Department of Marketing Faculty of Economics Store location: Evaluation and Selection based on Geographical Information Tammo H.A. Bijmolt Joint project with: Auke Hunneman and Paul Elhorst

Department of Marketing Faculty of Economics Importance of store location For many customers, store location is a key factor driving store choice. For many customers, store location is a key factor driving store choice. Store location determines the trade area. Store location determines the trade area. Store location can be a source of competitive advantage. Store location can be a source of competitive advantage. The decision is almost irreversible  costs of mistakes are high. The decision is almost irreversible  costs of mistakes are high.

Department of Marketing Faculty of Economics Situation: Chain of stores with many outlets Important issues: 1. Performance of current outlets 2. Site selection for new outlets  ?

Department of Marketing Faculty of Economics Modeling framework 1. Current outlets: Determine impact of drivers of store performance (characteristics of customers, outlet, and market/competition) 2. Copy relationships found in stage 1 to new sites to determine potential performance.

Department of Marketing Faculty of Economics Store Characteristics, including:  Location  Size Consumer Characteristics, including:  Geodemographics  Number of households Competitor Characteristics, including:  Number of competitors  Retail activity Store Performance  Existing stores  New stores Main and Interaction effects

Department of Marketing Faculty of Economics Which consumers? Trade area: geographical space from which the store gets most of its sales. Trade area definition: based on travel distance or travel time of the customers.  Loyalty cards provide information on purchase behavior and residence location (Zip code) of customers.  Databases provide demographic information per Zip code.

Department of Marketing Faculty of Economics Definition of the trade area = Trade area Our approach: 1. Rank the ZIP codes on decreasing sales. 2. Determine which ZIP codes yield 85% of the total sales. 3. Trade area includes all these ZIP codes and those closer to the store. Store

Department of Marketing Faculty of Economics Sales to members Sales from zip code j=1 Sales from zip code j=2 Sales from zip code j=3 Sales from zip code j=4 Penetration rate at j=3 Avg no of visits at j=3 Avg expenditures at j=3 No of HHs at j=3 Sales to non-members Store revenues Trade area +++ x xx + Sales from members outside trade area Sales from members within trade area +

Department of Marketing Faculty of Economics Model (1) Van Heerde and Bijmolt (JMR, 2005): Total sales of a store i in period t can be decomposed into: Sales to loyalty card holders Sales to other customers

Department of Marketing Faculty of Economics Model (2) Sales to loyalty card holders (within the trade area) can be further decomposed into: = number of households in zip code area j = penetration rate of the loyalty card in zip code area j = avg number of visits of loyalty card holders in j = avg expenditures per visit of loyalty card holders in j i: Store j: Zip code t: Time period

Department of Marketing Faculty of Economics Example HouseholdsLC holdersAvg number of visits Avg amount spent Penetration Rate ZIP Code € (75/100) ZIP Code € (100/200) Sales ZC 1 = NH*PR*NV*EP = 100*0.75*5*100 = €37,500 Sales ZC 2 = NH*PR*NV*EP = 200*0.50*10*75 = €75,000 Total sales to loyalty card holders = €37,500+ €75,000= €112,500

Department of Marketing Faculty of Economics Dependent variables Per Zip code:  Penetration of loyalty card (Logit)  Average number of visits (Ln)  Average purchase amount (Ln) Percentage of sales to loyalty card holders outside the trade area (Logit) Percentage of total sales to other customers (Logit)

Department of Marketing Faculty of Economics Explanatory variables Z j predictors that vary between zip code areas X i store specific predictors Components of the sales equation to be explained by factors concerning characteristics of: Store Consumer Market/Competition e.g.

Department of Marketing Faculty of Economics Spatial-lag Random-effects Hierarchical model Relation between ZIP codes that are close to each other. Here, spatial lag specification Spatial weight matrix in the error term accounts for spatial autocorrelation. Random-effects Hierarchical model: ZIP codes nested within stores. GLS estimation based on Elhorst (2003)

Department of Marketing Faculty of Economics Empirical study Dutch chain of clothing retailer 28 stores throughout The Netherlands Trade area: about 60 to 200 ZIP codes per store 3 years ( ) We have data for each store as well as data about characteristics of their market areas (consumer and competitor information).

Department of Marketing Faculty of Economics Average sales per store About 75% of the sales is by loyalty card holders.

Department of Marketing Faculty of Economics

Department of Marketing Faculty of Economics The relationship between travel distance and the penetration rate

Department of Marketing Faculty of Economics The relationship between number of visits and travel distance

Department of Marketing Faculty of Economics

Department of Marketing Faculty of Economics Model predictions: steps 1. Model for explaining revenue components (LP penetration, number of visits, etc.) based on data from existing stores. 2. Model predictions of the revenue components per ZIP code / store. 3. Per ZIP code: # households x LP penetration x # visits x average basket size = predicted revenues. 4. Aggregate predicted revenues across ZIP codes, add the percentage sales outside the trade area and percentage sales to customers without a loyalty card 5. Final result: Prediction of sales per store, per year.

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Department of Marketing Faculty of Economics Conclusions  New methodological tool based on geo- demographic and purchase behaviour to assess store performance.  We explain a substantial amount of variance in store performance.  We identify important drivers of store performance.  Drivers differ between penetration, number of visits and expenditures, e.g. distance and household composition.

Department of Marketing Faculty of Economics Further research Predictive validity:  Predict sales for potential new locations Comparison to benchmark models