department of marketing faculty of economics store location: evaluation and selection based on...

Department of MarketingFaculty of Economics

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


Situation: Chain of stores with many outlets

Important issues:

1. Performance of current outlets

2. Site selection for new outlets ?


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.


Store Characteristics, including:LocationSize

Consumer Characteristics, including:GeodemographicsNumber of households

Competitor Characteristics, including:Number of competitorsRetail activity

Store PerformanceExisting storesNew stores Main and

Interaction effects


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.


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


Sales tomembers

Sales fromzip code j=1




Penetration rateat j=3

Avg no of visitsat j=3

Avg expendituresat j=3

No of HHsat j=3

Sales tonon-members

Store revenues

Trade area

+ + +

x x x

+

Sales from membersoutside trade area

Sales from memberswithin trade area

+


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

ititit SNSLS

itSL

itSN


Model (2)

Sales to loyalty card holders (within the trade area) can be further decomposed into:

ii J

jjtjtjtjt

J

jijtit EPNVPRNHSLSL

11

jtNH

jtPR

jtEPjtNV

= 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


Example

Households LC holders Avg number of visits

Avg amount spent

Penetration Rate

ZIP Code 1 100 75 5 €100 0.75 (75/100)

ZIP Code 2 200 100 10 €75 0.50 (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


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)


Explanatory variables

Zj predictors that vary between zip code areas

Xi store specific predictors

Components of the sales equation to be explained by factors concerning characteristics of:

• Store

• Consumer

• Market/Competition

e.g. jNVjnNV

N

nnNViNVj RZNV

NV

,,1

,0,ln

iNVikNV

K

kkNVNViNV UX

NV

0,,1

0,00,0,


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)

llll ξWRλR


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).


Average sales per store

0

500000

1000000

1500000

2000000

2500000

2002 2003 2004

Year

Av

era

ge

sa

les

pe

r s

tore

unscanned

scanned

About 75% of the sales is by loyalty card holders.


The relationship between travel distance and the penetration rate

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0 1 2 3 4 5 6 7 8 9 10

travel distance

pre

dic

ted

pe

ne

tra

tio

n r

ate


The relationship between number of visits and travel distance

2,9

2,922,94

2,96

2,98

33,02

3,04

3,06

3,083,1

3,12

0 1 2 3 4 5 6 7 8 9 10

travel distance

pre

dic

ted

nu

mb

er

of

vis

its


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.


!

Legend

! Store

new.PENRATE

0.008 - 0.023

0.024 - 0.030

0.031 - 0.036

0.037 - 0.045


!

Legend

! Store

new.VISITS

2.00 - 3.00

3.01 - 4.00

4.01 - 5.00

5.01 - 7.00


!

Legend

! Store

new.EXPENDITUR

0.00

0.01 - 24.00

24.01 - 25.00

25.01 - 27.00


!

Legend

! Store

TOT.SALES

0 - 2062

2063 - 5101

5102 - 9067

9068 - 16055


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.


Further research

• Predictive validity: Predict sales for potential new locations

• Comparison to benchmark models

department of marketing faculty of economics store location: evaluation and selection based on...

Documents

store j

store slide

trade area sales

zip code area j

trade area slide

zip code j

store choice

members sales