New supermarket layout

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New supermarket layout
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  Toward a new supermarket layout: from industrial categories to one stop shopping organization through a data mining approach. Adilson Borges Chairholder Auchan Chair of Retailing Marketing Professor Reims Management School Track indication: Distribution Channels and Retailing Reims Management School 59 rue Pierre Taittinger BP 302 51061 Reims France +33 (0)3 26 77 46 04 adilson.borges@wanadoo.fr    Toward a new supermarket layout: from industrial categories to one stop shopping organization through a data mining approach. Abstract This paper proposes a new grocery store layout based on the association among product categories. The very first grocery stores displayed their products in an industrial approach, which have produced the present day grocery store layouts based on “sectors” as fruits, vegetables, magazines, cds, and so on. This approach is company oriented and it fails to respond to the needs of the time-pressured consumer. Some retailers are trying to move from this organization to something new, and are struggling to become ¨consumer oriented¨ in their layout approach. For example, Tesco has rethought their store layout with ¨plan-o-grams¨ to try to reflect local consumers needs (Shahidi, 2002). Other French retailers have used consumption universe layouts to make it easier for consumers to find their product in a more hedonic environment. One possibility to do so is to make the store layout construction through the introduction of the market basket analysis, improving one stop shopping experience. This allows retailers to cluster products around the consumer buying habits, avoiding time search spending, and then creating a very strong appeal for today’s busy consumers. We use 1,7 million transaction database to measure the buying association and to create a category correlation matrix. Then we applied the multidimensional scale technique to display the set of products in the store space. We will imply that the buying association, measured through the market basket analysis, is the best way to find product organization that are better suited to one stop shopping. Keywords: Data mining, market basket analysis, retailing, store layout. The store layout is a huge task for retail managers. The complexity of this task lies in the relationship between categories on sale as well as on the impact that it produces on the consumer spatial behaviour and in-store traffic. The very first grocery stores displayed their  product categories in an industrial approach, which have produced the present day grocery store layouts based on fruits, vegetables, magazines, cds, and so on. This merchandise organization seems logical, because 1)  consumers get to know where to find products in the store and 2)  consumers have learned the categorical scheme and vocabulary from retailers and manufacturers. This enables them to use store signs and paths to find the products that interest them in the store. This means that any change in the product location or store layout might have disastrous effects on store performance. Some little improvements have been made in that highly sensitive area. As a matter of fact, some categories have been placed side by side in their a priori  cognitively logical pairs. Displaying camera and films in the same area invites the consumer to remember that they need both to take photos, this allows the retailer to boost sales from the store visit. To find these logical complementary categories, the econometricians have developed cross-elasticity (CE), which measures the sales change of one category from a price change in another. It claims to capture the use association  among categories, because we suppose the  products will be used together. In spite of the importance of use association, the main goal of the supermarket is to provide one stop shopping. Shoppers will buy both products with strong and weak use associations on the same store visit. (Borges et alli , 2002).  As a matter of fact, 94% of American grocery shoppers seem to consider that a store layout that makes shopping easier as important when choosing their supermarket (FMI, 2000). Time conscious and empowered consumers will be more attracted by supermarket chains who adopt one stop shopping layouts. This paper proposes a new grocery store layout based on the association among categories. We use the buying association measure to create a category correlation matrix and we apply the multidimensional scale technique to display the set of products in the store space. We will imply that the buying association, measured through the market basket analysis, is the best way to find product organization that are best suited to one stop shopping. The Store Layout Increasing space productivity represents a powerful truism in retailing: the more well  presented merchandise customers are exposed to, the more they tend to buy. By careful  planning of the store layout, retailers can encourage customers to flow through more shopping areas, and see a wider variety of merchandise (Levy and Weitz, 1998). There are at least two layout approaches: the traditional and the consumption universes. The traditional approach consists in repeating the industrial logic implementation, which means  putting products that share some functional characteristics or srcins in the same area. So we will find the bakery area (with bread, cakes, biscuits, etc), the vegetable area (with carrots,  beans, etc), and so on. This traditional approach has been improved by the use of cross-elasticities, which should measure use association. Retailers have changed some categories and put more use related items together. If a consumer wants take photos at a family party, s/he needs at least the camera and the film. In these cases, both products are complementary, because consumers need both at same time to achieve a specific goal (Walters, 1991). The nature of the relationship among products could be twofold: the use association (UA) or the buying association (BA). UA is the relationship among two or more products that meet specific consumer need by their functional characteristics. We can classify the relationship among different categories by their uses: the products can be substitutes, independent and complementary (Henderson and Quandt, 1958 ; Walter, 1991). The BA is the relationship established by consumers through their transaction acts and it will be verified in the market  basket. While UA is not a necessary condition for BA, because UA depends much more on the products functional characteristics, BA depends on buying and re-buying cycles as well as on store marketing efforts. Despite improvements, the store remains organized in “product categories” as defined by the manufacturers or category buyers. This approach is company oriented and it fails to respond to the needs of the time pressured consumer. Some retailers are trying to move from this organization to something new, and are trying to become ¨consumer oriented¨ in their layout approach. Tesco has rethought their store layout with ¨plan-o-grams¨ to try to reflect local consumers needs (Shahidi, 2002). Other French retailers have used consumption universe layouts to make it easier for consumers to find their product in a more hedonic environment. This approach allows supermarkets to cluster products around meaningful purchase opportunities related to use association. Instead of finding coffee in the beverage section, cheese in fresh cheese, ham in the meat section, and cornflakes in the cereal section, we could find all those products in the breakfast consumption universe. Other universes, such as the  baby universe or tableware universe, propose the same scheme to cluster different product categories. It is too soon to foresee the financial results of such applications, but it shows, however, the retailer’s desire to improve in store product display.  These new layout applications do not take the one stop shop phenomenon into account. In fact, this approach is based on the principle that conjoint use of products will unconditionally  produce conjoint buying. The main problem with this rationale is that use association alone cannot be used to explain the associations carried out in the buying process (the market  basket), because it fails to take buying time cycles into account. For example, bread and butter should be classified as occasional complements, and then they should be found in the same market basket (Walters, 1991). However, this could be not true, since the products have different buying and re-buying cycles. In that case, buying association may be weak, because  bread is usually bought on a daily basis, and butter once every week or two. On the other hand, ‘independent products’ don’t have any use relationship, so they should not have any stable buying association. Meanwhile, Betancourt and Gautschi (1990) show that some products could be bought at the same time as a result of the store merchandising structure, store assortment, the marketing efforts and consumption cycles. So, the fact that two products are complementary is not a guarantee that those products will be present in the same market basket. In addition, some researchers have found that independent products have the same correlation intensity as complementary ones in the market baskets (Borges et alli , 2001). So, the store layout construction has to incorporate the market basket analysis to improve the one stop shopping experience. This allows retailers to cluster products around the consumer buying habits, and then to create a very strong appeal for today’s busy consumers. The Buying Association: a way to measure the relationship among products The relationship between categories has always been articulated through their use, but this is not enough to explain conjoint presence in the market basket. These two kinds of relationships were clear for Balderston (1956), who presented it as (1) use complementary, if  products are used together, and (2) buying complementary, if products are bought together. BA can be computed from supermarket tickets, and indicates real consumer behavior (it is not  based on consumers’ declaration or intention). Loyalty cards and store scanners have  produced a huge amount of data that is stored in data warehouses and analyzed by data mining techniques. Data Mining is regarded as the analysis step in the Knowledge Discovery in Databases (KDD) process, which is a "non-trivial process of extracting patterns from data that are useful, novel and comprehensive". In data mining, BA is considered as an association rule. This association rule is composed of an antecedent and consequence set : A ⇒  B, where A is an antecedent and B a consequent; or A,B ⇒  C, where there are two antecedents and one consequence (Fayyad et alli , 1996). The BA is calculated by the following formula: Equation 1 )()(  A f  AB f   AB  = δ   , where f(AB) represents the conjoint frequency of both products A and B and f(A) represents the product A frequency in the database. This equation is similar to the conditional  probability that could be written as (A ∩ B)/A, given that A intersection B represents the market baskets where both products, A and B, are present at same time. The buying association represents the percentages of consumers that buy product A and who also buy product B. It shows the relationship strength between products, considering only the relationships carried out on buying behavior  . This can be represented as a percentage: a BA of 35% between coffee and laundry is interpreted as 35% of consumers have bought coffee also bought laundry in the same shopping trip.  In the same way that cross-elasticity is not symmetric, BA is also not symmetric. The BA FC  can be different from BA CF (this relationship depends mainly on the category penetration rates over the total sales). Mathematically: ∀  F>C, so (F ∩ C)/F < (F ∩ C)/C. So, if A frequency is different from B frequency, then the relationship among those products will always be asymmetric. For example, “F” represents the film and “C” the camera. Suppose the condition F>C is confirmed, then the film has a larger penetration in the market  baskets than camera. If this condition is satisfied, then BA FC <BA CF . BA does not measure casual relationships, but only a correlated presence. It gives us a  probability P (AB) to find a product B since we have found the product A in a market basket. Hence the sample in the data mining applications is usually large (N →   ∞ ), we can consider this measure as a conditional probability by Bernoulli’s theorem. Therefore, we can state that BA AB  = P B  A , which allows us to use the entire mathematical arsenal from conditional  probability on BA (Hays, 1977).  Method and Results The first step toward a store layout map is to measure the relationship among products. To do so, we have got a two year database from three French supermarkets. The database has 1.700.000.000 transactions during the period. Each transaction has the consumer identification, the data, the EAN codes, the quantities of each product, and the total value. We have chosen 20 different categories to construct a correlation matrix, which are: water, bread, cornflakes, ham, detergent, cheese, pasta, butter, wine, sauce, mayonnaise, coffee, beverage, milk, yogurt, toothpaste, deodorant, shampoo, chips, beer. For space reasons, we will not show the correlation matrix, because this is not the main point of the article. Once we have established the correlation matrix, we are able to calculate the spatial representation of these relationships through the multidimensional scaling technique (MDS). We have used data as distance and an asymmetric matrix to produce the results. In order to use the correlation matrix as distances among categories, we have inversed the values by subtracting 1 from all values. So, if two products have a strong correlation (say 0,95) the proximities will be small (1-0,95 = 0,05), which means that those categories are similar and should be represented in a nearby space on the map. To assess validity we have make individual MDS analyses for each store, and we found the same structural results for the map representation. We will not show each map here for reasons of space. However, the model stress is 0,41110 and the square correlation is 0,20971, which means that we have to be cautious about accepting this model. We represent all categories in the multidimensional space as showed in the Figure 1. The first cluster in the Figure 1 is comprises cornflakes, ham, butter and cheese. These  products are usually bought together and buyers who require one stop shopping might get a  better experience from this categories layout. One can see in the cluster 1 a breakfast consumption universe. This can be true, even if we have other breakfast products, as coffee or milk (cluster 5) and bread (cluster 3) placed in other areas of the map. We have to stress that the buying association and the consumption universes are not incompatible. Common use  products could be present in the same basket, since they have the same time cycle purchase. Clusters 2 and 3 are of great interest in identifying the limits in the consumption universe approach. Cluster 2 shows beer, water and pasta being bought together in many shopping
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