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Store location: Evaluation and Selection based on Geographical Information

Store location: Evaluation and Selection based on Geographical Information. Tammo H.A. Bijmolt Joint project with: Auke Hunneman and Paul Elhorst. Importance of store location. For many customers, store location is a key factor driving store choice.

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Store location: Evaluation and Selection based on Geographical Information

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  1. Store location: Evaluation and Selection based on Geographical Information Tammo H.A. Bijmolt Joint project with: Auke Hunneman and Paul Elhorst

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

  3. Situation: Chain of stores with many outlets Important issues: • Performance of current outlets • Site selection for new outlets  ?

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

  5. Store Characteristics, including: • Location • Size • Consumer Characteristics, including: • Geodemographics • Number of households • Store Performance • Existing stores • New stores Main and Interaction effects • Competitor Characteristics, including: • Number of competitors • Retail activity

  6. 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.

  7. Definition of the trade area • Our approach: • Rank the ZIP codes on decreasing sales. • Determine which ZIP codes yield 85% of the total sales. • Trade area includes all these ZIP codes and those closer to the store. Store = Trade area

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

  9. 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

  10. = 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 Model (2) Sales to loyalty card holders (within the trade area) can be further decomposed into: i: Store j: Zip code t: Time period

  11. Example

  12. 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)

  13. Explanatory variables • Components of the sales equation to be explained by factors concerning characteristics of: • Store • Consumer • Market/Competition • e.g. Zj predictors that vary between zip code areas Xi store specific predictors

  14. 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)

  15. Empirical study • Dutch chain of clothing retailer • 28 stores throughout The Netherlands • Trade area: about 60 to 200 ZIP codes per store • 3 years (2002-2004) • We have data for each store as well as data about characteristics of their market areas (consumer and competitor information).

  16. Average sales per store About 75% of the sales is by loyalty card holders.

  17. The relationship between travel distance and the penetration rate

  18. The relationship between number of visits andtravel distance

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

  24. 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.

  25. Further research • Predictive validity: • Predict sales for potential new locations • Comparison to benchmark models

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