Why is Assortment Planning so Difficult for Retailers? A Framework and Research Agenda

Journal of Retailing 85 (1, 2009) 71–83

Why is Assortment Planning so Difficult for Retailers? A Framework and Research Agenda Murali K. Mantrala a,∗ , Michael Levy b,1 , Barbara E. Kahn c,2 , Edward J. Fox d,3 , Peter Gaidarev e , Bill Dankworth f , Denish Shah g a

University of Missouri, Columbia, MO 65211, United States b Babson College, Babson Park, MA, United States c School of Business Administration, University of Miami, Coral Gables, FL 33146, United States d JCPenney Center for Retail Excellence, Edwin L. Cox School of Business, Southern Methodist University, United States e Oracle Retail, Cambridge, MA 02141, United States f Direct Store Delivery, Kroger, United States g J. Mack Robinson College of Business, Georgia State University, 35 Broad St., Ste. 400, Atlanta, GA 30303, United States

Abstract When retailers conduct product assortment planning (PAP), they determine (1) The variety of merchandise, (2) The depth of merchandise, and (3) Service level or the amount of inventory to allocate to each stock-keeping unit (SKU). Despite longstanding recognition of its importance, no dominant PAP solution exists, and theoretical and decision support models address only some of the factors that complicate assortment planning. This article simultaneously addresses the variety, depth, and service level aspects of PAP to provide a more thorough understanding. A review of current academic literature and best trade practices identifies open questions and directions for further research and applications. © 2008 New York University. Published by Elsevier Inc. All rights reserved. Keywords: Product assortment planning; Stock-keeping unit; Consumer; Retailers

One of the most basic strategic decisions a retailer must make involves determining the product assortment to offer. Retailers attempt to offer a balance among variety (number of categories), depth (number of stock-keeping units [SKUs] within a category), and service level (the number of individual items of a particular SKU). Yet retailers also are constrained by the amount of money they can invest in inventory and by their physical space. Offering more variety thus may limit the depth within categories and the service level, or both. By making appropriate trade-offs with respect to variety, depth, and service levels, retailers hope to satisfy customers’ needs by providing the right merchandise in the right store at the right time. If the retailer fails to provide the expected assortment, customers defect, causing losses in both current and future sales. If a customer hopes to purchase ∗

Corresponding author. Tel.: +1 573 884 2734. E-mail addresses: [email protected] (M.K. Mantrala), [email protected] (M. Levy). 1 Tel.: +1 781 239 5629. 2 Tel.: +1 305 284 4643. 3 Tel.: +1 214 768 3943.

clothing but cannot find all the product categories necessary to put together an outfit (variety), his or her preferred style in the category (depth), or the proper size, the retailer has failed and may not be able to induce the customer to return. The heterogeneous nature of the marketplace also demands that retailers tailor their assortments to local tastes rather than making national-level product assortment planning (PAP) decisions. Macy’s, for instance, having realized that a “one size/style fits all” strategy is not adequate, is moving toward tailoring at least 15% of the merchandise in each of its store to local tastes (O’Connell 2008). Despite the longstanding recognition of the importance of PAP, practitioners have not adopted a dominant solution, and despite emerging academic literature on PAP, extant theoretical and decision support models address only subsets of the range of factors that make assortment planning so challenging. Researchers tend to focus on analytical solutions that deal almost exclusively with questions of depth—that is, which SKUs should be carried within a particular category—but fail to address all three issues associated with PAP decisions simultaneously. We attempt to correct for this omission and provide a more

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thorough understanding of the difficulties of assortment planning by reviewing current academic literature and best trade practices, as well as identifying questions and directions for further research and applications. Product assortment planning model To facilitate our discussion, we provide a conceptual framework for PAP-related decision making in Fig. 1. We use this framework to guide our exploration of the current state of practical and academic knowledge about PAP. Product assortment planning entails a series of trade-offs, during which retailers must consider consumer perceptions and preferences, their own supply-side constraints, and the external environmental factors, such as economic conditions and competitors’ strategies. Retailers then invest in people and systems according to the fundamental category assortment decisions they make. Customers benefit from these costly investments by finding and buying what they want; if their experience is favorable, they become loyal and generate revenues for the retailer. Therefore, an appropriate metric for assessing the long-term success of assortment decisions uses customer lifetime value (CLV), as we show in Fig. 1. Understanding inputs to product assortment planning decisions: where do we stand? The conceptual model in Fig. 1 illustrates three sets of inputs to PAP decisions: consumer perceptions and preferences, retailer constraints, and environmental factors. What do we know about consumer perceptions and preferences? A growing body of consumer behavior research focuses on consumer choice within a single category, that is, the depth

aspect of the retailer’s PAP related to how many and which SKUs to offer within a product category. Determining the optimal number of SKUs requires identifying the number of distinct brands or product types to offer, the number of variants of each brand or product type to offer, and the number of units of each variant of each brand or product type to carry in inventory. Because many factors govern consumers’ preferences, as the first box in Fig. 1 implies, these determinations are difficult. As a starting point, the retailer needs to identify consumers’ preferred brands. According to consumers, an optimal assortment includes the first choice preference for each consumer in the target market, but in some markets, the heterogeneity of preferences is so massive that even this seemingly simple solution becomes quite difficult (Green and Krieger 1985). Even if a retailer can determine and carry the first choice preference of each member of its target market, consumers frequently want options or flexibility in their choice set (Kahn and Lehmann 1991). Consumers’ desire for flexibility Consumers prefer flexibility because the purchase occasion often is separate in time from the consumption occasion. The consumer must predict his or her future utilities, which is considerably more difficult than predicting immediate utilities (Kahneman and Snell 1992; Simonson 1990). Consumers also try to avoid the difficulty or stress of making the inevitable trade-offs associated with choosing products (e.g., price for quality, health for taste). Finally, consumers’ preferences may change over time as a result of satiation (McAlister and Pessemier 1982) or the need for stimulation (Menon and Kahn 1995, 2002), prompting them to prefer a choice set that allows for variety-seeking behavior (Kahn 1998).

Fig. 1. Product assortment planning model.

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Assortment flexibility also enables consumers to acquire information about the items in the set (Brickman and D’Amato 1975; McAlister 1982), particularly when they lack knowledge or want to sample different options to learn about their own preferences. Changing goals, needs (Simonson 1990), or social situations (Ariely and Levav 2000; Ratner and Kahn 2002) also demand more flexibility. Drolet (2002) suggests that consumers crave variety among items within a choice set because they also desire variety in the decision rules that they use. For example, if a consumer traditionally uses a price rule that dictates choosing the highest priced option, he or she may opt to use a compromise option rule in a subsequent decision. Consumer preference instability Another factor associated with assortment size pertains to assumptions about the stability of consumer preferences over time. Economists frequently model preferences as stable and known with certainty (Luce 1959), but compelling evidence implies consumers’ preferences instead develop as a function of the choice set and task demands (Bettman, Luce, and Payne 1998). That is, preferences depend critically on the meta-goals of the decision maker (e.g., maximizing decision accuracy, minimizing cognitive effort, minimizing stress, maximizing choice justification), the complexity of the task, the other options in the choice set, and the representation of the choice set. An item that represents the first choice in one particular situation might not be the item chosen in another scenario. Therefore, in many circumstances, no single most preferred item exists, because the most preferred item gets constructed at the time of choice as a function of the decision circumstances. Of particular relevance when thinking about assortment decisions is the role that the configuration of the consumer’s choice set can have on the ultimate consumer choice. Research shows that adding items to an assortment strategically can affect the likelihood that a specific product is chosen. For example, Huber, Payne, and Puto (1982) identify an asymmetric dominance effect, such that adding a dominated alternative to a choice set can increase the likelihood of choosing the alternative that dominates. Simonson (1989) also identifies a compromise effect; the share of a product increases when it represents the intermediate or compromise alternative but diminishes when that choice is the extreme option (Simonson and Tversky 1992). Kivetz, Netzer, and Srinivasan (2004) further demonstrate that compromise effects are robust across assortments that are larger than three options, so as Rooderkerk, van Heerde, and Bijmolt (2008a) note, popular discrete choice models that do not account for such context effects may lead to suboptimal product line decisions. These latter authors propose an extension of the standard multinomial probit model that decomposes a product’s utility into a partworth utility and a context-dependent component that captures multiple (substitution, attraction, and compromise) context effects. They find evidence of context effects in choicebased conjoint data and demonstrate that their proposed model predicts a holdout choice set better than does a standard discrete choice model. Accommodating context effects thus appears to suggest product lines that differ systematically from product

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lines that ignore context effects—an observation highly relevant to retail assortment planning. Global versus local utility Consumers may not be able to choose a favorite item because they may try to maximize global utility across a sequence of choices rather than local utility at the time of the choice (Kahn, Ratner, and Kahneman 1997). For example, Loewenstein and Prelec (1993) show that consumers respond to the gestalt properties of a sequence of consumed goods rather than the properties of each item, such that they may prefer to spread out pleasurable items or construct improving sequences of consumption (e.g., save the best for last). Alternatively, they may choose to maximize the memory of their choices rather than the real-time utility (Ratner, Kahn, and Kahneman 1999). Such arguments and the significant heterogeneity across consumer preferences suggest retailers should construct huge assortments that provide a wide variety of items that appeal to every consumer taste and each consumer situation. The growth of specialty retailers such as Barnes & Noble (books), Best Buy (electronics), and Staples (office supplies) supports such a strategy. However, these retailers also face the perpetual risk, in attempting to satisfy multiple preferences across consumers and provide flexibility in product choices, of offering too much variety. Too much choice Consumers might perceive large assortments negatively if they create frustration or a sense of being overwhelming (Huffman and Kahn 1998; Iyengar and Lepper 2000). If customers become frustrated with the complexity of a large assortment and then direct that frustration toward the retailer, they may decide not to return to the store (Fitzsimons, Greenleaf, and Lehmann 1997). To maximize customer satisfaction yet provide a large enough assortment to ensure they carry the consumer’s first choice, retailers should control both the presentation of information and the input consumers provide (Huffman and Kahn 1998). With large assortments, retailers can increase consumer satisfaction by presenting information about the choice options according to attributes rather than alternatives. For example, rather than considering hundreds of different salads, a consumer should make a choice about each item (attribute) to be included, or not, in the completed salad (alternative). When the assortment contains fewer options however, presentation by alternatives is acceptable and does not cause dissatisfaction. Consider, for example, the successful strategy applied by retailers such as Costco, which offers relatively few SKUs within a category but constantly changes the SKUs offered. Costco pursues this strategy to make opportunistic buys of high-quality items that it sells to customers at lower prices, but the approach offers the additional benefit of creating a “treasure hunt” experience for consumers. Shallow depth within a product category, which changes over time without notice, prevents overwhelming the consumer with too much choice and offers surprises about what will be available at any one time. However, as the con-

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sumer gains more expertise, he or she can parse through deeper assortments better, which may lead to greater dissatisfaction if the desired option is not available, the key limitation of this strategy. Regular patrons of warehouse clubs, who are aware of the changing assortments, are less likely to be dissatisfied than those with less experience shopping at these types of stores. Another means to help consumers cope with a complex choice set requires organizing the external structure of the assortment to be consistent with the consumer’s internal categorization structure. For example, Morales et al. (2005) focus on the layout or organization of the assortment and the retailer-provided filtering or screening method for examining items in that assortment. The layout refers to the classification system the retailer uses to display the category (e.g., by brand, color, or style), whereas filtering pertains to the presentation of the assortment, either all at once or in sections. Filtering has great relevance for Internet assortments but also applies to physical spaces (e.g., apparel displays of complete outfits). If the way consumers organize the items in the assortment in their heads (e.g., their schemas [Alba and Hutchinson 1987] or shopping goals [Huffman and Houston 1993]) matches what they see on the shelf, they can process information about the items more easily (Fiske and Taylor 1991). Actual versus perceived variety To complicate the retailer’s challenge when assembling an assortment, the actual variety of the assortment may not match the perceived variety that the consumer experiences (Broniarczyk, Hoyer, and McAlister 1998). Displays of choice options can affect the perceived variety of the assortment, even if actual variety remains constant (Kahn and Wansink 2004). In particular, two features of assortment structure influence consumers’ perceptions of assortment variety: (1) the organization of the assortment (Hoch, Bradlow, and Wansink 1999) and (2) the relative symmetry in the frequencies of items in the assortment (Young and Wasserman 2001). Actual variety is the number of options offered in the assortment, whereas perceived variety may not be a direct function of the number of options offered. Some studies even show that people perceive assortments to provide more variety when SKUs are removed (Narisetti 1997). If consumers perceive more variety in the assortment, they may evaluate the product they select more positively and be willing to pay more for it (Godek, Yakes, and Auh 2001). These issues relate directly to consumers’ preferences for items in the choice set. However, other factors, independent of the choice set, also influence the retailer’s assortment decision. For example, search and substitution costs that a consumer incurs to find a preferred item affect the consumer’s ultimate decision to buy and therefore have an indirect effect on the assortment decision. Consumer search costs Even when a consumer finds an acceptable product at one retail store, he or she still may be uncertain whether similar products are available at other stores and be willing to go to another

store to explore other alternatives with the hope of finding a better product, though doing so involves search costs (e.g., Cachon, Terwiesch, and Xu 2005). To attract consumers to their stores, many retailers (e.g., the specialty electronics retailer Media Markt/Saturn in Europe; see Mantrala, Krafft, and Stiefel 2008) position themselves as a “one-stop” shopping destination in their specialty area by offering an assortment deep enough to make consumers anticipate insufficient returns from further search at competing stores. Such a strategy may have merit; 54% of shoppers prefer one-stop shopping (Fox and Sethuraman 2006). The strategy functions best when competing retailers carry overlapping rather than unique assortments. In this setting, Cachon, Terwiesch, and Xu (2005) show that in the presence of consumer search, it may be optimal to keep otherwise unprofitable products in the assortment, because they may attract consumers away from searching at other stores. If a retailer fails to incorporate the impact of possible consumer search into its PAP decisions, it could end up with a narrower assortment that adversely affects its profits. Consumer substitution behavior The last entry in the first box in Fig. 1 notes that even if it wanted to, a retailer cannot maintain a 100% service level and carry every SKU in stock at all times. Even if the retailer could determine the optimal assortment mix to carry, it may be unprofitable to stock such an assortment. Therefore, out-ofstock (OOS) situations are key realities for retailers, which must predict consumer reactions to these events. Consumers might substitute a similar item, such as a different package size or a different color, but easily substitutable SKUs increase the inventory investment unnecessarily. In the worst case scenario, the retailer stocks a substitutable item, but the consumer decides instead to buy the preferred item from a competitor’s store. This problem is even more severe for a retailer’s suppliers, because a substitute SKU may come from a different supplier. The specific cost of an OOS situation is significant for retailers, which can lose nearly half of intended purchases as a result of stockouts. Abandoned purchases translate into sales losses of approximately 4% for a typical retailer (Corsten and Gruen 2004). Verhoef and Sloot (2006) report that brand switching is the most common reaction (34%) to an OOS situation, followed by postponing the purchase (23%), store switching (19%), and item switching (18%). Therefore, carrying substitutes just for the sake of providing a substitution option may be a poor strategy. Summary of consumer issues Despite progress in understanding consumers’ influence on PAP decisions, much remains unknown. The challenges of PAP from a consumer perspective involve the complex phenomenon of dealing with product assortments that are attractive but pose choice difficulty (Broniarczyk 2008). More research should investigate the factors that moderate these effects. In attempting to answer a fundamental question—What is the optimal balance between having too many SKUs within a category and not enough?—research should attempt to determine

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how much depth will satisfy consumers’ desire for flexibility. Can retailers predict unstable consumer preferences in a timely and useful fashion and thus consider them during merchandise inventory buying decisions? Which retailers or retail formats benefit most from large assortments, and which would do better if the assortment were highly edited? If an edited assortment is desirable, how can the retailer determine which items to keep in the assortment and which to remove? Although some research suggests the influence of assortment layout on purchases, more work should guide retailers in their purchasing decisions. Retailers are experimenting with format blurring; for example, Walgreens is adding more convenience items to attract convenience customers. Despite this apparently growing trend, academic research into the impact of such strategies on consumer behavior in general and loyalty behavior in particular remains limited. Furthermore, both retailers and academics clearly recognize the costs associated with OOS situations. Online retailers can track clicks and estimate what customers buy instead, but in brick-and-mortar stores, it is far more difficult to determine when, if, and for what a substitution purchase occurs. Such information would be useful for PAP. Retail practitioners have devised various methods to capture changing customer tastes and translate those desires into new products or services, such as cool hunters who encourage others to pass on trend information or borrow ideas from runway fashion shows. More room exists for academics to incorporate structure, theory, and methodology into developing assortment strategies that capture and translate these rapidly changing consumer perceptions and preferences. Finally, extant research to date has only concentrated on one of the three factors that constitute the PAP decision, namely, assortment depth, or how many SKUs to carry within a product category. Additional research must examine how consumers’ perceptions and preferences impact variety, the service level, and the interrelations among the three. As previous research shows though, even if retailers knew the exact depth, variety, and service level that would best satisfy customers, they might not be able to achieve them because of the constraints these retailers invariably face. What do we know about retailer constraints? The most obvious constraint on the size and composition of retail assortments, at least in a brick-and-mortar context, is the space available in the store. Without considering costs, the ideal store size equals the sum of all ideal category assortments. Yet space requirements further depend on the physical dimensions of the individual items and their strategic importance. Average demand, variability of demand, and target service levels (i.e., percentage of demand satisfied) dictate how much merchandise should be in a store and, consequently, how much space is needed. Finally, supply chain characteristics such as delivery cycle and shelf pack size determine space requirements. Underlying all these factors, as outlined in the second box on the left-hand side of Fig. 1, are the retailer’s strategic choices with respect to its market position and image.

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Physical space Because expanding the physical dimensions of stores is very costly and often impossible, total floor space remains essentially fixed. Retailers typically plan the space requirements for their stores by first choosing the number of categories (variety or breadth), then how much space each category requires based on the number of SKUs within the category (depth), and finally the number of units within each SKU (desired service level). The addition of ancillary areas, such as cash wraps or dressing rooms, provides the total store size. The limitations of physical space often force retailers to make less-than-optimal space allocation decisions, which are further complicated when the space requirements within categories change over time. Retailers typically arrange certain products together within a category to enable customers to shop easily, though in some settings, complementary merchandise appears together to facilitate bundling strategies and encourage unplanned purchases. The number of SKUs (depth) in a category or individual items (service level) within a SKU depends on several factors. First, the physical dimensions of an item affect how much space is devoted to it. Second, the more shelf space allocated to a SKU, the greater is the probability that it will attract shoppers’ attention and be purchased as a result. The most commonly used heuristic allocates shelf space to SKUs in direct proportion to their sales, though more analytical approaches introduced by Corstjens and Doyle (1981, 1983) and Bultez and Naert (1988) propose models for optimizing the allocation of shelf space among SKUs in a category. These models are based on response functions that capture how product sales respond to changes in the number of facings allocated. Van Nierop, Fok and Franses (2006) extend the shelf-space optimization problem by considering the effect of shelf space on other marketing mix decisions. In general, optimizing shelf space, particularly among larger numbers of SKUs, requires solving a complex integer program problem using sophisticated methods such as simulated annealing. Third, both average demand and variability of demand affect shelf space requirements. As consumers demand more units per period on average, more shelf inventory is necessary; moreover, as demand varies from period to period, the retailer must stock additional inventory on the shelf to satisfy demand in excess of the average. Retailers and their supply chain partners can mitigate these additional shelf inventory requirements by shortening reorder and delivery times. For example, if the retailer orders fewer units of an SKU more frequently, it can allocate less space to that SKU without running out of stock. Fourth, in a related point, the retailer must consider its target service level when determining how many individual units of a SKU to carry. As the required service level increases from, say, 90–95%, the additional inventory required increases exponentially. Retailers must therefore make strategic decisions about higher versus lower service levels for specific items. For example, an office supply store such as Staples should never be out of paper or certain types of ink and toner for printers, so these items must have higher service levels despite the associated higher shelf inventory and space requirements. On the other hand, Staples may decide to assign a lower service level, and therefore

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allocate relatively less shelf inventory and space, to merchandise that is not as important strategically, such as desk chairs or expensive fountain pens. State-of-the-art inventory management systems enable retailers to forecast demand fairly accurately, control for demand variability, and provide targeted service levels. However, systems that apply to staple goods differ from those for fashion merchandise because their demand characteristics differ (e.g., Mantrala and Rao 2001). Specifically, fashion merchandise systems forecast sales at the category level, because specific SKUs vary from season to season. The buyer then must integrate consumer characteristics and environmental considerations to determine the specific SKUs to buy. Any fashion-based system also aims to have zero inventory at the end of the season, which is not relevant for staple items. Systems for staple goods, in contrast, tend to use previous sales history at the SKU level to forecast future demand. These systems are only useful for determining the number of individual units a retailer should carry within an SKU; they ignore the variety and depth problems. Both types of systems are readily available in commercial forms (e.g., Elmaghraby and Keskinocak 2003). Fifth, the retailer must consider the delivery cycle and case pack size when allocating space. Although some retailers can control how often they receive merchandise and influence the case pack size, others lack this power. Retailers without the power to control these variables must allocate space on the basis of the number of units that they typically sell during the delivery cycle and then allocate enough space to accommodate at least one case pack. In turn, they may allocate more shelf space to an item than either optimization models or heuristic approaches recommend. For example, if a retailer expects to sell fewer than 12 units per week of an SKU but the vendor only delivers once every 2 weeks or the shelf pack contains 24 units, the retailer must order 24 units at a time and overallocate space to the item.

premium). Kumar and Steenkamp (2007) note that private-label products account for 20% of U.S. sales in supermarkets and discount stores and appear in more than 95% of consumer packaged goods categories. Therefore, understanding the retailer positioning and the assortment of national and private-label brands sold by the retailer in relation to its image has critical importance (Ailawadi and Keller 2004). In particular, retailers of strong private-label brands can earn a differentiation advantage and thus build store loyalty (Corstjens and Lal 2000). Ailawadi, Pauwels, and Steenkamp (2007) reveal that a well-differentiated privatelabel program can induce a virtuous cycle, in which greater private-label share increases share of wallet (of customers), and greater share of wallet increases private-label share. However, there is still much to learn about the interactions between national and store brands in a retailer’s assortment, including the ability of high-equity brands to increase the value of lowerequity brands in the same retail department (Simmons, Bickart, and Buchanan 2000), as well as their impact on the retailer’s image. Furthermore, the use of such tactics may depend on elements external to the retailers themselves, including for example whether competitors have adopted private labels, whether economic conditions encourage consumers to consider private-label products, and so forth, as the next section addresses. What do we know about environmental factors? In addition to consumer response and retailer constraints, environmental factors external to the organization provide important inputs into a retailer’s PAP decisions. Competition-related assortment trends, changing economic and environmental conditions, shifting consumer profiles and lifestyle trends, and changes in trade areas are especially relevant (see bottom box on left-hand side of Fig. 1). Competition-related assortment trends

Market position, format choice, private versus national brands, and brand image The next several entries in the Retailer Constraints box in Fig. 1 pertain to the core of any assortment decision, namely, the retailer’s market position in terms of the amount of variety and brand image. For example, category killers and specialty retailers offer deep assortments in a narrow variety of product categories, while warehouse clubs offer shallow product assortments but a broad variety of product categories. The variety of categories and the depth of SKUs within categories thus define the type of retailer and its positioning in the marketplace. The assortment’s composition, in terms of quality, price levels, and brands, also determines a retailer’s market position and image. For example, Saks Fifth Avenue and Neiman Marcus both offer fashionable, exclusive, expensive brands to support their prestigious brand images. In contrast, more than 50% of the U.K. grocery retailer Tesco’s products are private labels, including premium private labels, such as Tesco’s Finest, that heighten its overall image (Kumar and Steenkamp 2007). In general, retailers are increasing their private-label presence and introducing multi-tier store brands (i.e., value, standard, and

The same product category might be purchased from stores that employ very different retail formats; for example, a consumer can buy soda at a grocery store, discount store, warehouse store, convenience store, drugstore, extreme value retailer, or even an office supply category killer like Staples. These customers have become accustomed to shopping for the same merchandise at multiple formats. Fox and Sethuraman (2006) observe that consumers face “format blurring,” because different retail formats increasingly stock similar categories, which increases competition among retailers. In terms of between-format assortment competition, Fox and Sethuraman (2006) also observe that consumers’ assortment preferences depend heavily on the purpose of the shopping trip. These preferences affect both the variety and depth of assortment decisions. If the goal is to stock up on groceries, shoppers prefer stores that offer larger assortments, because they can mitigate the cost of searching through the assortment by purchasing a larger basket of goods. For quick trips however, smaller assortments that require less search tend to be preferable. As a result, stockup trips often occur in supercenters and supermarkets, whereas quick trips tend to focus on convenience stores and drugstores.

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Retailers therefore should segment their markets on the basis of shopping trip purpose, such that each shopper may appear in the target segment for some trips but not others. In the middle, supermarkets must decide whether they want to compete for consumers’ stock-up or quick trips. Their strategic choice in this regard influences both assortment sizes and the specific inventory carried. Several factors also should determine within-category assortments. Recent econometric research by Briesch, Chintagunta, and Fox (2008), in which they use household-level market basket data, reveals that the number of brands offered in retail assortments has a positive effect on store choice for most households, whereas the number of SKUs per brand, sizes per brand, and proportion of SKUs unique to a store (proxy for private labels) have negative effects on store choice.

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conventional supermarkets offer more natural, organic, low-fat, low-sugar, and low-salt merchandise. Partially as a result of changing lifestyle trends, sales at natural food retailers such as Whole Foods are growing at 20% per year (Weitz and Whitfield 2006). In keeping with this trend, Target has pledged to carry more natural care items in its assortment, and Marks & Spencer has increased its organic product assortment to almost 500 products, which enabled it to enjoy a 48% sales increase in organic foods in one year. Marks & Spencer also has banned the use of 60 pesticides in its product lines. French retailers that belong to the Fédération des Entreprises du Commerce et de la Distribution (FECD)—which represents 93% of the nation’s hypermarkets and more than 80% of supermarkets (Wong 2008)—have signed an environmental charter, pledging to increase the proportion of organic foods in their merchandise mix by 15% each year.

Changing economic and environmental conditions Changes in trade areas Macroeconomic and environmental trends also influence both variety and depth PAP decisions. For example, retailers in developed economies must deal with the “boom–bust” nature of those economies—an issue very much on the minds of retailers during serious economic downturns. This scenario is especially problematic for high-ticket, durable goods retailers (e.g., automobile dealers) that must quickly adapt their assortments to ongoing cycles. As the issues of environmental responsibility and energy conservation become more significant for consumers, retailers experience pressure to supply ecologically friendly products. Lee Scott, Wal-Mart’s CEO, even announced that Wal-Mart would stock more affordable, energysaving products to counteract rising energy costs, with the goal of doubling the sale of products that enable homes to be more energy efficient. Thus, customers no longer have to pay premium prices to obtain energy-saving products (Rosenbloom 2008; Wong 2008). Similarly, rising concerns about climate change and global warming prompted Office Depot to introduce a “Green Store” with an emphasis on green products, such as office supplies, technology, and furniture that offer recycled content, remanufactured components, energy efficiency, and a lack of toxic chemicals (Reuters 2008). Tesco also has unveiled a plan to include carbon labels on the full spectrum of its 70,000 products, highlighting its awareness of the socially responsible need to minimize carbon emissions (Finch 2008). Shifting consumer profiles and lifestyle trends Retailers must adjust the variety and depth of their assortments to changing consumer tastes and profiles. Consider, for example, the impact of the aging population, particularly the giant segment of aging Baby Boomers, on assortment decisions. No longer having to support their children, this group generally has more disposable income than other age groups, yet much of that wealth is expended on services and experiences rather than products. Retailers targeting this age cohort therefore focus on improving the convenience and quality of the shopping experience and emphasizing wellness offerings. For example,

Most retailers segment their markets or trading areas primarily on the basis of customer-specific (e.g., demographics), market-specific (e.g., weather, region), or store-specific (e.g., urban, suburban, or rural; presence or absence of competing stores) factors, and then modify the variety and depth of their assortments on the basis of these factors (Grewal et al. 1999). For example, grocery stores in markets with larger Hispanic populations tend to offer more authentic Hispanic food items; drugstores in markets with more elderly populations carry larger assortments of incontinence products, whereas those in markets with more young children and families carry larger assortments of diapers; and department stores in trendier urban markets offer more haute couture fashions (Fox and Sethuraman 2006). Customizing assortments for individual stores, or micro-marketing (e.g., Hoch et al. 1995), offers an effective competitive weapon, to the extent that assortments can be customized in a costeffective manner. Buyers have an intuitive feel for how environmental factors affect their assortment decisions, but more research would be useful. In what circumstances does format blurring improve customer loyalty, revenues, and CLV? What impact does an assortment shift have on store brand image, and vice versa? How do various private-label programs interact with the remaining assortment, and what is their joint impact on loyalty, revenues, and CLV? Finally, how should trends like the green movement and shifts in trade area composition influence PAP decisions, and will such trends alter CLV and its antecedents? Summary of inputs to assortment decisions The three classes of input factors that help determine the optimal variety, depth, and service level of retail product assortments thus involve a host of complex trade-offs (summarized in Table 1). As our PAP framework in Fig. 1 indicates, the outcomes of these trade-offs should include positive influences of the customer experience, which affects customer loyalty and profits, with the ultimate objective of maximizing customer lifetime value.

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Table 1 What makes PAP so difficult? Dimension

Impact

CONSUMER PERCEPTIONS and PREFERENCES

Different consumers have different assortment preferences (Green and Krieger 1985) Consumer preferences can change overtime (McAlister and Pessemier 1982) Consumers often seek variety and or flexibility of choices (Drolet 2002; Kahn 1998; Kahn and Lehmann 1991; McAlister 1982; Simonson 1990) Consumer preferences are unstable (Bettman, Luce, and Payne 1998; Kahn et al. 1997; Loewenstein and Prelec 1993) Large assortments can sometimes frustrate or overwhelm the consumer (Fitzsimons et al. 1997; Huffman and Kahn 1998; Iyengar and Lepper 2000;) Actual variety of assortment may not be the same as variety perceived by consumers (Broniarczyk et al. 1998; Kahn and Wansink 2004; Narisetti 1997) Consumers may switch retail stores, even with high search costs (Cachon, Terwiesch, and Xu 2005; Mantrala, Krafft, and Stiefel 2008) Consumers may switch retail stores if their desired product is not stocked or out of stock (Corsten and Gruen 2004; Verhoef and Sloot 2006) Product assortment is constrained by the physical dimension of the items and the corresponding space available in the store (Corstjens and Doyle 1981) Relative shelf space allocated to a item depends on the strategic importance of the item (Bultez and Naert 1988) Variability of demand can impact product inventory levels Low probability of stockouts require high inventory levels Delivery cycles can govern shelf space allocation for individual products Retailer type or market position heavily influences assortment composition (Kumar and Steenkamp 2007) Different retail formats are increasingly stocking similar categories (Fox and Sethuraman 2006) Macroeconomic and environmental trends influence product assortment (Reuters 2008; Wong 2008) Product assortment needs to be periodically adjusted to account for shifting consumer profile and lifestyle trend (Wong 2008) Retailers need to modify assortment to suit demographics and other characteristics of each store location (Fox and Sethuraman 2006; Grewal et al. 1999)

RETAILER CONSTRAINTS

ENVIRONMENTAL FACTORS

Understanding the outputs of product assortment planning: where do we stand? The outputs of the PAP model in Fig. 1 are fairly straightforward: retailers invest in staff and information technology infrastructure to create a customer experience that generates revenues and loyal customers. As a long-term investment, the value of loyal customers reflects CLV. In the face of uncertain economic times, some of the world’s great fashion houses have, for the first time, invested in PAP software, whereas they previously left assortment decisions to head designers. As fashion cycles shorten and consumers’ pocketbooks shrink, retailers also have become less patient about accepting late merchandise. Valentino Fashion Group SpA, for example, recently invested more than $20 million in SAP AG software that enables it to track daily store performance, manufacturing, and shipping. Burberry and Gucci have made similar improvements (Passariello 2008), with the expectation that these sizable investments will translate into higher sales and more loyal customers. To acquire and retain loyal customers, retailers cannot simply satisfy them. Rather, customers expect the merchandise they want to be available, in their size, in the stores, when they want it. To build and maintain a loyal group of customers, retailers must attempt to augment satisfaction (Levitt 1983), move past delight (Kotler 1994), and achieve consumer affection (Peterson

1990). Delight goes beyond the basic expectations required for satisfaction by delivering unexpected, augmented attributes to the product/service, such as recycled paper packaging or special orders. Affection is patronage loyalty, built on both past unexpected experiences (delight) and future expectations (Taher, Leigh, and French 1996). A useful measure that considers costs, revenues, and, implicitly, customer loyalty—as its position on the right side of Fig. 1 indicates—CLV equals the expected financial contribution from the customer to the firm’s profits during their entire relationship (Gupta et al. 2006; Kumar 2006a,b; Kumar and George 2007; Kumar and Petersen 2004; Kumar, Ramani, and Bohling 2004; Kumar, Shah, and Venkatesan 2006; Kumar, Venkatesan, and Reinartz 2006; Kumar and Steenkamp 2007; Kumar, Petersen, and Leone 2007; Reinartz and Kumar 2000, 2002, 2003; Reinartz, Thomas, and Kumar 2005; Thomas, Reinartz, and Kumar 2004; Venkatesan and Kumar 2004). To estimate CLV, firms use prior behaviors to forecast future purchases, the gross margin from these purchases, and the costs associated with servicing customers, such as the costs of communicating through advertising, personal selling, or other promotional vehicles. Multiple marketing-related activities influence CLV, beyond simply PAP decisions, and it would be an analytical challenge to parse out PAP from the other variables. Yet CLV remains a theoretically justifiable measure that most retailers could incorporate into their arsenal of metrics.

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Practical tools and academic decision support models for assortment planning The three sets of PAP inputs imply complex trade-offs along the dimensions of breadth, depth, and customer service level, with both strategic and tactical implications. Because the ultimate goals of this planning process include specifying the right mix of SKUs that maximize the retailer’s sales, profit, or customer equity, subject to budgetary and space constraints, we consider how retailers typically tackle such trade-offs, the tools available to support their decisions, and emerging recommendations from academic research.

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open and close, and customer tastes evolve. As a result, it is almost impossible for a buyer or category manager to develop assortments intuitively, aided only by spreadsheets. The huge impact of the assortment’s composition on the retailer’s sales and profits places PAP at a high priority for most retailers. When they develop in-house approaches, these retailers need both business consultants and academics to provide tools that can help them make complicated assortment plans and decisions in a more efficient, timely, and profitable manner. We summarize several decision aids available commercially and emerging from academic research that may inform category assortment decisions, as depicted in the center of Fig. 1.

PAP in practice Commercial aids Typical PAP practice proceeds through strategic (long-term) and then tactical (short-term) elements, which generally involve several interrelated steps (e.g., Kok, Fisher, and Vaidyanathan 2006). The strategic or long-term step determines the breadth or variety of a store’s assortment by delineating various categories and subcategories of products to be carried. A grocery retailer like Albert Heijn divides its overall assortment into merchandise categories—chilled products, dry goods, and groceries—and then into subcategories such as wines, cereals, and breads. Similarly, a specialty retailer like Best Buy might determine how many and which product categories (e.g., computers, cameras) and subcategories (e.g., conventional still cameras, digital cameras) to offer in stores. The role of each category in the overall assortment, such as staples, variety enhancers, niche, or fillin products, also influences the store space configuration and allocation (Dhar, Hoch, and Kumar 2001). The retailer then implements more short-term planning steps, including demand forecasts and the ensuing sales, margin, and turnover goals across a planning horizon, as well as space allocations and inventory investments based on these goals. Conterminously, the retailer determines the specific depth of assortment, or the SKUs to carry in each subcategory. This step depends, in part, on the value that the target market places on the range of selection. That is, depth must be deeper for customers who are less willing to substitute for their preferred item. Regardless of whether the category consists of fashion or staple goods, decisions about which SKUs to carry and where to obtain them remains the responsibility of the buying team. Simultaneously, the retailer identifies the service level to provide for each SKU. These three strategic decisions must occur at the same time because, to a great degree, they define the retailer’s image and format. With a fixed amount of space and money for inventory, a retailer strategically chooses to provide variety (e.g., hypermarket Carrefour), depth of assortment (e.g., category killer Best Buy), and a high level of in-stock availability (e.g., The Gap), or both. This assortment planning process is, of course, extremely complex and challenging because of the interrelationships among the steps and the amount of calculations involved for each SKU. A large, national retailer must monitor and adjust such decisions across thousands of SKUs during each planning cycle, even as new products are constantly introduced, stores

As computing, data capture, storage and mining, and communications technologies have improved, consultants and software vendors have offered more PAP solutions, some of which replace homegrown spreadsheet decision aids with dedicated software applications that enhance the efficiency of the retailer’s decision making. Advanced analytical tools augment such tools by providing greater decision quality during specific steps of the process. Both large enterprise software vendors that aim to provide a suite or “one-stop” solution to the retailer and smaller retail software companies that specialize in “best-ofbreed” point solutions offer such tools. The former typically focus on workflow, integration, and efficiency, whereas the latter concentrate on a particular task in the planning process—though this distinction is blurring as large vendors acquire more small firms. Some of the best known service providers in this area include SAP, Oracle, JDA, SAS, NSB Group, Maple Lake, Torex Retail, and Manhattan Associates. The minimum benefit expected of a software solution requires that it enable the retailer to conduct analyses, just as it has done in the past, but with less effort. Any dedicated assortment planning application therefore should offer the following capabilities: • Workflow management to ensure all necessary tasks is performed in the correct order. • Scalability, or the ability to handle tens of thousands of SKUs and thousands of store locations. • Integration to provide data interfaces with other enterprise systems. • Automation of recurring tasks. • Reports summarizing historical and in-season performance, alerts, and exceptions. • User management that defines which users can employ a particular facility. • Management of the business rules that form the decision logic used by operational systems within the organization or enterprise. • Configuration points that enable the user to change the system’s behavior to suit customer practices. These functionalities are not particularly analytical but are important to users because they depict all relevant data on one

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screen, rather than requiring users to look at multiple printouts or spreadsheets. In terms of the analytical features though, an assortment planning application should be able to offer demand forecasts, both top-down and bottom-up, for both old and new items, and at any level of aggregation (e.g., store vs. region vs. chain; SKU vs. style vs. category). It should be able to determine optimal size profiles for apparel in individual stores. In addition, the system should be able to provide different assortments for different stores on the basis of individual demand characteristics. Most analytics deployed in large-scale applications are rather simple by academic standards, because they must be fast and robust to produce good answers within a reasonable time. Many of the best known analytics have been employed commercially since at least the early 1970s, and most are designed to solve two assortment planning issues: the correct depth within a category and service level. Few consider the appropriate variety. As we stated at the outset, no dominant solution exists for the retail PAP decision that tackles all these issues, leaving the door wide open for contributions from academics from various disciplines. Decision models from academic research Growing attention to developing PAP optimization models has emerged among scholars in operations research, marketing, and management. Kok, Fisher, and Vaidyanathan (2006) review various classes of published and emerging decision models, including assortment planning with multinomial logit models of consumer demand (e.g., Cachon, Terwiesch, and Xu 2005; Miller et al. 2006; Vaidyanathan and Fisher 2004; van Ryzin and Mahajan 1999) and assortment planning with exogenous demand models (e.g., Kök and Fisher 2007; Smith and Agrawal 2000). Despite considerable progress in addressing retailers’ needs, two key challenges demand more research attention (Kok, Fisher, and Vaidyanathan 2006): (1) knowing how to customize the retail assortment at the store level, rather than simply using a centrally planned assortment for all stores; and (2) developing attribute-based approaches to PAP, rather than the product-focused approaches that have dominated previous research. Using an attribute-based approach, retailers can predict sales of new products on the basis of information about the attributes of existing products. For example, a retailer’s assortment plan for a jeans category might consider size distribution, colors, and styles to predict demand for new jeans styles. Recent work by Rooderkerk, van Heerde, and Bijmolt (2008b) attempts to address both of these challenges by developing a normative model that provides optimal category assortment solutions at the SKU level by store, thus accommodating differences in store characteristics and the demographics of the trade area. The novel features of their methodology include a sales response model that extends the attribute-based approach of Fader and Hardie (1996) and a heuristic procedure for solving the resulting quadratic knapsack optimization problem that derives (near-) optimal solutions at the most disaggregated level. They estimate

a SKU-by-store level model that decomposes the sales of an SKU into (1) an attribute-based baseline component, unaffected by the presence of other SKUs; (2) the effects of the store’s own marketing mix; and (3) the cannibalizing effects of substitute SKUs carried and promoted by the store. Using attributes rather than SKU-specific effects enables the model to handle the sales of large sets of items and still predict sales for all products, even those not yet available, assuming they are composed of existing attributes. In an application of their methodology, Rooderkerk, van Heerde, and Bijmolt (2008b) show that the optimized assortments increase expected profits compared with current assortments. In a subsequent paper, Rooderkerk et al. (2008c) develop a counterpart to the knapsack problem that provides solutions robust across the uncertainty in the products’ profit contributions. Yet assortment planning challenges remain. First, most extant academic models apply to single-category assortment problems, even though consumers often buy products from different categories together on a particular shopping trip, perhaps because of their complementarity or similar purchase cycles (e.g., Manchanda, Ansari, and Gupta 1999; Russell et al. 1997). Bell and Lattin (1998) show that consumers make store choices on the basis of total basket utility. Thus, more work should incorporate the basket effect of consumer behavior and optimize the overall assortment (e.g., Agrawal and Smith 2003). Furthermore, multiple-category assortment models should address strategic considerations, such as the category’s designated role (e.g., staple, variety enhancer) in the retailer’s category management system (Cachon and Kok 2007). Second, retailers implicitly know that they should modify or adapt their assortments over time in response to environmental trends. Yet most academic models (cf. Caro and Gallien 2007) fail to consider the factors that drive such changes in assortments. Third, retailers must consider nonstore elements of the marketing mix, such as advertising, promotions, and pricing. McIntyre and Miller (1999) argue that for a given fixed shelf space allocation, the processes of selecting and pricing the assortment become inseparable if the retailer allows for acrossproduct effects (e.g., substitutability, complementarities). Yet surprisingly few academic models jointly optimize assortment planning, pricing, and promotions, though some commercial software vendors (e.g., DemandTec, see Prime Newswire 2008) claim to have developed comprehensive suites for such integrated planning. Fourth, academic assortment decision models tend to be stronger in their analytics and algorithms than are commercial solutions available in the marketplace. Yet they still must demonstrate their “implementability” and profitability to gain acceptance among practitioners. The main barriers to practitioners’ adoption of the latest models from research journals include: • Data requirements: are the data required to apply these models readily available? • Model complexity: do these models require significant human involvement and subjective judgments that cannot be automated easily?

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• Ease of integration: can the new models be easily integrated into the retailers’ existing systems, or do the latter need to be overhauled? • Ease of validation: is it possible to measure incremental benefits without field tests? • Cost–benefit considerations: do the benefits promised by advanced models outweigh the implementation costs? Successful field tests of decision models are the best way to expedite their adoption in practice. However, field tests demand significant investments of both executive time and organizational resources, along with close collaboration between the researcher and the practitioner (e.g., Mantrala et al. 2006). Gaining such support in a fast-moving business world, where managers tend to be preoccupied with their immediate problems, is difficult. Consequently, many academic models get published without adequate field testing, slowing down the adoption of academic modeling advances in practice. In summary, academic models should strive to provide more valuable insights and directions for PAP improvements to practitioners. Because assortment problems, in their full form, tend to be rather intractable and next to impossible to solve completely, practitioners could benefit significantly from models that provide insights into which factors dominate and which have less significance in their decision making. Conclusion In 1999, Journal of Retailing devoted a special issue to assortment planning, in which Kahn (1999) noted progress, beyond treating assortment planning as a space allocation problem. Articles in that special issue addressed more subtle issues, such as the customer decision process and the competitive relationship among stores in relation to their assortments. In the ensuing decade, many contributions have enhanced our understanding of PAP, but much more remains unsure, including the factors that moderate consumers’ responses to retail assortments (Broniarczyk 2008). Recent consumer research findings reveal that the retail management’s assortment planning problem is far more complex and challenging than the special issue authors perceived in 1999, highlighting the continuing need for research that can help retail executives manage and allocate assortments (Grewal and Levy 2007). Kok, Fisher, and Vaidyanathan (2006) specifically recommend that assortment model builders should draw on the significant body of recent marketing findings pertaining to consumers’ perceptions of variety and incorporate them into assortment optimization models. Another broad area of inquiry involves determining the optimal balance among variety, depth, and customer service level. Academic research and the analytical solutions offered to industry deal almost exclusively with questions about depth. Yet variety, depth, and service level decisions are interrelated, making it incumbent on researchers and practitioners to find solutions that integrate all three dimensions of PAP.

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In the course of our discussion, we have outlined various open questions and issues that deserve attention but have been barely addressed by retail practitioners, consultants, or academics. Finding suitable answers to these questions requires more basic consumer and operations research. We hope this review spurs further research and inquiry into retailers’ PAP from multiple disciplinary perspectives.

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