We focus on destination categories, so named because they have the greatest impact on where householdschoose to shop and, more generally, on how category positioning (e.g., long-run merchandising policies)
affects which store a household chooses. We propose a reduced-form model-based analytical approach to iden-tify categories that fill the destination role. Our approach determines which categories are most important toshoppers’ store choice decisions and helps determine in which categories the retailer provides superior value.In addition, our approach allows us to understand the impact of the retailer’s long-run merchandising policydecisions on the value it provides. Previous store choice research focused on the effects of pricing, assortmentand other merchandising decisions at the store level but did not consider the effect of specific categories onstore choice. This focus leads us to formulate a model that can (1) measure and explain the differential impactthat specific categories have on shoppers’ store choice decisions and (2) measure the relative value of retailers’category offerings, partitioning that value into the component resulting from retailer merchandising and thecomponent that is nonmerchandising related. The model form captures differences in category value acrossstores (i.e., the store’s category positioning) by specifying a spatial model for the store choice and categoryincidence intercepts. Our spatial model recognizes that stores position their offering vis-à-vis the category idealbased on long-run category merchandising decisions and that not all categories have the same importance instore choice decisions. We explore these issues for five retailers in the Charlotte, North Carolina market. We findthat (1) category impact on store choice is highly skewed; (2) although categories with higher sales generallyhave a higher impact on store choice decisions, there are exceptions; (3) impact on store choice decisions doesnot vary systematically by the type of category (e.g., perishable versus dry grocery); and (4) our measure ofcategory impact on store choice, although correlated with the category development index between retailers, issuperior in that it provides a basis for comparing category impact within a retailer and how relative categoryvalue, based on long-run merchandising decisions, attracts shoppers to a store.
Key words : category positioning; category and store choice modeling; spatial modelingHistory : Received: November 7, 2011; accepted: November 12, 2012; Preyas Desai served as the editor-in-chief
and Gary Russell served as associate editor for this article. Published online in Articles in Advance.
1. IntroductionA fundamental tenet of category management isthat individual categories play different roles. Themost widely used taxonomy identifies categories forthe “destination,” “routine,” “occasional/seasonal” or“convenience” role. These consumer-based roles helpretailers “understand how consumers view the cat-egory” (Blattberg et al. 1995, p. 22), which shouldenable them to manage “categories according to theirimportance to consumers” (ACNielsen 2006, p. 79).Surprisingly, these consumer-based category roles aredescribed not from the consumer’s perspective but bythe “positioning the retailer should take based on the cat-egory’s importance to the consumer” (Blattberg et al.1995, p. 23, emphasis added). The common thinking isthat categories should be managed according to theirroles, thereby enabling retailers to use their productoffering like a portfolio that attracts shoppers whileprofitably generating revenues.
Our focus is on destination categories, so namedbecause they have the greatest impact on wherehouseholds choose to shop and, more generally, onhow category positioning (e.g., long-run merchandis-ing policies) affects which store a household chooses.In other words, we are interested in which spe-cific categories drive store choice decisions and howretailer merchandising influences those categories.
To our knowledge, no definitive definition of des-tination categories exists, yet destination categorieshave been discussed in the context of the differ-ent roles that categories play. For example, the JointIndustry Project on Efficient Consumer Reponse statesin their Category Management Best Practices reportthat a destination category is “to be the primary cate-gory provider and help define the retailer as the storeof choice by delivering consistent, superior target cus-tomer value” (Food Marketing Institute 1993, p. 26).Two implications of this statement help us understand
the meaning of a destination category. First, beingthe primary provider helps the retailer become thestore of choice, so destination categories should drivestore traffic. Second, to be the store of choice requiresthat the retailer deliver more value in the categorythan its competitors do. Although retailers can allo-cate resources in an attempt to create destination cat-egories, it is ultimately the shopper who determineswhether the retailer is offering superior value in a par-ticular category. We discuss the concept of destinationcategories in more depth in §3.
1.1. ObjectivesIn this paper, we propose a reduced-form model-based analytical approach to identifying categoriesthat play the destination role. Because our modelfocuses on how shoppers respond to retailers’ mar-keting mix decisions—not retailer decision making—it allows us to determine which categories are mostimportant to shoppers’ store choice decisions andwhich retailers provide superior value in different cat-egories.1 Our approach also allows us to understandthe impact of a retailer’s long-run merchandising pol-icy decisions on the value it provides; specifically,we formulate a model that can (1) measure andexplain the differential impact of specific categorieson shoppers’ store choice decisions and (2) measurethe relative value of retailers’ category offerings, par-titioning that value into the component resulting fromretailer merchandising and the component that is notmerchandising related. This value partitioning clari-fies the contribution of a retailer’s merchandising tothe value of its category offerings. It is also importantto note that although previous store choice researchfocused on the effects of pricing, assortment, and othermerchandising decisions at the store level, it did notconsider the effect of specific categories on store choice.
To understand the role of category positioning instore choice, we formulate a mixed nested logit modelthat incorporates both store choice and category inci-dence decisions in a way that captures the effect ofa store’s weekly category merchandising decisions,as well as its long-run merchandising policies, on theprobability of choosing that store. This reduced-formmodel of shopping behavior captures differences incategory value across stores by specifying a spatialmodel for the store choice and category incidenceintercepts. The store choice intercepts position eachstore in latent multiattribute space on the basis ofstore characteristics and the value that the store pro-vides to consumers across all categories. The category
1 For this reason we do not claim to recommend categories thatthe retailer could or should develop to serve the destination role.As we discuss in this section, and in more detail in §4, we specify areduced-form model conditioned on current strategic retailer deci-sions, and consequently, we focus only on identifying categoriesthat currently play the destination role.
incidence intercepts are parameterized in terms of thespatial distance between a household’s perception ofstores’ offerings in a category and the household’sideal point for that category—in other words, the dis-tance between what the store offers in a specific cate-gory and what the household wants in that category.Thus, our spatial model recognizes that stores canposition their offerings vis-à-vis the category idealbased on long-run category merchandising decisionsand that not all categories have the same importancein store choice decisions.
The remainder of this paper is organized as follows:Section 2 briefly reviews the extant literature andpositions our work relative to this literature. The con-cept of a destination category is discussed in §3 andis specifically related to category development mea-sures (i.e., category development indices, or CDIs).Section 4 presents two conceptual frameworks thatexplain how category incidence (conditional on theshopper visiting the store) is used to estimate cate-gory utility; these two conceptual frameworks guideour model specification. The store choice and categoryincidence component models are presented in §5,where we also explain how these model intercepts areparameterized and offer a baseline model that can beused to assess the usefulness of the proposed spatialrepresentation. Section 6 discusses estimation issues.Our data set is described in §7. Modeling results fol-low in §8. Section 9 presents a policy analysis thatinvestigates the impact of categories on store choice.We end with a discussion of limitations and avenuesfor future research.
2. Background and ContributionExplaining store choice decisions has been ofgreat interest to academics and practitioners alike.Researchers have studied a wide variety of fac-tors that may influence a consumer’s decision aboutwhere to shop including pricing, promotion, featureadvertising, assortment, retail price format (HiLo ver-sus everyday low pricing (EDLP)), shopping bas-ket size and composition, travel distance/time, priorshopping experiences, the need for variety, shoppers’fixed and variable costs, cherry-picking, and house-hold characteristics.2
This study contributes to the extant literature onstore choice in a number of important ways.
• First, unlike previous store choice research, ourprimary interest is to understand the role that spe-cific categories play in store choice decisions. To our
2 Representative papers include Reilly (1931), Baumol and Ide(1956), Huff (1964), Arnold et al. (1978, 1981), Arnold and Tigert(1982), Arnold et al. (1983), Broniarczyk et al. (2006), Chernev et al.(2003), Chernev and Hamilton (2009), Bell et al. (1998), Bell andLattin (1998), Rhee and Bell (2002), Fox and Hoch (2005), Brieschet al. (2009), and Zhang et al. (2010).
knowledge, this is the first study to (1) address con-ceptually and empirically the issue of destinationcategories and (2) isolate the differential effects ofcategory positioning decisions on store choice.
• Second, because we incorporate a spatial modelof household preferences in which stores and cate-gories are positioned in multiattribute space, we canassess the extent to which different categories drivestore choice decisions. Furthermore, we show how thepositions of a given store and category in multiat-tribute space reflect the value of the store’s categoryoffering.
• Third, in contrast to Bell et al. (1998) and Brieschet al. (2009), we do not adopt a shopping listmetaphor or need-based approach to category inci-dence. By not conditioning on a shopping list or oncategory needs, we capture all purchase incidencesand can therefore account for impulse purchases.3
• Fourth, we explicitly model both store choice andcategory incidence and consider a large number ofcategories (i.e., 80) in multiple retail formats (groceryand supercenter).
• Finally, in contrast to the earlier literature thateither assumed that category value did not varyacross consumers (e.g., Baumol and Ide 1956, Huff1964) or allowed categories to have differential appealacross shoppers based on an unobserved shoppinglist or predetermined set of needs (e.g., Bell et al.1998, Briesch et al. 2009), we allow stores to differen-tiate their category offerings based on assortment andother long-run merchandising policies. We find thatcategories do not necessarily have the same impor-tance in store choice decisions. Holding price and pro-motion constant, the likelihood of category purchasedepends on which store a shopper chooses.
3. Destination Categories and CDIs3.1. Destination CategoriesACNielsen (2006) proposes an analytical basis for cat-egory role selection in which the first question to beanswered is how important the category is to con-sumers. ACNielsen argues that high category impor-tance is a necessary, but not sufficient, condition fora destination category. The retailer must also providesuperior value in the category based on merchandis-ing effort—in particular, product assortment.
Adopting this perspective, the retailer selects cat-egories for the destination role.4 That selection
3 To be clear, although we capture multiple store visits, we do notexplicitly model this behavior.4 Our focus is not on how a retailer selects a category for thedestination role, but rather on identifying which categories haveassumed that role.
influences retailer actions, in particular merchandis-ing (including product assortment) decisions. How-ever, whether a category selected for the destinationrole achieves its objectives for the retailer is ultimatelydetermined by consumers. They are the ones whodetermine the value a retailer is delivering in a spe-cific category and where they will shop.5 Although wecan assume that all categories influence store choiceto varying degrees, a destination category dispropor-tionally increases the likelihood of a store being cho-sen because this store offers consumers superior valuein the category. Following first-order principles, wecapture value by estimating the utility that a shop-per derives from a category at each store. As we dis-cuss in §§4 and 5, the utility that a consumer derivesfrom a category is determined by the attractivenessof that category at a specific store, along with otherfactors including category needs and in-store factorsthat influence the consumer while shopping.
Continuing with first-order principles, the moreattractive the category is at a store, the higher theprobability that a consumer chooses that store. Attrac-tiveness is defined in terms of a long-run merchandis-ing score that reflects how well positioned a categoryis relative to what consumers want in the category.The purpose of the merchandising score is to movethe position of a store based on the perceived attrac-tiveness of the store’s long-run merchandising poli-cies. The tacit assumption is that the smaller thedistance between a store’s location, adjusted for itsmerchandising attractiveness and what consumerswant in a category, the higher the probability of shop-pers choosing that store and the more that categoryplays the destination role for this retailer.
To fix ideas, in this paper we define a destina-tion category as one that (1) substantially affectsstore choice decisions and (2) delivers superior value(at least in part) as a result of the retailers’ long-run merchandising decisions. Thus, destination cate-gories are jointly determined by the retailer and theconsumer, in the sense that the retailer anticipates orresponds to consumers’ category preferences. Accord-ingly, the retailer does not select categories to serve inthe destination role; rather, the retailer identifies cate-gories that motivate and attract consumers. Note thatwe define a destination category as one that increasesstore traffic, not in terms of how much a consumerpurchases while shopping. This perspective is consis-tent with the view taken by practitioners. Obviously,
5 A brand manager can be thought of as playing a role similar tothe retailer. The brand manager determines a brand’s value propo-sition, one that they hope will resonate with consumers. However,it is consumers who ultimately determine whether the value propo-sition is relevant and believable and whether they will purchasethe brand.
retailers welcome larger basket sizes and/or bas-ket expenditures. However, interviews with categorymanagers at large retailers revealed that higher cat-egory purchase quantities are associated with othercategory roles; these include commonly defined rolessuch as “routine” and “seasonal” as well as retailer-defined roles such as “basket builder” and “impulse.”By contrast, the ability to drive store traffic wasthe most distinguishing feature of destination cate-gories cited by the retail managers. Specific responsesincluded that such categories “are trip drivers,” “canbe a traffic driver,” and “can increase awareness andcustomer count.”6
Our perspective that destination categories resultin large measure from a retailer’s long-run merchan-dising decisions has two salutary benefits. First, suchdecisions can be differentiated from short-run mer-chandising decisions (e.g., feature advertising) thatmay also increase store traffic. Second, this definitionmitigates possible endogeneity concerns, as long-rundecisions are, by definition, independent of short-runshocks to the system. In other words, strategic reac-tions by retailers to shopper behavior operate over alonger time frame, so it is possible to condition oncurrent retailer behavior and then build a consumermodel, relative to this conditioning.
Our empirical analysis will consider only categoriesthat are offered by the five retailers in our sample,but in principle, this need not be the case.7 To identifya category as playing the destination role, we mustdemonstrate that the category influences store choiceand, just as important, that the long-run merchandis-ing policies have created value as evinced by the levelof attractiveness consumers place on the category.As we demonstrate in §5, our modeling framework,which adopts a spatial parameterization for the storeand category intercepts, allows us to estimate the util-ity that the shopper derives from a category and theimpact that category utility—in particular, categorymerchandising—has on store choice decisions.
3.2. Category Development IndexOne measure that has been proposed as an indica-tor of shoppers’ preference for a specific category ata specific retailer is the CDI. At an aggregate level,the CDI reflects the preference of shoppers to pur-chase specific categories at one retailer (rather thanat others) and is defined as the retailer’s share ina particular category divided by its overall marketshare, multiplied by 100 (e.g., Dhar et al. 2001). Table 1presents the CDIs of 80 product categories for five
6 Information on our interviews with retail category managers canbe obtained by contacting the third author.7 However, in such a case, the determination of value would bedifficult to assess under quasi-monopolistic conditions.
retail chains in the Charlotte, North Carolina mar-ket. These chains and product categories will be thefocus of the empirical analysis that follows in §8. CDIscan vary markedly across retail chains, as is the casefor the five retail chains shown in Table 1. AlthoughCDIs can undoubtedly identify store-by-category dif-ferences, they provide little insight into the reasonsfor, or consequences of, these differences for the fol-lowing reasons.
• First, CDIs provide no information about theimportance of different categories in store choice deci-sions, a key consideration in the identification ofdestination categories. For example, categories withhigh purchase frequency and/or that represent a highannual expenditure may have greater impact on storechoice.
• Second, CDIs provide no insights as to the valueadded by the retailer’s long-run merchandising poli-cies. In contrast, our model framework separatesfactors that the shopper knows before choosing astore, which reflect a store’s long-run category assort-ment and merchandising policies, from those that areonly observable after choosing the store, i.e., whileshopping.
• Third, CDIs have limited explanatory value;a high CDI (>100) might indicate that the category isa complement to other categories that actually influ-enced the store choice decision (see, for example,Manchanda et al. 1999), or it might indicate that thecategory is a complement to other purchases thatwere made because of advertised prices.
• Finally, because CDIs are neither household- nortime-specific, they are not at all informative as towhich store a household may choose on any givenshopping trip.
As we demonstrate in §9, our model form allows usto derive a metric that is highly correlated with CDIsacross a store (within a category) but that remediesthese limitations.
4. Conceptual FrameworkIt is well known that modeling store choice presentssubstantial challenges. Consider that the expectedcosts of shopping at each store, and hence the prob-ability of choosing each store, depends on the shop-per’s intended purchases. Yet purchase intentions,reflected in either a shopping list (Bell et al. 1998)or a priori category needs (Briesch et al. 2009), arenot observed—they must be inferred from actualpurchases. Actual purchases, however, include bothintended and unplanned purchases (see Bell et al. 1998,p. 354 for a detailed discussion). Unplanned pur-chases are made as a result of in-store displays andpromotions (Kollat and Willett 1967), but shoppers arenot exposed to these in-store stimuli until after theyhave chosen a store. Moreover, in-store stimuli arespecific to a store, so unplanned purchases are depen-dent on which store is chosen.
To address this challenge, we will model observedpurchases in such a way that intended and unplannedpurchases are partitioned. Specifically, informationthat shoppers know prior to choosing a store willbe reflected in the intended purchase probability,whereas in-store displays and promotions—to whichshoppers are exposed only after choosing a store—will be reflected in the unplanned purchase proba-bility. Note that these purchase probabilities will bemodeled at the category, rather than the stock-keepingunit (SKU) level. In this way, we parsimoniously cap-ture a store’s diverse product offerings while avoid-ing the complexity of cross-product effects withincategories and incorporate retailers’ assortment poli-cies, which are only relevant at the category level.Finally, we use intended category purchase probabil-ity and other relevant factors to estimate the utilitythat the shopper derives from each category, whichis then incorporated directly into the store choicemodel. Note that the uncertainty of purchase inten-tions will be taken into account by estimating thecategory purchase (i.e., category incidence) and storechoice models simultaneously.
To better illustrate our proposed approach and,specifically, how we will use category incidence (con-ditional on the shopper visiting the store) to estimatecategory utility, Figure 1 provides two conceptualframeworks. Figure 1, panel A, is representative ofthe mental model (we hypothesize) that shoppers usein deciding which store to visit, and panel B trans-lates the shopper’s conceptual framework into ele-ments that are more closely aligned with the modelproposed in the next section.
Figure 1 Conceptual Frameworks: (A) Conceptual Consumer Model and (B) Conceptual Modeling Framework
Consumer “evaluates” a storewith respect to the categories
of need
Consumer assessescategory needs
Need assessment can be driven by anumber of factors (e.g., inventoryassessment, or “looking in the pantry”).
Attractiveness is determined by whatthe consumer wants to purchase in acategory and what the store offers (i.e.,assortment and merchandising-relatedfactors).
Consumer assigns a weight toeach category that reflects
the importance of the category
This weight is a function of a number offactors: (e.g., money spent inthe category, average time betweenpurchases in the category).
Consumer “computes” thecomposite utility of the“basket” for each store
Consumer compares the basketutility with store-specific
“shopping costs”
Store-specific costs include traveldistance to the store, store loyalty, andcategory-specific store preference.
Consumer selects the storeto visit that maximizes
net utility
Once at the store, in-storemerchandising influences
category purchases
Category incidence isobserved
Needs and categoryattractiveness arenot directly observable.
4.1. Conceptual Shopper ModelAs depicted in panel A of Figure 1, the process beginswith the shopper recognizing one or more categoryneeds. He or she then, either consciously or uncon-sciously, evaluates each store’s offering in the cate-gories of need. In essence, this involves an evaluationof what the shopper wants to purchase and what thestore offers. It is important to note that this evalua-tion reflects what the shopper knows before choos-ing a store—each store’s long-run category assortmentand merchandising policies, not the actual displaysand (unadvertised) prices, which are only observ-able after choosing the store. In addition, the shopperweighs the importance of individual categories in sat-isfying his or her needs. These importance weightsreflect the fact that certain categories may have agreater impact on store choice. At this point, the shop-per is in a position to assess the total basket utilitythat he or she would derive from purchasing at eachstore under consideration. To determine which storeto visit, the shopper compares the store-specific totalbasket utility with store-specific shopping costs. Theshopper then chooses the store that maximizes his orher net utility, which in turn determines which in-store stimuli he or she sees and hence whether cat-egory purchases are consummated. Recall that onlythe household’s category purchases (incidence) areobserved, not the household’s category needs or thestore-specific category value. In our model, categoryneeds and store-specific category value are identifiedby the household’s category purchases.
4.2. Conceptual Modeling FrameworkThe modeling framework shown in panel B of Fig-ure 1 adds further specificity to the shopper modeldescribed above. In the figure, ovals depict inherentlyunobservable factors (i.e., constructs) and squaresdepict observable variables. We see that factorshypothesized to affect category incidence are dividedinto those that influence the shopper prior to visit-ing a store (category needs and store-specific cate-gory value, labeled SSCV in the figure) and those thatare experienced while shopping (Price and Display).We use inventory-related variables such as Time sincelast category purchase and Lagged purchase quantity,whether the households’ favorite brands are adver-tised as Features, as well as the Day of the week theshopping trip took place to operationalize categoryneeds. Inventory assessment and feature advertisingoccur prior to the store visit; the day of the weekthat the shopping trip took place provides informa-tion about the type of trip planned—stock-up/majorversus fill-in (Kahn and Schmittlein 1989, Kim andPark 1997). And, as detailed in §5.1.1, we develop aspatial model to estimate the store-specific categoryvalue by reparameterizing the intercept term in the
category incidence model. Store choice is hypothe-sized to be a function of category needs, store-specificcategory value, and store-specific fixed costs. How-ever, the effects of category needs and store-specificcategory value are weighted by the importance of thecategory to the shopper, which, following ACNielsen(2006), is hypothesized to depend on the amount ofmoney spent ($Spend) in the category and averagetime between category purchases (APT).
4.3. CommentWe acknowledge that there is a conditioning or endo-geneity problem inherent in the notion of a destinationcategory, given the dependency between observedpurchases and store choice. Although we do notexplicitly address the endogeneity issue, note thefollowing. First, as discussed above we distinguishbetween factors that shoppers experience (or areexposed to) before choosing a store and after they areat the store, i.e., while shopping. Second, we adopta utility maximizing approach as opposed to a costminimization approach, as implemented by Bell et al.(1998) and Briesch et al. (2009).8 Because we focus onutility maximization, we do not need to estimate thequantity that the household plans to buy because wedo not calculate category costs, but rather we focus oncategory attractiveness.9 Third, although our modelform requires (via the conditioning argument) that ahousehold be in the store to make a purchase, the con-ditioning is used only to estimate the attractiveness ofthe category; it is not assumed to be part of the house-hold decision process. In our analysis, we considerthe 80 largest of the 290 categories for which we havedetailed price information. Consequently, there is nobinding constraint that the household must purchasein one of the 80 largest categories given that theyselected a store to visit. Finally, there is another reasonto view this putative endogeneity problem as perhapsless severe. Although to some extent store position-ing as shown in category merchandising may impactstore choice, it is unlikely that retailers can respondto consumer purchasing patterns by changing prod-uct assortments in the short run, i.e., weekly. Rather,it is more likely that the majority of category mer-chandising and product assortment policies are fixedover short periods. In other words, it takes retailerslonger to understand how category positioning mightbe changed to impact store choice. This argument issimilar to the rationale for treating prices as exoge-nous. Retailers cannot detect consumers’ responses toprices quickly enough to influence weekly price set-ting decisions (see Erdem et al. 2006).
8 Utility maximization is the dual of cost minimization.9 As discussed in §2, this also relieves the need to adopt a shoppinglist metaphor (Bell et al. 1998) or need-based approach (Brieschet al. 2009).
5. Model Forms5.1. Category Incidence ModelThe indirect utility for household h purchasingcategory c on trip t (at store s5 can be written as
U Chsct = V C
hsct + �Chsct1 (1)
where V Chsct denotes the deterministic component
of utility. Consistent with our conceptual modelingframework, we partition the deterministic componentof the utility of purchasing in the category in termsof category needs, in-store factors, and store-specificcategory value as follows:
V Chsct = �hsct +�0hc +�1hcTimehsct +�2hcQntyhsct−1
+�3hcTimehsct ×Qntyhsct−1 +�4hcFAdvhsct
+�5hcWKEndhct +�6hcPricesct +�7hcDisphsct0 (2)
In Equation (2), the first five covariates relate to fac-tors that influence a household before shopping andwhich assist in determining the household’s categoryneeds:
Time = The number of days since the house-hold last purchased in the category.
Qnty = Quantity purchased in a category onlast visit.
Time × Qnty = The mean-centered interaction of num-ber of days since the last purchase andthe quantity last purchased.
FAdv = The proportion of each household’sthree favorite brands that are featureadvertised at store s during trip t,weighted by the brands’ share ofhousehold category purchases duringthe 26-week initialization period.
WKEnd = A dummy variable equal to 1 if theshopping trip occurred between Fridayand Sunday; 0 otherwise.
The last two covariates in Equation (2) are in-storefactors that influence the consumer while shopping:
Price = The average price of those brands in the cat-egory that are carried by all stores, weightedby each brand’s long-run market share of thecategory (see Ainslie and Rossi 1998).
Disp = The proportion of each household’s threefavorite brands that are displayed at store sduring trip t, weighted by the brands’ shareof household category purchases during the26-week initialization period.
With respect to parameters, �hsct denotes store-specific category value (see Figure 1), which indicateshow attractive category c at store s is for household h(how this parameter is estimated will be explained
in the next section); �0hc represents the (estimated)baseline purchase frequency for the category, whichaccounts for differences in frequency of category pur-chases when estimating spatial parameters (spatialparameters will be discussed below); and, �jhc, j =
1121 0 0 0 17, reflect the impact of each covariate on theindirect utility of purchasing in category c, wherewe assume a variance components representation forthese effects:
�jhc = �j + �c + �h0 (3)
In Equation (3), �j gives the mean impact of covari-ate j , �c gives the variance across categories, and �h
gives the variance across households.Note that we selected the covariates appearing in
Equation (2) on the basis of our conceptual modelframework, which partitions factors that could pos-sibly influence category purchases and store choicebased on whether the shopper experiences thosefactors before or after entering the store. Thereare, however, several covariates that warrant furtherdiscussion.
Because category needs depend on householdinventory levels and the rate at which the inventoryis consumed, we need to operationalize these vari-ables; we use lagged quantity and time since last cat-egory purchase (and their interaction) as surrogatesfor these inventory levels, respectively, which is con-sistent with the extant literature (see, for example,Erdem et al. 2003, Briesch et al. 2009, Hendel andNevo 2009). Accordingly, we expect that the prob-ability of category purchase is negatively related toquantity last purchased and positively related to timesince the most recent category purchase (Chib et al.2004). We include WKEnd to account for the differentshopping behaviors that are known to be associatedwith weekday and weekend trips; in particular, basketsizes on weekend trips are generally larger than bas-ket sizes on weekday trips (see Kahn and Schmittlein1989, McAlister et al. 2009). Thus, WKEnd is a plausi-ble surrogate for both basket size and basket expen-diture as well as other behaviors driven by the typeof trip, i.e., stock up (major) versus fill in.
Finally, we chose not to model basket dollar expen-diture directly for two reasons. First, our interviewswith retail category managers indicated that desti-nation categories are associated with driving traf-fic to the store as opposed to increasing basket sizeor basket dollar expenditure. Second, we performeda natural experiment in which we conditionedCDIs according to a household’s basket expenditure;in other words, we recalculated the CDIs in Table 1conditioning on high versus low dollar categoryspend (detailed results are in the Web appendix, avail-able at http://dx.doi.org/10.1287/mksc.2013.0775).10
10 We used median dollar category expenditure to form the highversus low household groups for each retailer.
The differences are, for the most part, quite small, andthere appears to be no systematic pattern to suggestthat category dollar expenditures are driving categoryincidence. Although we do not explicitly include cat-egory dollar expenditure in the category incidencemodel, we recognize that category dollar spend mayinfluence the importance of a category in driving storechoice decisions. We discuss this issue in §5.2.
5.1.1. Category Attraction Parameter. To allowstore choice to be influenced by what the shopperwants vis-à-vis what stores offer in a category, weadopt an ideal point representation for �hsct, the store-specific category value, which gives the attraction ofcategory c at store s for household h. Accordingly,we define �hsct as
�hsct =∑
d
wd4�sd +ãhscdt −�Ihcd5
21 (4)
where
�sd = the location of store s on latent attributedimension d,
wd = the importance weight of latent attributedimension d,
�Ihcd = household h′s ideal point for category c on
latent attribute dimension d, andãhscdt = long-run merchandising score for household h
of category c at store s during trip t on latentattribute dimension d.
In the interest of parsimony, ãhscdt is defined as a linearcomposite of long-run merchandising covariates suchthat
ãhscdt
=b1hdFavhsct +b2hdNUPCsct +b3hdNBrdsct
+b4hdNSizesct +b5hd%PLsct +b6hd%Natlsct
+b7hdAdvFsct +b8hdDispFsct +b9hdAvgPricesct1 (5)
where
Fav = Favorite brand availability—the summa-tion of the purchase shares of the top threebrands for the household if the brand issold in store s during period t or is 0otherwise;
NUPC = The number of universal product codes(UPCs) per brand available at store s in cat-egory c during trip t divided by the aver-age number of UPCs per brand carried byall stores in all periods;
NBrd = The number of brands available at store sin category c during trip t divided by theaverage number of brands carried by allstores in category c over all periods;
NSize = The number of sizes per brand available atstore s during trip t divided by the aver-age number of sizes per brand for all storesover all periods;
%PL = the percentage of private label UPCs avail-able in category c at store s during trip t;
%Natl = the percentage of national brand UPCsavailable in category c at store s duringtrip t;
AdvF = the frequency of brand advertisements—the number of weeks that at least oneSKU in the brand was feature advertiseddivided by the total number of weeks;
DispF = the frequency of displays—the number ofweeks that at least one SKU in the cate-gory was on display divided by the totalnumber of weeks;
AvgPrice = the average volume-weighted prices acrossall SKUs in a category; and
bkhd = the component weight for long-run mer-chandising covariate k for household h ondimension d.
We use the first 26 weeks of data to initializeeach covariate and then apply a 26-week window toassess availability. For example, if we observe that arespondents’ favorite brand is available at store s, say,at week 2, we assume that the brand was also avail-able in weeks 3–28. With the exception of AvgPrice, allof these merchandising covariates have been inves-tigated in the context of the impact of assortmentson store choice (see Briesch et al. 2009) and so areincluded in this study. However, in this study, we areinterested in the longer-run impact of each covariatebecause we believe that consumers develop beliefsabout store and category positioning based on theircollective shopping experiences. We have includedAvgPrice because the longer-run pricing practices ofretailers play a role in shaping category perceptionsas well; for example, EDLP versus HiLo.
Note that the role of ãhscdt, the long-run merchandis-ing score, in our modeling framework is to move theposition of store s on dimension d based on the per-ceived attractiveness of store s’s long-run merchan-dising policies in category c. The tacit assumption isthat the smaller the distance between the store’s loca-tion (adjusted for merchandising attractiveness) andthe category ideal point, the higher the probability ofchoosing that store. The magnitude and statistical sig-nificance of each component weight associated with along-run merchandising covariate (bkhd) indicates theextent to which the covariate impacts the compositeand, perhaps more importantly, the covariate’s role indisplacing a store along each latent attribute dimen-sion. In other words, we will use the componentweights to “adjust” the store’s position on the latent
attribute dimensions. Note also that (1) AdvF andDispF are incorporated in the merchandising score butonly as long-term frequencies (see Ainslie and Rossi1998), and (2) in deriving the store locations (�sd) andcategory ideal points (�I
hcd), we remove the baselinecategory purchase frequencies (�0hc) so that the impactof assortment and merchandising is independent ofhow often the household purchases in the category.We establish the conditions for identification of thespatial parameters below (proofs are available in theWeb appendix).
Finally, we would argue that the merchandisingand product assortment variables (and their defini-tions) that appear in Equation (5) reasonably capturea retailer’s merchandising and product assortmentdecisions and are similar to the operationalizationsused by others (e.g., Boatwright and Nunes 2001,Briesch et al. 2009); however, we recognize that thesecovariates may not be reflective of how householdsencode category information or form impressionsabout categories. For example, Hoch et al. (1999)present an interesting approach to capturing howhouseholds perceive the “variety” of an assortment.Their approach, however, is best suited to studies thatconsider a small number of items per category, unlikethe present study, because it is based on computingthe psychological distance between all items in a cate-gory (i.e., all pairwise comparisons).11 We discuss thislimitation of the current study further in §10.
5.1.2. Identification Conditions for SpatialParameters. To identify the spatial parameters, i.e.,store locations (�sd) and category ideal points (�I
hcd),we use category purchase incidence data along with anumber of identifying constraints. In this section weprovide general identification conditions. Conditionsfor identification of a k-dimensional solution relyon the identifying restrictions associated with thek− 1 dimensional solution (see the Web appendix forproofs).
Condition 1. The weights for the dimensions are setto −1 for all dimensions. This identifies the scale ofthe map and ensures that all dimensions have thesame scale.
Condition 2. One store is located at the origin(or the stores are centered at the origin; i.e., thesum of the store positions on each dimension addto 0). This restriction provides translational invari-ance for the stores and helps identify the categoryintercepts.
11 Another interesting approach is presented by Morales et al.(2005), who capture how a consumer organizes category assortmentinternally. We thank a reviewer for raising this issue and pointingus to the work of Hoch et al. (1999) and Morales et al. (2005).
Condition 3. One category is located at the origin (orthe categories are centered at the origin; i.e., the sumof the category positions on each dimension add to 0).This restriction provides translational invariance forthe categories and helps identify the category inter-cepts and other store positions.
Condition 4. For each dimension d, �4s=d+15d > 0.This restriction provides rotational invariance for thedimensions.
Condition 5. For each dimension d, �4s=d+15j = 0 and�Ih4c=d+15j = 0, j = d+ 11 0 0 0 1D.Condition 6. The number of dimensions (D) is less
than the number of stores (S), as D + 1 storesare required to identify the category positions andintercepts.
5.2. Store Choice ModelThe indirect utility for household h selecting store son trip t is
U Shst = V S
hst + �Shst1 (6)
where V Shst denotes the deterministic component and
�Shst denotes the error term. Consistent with our con-
ceptual modeling framework presented in §4, weparameterize the deterministic component of the indi-rect utility of store choice in terms of “store fixedcosts” (e.g., travel distance/time), category needs, andstore-specific category value. Recall that the latter twoterms were used in specifying the deterministic com-ponent of the indirect utility of purchasing in the cate-gory shown in Equation (2). However, in-store factorsrelating to Price and Display should not be includedbecause they are only observed by the shopper afterselecting a store. Thus, for the purpose of specify-ing the store choice model, we define a reduced formof the deterministic component of the indirect utilityof purchasing in a category previously introduced inEquation (2) as follows:
V Rhsct = �hsct +�1hcTimehsct +�2hcQntyhsct−1 +�3hcTimehsct
×Qntyhsct−1 +�4hcFAdvhsct+�5hcWKEndhct1 (7)
where all terms have been previously defined.12
Whereas V Rhsct gives the deterministic component of
the indirect utility of a category, it is the total util-ity (i.e., value) of the basket that a household intendsto purchase at a given store that should influencethe household’s decision as to which store to visit.
12 Notice that the parameter �0hc is also absent. Recall that �0hc rep-resents the estimated baseline purchase frequency for the category,which is included to account for differences in frequency of cat-egory purchase when estimating spatial parameters. In the storechoice model, adjusting for baseline purchase frequencies is notneeded.
To develop such a measure, we first consider theextent to which categories should be weighted dif-ferently in determining the impact that a categoryhas on store choice. As discussed in §4, it is reason-able to expect that certain categories will have moreweight in driving store choice; for example, categoriessuch as carbonated beverages, which have high pur-chase frequency and relatively high dollar value areperhaps more likely to affect store choice decisionsthan categories such as salt, which have low purchasefrequency and low dollar value (ACNielsen 2006).Letting �hc denote the weight that household h placeson category c, we can write
�hc = exp4uc + �c1$Spendhc + �c2APThc
+�c3$Spendhc × APThc + �hc51
where $Spend denotes the household’s average dollarspend in the category and APT denotes the averagetime between purchases in the category. In estimat-ing �hc, we mean center both covariates and thereforeexpect that �c1 > 0 and �c2 < 0, reflecting that, all elsethe same, the greater the dollar amount spent in a cat-egory and the shorter the time between purchases, thegreater the category’s weight in store choice decisions.
The category importance weight, �hc, is used toscale the value of the store’s category offering for agiven household, and aggregating over all categoriesthat the household needs, we define a measure of theutility that the store offers for the household’s entirebasket of intended purchases. We denote the utilityof the entire basket of intended purchases for house-hold h on trip t as BaskUtil, where
BaskUtilhst =∑
c∈S4C5
�hc ln41 + eVRhsct50 (8)
In Equation (8), the term ln41 + eVRhsct5 is the inclu-
sive value of a category, i.e., the maximum value ofthe utility for the category, excluding in-store factors.We can see from this specification that BaskUtil is animportance-weighted measure of utility that capturesall categories the household intends to purchase at agiven store on a particular trip.
Having defined all of the necessary covariates,we can express the deterministic component of theindirect utility of store s for household h on shoppingtrip t as
V Shst = �hs +�1hLoyalhs +�2hDisths + BaskUtilhst1 (9)
where
�hs = the intrinsic attraction of the store to thehousehold;
Loyal = a category-independent store loyaltymeasure—for the initialization period, thenumber of visits made by the household toa given store divided by the total numberof stores visited during this same period;
Dist = natural logarithm of the travel time(in minutes) from the centroid of thehousehold’s zip + 4 to each store;
BaskUtil = importance-weighted measure of basketutility; and
�jh = the parameters associated with the storeand household-level covariates.
Household heterogeneity is captured by adopting avariance components representation for �hs and �jh asfollows:
�hs =�s +�h1 (10a)
�jh = lj + �h0 (10b)
In Equation (10a), �s denotes the mean attractionof store s, and �h gives the variance across house-holds; in Equation (10b), lj denotes the mean impactof covariate j , and �h gives the variance acrosshouseholds.
Note that in deciding the specification for thestore choice model, we had to make trade-offs asto the number and types of covariates to includeto minimize the computational requirements to esti-mate a rather large set of free parameters with anobjective function that requires approximating high-dimensional integration. For this reason, and becauseour primary focus is on the role that destination cate-gories play in influencing where a consumer choosesto shop, we did not include household demographicvariables in the store choice model. Rather, we focuson two household-level covariates that have beenshown to influence store choice (see Bell et al. 1998).The Loyal covariate is intended to capture category-independent store loyalty, which reflects the house-hold’s intrinsic preference for a store. That is, thepreference for a store that does not vary from tripto trip. This measure is analogous to the brand loy-alty measure discussed in the brand choice literature(see, for example, Keane 1997) and is computed,as described above, in the same manner as brandloyalty (see Bucklin and Lattin 1992). The otherhousehold-level covariate, Dist, which measures dis-tance traveled, uses information on the location of thehousehold and the store visited to estimate the traveltime from each household to each store. Travel timeswere estimated by geographic services provider ESRIusing a proprietary algorithm that incorporates trafficand driving speeds. Finally, as stated above, we donot include household demographics in the modelexplicitly, but we do investigate the role that demo-graphics play in the importance of a category to ahousehold. To accomplish this, we follow a two-step
process. First, for each household, we harvested thecategory importance parameter (�hc5 using Bayesianposteriors from the estimated covariance matrix andparameters. Next, we regressed the interhouseholdcategory importance parameters on the household’sdemographic covariates.13 We report these resultsin §8.2.3.
5.3. Baseline ModelCentral to the proposed model is the spatial repre-sentation in which an ideal-point specification (i.e.,Euclidean distance) is used to define the attractionof a category. A tacit assumption in this formula-tion is that the attraction parameter plays a significantand informative role in defining the deterministiccomponent of the indirect utility of purchasing inthe category and ultimately in driving which store ahousehold chooses.
Our baseline model tests this assumption by adopt-ing a linear form for the deterministic component ofthe indirect utility of purchasing in a category origi-nally defined in Equation (2). Specifically, for the pur-pose of estimating the baseline model, we define thedeterministic component of the indirect utility of pur-chasing in a category as
where all terms have been previously defined. Thereare several important differences to note. First, wehave removed �hsct, the category attraction param-eter, and as such moved away from the proposedspatial model. Second, we include ãhsct, the long-run merchandising score, directly in the deterministiccomponent, but (contrary to Equation (5)) we redefineãhsct as
ãhsct = b1hcFavhsct + b2hcNUPCsct + b3hcNBrdsct
+ b4hcNSizesct + b5hc%PLsct + b6hc%Natlsct
+ b7hcAdvFsct + b8hcDispFsct + b9hcAvgPricesct0
Notice that the coefficients that give the impact ofeach long-run merchandising covariate do not varyby dimension, although we will still incorporatehousehold and category heterogeneity:14
bjhc =�j + �c + �h1 (12)
13 We could have harvested all covariates used in the store choicemodel; however, our primary interest is the extent to which house-hold demographics determine the importance of the category indriving store choice.14 Recall in our proposed model that category heterogeneity isparameterized in the spatial model specification.
where �j gives the mean impact for covariate j , �cgives the variance across categories, and �h givesthe variance across households. Thus, this baselinemodel not only tests the usefulness of the reduced-space spatial representation but also the nonlinear/distance specification for category attractiveness;in other words, it allows us to test the hypothesis thata simpler model—one that does not recognize thatretailers can position their category offerings closerto what consumers want using long-run merchandis-ing policies and that not all categories have the sameimportance in driving store choice—fits as well as ourproposed model.
6. EstimationLet �V S denote the set of parameters specified in V S
hst,the deterministic component of the indirect utility ofhousehold h choosing store s on trip t. Similarly, let�V C denote the set of parameters specified in V C
hsct,the deterministic component of the indirect utility ofhousehold h choosing to purchase in category c atstore s on trip t. The probability that we observehousehold h (h = 1121 0 0 0 1H ) purchasing a subset ofcategories c at store s (s = 1121 0 0 0 1 S5 on trip t (t =
where yhcst = 1 if household h purchases in categoryc at store s on trip t, 0 otherwise. The first leadingterm in Equation (13) gives the probability that house-hold h will visit store s on trip t. Under the usualassumption that the error terms �hst have Gumbel dis-tributions, the probability that household h will visitstore s on trip t is given by
Pr4yhst = 1 � �V S 5=eV
Shst
∑Sj=1 e
V Shjt0 (14)
The remaining terms in Equation (13) give the prob-ability that household h purchases in category c ontrip t, conditional on shopping at store s. Assuminga binary logit model for the distribution of �hsct, theprobability that household h purchases in category con trip t conditional on choosing store s can be writ-ten as
Pr4yhct = 1 � s5=1
1 + e−V Chsct
0 (15)
Letting �h = 6�V S 1 �V C ], we can write the likelihoodfor the store choice and category incidence models as
where � denotes the global set of store choice andcategory incidence parameters, è denotes the param-eter covariance matrix, and f 4� �è5 is the distributionof the parameter vector � conditional on the covari-ance matrix è.15 We assume that this distributionis multivariate normal. To account for heterogene-ity across household purchase incidence and storechoice decisions, we use a continuous distributionwith the parameter covariance matrix è. To reducethe dimensionality of the covariance matrix è, weuse a two-factor structure of the Cholesky, includingparameters for unique components of the variance(see, e.g., Hansen et al. 2006, Briesch et al. 2009). Theparameters in Equation (16) can be estimated usingsimulated maximum likelihood. Here, we use what isanalogous to a mixed nested logit estimation proce-dure (see Train 2003, Chapter 6 and §7.6) with Haltonsequences for the numerical integration implementedwith a quasi-Newton algorithm and user-supplied(i.e., analytic) gradients.16
7. DataWe use a multioutlet panel data set from Charlotte,North Carolina that covers a 104-week periodbetween September 2002 and September 2004. Pan-elists recorded all packaged and nonpackaged goodspurchases using in-home scanning equipment; pur-chases made in all grocery and nongrocery stores areincluded so that the data are not limited to a smallsample of grocery stores. This is important becausepackaged goods purchases are frequently made out-side of grocery stores. Households are included in thesample if at least 80% of their purchases were madeat the five store chains (four supermarkets and onemass merchandise supercenter) for which we havegeolocation data and if they spent at least $10 everymonth.17 The resulting data set includes 368 familieswith a total of almost 40,000 shopping trips. Descrip-tive statistics for these households are provided inTable 2, which provides information on householdshopping behaviors and demographics. We use thefirst 26 weeks of data as an initialization period to
15 It is true that a store visit implies that the shopper made a pur-chase in at least one category. However, there are a total of 290nonperishable categories (of which the 80 largest are modeled),along with perishable categories that are not included in the dataset. Thus, it is possible that a shopping trip could be made with-out buying one of the 80 modeled categories. As a result, we donot have to impose the binding constraint that given a store choicedecision, at least one modeled category must be purchased.16 To test the performance of our model, we performed a series ofsimulation experiments; details can be obtained by contacting thefirst author.17 The last criterion was used to ensure that panelists were faith-ful in recording their purchases and remained in the panel for theentire 104-week period.
Table 2 Household Descriptive Statistics
Mean SD
Shopping behaviorsNumber of households 368Monthly spending 21601 107076Average spend per trip 3408 12082Number of trips 16507 64086Number of stores visited 403 1013Trip share at favorite store (%) 57 18
identify categories purchased by each household aswell as to identify the intertemporal variables for cat-egories. We use the middle 52 weeks as an estimationsample and the final 26 weeks as a validation sample.
We have detailed price information for 290 cate-gories. From those categories, we selected the top80 based on total dollars spent in the category; onlycategories that were not substantial (i.e., in whichfewer than 10% of the households purchased) wereexcluded. The top 80 categories comprise more than75% of the average market basket (of products trackedby UPC). Table 3 presents category penetration ratesand share of total grocery spending for each category,along with the average price for each retail chain.Store statistics are shown in Table 4; this table pro-vides information about store loyalty, travel time fromhome to the closest store of the retail chain, spendingper trip, and averages of the long-run merchandis-ing covariates used in the spatial model, where fiveof the nine long-run merchandising covariates havebeen indexed to provide a relative measure. The long-run merchandising covariate statistics tells us some-thing about the (relative) character of each retailer.For example, Walmart offers shoppers more nationalbrands to choose from and, consistent with its EDLPformat, offers fewer advertised items but uses moredisplays. Winn-Dixie, on the other hand, offers shop-pers fewer brand choices but overindexes on the per-centage of private label brands, whereas Food Lionoverindexes on the number of UPCs per brand andBI-LO utilizes more brand advertisements than theother retailers. As we might expect, Walmart offersthe lowest long-run average prices of any retailer.
It is worth highlighting two potentially interestingcharacteristics of the shopping behavior of our pan-elists. First, each household visits, on average, fourdifferent stores over the duration of the data andvisits their favorite store on 57% of their trips. Thus,
Note. Standard deviations are in parentheses.aNumbers are indexed to provide a relative measure.
the data show substantial intertemporal variability instore choice.
It is also useful to examine single-category shop-ping trips. Our data set includes 573 trips duringwhich only a single category (one out of the 290 cat-egories18 with UPCs captured in our data set) waspurchased. On 36% of these single-category trips, the
18 To provide a more extensive examination of single purchaseshopping trips we considered all 290 categories in our database asopposed to the 80 categories we selected for modeling purposes.
shopper chose the store he or she visited most often.On 69% of single-category trips, however, the shopperchose the store with the highest CDI (recall that CDImeasures the extent to which retailers get more or lessthan their fair share of category sales). Thus, withoutthe confounding effects of other categories in the mar-ket basket, we find that shoppers were almost twiceas likely to choose the store “specializing” in the cat-egory as their favorite store, which strongly suggeststhat specific categories do indeed affect store choice.19
8. ResultsWe began by fitting several models and progressivelyincreasing the number of dimensions specified forthe latent multiattribute space. We stopped increasingthe number of latent attribute dimensions when theBIC and CAIC information-theoretic statistics indi-cated that the improvement in log likelihood fromadding an additional dimension did not compensatefor the increase in model complexity.
8.1. Model FitTable 5 provides goodness-of-fit statistics for both in-sample and out-of-sample results. First, we see thatall of the proposed models fit better than the base-line model. Recall that these proposed models includea spatial representation for the attraction parameter.Thus, the superior fit of the proposed models sug-gests that the relative positions of stores and categoryideals in perceptual space may provide insights intothe role of categories and category merchandising instore choice decisions and enable retailers to maketheir stores more attractive by better accommodatingshoppers’ preferences.
In terms of in-sample fit, the three-dimensionalsolution has lower BIC and CAIC information-theoretic statistics than either the two- or four-dimensional solutions. Table 5 also reports in- andout-of-sample log likelihoods along with hit rates.In terms of hit rates, all of the models perform equallywell, although the three-dimensional solution pro-vides the highest hit rates in and out of sample.The three-dimensional solution also yields the low-est out-of-sample log likelihood. Given that the three-dimensional solution fits the data best, we will focuson this solution in the remainder of our analyses anddiscussion.20
19 This analysis, similar to the CDI metric, is not informative aboutthe relative impact of different categories on store choice, however.20 In response to one anonymous reviewer, we also compared thefit of the three-dimensional model to a simple model that includesonly two covariates: Dist and Loyal. Whereas the simple modelhas only a slightly lower store-choice likelihood, it is less attrac-tive than the proposed model for a number of reasons. One wayto think about the proposed model is that it corrects bias in the
Hold out sampleHouseholds 368 368 368 368No. of trips 12,016 12,016 12,016 12,016Log likelihood −2581465 −2421439 −2401748 −2401765Choice hit rate (%) 43.3 43.4 44.6 43.6Incidence hit rate (%) 87.6 87.7 88.8 87.7
8.2. Parameter EstimatesTable 6 presents estimates of the store choice and cat-egory incidence parameters. The first set of columnspertains to the mean parameter estimates and thesecond set of columns to the heterogeneity standarderrors.
8.2.1. Store Choice Equation. Focusing first onthe store choice model, we see that all of the storeintercept mean estimates, which reflect the intrinsicattraction of a store, are statistically significant.21 TheLoyal mean parameter estimate is also statistically sig-nificant and positive, consistent with previous find-ings of inertial behavior in store choice (Rhee and Bell2002). The Dist mean parameter estimate is statisticallysignificant and negative; i.e., all else the same, house-holds prefer to shop at stores that are closer to them.
We see that almost all of the heterogeneity stan-dard deviations in the rightmost panel of Table 6 arestatistically significant. The exception is Dist, whichindicates that households are homogeneous in theirpreference to shop at stores closer to them. It appearsthat households are heterogeneous in their inertialbehavior (Loyal) and are slightly more heterogeneousin their intrinsic preference for Walmart comparedwith the other retailers.22
simple model estimates arising from omitted variables and correla-tion with the error term—note that we observed large differencesnot only in the intercepts in the two models but also in the dis-tance and loyalty parameters. Perhaps more importantly, the sim-ple model has far less diagnostic or informative value to retailers;it does not include any of the category data. We believe that thealternative model described in §5.3 stands as the “theoretically rele-vant” baseline model because, by not including the category attrac-tion parameter, it addresses the key hypothesis of the proposedmodel that category positioning does indeed matter.21 Unless otherwise noted, all parameters are statistically significantat the p < 0005 level.22 Dividing a parameter mean value by its heterogeneity value givesa relative measure of response heterogeneity.
8.2.2. Category Incidence Equation. Turning tothe category incidence model, we see that all of thecovariates are statistically significant with one excep-tion: whether items in the category were featureadvertised (Fadv). The Fadv result is consistent withthe findings of Bodapati and Srinivasan (2006) andsuggests that, after controlling for other covariates,shoppers are not significantly more likely to purchasein a category because of feature advertising. The Priceand Disp parameter mean values have the expectedalgebraic signs—lower weekly prices and more fre-quent displays in the category increase the probabil-ity of category incidence. The Time parameter’s meanestimate is positive, suggesting that the more timesince the last category purchase, the more likely thehousehold will purchase in the category on the cur-rent trip. The Qnty parameter’s mean estimate is neg-ative, suggesting that the greater the quantity of thelast category purchase, the less likely the householdis to purchase in the category on the current trip.The Time × Qnty interaction’s positive mean estimatesuggests that shoppers feel significant stock pressure,consistent with the findings of Assunção and Meyer(1993). Finally, perhaps not too surprising, initiatingthe shopping trip on the weekend increases the prob-ability that the household will purchase in a category,as weekend trips may reflect stock-up trips that resultin larger baskets.
Turning next to the household heterogeneity para-meters, we see that four of the eight heterogeneitystandard deviations in the first right panel of Table 6are statistically significant. It appears that house-holds are homogeneous in their response to Qnty,
Time × Qnty, Fadv, and Disp but are heterogeneouswith respect to category purchase incidence(�-intercept), days since they last purchased in thecategory (Time), shopping day preferences (WKEnd),and weekly prices (Price). It is interesting to notethat households appear to be somewhat more het-erogeneous in their responses to Time, Qnty, andDisp compared with Price. Turning next to cate-gory heterogeneity, five of the eight heterogeneitystandard deviations are statistically significant. Cat-egories appear to be homogeneous with respect toTime × Qnty, Fadv, and Disp. Finally, there appearsto be much more heterogeneity in category priceresponse as compared to the other covariates.
8.2.3. Category Weights. As shown in Table 6,both APT and $Spnd parameter estimates are sta-tistically significant and have the expected alge-braic sign, although their interaction is nonsignificant.As expected, it appears that the impact of a categoryon store choice is greater if there is less time betweencategory purchases (i.e., the category is purchasedmore frequently) and if the household’s spending inthe category is greater.
Table 7 summarizes the influence of householddemographic characteristics on the importance thatshoppers place on a category. For each householddemographic variable shown in Table 2, Table 7 showswhere we found a significant relationship betweeninterhousehold variation in �hc and the households’demographic characteristic. We use a “S+” or “S−” inTable 7 to denote a positive or negative relationship.We see from the table that in 38 of the 80 categories,interhousehold variation in category importance is
associated with one or more of the householddemographic variables under consideration. Amongthe statistically significant relationships, we find thatelderly households (65 years of age or older) placegreater importance on crackers, breakfast meats, cof-fee, and vitamins, for example, whereas householdswith children place more importance on yogurt, papertowels, frankfurters, salty snacks, spaghetti, and Ital-ian sauce. In general, although we find significantcovariation between interhousehold category impor-tance and household demographics, the relationshipswere for the most part weak; household demographicsaccounted for less than 2% of the variation in categoryimportance and, across all comparisons, about 20% ofthe possible relationships were statistically significantat the p < 0010 level and less than 10% at the p < 0005level.
8.2.4. Store Positions and Long-Run Merchandis-ing Parameters. We find that all of the store position
parameters are statistically significant across all threelatent attribute dimensions. Interestingly, althoughthere is a demonstrable relationship between physi-cal geography and the derived perceptual store dis-tance (i.e., we find that approximately 22% of thevariation in the perceptual distances between storesis explained by median travel time) nearly four-fifthsof the variation in perceptual store distances is notexplained by the geographic location of the stores.
The long-run merchandising parameters wereestimated for each of the three latent attributedimensions. We find that 20 of the 27 mean parameterestimates are significant. There is not much intuitionin the store positioning or long-run merchandisingparameters, however, because of the dimensionalityof our model. As a consequence, we have left adetailed discussion of these parameter estimates forthe Web appendix.
9. Policy AnalysisThough our reduced-form model does not allow us torecommend categories that could potentially serve inthe destination role for a retailer, it does allow us toidentify categories that actually serve in the destina-tion role. In this section, we investigate the influenceof BaskUtil and its components, as well as long-termmerchandising decisions in influencing store choicedecisions.
Recall from Equation (8) that BaskUtil measures ahousehold’s utility for the entire basket of intendedcategory purchases on a particular trip if the retailer’sstore were chosen. BaskUtil incorporates the effects ofretailers’ long-run merchandising policies and cate-gory positions, weighting each category in the basketby its importance to the household’s store choice deci-sion. By decomposing BaskUtil into its category com-ponents, we can acquire a more nuanced view of theimpact of a store’s merchandising decisions and iden-tify which stores have been delivering more value inthe category. The decomposition is accomplished byaveraging the category’s contribution to BaskUtil for agiven store across households and trips, then scalingby household penetration (percent households) andaverage purchase frequency (number per year). Scal-ing by household penetration and purchase frequencyadjusts the utility of the category on an “average” tripto reflect the number of trips on which the categoryis sought.
Table 8 presents the decomposed BaskUtil values.Categories are arranged in descending order of util-ity. The table also provides the average category util-ities as well as the sales rank of each category. Webegin by looking across categories to assess differ-ences in category utility. Three interesting results areevident:
1. Category utility is highly skewed—the top fourcategories have nearly the same collective impact onstore choice decisions as the other 76 categories.
2. There appears to be a relationship between aver-age category utility and category sales—the top tencategories in terms of utility include five of the topten categories in sales ranking.23 That categories withhigher sales generally have a greater impact on storechoice decisions may not be too surprising;24 however,there are categories with relatively low sales such as
23 Category sales explain 48.1% of the variation in average categoryutility values.24 BaskUtil depends on �, which is a linear function of householdspending in the category, and our decomposed category utilityvalue is adjusted to reflect the number of trips. Both categoryspending and number of trips play a role in determining categorysales.
yogurt, refrigerated salad/coleslaw, and coffee thathave a high impact on store choice decisions.
3. We find no systematic pattern in average cate-gory utility rankings in terms of category type (e.g.,perishable, dry grocery, nonfood). Across the topquartile of category utilities, we find nearly an evensplit between perishable/fresh and dry grocery cat-egories with somewhat fewer nonfood categories.If anything, dry grocery categories are slightly moreprominent among the top quartile of categories thanother types.
We now turn our attention to differences in cat-egory utilities across retailers. Overall, we find thatHarris Teeter has the highest average category util-ity (00378) followed in order by BI-LO (00333), Winn-Dixie (00311), Food Lion (00285), and Walmart (00153).These average utilities reflect the percentage of cate-gories in which each retailer offers shoppers higher-than-average utility: Harris Teeter (85% of categories),BI-LO (68% of categories), Winn-Dixie (51% of cate-gories), Food Lion (36% of categories), and Walmart(19% of categories). Relative to the other retailers,Harris Teeter appears to be providing relatively highvalue for shoppers in more categories than anyretailer, whereas Walmart provides relatively highvalue in the fewest categories. In which categoriesdoes Walmart offer high value? All are nonfoods—deodorant, cold/allergy/sinus, batteries, householdcleaners, shampoo, soap, toothbrushes, toothpaste,skin care, vitamins, pet supplies and air fresheners—with the exception of chocolate and nonchocolate can-dies.25 Walmart is the only mass merchandiser amongthe retailers in our data set, so our finding that itoffers higher-than-average value in non-food cate-gories provides face validity for the BaskUtil measure.In light of Walmart’s strong competitive position,it may seem counterintuitive that it does not offerhigh value in food categories compared with gro-cery retailers. Yet Walmart has by far the largeststore choice intercept (see Table 6), suggesting thatthe retailer is generally preferred to its grocery retailcompetitors.26 In addition, Walmart’s low averageprices (see Table 4) together with the large nega-tive price parameter (see Table 6) imply that shop-pers are drawn to Walmart for its low prices, notnecessarily because of its expertise in specific foodcategories.
Earlier, we argued that CDIs lack diagnostic valuein terms of category importance in store choice
25 An analysis of retailer merchandising for total chocolate and non-chocolate candy categories shows that Walmart offers far morebrands and SKUs per brand and is much more likely to carry shop-pers’ favorite brands than any other retailer.26 As a mass merchandiser, Walmart offers many categories that thegrocery retailers do not; this is also likely reflected in its store choiceintercept.
decisions. Indeed, our primary motivation for devel-oping BaskUtil and its decomposition was to create ametric that shows which categories are most impor-tant to shoppers when choosing a store while alsocapturing the relative value that different stores pro-vide in each category. Analyzing category utilitiesacross retailers within a category enables us to deter-mine the relative value provided by a retailer in aspecific category. If a retailer provides more valuein a specific category than competing retailers, wewould expect shoppers to be more likely to choosethat retailer when they intend to purchase in the cat-egory and, as a result, be more likely to actuallypurchase in the category at that retailer. Accordingly,we calculated within-category correlations (i.e., acrossstores) between the category utility values shown inTable 8 and their corresponding CDI values, whichappear in Table 1. Across the 80 categories in our dataset, we generally find strong positive correlations: themean correlation is 0.684, 81.3% of the correlationsare greater than 0.5, and 65.0% of the correlationsare greater than 0.75.27 Interestingly, computing cor-relations within-store (i.e., across category) for the fiveretailers reveals small and nonsignificant relationships(the mean correlation is 0.105). Thus, although thecategory utilities obtained from decomposing BaskUtilare correlated with a store’s relative category devel-opment, they are more informative about which cate-gories have greater impact on store choice for a givenretailer. This is important in understanding how rel-ative category value, based in part on long-run mer-chandising policies, attracts shoppers to a retailer’sstores.
27 A nonparametric sign test for correlations greater than 0.75 yieldsless than a 0.01 probability that this would occur by chance.
9.2. Capturing the Impact of Long-TermMerchandising on Store Choice
The category utilities shown in Table 8 depend onthe store-specific category attraction parameter (seeEquation (4)) and the underlying multidimensionalspatial model. In the perceptual model, the distancebetween a store’s position and the shopper’s cate-gory ideal is adjusted based on the effectiveness ofthe retailer’s long-term category merchandising poli-cies (see Equation (5)). However, because our best-fitting model includes three latent dimensions, andbecause store positions, category ideal points, and thealgebraic signs of merchandising parameters all dif-fer across dimensions, specific merchandising recom-mendations would be idiosyncratic and difficult toexplain. On the other hand, the long-term categorymerchandising variables (e.g., favorite brands carried,number of brands, number of UPCs per brand, num-ber of sizes per brand) are all mean-centered. Byreplacing the estimated parameters for these variableswith zeros, we can thus determine what the categoryutility would have been if the retailer’s long-termmerchandising policies had been “average.” By com-paring these hypothetical values with the estimatedcategory utilities in Table 8, we can parsimoniouslyevaluate the impact of retailers’ long-term merchan-dising policies on store choice decisions.
Table 9 investigates the extent to which actuallong-term merchandising policies adopted by eachretailer resulted in higher category utilities than aver-age policies would have produced for the top quar-tile of categories shown in Table 8. In the table, thevalue 1 indicates that the retailer’s category utilityis higher than that which would have been obtainedunder average merchandising, and the value 0 indi-cates that the retailer’s category utility is lower than
what average merchandising policies would haveproduced. Interestingly, in 14 of the top 20 categories,at least one retailer merchandizes the category rela-tively effectively, and in 7 of the top 20 categories,a single retailer is merchandising effectively.28 BI-LOand Winn-Dixie both merchandise 3 of the top 5and 8 of the 20 most high-impact categories effec-tively. However, Winn-Dixie is the only retailer thatis merchandising effectively in carbonated bever-ages, salty snacks, and milk—three categories thatrank in the top 10 in terms of sales. In contrast,Food Lion merchandises only 1 of the top 20 cat-egories (pastry and doughnuts) effectively, whereasWalmart merchandises only 2 of the top 20 categories(beer/ale/alcoholic cider and wine) effectively.
10. Concluding RemarksOur study has focused on how individual categoriesimpact store choice decisions and on developing amethod for identifying destination categories. Wemodel only consumer shopping behavior in responseto a retailers’ marketing mix decisions, not a retailers’response to consumer demand. This reduced-formmodeling approach precludes us from recommend-ing specific actions that retailers could implement todevelop new destination categories.29 Our data setincludes the 80 largest of 290 total categories thatare tracked by universal product code. Unfortunately,
28 This is not to say that retailers may have other goals (e.g., prof-itability) that are not considered in our analysis.29 Our discussion of these issues has benefited from the commentsof the associate editor and two anonymous reviewers.
perishable categories are not included in our data set,so their impact on store choice is not incorporated inour model. This is a common shortcoming of paneldata from syndicated data providers but is neverthe-less a limitation of our analysis. Our analysis is alsolimited by geographic information. Store choices arerecorded at the trip level, but we do not know whereeach trip originated and terminated. We thereforehave assumed that each trip begins and ends at thepanelist’s home. Again, this is a common characteris-tic of panel data, but it does introduce measurementerror into our model and underestimates the effect ofgeographic convenience on store choice. A final lim-itation of our study is that causal data for categorypurchases are pooled across stores. We assume that, ifany store in a retail chain displays a particular SKU,then all stores in that chain display the SKU. In otherwords, we assume uniform implementation of dis-play within a retail chain. This assumption also resultsin measurement error, underestimating the effect ofdisplay on category purchases.
Our research could be extended in the future indifferent ways. First, to recommend specific actionsthat retailers could implement to develop destinationcategories, a structural model that comprehensivelycaptures the relationship between consumer shoppingbehavior and retailers’ strategic marketing mix deci-sions (including merchandising) incorporating equi-librium behavior should be developed. Second, futureresearchers can develop more direct analytic methodsto address the inherent endogeneity of category pur-chase and store choice. Third, our use of perceptional
distance mapping could be used to evaluate mer-chandising effectiveness at the category level andto suggest approaches to improving merchandisingeffectiveness category by category. Fourth, the efficacyof our model for selecting destination categories couldbe tested experimentally, either by matching storesof a given retailer within a geographic market or bycomparing stores across geographic markets for thesame retailer. Finally, the framework we have devel-oped could be extended to address the question ofhow much shoppers buy; i.e., purchase quantity.30
AcknowledgmentsThe authors thank Information Resources, Inc. for providingthe data used in this paper. Any mistakes or omissions arethe sole responsibility of the authors.
Electronic CompanionAn electronic companion to this paper is available aspart of the online version at http://dx.doi.org/10.1287/mksc.2013.0775.
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