Store location: Evaluation and Selection based on Geographical Information

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
(No Transcript)
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 j1
Sales from zip code j2
Sales from zip code j3
Sales from zip code j4



Trade area
Penetration rate at j3
Avg no of visits at j3
Avg expenditures at j3
No of HHs at j3
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
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
(No Transcript)
18
The relationship between travel distance and the
penetration rate
19
The relationship between number of visits and
travel distance
20
(No Transcript)
21
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.

22
?
?
23
?
?
24
?
?
25
?
?
26
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.

27
Further research

• Predictive validity
• Predict sales for potential new locations
• Comparison to benchmark models