View
222
Download
0
Category
Preview:
Citation preview
1
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.
2
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
3
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.
4
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
5
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.
6
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.
7
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
8
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).
9
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.
10
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
11
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
12
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.
13
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-
14
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.
15
(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.
16
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.
17
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).
18
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).
19
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
REFERENCES
Aaker, D. A., & Jones, J. M. (1971). Modeling Store Choice Behavior. Journal of Marketing
Research, 8(1), 38-42.
Ailawadi, K., Pauwels, K., & Steenkamp. J-B. (2008). Private Label Use and Store Loyalty.
Journal of Marketing, 72(6), 19-30.
Ailawadi, K., & Keller, K. L. (2004). Understanding Retail Branding: Conceptual Insights and
Research Priorities. Journal of Retailing, 80, 331-342.
Ainslie, A., & Rossi, P. E. (1998). Similarities in Choice Behavior across Multiple Categories.
Marketing Science, 17(2), 91-106.
Arnold, S. J., Oum, T. H., & Tigert, D. J. (1983). Determinant Attributes in Retail Patronage:
Seasonal, Temporal, Regional, and International Comparisons. Journal of Marketing Research,
20( 2), 149-157.
Baumol, W. J., & Ide, E. A. (1956). Variety in Retailing. Management Science, 3(1), 93-101.
Bell, D. R., Bonfrer, A., & Chintagunta, K. (2005). Recovering Stockkeeping-Unit-Level
Preferences and Response Sensitivities from Market Share Models Estimation on Item
Aggregates. Journal of Marketing Research, XLII, 169-182.
Bell, D. R., Ho, T., & Tang, C. S. (1998). Determining Where to Shop: Fixed and Variable Costs
of Shopping. Journal of Marketing Research, 35(3), 352-369.
Bell, D. R., & Lattin, J. M. (1998). Shopping Behavior and Consumer Preference for Store Price
Format: Why “Large Basket” Shoppers Prefer EDLP. Marketing Science, 17(1), 66-88.
Bell, D. E., Keeney, R. L., & Little, J. D. C. (1975). A Market Share Theorem. Journal of
Marketing Research, 12(2), 136-141.
Bells, S. (2015). An Analysis of the US Grocery Market, Market Realist.
Besanko D., Gupta S., & Jain, D. (1998), Logit Demand Estimation under Competitive Pricing
Behavior: An Equilibrium Framework. Management Science, 44,1533-1547
Blattberg, R. C., Peacock, P., & Sen, S. K. (1976). Purchasing Strategies across Product
Categories. Journal of Consumer Research, 3(3), 143-154.
Boatwright, P., & Nunes, J. C. (2001). Reducing Assortment: An Attribute-Based Approach.
Journal of Marketing, 65(2), 50-63.
Bodapati, A. V., & Srinivasan, V. (2006). The Impact of Feature Advertising on Consumer Store
Choice, Research Paper No. 1935, Research Paper Series, Stanford Graduate School of
Business.
Borle, S., Boatwright, P., Kadane, J. B., Nunes, J. C., & Shmueli, G. (2005). The Effect of Product
Assortment Changes on Consumer Retention. Marketing Science, 24(4), 616-622.
Briesch, R. A., Dillon, W. R., & Fox, E. (2013). Category positioning and store choice: The role of
destination categories. Marketing Science, 32(3), 488-509.
Briesch, R., Chintagunta, P. K., & Fox, E. J. (2009). How does Assortment Affect Grocery Store
Choice? Journal of Marketing Research, 46(2), 176-189.
36
Broniarczyk, S., Hoyer, W. D., & McAlister, L. (1998). Consumers’ Perceptions of the
Assortment Offered in a Grocery Category: The Impact of Item Reduction. Journal of
Marketing Research, 35(2), 166-176.
Brown, D. J. (1978). An Examination of Consumer Grocery Store Choice: Considering the
Attraction of Size and Friction of Travel Time. In Advances in Consumer Research, (5, 243-
246). Association of Consumer Research, Ann Arbor, MI.
Bucklin, R. E., & Lattin, J. M. (1991). A Two-state Model of Purchase Incidence and Brand
Choice. Marketing Science, 10(1), 24-39.
Bucklin, R. E., & Lattin, J. M. (1992). A Model of Product Category Competition among Grocery
Retailers. Journal of Retailing, 68(3), 271-293.
Chernev, A., & Hamilton, R. (2009). Assortment Size and Option Attraction in Consumer Choice
Among Retailers. Journal of Marketing Research, 46(3), 410-420.
Chiang, J. (1991). A Simultaneous Approach to the Whether, What and How Much to Buy
Questions. Marketing Science, 10(4), 297-315.
Chib, S., Seetharaman, P. B., & Strijnev, A. (2002). Analysis of Multicategory Purchase Incidence
Decisions Using IRI Market Basket Data. Advanced Econometrics,16, 57-92.
Chintagunta, P. K. (1993). Investigating Purchase Incidence, Brand Choice and Purchase Quantity
Decisions of Households. Marketing Science, 12(2) 184-208.
Cooper, L. G., & Nakanishi, M. (1988). Market Share Analysis: Evaluating Competitive
Marketing Effectiveness. Boston: Kluwer Academic Publishers.
Cooper, L. G. (1993). Market-Share Models. In J. Eliashberg and G. L. Lilien (Eds.), Handbook in
Operations Research and Management Science (vol. 5, chapter 6, 259-314). Amsterdam:
North-Holland.
Corstjens, M., & Lal, R. (2000). Building Store Loyalty through Store Brands, Journal of
Marketing Research, 37(3), 281-291.
Craig, C. S., Ghosh, A., & McLafferty, S. (1984). Models of Retail Location Process: A Review.
Journal of Retailing, 60(1), 5-36.
Dhar, S. K., & Sherman, S. J. (1996). The Effect of Common and Unique Features in Consumer
Choice. Journal of Consumer Research, 23(4), 193-203.
Deloitte. (2013). The 2013 American Pantry Study. Deloitte, New York.
Dowling, G. R., & Uncles, M. (1997). Do Consumer Loyalty Programs Really Work? Sloan
Management Review, 38(4), 71-82.
Dreze, X., & Hoch, S. J. (1998). Exploiting the Installed Base Using Cross-Merchandising and
Category Destination Programs. International Journal of Research in Marketing, 15(2), 459-
471.
Dreze, X., Hoch, S. J., & Purk, M. E. (1994). Shelf Management and Space Elasticity. Journal of
Retailing, 70(4), 301-326.
Fok, D., Franses, P. H., & Paap, R. (2002). Econometric Analysis of the Market Share Attraction
Model. In P.H. Franses & A. L. Montgomery (Eds.), Advances in Econometrics (vol. 16,
chapter 10, 223-256). Amsterdam: JAI Press.
37
Fox, E. J., & Hoch, S. J. (2005). Cherry-Picking. Journal of Marketing, 69(1), 46-62.
Gauri, D. K., Sudhir, K., & Talukdar, D. (2008). The Temporal and Spatial Dimensions of Price
Search: Insights from Matching Household Survey and Purchase Data. Journal of Marketing
Research, 45(2), 226-240.
Gauri, D. K., Trivedi, M., & Grewal, D. (2008). Understanding the Determinants of Retail
Strategy: An Empirical Analysis. Journal of Retailing, 84(3), 256-267.
Gijsbrechts, E., Campo, K., & Nisol, P. (2008). Beyond Promotion-based Store Switching:
Antecedents and Patterns of Systematic Multiple-store Shopping. International Journal of
Research in Marketing, 25(1), 5-21.
Houston, D. A., & Sherman, S. J. (1995). Cancellation and Focus: The Role of Shared and Unique
Features in the Choice Process. Journal of Experimental Social Psychology, 31(3), 357-378.
Keng, K. A., & Ehrenberg, A. S. C. (1984). Patterns of Store Choice. Journal of Marketing
Research, 21(4), 399-409.
Kotler, P., & Keller, K. L. (2011). Marketing Management (14th ed.). Eaglewood Cliffs, NJ:
Prentice Hall.
Louviere, J. J., & Gaeth, G. J. (1987). Decomposing the Determinants of Retail Facility Choice
Using the Method of Hierarchical Information Integration: A Supermarket Illustration. Journal
of Retailing, 63(1), 25-48.
Ma, Y., Seetharaman, P. B., & Narasimhan, C. (2012). Modeling Dependencies in Brand Choice
Outcomes across Complementary Categories. Journal of Retailing, 88, 47-62.
Manchanda, P., Ansari, A., & Gupta, S. (1999). The Shopping Basket: a Model of Multi-Category
Purchase Incidence Decisions. Marketing Science, 18(2) 95-114.
Mehta, N. (2007). Investigating Consumers’ Brand Choice and Purchase Incidence Decisions
across Multiple Product Categories: A Theoretical and Empirical Analysis. Marketing Science,
26(2), 196-217.
Meyer, R. J., & Eagle, T. C. (1982). Context-Induced Parameter Instability in a Disaggregate-
Stochastic Model of Store Choice. Journal of Marketing Research, 19(1), 62-71.
Nijs, V. R., Dekimpe, M. G., Steenkamp, J-B., & Hanssens, D. (2001). The Category-demand
Effects of Price Promotions. Marketing Science, 20, 1–22
Prasad, V. K. (1972). Correlates of Multistore Food Shopping. Journal of Retailing, 48(2), 74-81.
Prasad, A., Strijnev, A., & Zhang, Q. (2008). What Can Grocery Basket Data Tell Us about Health
Consciousness? International Journal of Research in Marketing, 25, 301-309.
Rhee, H., & Bell, D. R. (2002). The Inter-Store Mobility of Supermarket Shoppers. Journal of
Retailing, 78(1), 225-237.
Rossi, P. E., Allenby, G. M., & McCulloch, R. E. (2005). Bayesian Statistics and Marketing.
Hoboken, NJ: Wiley.
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.
38
Singh, V. P., Hansen, K., & Gupta, S. (2005). Modeling Attractiveness for Common Attributes in
Multi-category Brand Choice. Journal of Marketing Research, 42(2), 195-209.
Stephenson, P. R. (1969). Identifying Determinants of Retail Patronage. Journal of Marketing,
33(3), 57-61.
Tate, R. S. (1961). The Supermarket Battle for Store Loyalty. Journal of Marketing, 25(6), 8-13.
Talukdar, D., Gauri, D. K., & Grewal, D. (2010). An Empirical Analysis of the Extreme Cherry
Picking Behavior of Consumers in the Frequently Purchased Goods Market. Journal of
Retailing, 86(4), 336-354.
The Hartman Group Inc. (2014). U.S. Grocery Shopping Trends 2014. Food Marketing Institute,
Arlington, Virginia.
Uncles, M. D., & Ehrenberg, A. S. C. (1990). The Buying of Packaged Goods at US Retail Chains,
Journal of Retailing, 66(3), 278-296.
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
er o
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.
Recommended