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Shopping behavior and retail merchandising strategies
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Shopping Behavior and Retail Merchandising Strategies Robert A. Mittelstaedt University of Nebraska-Lincoln
Robert E. Stassen University of Arkansas
This article examines consumer shopping behavior and retail assortments by presenting a model of consumer shopping behavior within a competitive retail market with partially overlapping assortments. It first analyzes the search for a single product and, later, looks at the joint search for several products. The analysis indicates that a high degree of cross shopping is to be expected and, for the retailer to build patronage, it would require very high levels of inventory. The article concludes with some implications for retailers’ assortment strategies. Introduction This article examines the interrelatedness of consumer shopping behavior and retail assortments. A two-part model is presented. The first part models retail assortments in a competitive market in which each retailer carries an incomplete assortment that partially overlaps those of its competitors; the second part models consumer search behavior. The two parts are combined to analyze shopping behavior in a competitive market, beginning with the search for a single product and, later, looking at the joint search for several products. The analysis indicates that, to the extent consumers’ shopping trips are to find particular preselected items, a high degree of cross shopping is to be expected and, for the retailer to build patronage, very high levels of inventory are required. The article concludes with some implications for retailers’ assortment strategies. Merchandising
Strategies
and Inventory
Overlap
Duplication and Differentiation: According to Alderson (1965), “There are two opposing tendencies among competitors at any given level. One is to meet competition directly by offering an
Address correspondence to Robert A. Mittelstaedt, College of Business Administration, University of Nebraska, Lincoln, NE 68588.
Journal of Business Research 21, 243-2.58 (1990) 0 1990 Elsevier Science Publishing Co., Inc. 655 Avenue of the Americas, New York, NY 10010
0148-2963FXW3.50
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identical product. The other is to try to get some advantage over competitors by offering something different” (p. 80). This section examines these opposing tendencies in the inventory assortments of competing retailers. In forming their merchandising policies, retailers may choose to differentiate themselves from their competitors or duplicate their competitor’s offerings, or some combination of the two. Consider a market with N retailers, each carrying an assortment of A items out of a total of P items available to be stocked; thus, there is a maximum potential aggregate of N*P items that could be carried in the market. The sum of the assortments of all retailers (CA,) divided by the maximum potential aggregate (N-P)would be the “average” or “market” stocking rate. At one extreme, if every retail establishment completely duplicated the selection of its competitors, each of the N retailers would carry all of the P products (i.e., A, = P).In total, this market would contain A,*Nitems for sale and the market’s stocking rate would be (A,*N)I(P*N) = 1.0. At the other extreme, in a market in which every retailer completely differentiated its assortment, each would carry an assortment of (Ai = P/N) items (on the average), equalling P items available in total. With no overlap among competitors’ assortments, the market’s stocking rate would be P/(P-N), or simply l/N. As implied by the quotation above, Alderson suggested a third possibility that combines the tendencies toward duplication and differentiation. Based on a detailed study of grocery retailers in Philadelphia (Alderson and Shapiro, 1964), he argued that each retailer is driven to carry some products that its competitors carry and some that they do not (Alderson, 1965, p. 80). Within a competitive market, each retailer acts as if it drew a random sample of brands and models from the total pool of those available to all retailers, including those carried by others and those that are not. In his hypothetical example, Alderson plotted the proportion of the total available items against the number of retailers carrying each. The process can be simulated by random sampling, with replacement, with N retailers each drawing A items from the pool of P products, yielding a binomial expansion. An example of the outcome of such a process is shown in Figure 1 (taken from Alderson, 1965, p. 81), which assumes that each of four retailers carries 1,000 items out of a total of 2,000 available, namely, a 50% market stocking rate. A market of complete duplication would be represented by a severe rightward skew of the distribution in Figure 1; the consumer would be able to find every item at every store. By contrast, a market characterized by complete differentiation would be represented by a severe leftward skew of Figure 1 with each item available in only one retail assortment. With a partial overlap of assortments, the market stocking rate would be between UN and 1. Given N, the market stocking rate would determine the binomial expansion and, therefore, the expected distribution of product availability.
Empirical Evidence About Product Assortments Before going on, evidence on the distribution of product assortments should be examined. Table 1 summarizes seven studies, contained in five reports, of the distribution of product assortments in retail markets. The studies by MLH Consultants (1979) and Thomas and Fishwick (1979) examined the inventories of recordings of 60 standard classical works held by retailers
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0.3 -
0.2 -
0.1 -
125
125
0.0 .-
7
0
1
Figure 1. Proportion
(Alderson,
of total items available 1965, Fig. 3-3)
in Cork, Dublin, and London.
-r
T
2
3
by the number
4
of retailers
carrying each.
In Table 1, these data have been aggregated at the individual recording levei; thus, Beethoven’s Sixth Symphony recorded on RCA by Bernstein and the New York Philharmonic is a different item from the same work recorded by von Karajan and the Berlin Philharmonic on Deutche Grammaphon. The Cork and Dublin retailers represent substantially all stores that carry classical records in Cork and the central district of Dublin, respectively. The London data come from 10 stores that represent substantially all sellers of classical recordings in the Soho district. There are no distributor labels, and all recordings are available to be carried by all retailers. The syrup and sauce data (Mittelstaedt and Stassen, 1988) were collected from 11 supermarkets located within an area approximately 2 miles in diameter in Lincoln, Nebraska. Data are reported at the SKU level; each brand/flavor/size unit is a different product. The use of shelf tags by all retailers ameliorated any potential problem from stockouts. About 10% of each product category consists of distributor brands and generics. While not available to all retailers, they are available to consumers and, therefore, were retained. Maynes (1976, Chap. 3) shopped all retailers in Ann Arbor, Michigan, for those models of lo-speed racing bicycles priced under $225 and single-lens reflex cameras that had been reported in (then) current issues of Consumer Reports. Included in those reports was a distributor brand (Sears) of one bicycle and two cameras, but it is not known whether these items were actually found in the market. Information on products available by mail order was not collected. The nature of the available data precluded basing the market stocking rate on all items available to retailers in the market. Therefore, each market stocking rate reported in Table 1 is based on the total number of items carried in the market; namely P in the denominator (IV-P) is the total number of separate items actually available to consumers in that market. Alderson (1965) who used a market stocking rate of 50% as his example, thought that higher rates were unlikely, and, indeed, they appear to be, as only spaghetti
-.
Cork, Ireland MLH Consultants, 1979 Dublin, Ireland MLH Consultants, 1979 London, U.K. Thomas and Fishwick, 1979 Lincoln, Nebraska Mittelstaedt and Stamen, 1988 Lincoln, Nebraska Mittelstaedt and Stassen, 1988 Lansing, Michigan Maynes, 1976 Lansing, Michigan Maynes, 1976
Market and source
5 9 11 11 2 6
8 10 11 11 9 11
Classical records
Classical records
Pancake syrups
Spaghetti sauces IO-Speed bicycles
35-mm Cameras
Carrying most popular item 3
In study 3
Product
Number of retailers
Classical records
Table 1. Comparison of Nine Markets
n.a.
n.a.
71.1
54.6
55.6
31.3
48.5
Largest
26.6
14.6
55.0
41.1
30.9
20.5
39.5
Market
na.
n.a.
40.8
33.8
11.0
10.6
26.8
Smallest
Stocking rates
0
0
14.5
10.4
0
0
1.0
At every outlet
28.6
68.8
17.1
27.3
29.5
59.1
82.5
At only one retailer
Items available
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Table 2. Proportion of Items Available at Only One Retailer Ann Arbor
Ithaca
Minne&polis/St. Paul
Porch/deck paint Blankets
.615 .688
l.ooo .700
.455 .588
IO-Speed bicycles Dishwashers Microwave ovens Pocket cameras
,690 .500 .778 304
,733 600 ,667 ,417
,514 .214 .417 .423
Note: Adapted from Geistfeldet al. (1980)
sauces in Lincoln, Nebraska, exceeded that figure. In other words, on the average, these retailers are carrying fewer than half of the total items being offered somewhere in the markets in which they compete. Obviously, there is variation in stocking rates among retailers, as shown by the stocking rates of the largest and smallest selections, but even the largest assortments offer the shopper less than complete selections. Table 2 presents data drawn from a study of markets in three cities by Geistfeld et al. (1980) in which trained shoppers were sent to find and report the prices of all varieties of six product classes reviewed in then current articles in Consumer Reports. Because the number of stores shopped for each product in each city was not reported, it is not possible to calculate market stocking rates. However, the stores were selected according to the definition of a market as “the set of sellers the consumer might consider if he possessed accurate information regarding the existence of sellers and brands as well as the range of prices and qualities available” (p. 181). Table 2 shows the proportion of the varieties of each product class available in that market that were carried by only one seller. For example, in Ann Arbor, of the 13 varieties of porch and deck paint available in the market, 8 (.615) were stocked by only one of the stores visited. Note that in 12 of the 18 product markets, half or more of the varieties found in that market were each available in only one store; the mean of these data points is 0.572. Shopping
in a Market
of Partially
Duplicated
Assortments
Assumptions About Shopping Behavior This section presents a model of shopping behavior within markets of partially duplicated assortments. It is based on four assumptions, stated here in the form of assertions. A. Shopping is a purposeful activity undertaken to provide consumers with the opportunities to find products that best satisfy their needs and wants at favorable prices. B. Shopping is a costly activity in the sense that it requires time and effort, in addition to the foregoing of other pleasurable activities. C. It is assumed that the consumer begins a shopping trip with a relatively well developed concept of the product being sought. This concept may range from a very specific definition (e.g., a 32-ounce jar of Ragu Chunky Garden Style Spaghetti Sauce, with mushrooms) to a moderately constrained definition (e.g., a pair of unlined kid gloves, size 8, in a color that would complement
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a gray coat.) Put differently in the context of another example, to the book shopper, Tom Wolfe’s The Bonfire of the Vanities is not a substitute for Garrison Keillor’s Leaving Home. Note that this assumption excludes shopping done with only a vaguely defined purchase goal (e.g., any current bestselling book would be a suitable birthday gift for Aunt Myrtle). D. It is assumed that there are no strong preferences for individual sellers, except as they offer the opportunity for shopping efficiency or effectiveness; for example, the Bonfire of the Vunities is the same item in all retail establishments. Admittedly, any or all of these assumptions may be challenged. It is explicitly recognized that people visit stores for other reasons, including information seeking (Beatty and Smith, 1987) and recreation (Ballenger and Korganonkar, 1980). However, these assumptions are made for purposes of simplification, and these other possibilities are discussed later. Gains from Shopping Put together, the assumptions make up a model in the “economics of information” tradition of Stigler (1961). In that view, shopping is a process of visiting 1 to N retailers serially, and the gain from continued shopping involves the assessment of its marginal benefits. While accepting that general point of view, the model presented here differs in three ways. First, given the twin goals of shopping in the first assumption, two potential benefits to shopping are recognized and defined. From the consumer’s perspective, shopping effectiveness is achieved when he or she is able to obtain the product which best fits his or her needs and wants, and shopping eficiency is realized when the shopper is able to find the lowest priced alternative among substitutable products. The potential gain to interstore shopping is the sum of incremental benefits from efficiency and effectiveness less the incremental costs of search. Specifically, and given that the stores must be visited serially, an individual’s gain (GJ to shopping the ith store can be expressed in the general model below. Gi = aV; + f3Yj -
yS,
Where Vi = the incremental gain in shopping effectiveness, realized in finding a product at store i not carried at stores previously shopped
Yi = the incremental gain in shopping efficiency, realized through price comparison on items carried by store i and stores previously shopped Si = the incremental cost of shopping store i with a, f3, and y being subjective weights associated with the respective incremental
benefits and costs of shopping Second, rather than contend that consumers will continue “to search for a lower price to the point where the savings from an additional search does not exceed the costs of that additional search” (Geistfeld et al., 1980, p. 180), all that is asserted
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here is that there exists a cost-benefit ratio that captures the potential value of interstore shopping. In other words, it is not claimed that the condition G > 0 necessarily leads to further interstore shopping. All that is intended is that, given the purposeful nature of shopping (Assumption A), the propensity to visit more stores is a function of the magnitude of G. Finally, although the model of Equation (1) assumes that shopping is a purposeful activity (Assumption A), it does not imply that shoppers are motivated only to make purchases. Thus, it is possible for the consumer to achieve the incremental gain of shopping (Gi) by acquiring information as well as by purchasing desired products. On the other hand, if a consumer chooses to limit search to a particular store for its special “atmospherics” rather than for its merchandise selection or price (i.e., a denial of Assumption D), the model, as stated, cannot incorporate this phenomenon. A later section discusses motivation and its relationship to consumer typologies.
Gains to Shopping with Incomplete,
Overlapping Assortments
Generally, the economics of information/shopping literature has assumed that all retailers stocked the same goods (cf. Anglin and Baye, 1987; Carlson and McAfee, 1984; Stigler, 1961). The data summarized in Tables 1 and 2 suggest that this is not descriptive of retail markets, so the question becomes one of the effect of incomplete, partially overlapping assortments on the gains to shopping. To calculate the probabilities associated with the values of “V” and “Y” in Equation (l), it is necessary to specify the distribution of assortments among competitors within the market being shopped. Based on the earlier discussion, it appears reasonable to assume that competing retailers’ assortments of a given product class are distributed as a binomial expansion (as described earlier) in which the probability of “success” is equal to the average stocking rate for the product category. The effects of this can be seen in Figure 2, which recasts the hypothetical market of four retailers (with an average stocking rate of SO) as two cumulative probability functions. The bars labeled “shopping effectiveness” indicate that the probability of a consumer finding any particular item at least once would be .5 at the first store visited, rise to .75 at the second store, and become 875 by visiting any three retailers. Similarly, the bars labeled “shopping efficiency” indicate that the probability of finding any one product at least twice (thus allowing a price comparison) is .25 with a visit to the second retailer, increasing to .5 with the visit to the third retailer. The differences in the magnitudes from store ‘3” to store “i + 1” of each cumulative probability function are the incremental effectiveness of shopping and incremental efficiency of shopping, respectively. It should be noted that in a market of completely differentiated assortments, with no overlap of retail assortments, there is no incremental efficiency to shopping. Incremental effectiveness, while positive, would be determined by the average stocking rate. By contrast, in a market of completely duplicated assortments, a visit to the first retailer exposes the consumer to every available product, and the incremental effectiveness of further shopping would be zero. By visiting only one other store, the consumer would have a chance to compare prices on any given item; the
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Cumulative Probabilities
0.6
0.4
0.2
0.0
Number of Stores Visited
Figure 2. Probabilities
of success.
incremental efficiency of extensive shopping to more retailers would be relatively low. Of course, complete duplication and complete differentiation are extreme cases. While unlikely to occur, they emphasize that a market in which retailers tend to duplicate their competitors’ assortments (i.e., a rightward skew in Fig. 1) enhances the consumers’ absolute levels of shopping effectiveness and efficiency but diminishes the incremental effectiveness and efficiency of extended shopping beyond the second or third store. By contrast, a market in which retailers tend to differentiate their assortments (i.e., a leftward skew in Fig. 1) diminishes the absolute levels of both shopping effectiveness and efficiency for the consumers who shop there. Although there would be positive incremental effectiveness and efficiency from extended shopping in such a market, these would diminish as the number of competing retailers increased. Differences
Among
Shoppers:
As Westbrook and Black (1985) have noted, “One of the most enduring interests in contemporary retailing literature has been the pursuit of shopper typologies”
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because of their potential to enable retailers to “ . . . differentiate and target their offerings, locations, and promotional efforts according to the varying patronage responses of the basic shopper types” (p. 78). According to their review of the major taxonomies, one recurring type is the “price shopper.” In this section, the incremental efficiency and effectiveness of shopping are investigated from the perspective of two consumer types, the price shopper (p) and the variety shopper (v). Equation (1) has been weighted to exemplify each type. Thus, the gains to shopping the ith store for the price shopper: GPi = (.75)Y, + (.25)V, - rSj
(2)
and the variety shopper: G,i = (.25)Y; + (075)Vi - ySi
(3)
reflect differing emphases on finding the lowest price and finding the “right” product, respectively. It should be noted again that consumers shop for many reasons. Downs (1961) argued that the “units of output” produced by shopping fall into three categories: the goods themselves, the information obtained, and the pleasure received from the process itself. More recently, Bloch et al. (1989) have subsumed the latter two goals into the concept of “browsing,” which they define as “the in-store examination of a retailer’s merchandise for informational and/or recreational purposes without an immediate intent to buy” (p. 14). They argue that consumers may seek information as an aid to future purchases or to enhance their expertise levels. As noted above, this form of shopping or browsing can be accommodated within the model to the extent that it is product specific (e.g., Store “X” carries product “g” at price “P’). Of course, over time, the acquired information could become generalized (e.g., store “X carries the largest assortment of product class “G”; store “X” has high prices for product class “G”) and used in shopping. For example, in any market, the stocking rates of competitors will vary and, if the consumer knows the size of each retailer’s assortment, the gains to shopping (for whatever reason) are maximized by visiting stores in order of the size of their assortments. In any given situation, this would, in effect, reduce the incremental gain in shopping effectiveness. Generalized knowledge of prices would have the same effect on shopping efficiency. In other words, generalized knowledge would change the shape of the effectiveness and efficiency functions but not the basic terms of the model. On the other hand, Bloch et al. (1989) suggest that recreational shopping may be a function of both retail atmospherics and merchandise variety. As noted earlier, the likely effects of atmospherics cannot be accommodated in the model. However, the latter aspect of recreational shopping would be captured by proper weighting of the subjective importance of variety (“B”) for the recreational shopper. Figure 3(a-c) shows incremental benefits (G,) to price shoppers, variety shoppers, and “average” shoppers (Equation [I] with equal weighting) who successively visit the second, third, and fourth stores in a market with four stores. It is assumed that the assortments offered by each retailer are distributed as in a binomial expansion. Figure 3(a) shows the incremental benefits when the market stocking rate is 20%; the market stocking rates in Figure 3(b, c) are 40% and 60%, respectively. Several points should be noted.
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3a. 20% Stocking Rate
2
3
3b. 40% Stocking Rate
0.0 2
3
0.4 3c. 60% Stocking Rate
0.3
0.2
0.1
0.0
t
Number of Stores Shopped Figure 3. Marginal benefits to shopping.
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First, for all shoppers, the incremental benefits increase as the stocking rates increase. Second, for the variety and average shoppers, there is a decreasing incremental benefit to visiting the third and fourth stores, regardless of the stocking rate. By contrast, for the price shoppers, at the 20% stocking rate, the incremental benefit of visiting the third and fourth stores actually increases. In the market characterized by a 40% stocking rate, the incremental benefit of visiting the third store is the same as visiting the second store. Finally, if one compares the price shoppers with the variety shoppers, it appears that, as the stocking rate increases, there is a shift in the relative values of the incremental benefits. At relatively low stocking rates, 20% and 40%, the incremental benefit of visiting a second store is greater for the variety shoppers than for the price shoppers. However, at the 60% rate, the incremental benefit of visiting the second store is greater for the price shopper.
Shopping
Behavior
and Patronage
Building
Patronage and Multiple Purchases: To say that the goal of the retailer is to build patronage is to imply that the consumer will seek more than one item per trip and/or more than one item over repeated trips. Within any one shopping trip, there are at least two ways in which shopping processes for several products become so strongly linked that the consumer wants to buy them at the same place. First, there are products that are complementary in use, for example, the consumer wants to buy a blue striped shirt if, and only if, a suitable tie can be found. Second, there are situations in which the consumer is seeking only one product but, before buying, wants to make a side-by-side comparison, for example, before buying a pair of a particular brand/model of sport shoes the consumer wants to try on both the 9 medium and the 9 narrow. On the other hand, the linkages among products that operate over time imply the existence not only of traditionally discussed patronage motives such as convenience, credit availability, and helpfulness of store personnel (cf. Markin, 1977) but, in addition, a degree of certainty on the part of the consumer that he or she can find what is wanted at that particular store. It is important to note that this consumer certainty may not reflect the store’s “true” assortment. After all, any one consumer considers and selects from only a subset, and probably a small subset, of all the items carried in any store. For example, out of any one supermarket’s thousands of SKUs, the typical household purchases only a few hundred per year and, in all likelihood, does not care whether the store carries other items or not. To use a different example, to a man who wears a size 42long coat, a store’s inventory of other sizes is irrelevant; he will make his judgment of its selection based on its assortment of coats and/or suits available in his size. In spite of the “narrowness” of consumers’ views of any retailer’s assortment, purchases do become linked over time because households are trying to build and maintain their own assortments to solve their particular consumption problems (Alder-son, 1965, Chap. 6). Thus, a consumer expects to be able to purchase the items needed to prepare meals in one location. Even if the household serves
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hamburgers, tacos, and hot dogs 6 nights a week but, on the seventh, chooses to grill fresh salmon steaks, it will likely expect to find fresh salmon steaks at the same store where it buys the “staples” of its diet. Shopping and Joint Probabilities Whether purchases of several items become linked for one shopping trip or over time, the impact on the magnitude of shopping effectiveness (VJ and efficiency (Y,) is the same; linking makes each a function of the joint probabilities of finding the right products and finding them more than once. Consider a shopping trip for two products. Obviously, in the first store visited, the potential for shopping effectiveness is equal to the joint probability of finding both products. Unless the stocking rate of both is lOO%, the joint probability is less than that of either product taken separately. With average stocking rates of 50% (or less), the joint probabilities of finding two or more products can become remote. Of course, the incremental benefits of visiting further stores depend on the customer’s commitment to the idea of finding both products in the same store; however, given a low probability of finding both products in the first store, there would seem an enhanced benefit to visiting more stores when more than one product is sought on one trip. In all product lines, some brands/models will sell more than other brands/ models. For example, according to Nielsen scanner data, there were 54,762 active branded UPC items offered by U.S. supermarkets during the fourth quarter of 1985. Of these, only 7,562 (13.7%) sold 12 or more units per week in the typical supermarket (Progressive Grocer, 1986). Obviously, the more popular brands/models are likely to be carried by more retailers; some are likely to enjoy such popularity as to be carried by nearly every store. It should be noted that this statement does not imply that knowing a retailer’s depth in a particular product line would allow prediction of the specific items carried, a proposition for which there is no empirical support (Mittelstaedt and Stassen, 1987). What the existence of different stocking rates for different brands/models does imply is that the set of “linked” items that consumers seek will likely include at least one of the less popular brands/models. If so, the effect would be to pull down the joint probabilities of finding all items sought to, at least, the average stocking rate of the least stocked item in the set. Consider this admittedly hypothetical example. For each of its product lines, store X carries 50% of the brands/models available in the market, of which 20% (or 10% of the total available) account for 80% of its sales and are carried by all competitors in the market. In other words, store Xcarries about 40% of the “minor” brands/models. For a consumer seeking 10 items, 9 of which are carried by all competitors and 1 of which is a minor brand/model, the probability of finding all items in this store is 0.4. If 2 of the 10 sought items are minor brands/models, the probability of finding all ten items in store X falls to 0.16. Figure 4 shows the stocking levels required to maintain the probability of satisfying a given consumer seeking more than one product. For example, the 50% line shows the stocking levels needed to maintain the joint probability at 0.50 with one through seven products being sought; in general, the stocking level required for n jointly sought products is the nth root of the retailer’s desired
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0.6 -
0
2
4
6
6
Number of Stores Shopped
Figure 4. Stocking rate requirements. probability of satisfying the customer. As can be seen, the costs of maintaining inventory levels sufficient to maintain the joint probabilities at attractive levels may be prohibitive.
The Retailer’s Dilemma The model presented here suggests the nature of the dilemma facing retailers as they attempt to build and keep consumers’ patronage. On the one hand, differentiating strategies add to the incremental effectiveness of consumers shopping in more than one store, especially “variety-seeking” shoppers. On the other hand, duplicating strategies increase the incremental efficiency of shopping; the attractiveness of visiting other stores is enhanced, especially for “price” shoppers. Furthermore, to the extent there are consumers seeking more than one relatively “unpopular” product (at one time or over time), the stocking levels required to maintain the probability of satisfying customers’ needs and wants may require inventory levels that are very costly. The section discusses two general ways for retailers to cope with the situation implied by the model. A third alternative that may combine the first two, private labeling, is discussed.
Coping Strategies As one strategy alternative, retailers can make efforts to increase the substitutability of items carried within a product category. The effective stocking rates of any one
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retailer are enhanced by increased substitutability of the items carried. As Gist put it, “The absence of strong brand/style preferences may represent an opportunity to reduce the depth of the product line without a proportional reduction in the level of probability that we can fill a customer’s order (Gist, 1968, p. 362). Of course, it would not represent any saving of inventory costs unless the product line was such that multiple units of each brand/model were stocked. For example, to show a shopper that the brand D dining room set was essentially the same as brand F, a furniture store might have to carry at least one set of each brand and, if so, this would allow no reduction of total inventory. Furthermore, this strategy would not be useful for the sorts of products for which truly ingrained preferences exist. Because the manufacturers’ marketing strategies are designed to decrease substitutability within product lines, it would be the retailer’s task to overcome manufacturers’ efforts to build brand loyalty with counter efforts within the store. Through display and/or personal selling, comparative information that dwelt on similarities of brands/models would be presented to shoppers. The issue for the retailer would become whether these would cost more than the savings in inventory costs associated with the reduced selections. A second strategy alternative might be described as “trying to be all things to some people.” It is likely that, for most consumers, the joint probabilities of choice for particular brands/models are not completely independent across product lines. In other words, knowing that a consumer wanted a particular brand/model within one product category might allow the retailer to predict which brands/models they prefer in other product categories. By targeting on a particular segment of consumers, the store would carry those brands/models that are most likely to be chosen by that segment and control its inventory costs by dropping items less favored by that segment. While it might be claimed that such a strategy would be difficult for a mass merchandiser to implement, evidence suggests that, with respect to department stores, there are demand segments that respond differently to the several merchandise lines typically carried by department stores (Hirschman, 1979). Thus, it would seem that even a store with a very broad assortment would begin by building depth in the product line or lines most important to its customers and adding items that complemented particular brands/models within its core lines. For example, as part of its overall strategic plan, Penney’s is building deep assortments in coordinated apparel items for young women and upscale furniture (Dunkin, 1989). However, the model points out that there are limits to this strategy. By adding more national brand items to the assortment, the retailer brings itself into conflict with retailers with whom it has not previously competed. Intensified price competition is the likely consequence.
Private Labels Finally, the use of private-label merchandise could be used to both deepen assortments to appeal to a market segment and, to the extent the products are successful, become substitutes for other products. In short, the successful private brander could both deepen and differentiate its assortment. As Salmon and Cmar (1987) point out, this is most likely to be effective for the retailer that is large
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enough to implement a private-label program and, at the same time, appeal to a relatively narrow market niche. The dangers of an unsuccessful attempt at private labeling are most noticeable in the case of the firm that is forced, by the strength of consumer demand, to add national-brand merchandise to its existing line of private-label goods. As modeled by this analysis, it would not only bring it into competition with other retailers from whom it had previously been differentiated but would also appeal to those shoppers motivated to find the lowest prices. Thus, Sears’ highly publicized additions of national-brand appliances and electronics to its existing lines appear to have put them into new dimensions of price competition (Graham, 1989).
Avoiding the Depot Effect Over two decades ago, Aspinwall(1962) argued that retailers were becoming “depots” that consumers visit, not to shop, but to pick up goods that had been presold to them by manufacturers. The better control afforded by improved transportation and communications would eventually force the flow of merchandise to the consumer to be regulated completely by consumer demand. Whatever benefits this might have, he predicted that this trend would eliminate retailer’ merchandising profits, namely, those that arise from the risk associated with carrying inventories at a sufficient level to anticipate consumer needs and wants. The model of retail competition and shopping discussed in this article points up the difficulty of building and maintaining assortments in an environment in which consumers seek specific items from any particular retailer’s inventory. Since much of this difficulty results from the consumer’s brand/model insistence before a visit to a retail establishment, the prospect of Aspinwall’s “depot effect” is enhanced by the selling efforts of manufacturers. The model suggests that retailers forced to deepen their assortments are likely to become more of a depot. In effect, this may be interpreted as a concession to the power of manufacturer’s brands, rather than a move to establish competitive advantage. The strategy implication for retailers in today’s competitive environment would seem to be an old one: draw a distinction between “consumers” and “customers” and, in the words of Marcus (1979), “give more than passing attention to the ‘anatomy of the customer’ and investigate those things which customers like and those they don’t” (p. 211). References
Alderson, Wroe, Dynamic Marketing Behavior: A Functionalist Theory of Marketing, Richard D. Irwin, Inc., Homewood, Ill., 1%5. Alderson, Wroe, and Shapiro, Stanley J., Towards a Theory of Retail Competition, in Marketing Theory (Second Series). Reavis Cox, Wroe Alderson, and Stanley J. Shapiro, eds., Richard D. Irwin, Inc., Homewood, Ill., 1964, pp. 190-213. An&n, Paul M., and Baye, Michael R., Information, Multiprice Search, and Cost-of-Living Index Theory, Journal of Political Economy 95 (December 1987): 1179-1196. Aspinwall, Leo, The Depot Theory of Retailing, in Managerial Marketing: Perspectives and Viewpoints. William Lazer and Eugene J. Kelley, eds., Richard D. Irwin, Inc., Homewood, Ill., 1962, pp. 652-659.
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Ballenger,
R. A. Mittelstaedt Danny N., and Korgaonkar,
and R. E. Stassen
Pradeep K., Profiling the Recreational
Shopper,
Journal of Retailing 56 (Fall 1980): 77-92.
Beatty, Sharon E., and Smith, Scott M., External Search Effort: An Investigation Across Several Product Categories, Journal of Consumer Research 14 (June 1987): 83-94. Bloch, Peter H., Ridgway, Nancy M. and Sherrell, Daniel L., Extending the Concept of Shopping: An Investigation of Browsing Activity, Journal of the Academy of Marketing Science 17 (Winter 1989): 13-21. Carlson, John A., and McAfee, R. Preston, Joint Search for Several Goods, Journal of Economic Theory 32 (April 1984): 337-345. Downs, Anthony, 6-12, 50.
A Theory of Consumer Efficiency, Journal of Retailing 37 (Spring 1961):
Dunkin, Amy, The Newly Minted Penney: Where Fashion Rules, Business Week (April 17, 1989): 88-90. Geistfeld, Loren V., Maynes, E. Scott, and Duncan, Greg, Informational Imperfections in Local Consumer Markets: A Preliminary Analysis, in Advances in Consumer Behavior VZZ, Jerry C. Olson, ed., Association for Consumer Research, Ann Arbor, Mich., 1980, pp. 180-185. Gist, Ronald E., Retailing: Concepts and Decisions, John Wiley and Sons, Inc., New York, 1968. Graham, Judith, Retail Inc.: It’s a Brand New Ball Game, Advertising Age 60 (August 21, 1989): R-l. Hirschman, Elizabeth C., Intratype Competition Among Department Stores, Journal of Retailing 55 (Winter 1979): 20-34. Markin, Rom J., Retailing Management, Macmillan Publishing Co., New York. 1977. Marcus, Stanley, Quest for the Best, Viking Press, New York. 1979. Maynes, E. Scott, Decision-Making for Consumers, Macmillan Publishing Co., New York, 1976. Mittelstaedt, Robert A., and Stassen, Robert E., Niche Width and Competition among Retailers, presented at 12th Annual Macromarketing Seminar, Montreal, August 1987. Mittelstaedt, Robert A., and Stassen, Robert E., The Role of Retail Assortments in Shopping Effectiveness and Efficiency, presented at Summer Educators’ Conference, American Marketing Association, San Francisco, August 1988. MLH Consultants, A Study of the Evolution of Concentration in the Irish Classical Records Industry. Evolution of Concentration and Competition Series, Number 25. Commission of the European Communities, Brussels. 1979. Progressive Grocer, Nielsen Review of Retail Grocery Trends, September 1986. Salmon, Walter J. and Cmar, Karen A., Private Labels Are Back in Fashion, Harvard Business Review 65 (May-June 1987): 99-106. Stigler, George J., The Economics of Information, Journal of Political Economy 69 (June 1961): 213-225. Thomas, Gordon D., and Fishwick, Francis, A Study on Classical Records Prices and Markups in the United Kingdom. Evolution of Concentration and Competition Series, Number 27. Commission of the European Communities, Brussels, 1979. Westbrook, Robert A, and Black, William C., A Motivation-Based Shopper Typology, Journal of Retailing 61 (Spring 1985): 78-103.
243
Shopping Behavior and Retail Merchandising Strategies Robert A. Mittelstaedt University of Nebraska-Lincoln
Robert E. Stassen University of Arkansas
This article examines consumer shopping behavior and retail assortments by presenting a model of consumer shopping behavior within a competitive retail market with partially overlapping assortments. It first analyzes the search for a single product and, later, looks at the joint search for several products. The analysis indicates that a high degree of cross shopping is to be expected and, for the retailer to build patronage, it would require very high levels of inventory. The article concludes with some implications for retailers’ assortment strategies. Introduction This article examines the interrelatedness of consumer shopping behavior and retail assortments. A two-part model is presented. The first part models retail assortments in a competitive market in which each retailer carries an incomplete assortment that partially overlaps those of its competitors; the second part models consumer search behavior. The two parts are combined to analyze shopping behavior in a competitive market, beginning with the search for a single product and, later, looking at the joint search for several products. The analysis indicates that, to the extent consumers’ shopping trips are to find particular preselected items, a high degree of cross shopping is to be expected and, for the retailer to build patronage, very high levels of inventory are required. The article concludes with some implications for retailers’ assortment strategies. Merchandising
Strategies
and Inventory
Overlap
Duplication and Differentiation: According to Alderson (1965), “There are two opposing tendencies among competitors at any given level. One is to meet competition directly by offering an
Address correspondence to Robert A. Mittelstaedt, College of Business Administration, University of Nebraska, Lincoln, NE 68588.
Journal of Business Research 21, 243-2.58 (1990) 0 1990 Elsevier Science Publishing Co., Inc. 655 Avenue of the Americas, New York, NY 10010
0148-2963FXW3.50
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identical product. The other is to try to get some advantage over competitors by offering something different” (p. 80). This section examines these opposing tendencies in the inventory assortments of competing retailers. In forming their merchandising policies, retailers may choose to differentiate themselves from their competitors or duplicate their competitor’s offerings, or some combination of the two. Consider a market with N retailers, each carrying an assortment of A items out of a total of P items available to be stocked; thus, there is a maximum potential aggregate of N*P items that could be carried in the market. The sum of the assortments of all retailers (CA,) divided by the maximum potential aggregate (N-P)would be the “average” or “market” stocking rate. At one extreme, if every retail establishment completely duplicated the selection of its competitors, each of the N retailers would carry all of the P products (i.e., A, = P).In total, this market would contain A,*Nitems for sale and the market’s stocking rate would be (A,*N)I(P*N) = 1.0. At the other extreme, in a market in which every retailer completely differentiated its assortment, each would carry an assortment of (Ai = P/N) items (on the average), equalling P items available in total. With no overlap among competitors’ assortments, the market’s stocking rate would be P/(P-N), or simply l/N. As implied by the quotation above, Alderson suggested a third possibility that combines the tendencies toward duplication and differentiation. Based on a detailed study of grocery retailers in Philadelphia (Alderson and Shapiro, 1964), he argued that each retailer is driven to carry some products that its competitors carry and some that they do not (Alderson, 1965, p. 80). Within a competitive market, each retailer acts as if it drew a random sample of brands and models from the total pool of those available to all retailers, including those carried by others and those that are not. In his hypothetical example, Alderson plotted the proportion of the total available items against the number of retailers carrying each. The process can be simulated by random sampling, with replacement, with N retailers each drawing A items from the pool of P products, yielding a binomial expansion. An example of the outcome of such a process is shown in Figure 1 (taken from Alderson, 1965, p. 81), which assumes that each of four retailers carries 1,000 items out of a total of 2,000 available, namely, a 50% market stocking rate. A market of complete duplication would be represented by a severe rightward skew of the distribution in Figure 1; the consumer would be able to find every item at every store. By contrast, a market characterized by complete differentiation would be represented by a severe leftward skew of Figure 1 with each item available in only one retail assortment. With a partial overlap of assortments, the market stocking rate would be between UN and 1. Given N, the market stocking rate would determine the binomial expansion and, therefore, the expected distribution of product availability.
Empirical Evidence About Product Assortments Before going on, evidence on the distribution of product assortments should be examined. Table 1 summarizes seven studies, contained in five reports, of the distribution of product assortments in retail markets. The studies by MLH Consultants (1979) and Thomas and Fishwick (1979) examined the inventories of recordings of 60 standard classical works held by retailers
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245
0.3 -
0.2 -
0.1 -
125
125
0.0 .-
7
0
1
Figure 1. Proportion
(Alderson,
of total items available 1965, Fig. 3-3)
in Cork, Dublin, and London.
-r
T
2
3
by the number
4
of retailers
carrying each.
In Table 1, these data have been aggregated at the individual recording levei; thus, Beethoven’s Sixth Symphony recorded on RCA by Bernstein and the New York Philharmonic is a different item from the same work recorded by von Karajan and the Berlin Philharmonic on Deutche Grammaphon. The Cork and Dublin retailers represent substantially all stores that carry classical records in Cork and the central district of Dublin, respectively. The London data come from 10 stores that represent substantially all sellers of classical recordings in the Soho district. There are no distributor labels, and all recordings are available to be carried by all retailers. The syrup and sauce data (Mittelstaedt and Stassen, 1988) were collected from 11 supermarkets located within an area approximately 2 miles in diameter in Lincoln, Nebraska. Data are reported at the SKU level; each brand/flavor/size unit is a different product. The use of shelf tags by all retailers ameliorated any potential problem from stockouts. About 10% of each product category consists of distributor brands and generics. While not available to all retailers, they are available to consumers and, therefore, were retained. Maynes (1976, Chap. 3) shopped all retailers in Ann Arbor, Michigan, for those models of lo-speed racing bicycles priced under $225 and single-lens reflex cameras that had been reported in (then) current issues of Consumer Reports. Included in those reports was a distributor brand (Sears) of one bicycle and two cameras, but it is not known whether these items were actually found in the market. Information on products available by mail order was not collected. The nature of the available data precluded basing the market stocking rate on all items available to retailers in the market. Therefore, each market stocking rate reported in Table 1 is based on the total number of items carried in the market; namely P in the denominator (IV-P) is the total number of separate items actually available to consumers in that market. Alderson (1965) who used a market stocking rate of 50% as his example, thought that higher rates were unlikely, and, indeed, they appear to be, as only spaghetti
-.
Cork, Ireland MLH Consultants, 1979 Dublin, Ireland MLH Consultants, 1979 London, U.K. Thomas and Fishwick, 1979 Lincoln, Nebraska Mittelstaedt and Stamen, 1988 Lincoln, Nebraska Mittelstaedt and Stassen, 1988 Lansing, Michigan Maynes, 1976 Lansing, Michigan Maynes, 1976
Market and source
5 9 11 11 2 6
8 10 11 11 9 11
Classical records
Classical records
Pancake syrups
Spaghetti sauces IO-Speed bicycles
35-mm Cameras
Carrying most popular item 3
In study 3
Product
Number of retailers
Classical records
Table 1. Comparison of Nine Markets
n.a.
n.a.
71.1
54.6
55.6
31.3
48.5
Largest
26.6
14.6
55.0
41.1
30.9
20.5
39.5
Market
na.
n.a.
40.8
33.8
11.0
10.6
26.8
Smallest
Stocking rates
0
0
14.5
10.4
0
0
1.0
At every outlet
28.6
68.8
17.1
27.3
29.5
59.1
82.5
At only one retailer
Items available
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Table 2. Proportion of Items Available at Only One Retailer Ann Arbor
Ithaca
Minne&polis/St. Paul
Porch/deck paint Blankets
.615 .688
l.ooo .700
.455 .588
IO-Speed bicycles Dishwashers Microwave ovens Pocket cameras
,690 .500 .778 304
,733 600 ,667 ,417
,514 .214 .417 .423
Note: Adapted from Geistfeldet al. (1980)
sauces in Lincoln, Nebraska, exceeded that figure. In other words, on the average, these retailers are carrying fewer than half of the total items being offered somewhere in the markets in which they compete. Obviously, there is variation in stocking rates among retailers, as shown by the stocking rates of the largest and smallest selections, but even the largest assortments offer the shopper less than complete selections. Table 2 presents data drawn from a study of markets in three cities by Geistfeld et al. (1980) in which trained shoppers were sent to find and report the prices of all varieties of six product classes reviewed in then current articles in Consumer Reports. Because the number of stores shopped for each product in each city was not reported, it is not possible to calculate market stocking rates. However, the stores were selected according to the definition of a market as “the set of sellers the consumer might consider if he possessed accurate information regarding the existence of sellers and brands as well as the range of prices and qualities available” (p. 181). Table 2 shows the proportion of the varieties of each product class available in that market that were carried by only one seller. For example, in Ann Arbor, of the 13 varieties of porch and deck paint available in the market, 8 (.615) were stocked by only one of the stores visited. Note that in 12 of the 18 product markets, half or more of the varieties found in that market were each available in only one store; the mean of these data points is 0.572. Shopping
in a Market
of Partially
Duplicated
Assortments
Assumptions About Shopping Behavior This section presents a model of shopping behavior within markets of partially duplicated assortments. It is based on four assumptions, stated here in the form of assertions. A. Shopping is a purposeful activity undertaken to provide consumers with the opportunities to find products that best satisfy their needs and wants at favorable prices. B. Shopping is a costly activity in the sense that it requires time and effort, in addition to the foregoing of other pleasurable activities. C. It is assumed that the consumer begins a shopping trip with a relatively well developed concept of the product being sought. This concept may range from a very specific definition (e.g., a 32-ounce jar of Ragu Chunky Garden Style Spaghetti Sauce, with mushrooms) to a moderately constrained definition (e.g., a pair of unlined kid gloves, size 8, in a color that would complement
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a gray coat.) Put differently in the context of another example, to the book shopper, Tom Wolfe’s The Bonfire of the Vanities is not a substitute for Garrison Keillor’s Leaving Home. Note that this assumption excludes shopping done with only a vaguely defined purchase goal (e.g., any current bestselling book would be a suitable birthday gift for Aunt Myrtle). D. It is assumed that there are no strong preferences for individual sellers, except as they offer the opportunity for shopping efficiency or effectiveness; for example, the Bonfire of the Vunities is the same item in all retail establishments. Admittedly, any or all of these assumptions may be challenged. It is explicitly recognized that people visit stores for other reasons, including information seeking (Beatty and Smith, 1987) and recreation (Ballenger and Korganonkar, 1980). However, these assumptions are made for purposes of simplification, and these other possibilities are discussed later. Gains from Shopping Put together, the assumptions make up a model in the “economics of information” tradition of Stigler (1961). In that view, shopping is a process of visiting 1 to N retailers serially, and the gain from continued shopping involves the assessment of its marginal benefits. While accepting that general point of view, the model presented here differs in three ways. First, given the twin goals of shopping in the first assumption, two potential benefits to shopping are recognized and defined. From the consumer’s perspective, shopping effectiveness is achieved when he or she is able to obtain the product which best fits his or her needs and wants, and shopping eficiency is realized when the shopper is able to find the lowest priced alternative among substitutable products. The potential gain to interstore shopping is the sum of incremental benefits from efficiency and effectiveness less the incremental costs of search. Specifically, and given that the stores must be visited serially, an individual’s gain (GJ to shopping the ith store can be expressed in the general model below. Gi = aV; + f3Yj -
yS,
Where Vi = the incremental gain in shopping effectiveness, realized in finding a product at store i not carried at stores previously shopped
Yi = the incremental gain in shopping efficiency, realized through price comparison on items carried by store i and stores previously shopped Si = the incremental cost of shopping store i with a, f3, and y being subjective weights associated with the respective incremental
benefits and costs of shopping Second, rather than contend that consumers will continue “to search for a lower price to the point where the savings from an additional search does not exceed the costs of that additional search” (Geistfeld et al., 1980, p. 180), all that is asserted
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here is that there exists a cost-benefit ratio that captures the potential value of interstore shopping. In other words, it is not claimed that the condition G > 0 necessarily leads to further interstore shopping. All that is intended is that, given the purposeful nature of shopping (Assumption A), the propensity to visit more stores is a function of the magnitude of G. Finally, although the model of Equation (1) assumes that shopping is a purposeful activity (Assumption A), it does not imply that shoppers are motivated only to make purchases. Thus, it is possible for the consumer to achieve the incremental gain of shopping (Gi) by acquiring information as well as by purchasing desired products. On the other hand, if a consumer chooses to limit search to a particular store for its special “atmospherics” rather than for its merchandise selection or price (i.e., a denial of Assumption D), the model, as stated, cannot incorporate this phenomenon. A later section discusses motivation and its relationship to consumer typologies.
Gains to Shopping with Incomplete,
Overlapping Assortments
Generally, the economics of information/shopping literature has assumed that all retailers stocked the same goods (cf. Anglin and Baye, 1987; Carlson and McAfee, 1984; Stigler, 1961). The data summarized in Tables 1 and 2 suggest that this is not descriptive of retail markets, so the question becomes one of the effect of incomplete, partially overlapping assortments on the gains to shopping. To calculate the probabilities associated with the values of “V” and “Y” in Equation (l), it is necessary to specify the distribution of assortments among competitors within the market being shopped. Based on the earlier discussion, it appears reasonable to assume that competing retailers’ assortments of a given product class are distributed as a binomial expansion (as described earlier) in which the probability of “success” is equal to the average stocking rate for the product category. The effects of this can be seen in Figure 2, which recasts the hypothetical market of four retailers (with an average stocking rate of SO) as two cumulative probability functions. The bars labeled “shopping effectiveness” indicate that the probability of a consumer finding any particular item at least once would be .5 at the first store visited, rise to .75 at the second store, and become 875 by visiting any three retailers. Similarly, the bars labeled “shopping efficiency” indicate that the probability of finding any one product at least twice (thus allowing a price comparison) is .25 with a visit to the second retailer, increasing to .5 with the visit to the third retailer. The differences in the magnitudes from store ‘3” to store “i + 1” of each cumulative probability function are the incremental effectiveness of shopping and incremental efficiency of shopping, respectively. It should be noted that in a market of completely differentiated assortments, with no overlap of retail assortments, there is no incremental efficiency to shopping. Incremental effectiveness, while positive, would be determined by the average stocking rate. By contrast, in a market of completely duplicated assortments, a visit to the first retailer exposes the consumer to every available product, and the incremental effectiveness of further shopping would be zero. By visiting only one other store, the consumer would have a chance to compare prices on any given item; the
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Cumulative Probabilities
0.6
0.4
0.2
0.0
Number of Stores Visited
Figure 2. Probabilities
of success.
incremental efficiency of extensive shopping to more retailers would be relatively low. Of course, complete duplication and complete differentiation are extreme cases. While unlikely to occur, they emphasize that a market in which retailers tend to duplicate their competitors’ assortments (i.e., a rightward skew in Fig. 1) enhances the consumers’ absolute levels of shopping effectiveness and efficiency but diminishes the incremental effectiveness and efficiency of extended shopping beyond the second or third store. By contrast, a market in which retailers tend to differentiate their assortments (i.e., a leftward skew in Fig. 1) diminishes the absolute levels of both shopping effectiveness and efficiency for the consumers who shop there. Although there would be positive incremental effectiveness and efficiency from extended shopping in such a market, these would diminish as the number of competing retailers increased. Differences
Among
Shoppers:
As Westbrook and Black (1985) have noted, “One of the most enduring interests in contemporary retailing literature has been the pursuit of shopper typologies”
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251
because of their potential to enable retailers to “ . . . differentiate and target their offerings, locations, and promotional efforts according to the varying patronage responses of the basic shopper types” (p. 78). According to their review of the major taxonomies, one recurring type is the “price shopper.” In this section, the incremental efficiency and effectiveness of shopping are investigated from the perspective of two consumer types, the price shopper (p) and the variety shopper (v). Equation (1) has been weighted to exemplify each type. Thus, the gains to shopping the ith store for the price shopper: GPi = (.75)Y, + (.25)V, - rSj
(2)
and the variety shopper: G,i = (.25)Y; + (075)Vi - ySi
(3)
reflect differing emphases on finding the lowest price and finding the “right” product, respectively. It should be noted again that consumers shop for many reasons. Downs (1961) argued that the “units of output” produced by shopping fall into three categories: the goods themselves, the information obtained, and the pleasure received from the process itself. More recently, Bloch et al. (1989) have subsumed the latter two goals into the concept of “browsing,” which they define as “the in-store examination of a retailer’s merchandise for informational and/or recreational purposes without an immediate intent to buy” (p. 14). They argue that consumers may seek information as an aid to future purchases or to enhance their expertise levels. As noted above, this form of shopping or browsing can be accommodated within the model to the extent that it is product specific (e.g., Store “X” carries product “g” at price “P’). Of course, over time, the acquired information could become generalized (e.g., store “X carries the largest assortment of product class “G”; store “X” has high prices for product class “G”) and used in shopping. For example, in any market, the stocking rates of competitors will vary and, if the consumer knows the size of each retailer’s assortment, the gains to shopping (for whatever reason) are maximized by visiting stores in order of the size of their assortments. In any given situation, this would, in effect, reduce the incremental gain in shopping effectiveness. Generalized knowledge of prices would have the same effect on shopping efficiency. In other words, generalized knowledge would change the shape of the effectiveness and efficiency functions but not the basic terms of the model. On the other hand, Bloch et al. (1989) suggest that recreational shopping may be a function of both retail atmospherics and merchandise variety. As noted earlier, the likely effects of atmospherics cannot be accommodated in the model. However, the latter aspect of recreational shopping would be captured by proper weighting of the subjective importance of variety (“B”) for the recreational shopper. Figure 3(a-c) shows incremental benefits (G,) to price shoppers, variety shoppers, and “average” shoppers (Equation [I] with equal weighting) who successively visit the second, third, and fourth stores in a market with four stores. It is assumed that the assortments offered by each retailer are distributed as in a binomial expansion. Figure 3(a) shows the incremental benefits when the market stocking rate is 20%; the market stocking rates in Figure 3(b, c) are 40% and 60%, respectively. Several points should be noted.
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3a. 20% Stocking Rate
2
3
3b. 40% Stocking Rate
0.0 2
3
0.4 3c. 60% Stocking Rate
0.3
0.2
0.1
0.0
t
Number of Stores Shopped Figure 3. Marginal benefits to shopping.
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First, for all shoppers, the incremental benefits increase as the stocking rates increase. Second, for the variety and average shoppers, there is a decreasing incremental benefit to visiting the third and fourth stores, regardless of the stocking rate. By contrast, for the price shoppers, at the 20% stocking rate, the incremental benefit of visiting the third and fourth stores actually increases. In the market characterized by a 40% stocking rate, the incremental benefit of visiting the third store is the same as visiting the second store. Finally, if one compares the price shoppers with the variety shoppers, it appears that, as the stocking rate increases, there is a shift in the relative values of the incremental benefits. At relatively low stocking rates, 20% and 40%, the incremental benefit of visiting a second store is greater for the variety shoppers than for the price shoppers. However, at the 60% rate, the incremental benefit of visiting the second store is greater for the price shopper.
Shopping
Behavior
and Patronage
Building
Patronage and Multiple Purchases: To say that the goal of the retailer is to build patronage is to imply that the consumer will seek more than one item per trip and/or more than one item over repeated trips. Within any one shopping trip, there are at least two ways in which shopping processes for several products become so strongly linked that the consumer wants to buy them at the same place. First, there are products that are complementary in use, for example, the consumer wants to buy a blue striped shirt if, and only if, a suitable tie can be found. Second, there are situations in which the consumer is seeking only one product but, before buying, wants to make a side-by-side comparison, for example, before buying a pair of a particular brand/model of sport shoes the consumer wants to try on both the 9 medium and the 9 narrow. On the other hand, the linkages among products that operate over time imply the existence not only of traditionally discussed patronage motives such as convenience, credit availability, and helpfulness of store personnel (cf. Markin, 1977) but, in addition, a degree of certainty on the part of the consumer that he or she can find what is wanted at that particular store. It is important to note that this consumer certainty may not reflect the store’s “true” assortment. After all, any one consumer considers and selects from only a subset, and probably a small subset, of all the items carried in any store. For example, out of any one supermarket’s thousands of SKUs, the typical household purchases only a few hundred per year and, in all likelihood, does not care whether the store carries other items or not. To use a different example, to a man who wears a size 42long coat, a store’s inventory of other sizes is irrelevant; he will make his judgment of its selection based on its assortment of coats and/or suits available in his size. In spite of the “narrowness” of consumers’ views of any retailer’s assortment, purchases do become linked over time because households are trying to build and maintain their own assortments to solve their particular consumption problems (Alder-son, 1965, Chap. 6). Thus, a consumer expects to be able to purchase the items needed to prepare meals in one location. Even if the household serves
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hamburgers, tacos, and hot dogs 6 nights a week but, on the seventh, chooses to grill fresh salmon steaks, it will likely expect to find fresh salmon steaks at the same store where it buys the “staples” of its diet. Shopping and Joint Probabilities Whether purchases of several items become linked for one shopping trip or over time, the impact on the magnitude of shopping effectiveness (VJ and efficiency (Y,) is the same; linking makes each a function of the joint probabilities of finding the right products and finding them more than once. Consider a shopping trip for two products. Obviously, in the first store visited, the potential for shopping effectiveness is equal to the joint probability of finding both products. Unless the stocking rate of both is lOO%, the joint probability is less than that of either product taken separately. With average stocking rates of 50% (or less), the joint probabilities of finding two or more products can become remote. Of course, the incremental benefits of visiting further stores depend on the customer’s commitment to the idea of finding both products in the same store; however, given a low probability of finding both products in the first store, there would seem an enhanced benefit to visiting more stores when more than one product is sought on one trip. In all product lines, some brands/models will sell more than other brands/ models. For example, according to Nielsen scanner data, there were 54,762 active branded UPC items offered by U.S. supermarkets during the fourth quarter of 1985. Of these, only 7,562 (13.7%) sold 12 or more units per week in the typical supermarket (Progressive Grocer, 1986). Obviously, the more popular brands/models are likely to be carried by more retailers; some are likely to enjoy such popularity as to be carried by nearly every store. It should be noted that this statement does not imply that knowing a retailer’s depth in a particular product line would allow prediction of the specific items carried, a proposition for which there is no empirical support (Mittelstaedt and Stassen, 1987). What the existence of different stocking rates for different brands/models does imply is that the set of “linked” items that consumers seek will likely include at least one of the less popular brands/models. If so, the effect would be to pull down the joint probabilities of finding all items sought to, at least, the average stocking rate of the least stocked item in the set. Consider this admittedly hypothetical example. For each of its product lines, store X carries 50% of the brands/models available in the market, of which 20% (or 10% of the total available) account for 80% of its sales and are carried by all competitors in the market. In other words, store Xcarries about 40% of the “minor” brands/models. For a consumer seeking 10 items, 9 of which are carried by all competitors and 1 of which is a minor brand/model, the probability of finding all items in this store is 0.4. If 2 of the 10 sought items are minor brands/models, the probability of finding all ten items in store X falls to 0.16. Figure 4 shows the stocking levels required to maintain the probability of satisfying a given consumer seeking more than one product. For example, the 50% line shows the stocking levels needed to maintain the joint probability at 0.50 with one through seven products being sought; in general, the stocking level required for n jointly sought products is the nth root of the retailer’s desired
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0.6 -
0
2
4
6
6
Number of Stores Shopped
Figure 4. Stocking rate requirements. probability of satisfying the customer. As can be seen, the costs of maintaining inventory levels sufficient to maintain the joint probabilities at attractive levels may be prohibitive.
The Retailer’s Dilemma The model presented here suggests the nature of the dilemma facing retailers as they attempt to build and keep consumers’ patronage. On the one hand, differentiating strategies add to the incremental effectiveness of consumers shopping in more than one store, especially “variety-seeking” shoppers. On the other hand, duplicating strategies increase the incremental efficiency of shopping; the attractiveness of visiting other stores is enhanced, especially for “price” shoppers. Furthermore, to the extent there are consumers seeking more than one relatively “unpopular” product (at one time or over time), the stocking levels required to maintain the probability of satisfying customers’ needs and wants may require inventory levels that are very costly. The section discusses two general ways for retailers to cope with the situation implied by the model. A third alternative that may combine the first two, private labeling, is discussed.
Coping Strategies As one strategy alternative, retailers can make efforts to increase the substitutability of items carried within a product category. The effective stocking rates of any one
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retailer are enhanced by increased substitutability of the items carried. As Gist put it, “The absence of strong brand/style preferences may represent an opportunity to reduce the depth of the product line without a proportional reduction in the level of probability that we can fill a customer’s order (Gist, 1968, p. 362). Of course, it would not represent any saving of inventory costs unless the product line was such that multiple units of each brand/model were stocked. For example, to show a shopper that the brand D dining room set was essentially the same as brand F, a furniture store might have to carry at least one set of each brand and, if so, this would allow no reduction of total inventory. Furthermore, this strategy would not be useful for the sorts of products for which truly ingrained preferences exist. Because the manufacturers’ marketing strategies are designed to decrease substitutability within product lines, it would be the retailer’s task to overcome manufacturers’ efforts to build brand loyalty with counter efforts within the store. Through display and/or personal selling, comparative information that dwelt on similarities of brands/models would be presented to shoppers. The issue for the retailer would become whether these would cost more than the savings in inventory costs associated with the reduced selections. A second strategy alternative might be described as “trying to be all things to some people.” It is likely that, for most consumers, the joint probabilities of choice for particular brands/models are not completely independent across product lines. In other words, knowing that a consumer wanted a particular brand/model within one product category might allow the retailer to predict which brands/models they prefer in other product categories. By targeting on a particular segment of consumers, the store would carry those brands/models that are most likely to be chosen by that segment and control its inventory costs by dropping items less favored by that segment. While it might be claimed that such a strategy would be difficult for a mass merchandiser to implement, evidence suggests that, with respect to department stores, there are demand segments that respond differently to the several merchandise lines typically carried by department stores (Hirschman, 1979). Thus, it would seem that even a store with a very broad assortment would begin by building depth in the product line or lines most important to its customers and adding items that complemented particular brands/models within its core lines. For example, as part of its overall strategic plan, Penney’s is building deep assortments in coordinated apparel items for young women and upscale furniture (Dunkin, 1989). However, the model points out that there are limits to this strategy. By adding more national brand items to the assortment, the retailer brings itself into conflict with retailers with whom it has not previously competed. Intensified price competition is the likely consequence.
Private Labels Finally, the use of private-label merchandise could be used to both deepen assortments to appeal to a market segment and, to the extent the products are successful, become substitutes for other products. In short, the successful private brander could both deepen and differentiate its assortment. As Salmon and Cmar (1987) point out, this is most likely to be effective for the retailer that is large
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enough to implement a private-label program and, at the same time, appeal to a relatively narrow market niche. The dangers of an unsuccessful attempt at private labeling are most noticeable in the case of the firm that is forced, by the strength of consumer demand, to add national-brand merchandise to its existing line of private-label goods. As modeled by this analysis, it would not only bring it into competition with other retailers from whom it had previously been differentiated but would also appeal to those shoppers motivated to find the lowest prices. Thus, Sears’ highly publicized additions of national-brand appliances and electronics to its existing lines appear to have put them into new dimensions of price competition (Graham, 1989).
Avoiding the Depot Effect Over two decades ago, Aspinwall(1962) argued that retailers were becoming “depots” that consumers visit, not to shop, but to pick up goods that had been presold to them by manufacturers. The better control afforded by improved transportation and communications would eventually force the flow of merchandise to the consumer to be regulated completely by consumer demand. Whatever benefits this might have, he predicted that this trend would eliminate retailer’ merchandising profits, namely, those that arise from the risk associated with carrying inventories at a sufficient level to anticipate consumer needs and wants. The model of retail competition and shopping discussed in this article points up the difficulty of building and maintaining assortments in an environment in which consumers seek specific items from any particular retailer’s inventory. Since much of this difficulty results from the consumer’s brand/model insistence before a visit to a retail establishment, the prospect of Aspinwall’s “depot effect” is enhanced by the selling efforts of manufacturers. The model suggests that retailers forced to deepen their assortments are likely to become more of a depot. In effect, this may be interpreted as a concession to the power of manufacturer’s brands, rather than a move to establish competitive advantage. The strategy implication for retailers in today’s competitive environment would seem to be an old one: draw a distinction between “consumers” and “customers” and, in the words of Marcus (1979), “give more than passing attention to the ‘anatomy of the customer’ and investigate those things which customers like and those they don’t” (p. 211). References
Alderson, Wroe, Dynamic Marketing Behavior: A Functionalist Theory of Marketing, Richard D. Irwin, Inc., Homewood, Ill., 1%5. Alderson, Wroe, and Shapiro, Stanley J., Towards a Theory of Retail Competition, in Marketing Theory (Second Series). Reavis Cox, Wroe Alderson, and Stanley J. Shapiro, eds., Richard D. Irwin, Inc., Homewood, Ill., 1964, pp. 190-213. An&n, Paul M., and Baye, Michael R., Information, Multiprice Search, and Cost-of-Living Index Theory, Journal of Political Economy 95 (December 1987): 1179-1196. Aspinwall, Leo, The Depot Theory of Retailing, in Managerial Marketing: Perspectives and Viewpoints. William Lazer and Eugene J. Kelley, eds., Richard D. Irwin, Inc., Homewood, Ill., 1962, pp. 652-659.
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Ballenger,
R. A. Mittelstaedt Danny N., and Korgaonkar,
and R. E. Stassen
Pradeep K., Profiling the Recreational
Shopper,
Journal of Retailing 56 (Fall 1980): 77-92.
Beatty, Sharon E., and Smith, Scott M., External Search Effort: An Investigation Across Several Product Categories, Journal of Consumer Research 14 (June 1987): 83-94. Bloch, Peter H., Ridgway, Nancy M. and Sherrell, Daniel L., Extending the Concept of Shopping: An Investigation of Browsing Activity, Journal of the Academy of Marketing Science 17 (Winter 1989): 13-21. Carlson, John A., and McAfee, R. Preston, Joint Search for Several Goods, Journal of Economic Theory 32 (April 1984): 337-345. Downs, Anthony, 6-12, 50.
A Theory of Consumer Efficiency, Journal of Retailing 37 (Spring 1961):
Dunkin, Amy, The Newly Minted Penney: Where Fashion Rules, Business Week (April 17, 1989): 88-90. Geistfeld, Loren V., Maynes, E. Scott, and Duncan, Greg, Informational Imperfections in Local Consumer Markets: A Preliminary Analysis, in Advances in Consumer Behavior VZZ, Jerry C. Olson, ed., Association for Consumer Research, Ann Arbor, Mich., 1980, pp. 180-185. Gist, Ronald E., Retailing: Concepts and Decisions, John Wiley and Sons, Inc., New York, 1968. Graham, Judith, Retail Inc.: It’s a Brand New Ball Game, Advertising Age 60 (August 21, 1989): R-l. Hirschman, Elizabeth C., Intratype Competition Among Department Stores, Journal of Retailing 55 (Winter 1979): 20-34. Markin, Rom J., Retailing Management, Macmillan Publishing Co., New York. 1977. Marcus, Stanley, Quest for the Best, Viking Press, New York. 1979. Maynes, E. Scott, Decision-Making for Consumers, Macmillan Publishing Co., New York, 1976. Mittelstaedt, Robert A., and Stassen, Robert E., Niche Width and Competition among Retailers, presented at 12th Annual Macromarketing Seminar, Montreal, August 1987. Mittelstaedt, Robert A., and Stassen, Robert E., The Role of Retail Assortments in Shopping Effectiveness and Efficiency, presented at Summer Educators’ Conference, American Marketing Association, San Francisco, August 1988. MLH Consultants, A Study of the Evolution of Concentration in the Irish Classical Records Industry. Evolution of Concentration and Competition Series, Number 25. Commission of the European Communities, Brussels. 1979. Progressive Grocer, Nielsen Review of Retail Grocery Trends, September 1986. Salmon, Walter J. and Cmar, Karen A., Private Labels Are Back in Fashion, Harvard Business Review 65 (May-June 1987): 99-106. Stigler, George J., The Economics of Information, Journal of Political Economy 69 (June 1961): 213-225. Thomas, Gordon D., and Fishwick, Francis, A Study on Classical Records Prices and Markups in the United Kingdom. Evolution of Concentration and Competition Series, Number 27. Commission of the European Communities, Brussels, 1979. Westbrook, Robert A, and Black, William C., A Motivation-Based Shopper Typology, Journal of Retailing 61 (Spring 1985): 78-103.