter 1
Supercenter 2
Club 3
1.1
Club 4
1.0
Club 1
Supermarket 4
Supermarket 10 Supermarket 6
Supermarket 3
Supermarket 8
Club 2
Supermarket 2
Supermarket 1
Supermarket 5
Supermarket 9
0.9
Supermarket 7
0.8
0.00
0.50
1.00
1.50
2.00
Retail Chain Brand Breadth Index
The x-axis tracks the breadth of the brand assortment available at the retail chain, on
average, across all categories. The y-axis tracks the price advantage of products available at the
retail chain, on average, across all categories.17 From this map, one can see that Supercenter 1
offers the best prices and the second best brand assortments, on average, among all 16 retail
chains. Supercenter 2 offers the next best prices, while the 4 warehouse club chains – Clubs 1, 2,
3 and 4 – are third best. Note that these 4 warehouse club chains are also clustered in the left part
of the map, which indicates that they have the worst brand assortments among the 16 retail
chains. Supermarket 7 appears to be the most expensive retail chain in the market, while
Supermarkets 5 and 9 both offer good brand assortments.
17
Price and assortment measures at the category-level are constructed as unit-free indices that are then averaged
across categories. Mathematical operationalizations of these measures are explained in section 3. We also use other
assortment and price variables in our empirical analysis. We report these two measures in this map for expositional
convenience.
37