Store Location: Evaluation and Choice Based on Geographical Consumer Information Auke Hunneman Tammo H.A. Bijmolt J. Paul Elhorst University of Groningen The Netherlands ERSA Summerschool in Regional Science 4-12 July, 2006

Importance of Store Location For many consumers, store location is a key factor driving store choice. Store location determines the trade area. Store location is a source of competitive advantage. The decision is almost irreversible  costs of mistakes are high. Changing environment  experience becomes a less reliable guide. Competition  importance of growth. First a few words on why store locations are important. There are several reasons why managers should care about location decisions. First, travel distance strongly affect where consumers buy. Although trends like large warehouse formats and multipurpose shopping may have reduced the importance of travel distance somewhat, store location is still a key factor driving store choice. Second, store location determines trade area. This is the geographical area from which the store attracts most of its customers. Since the trade area needs to be an area with many target consumers, geographical information on the number and type of consumers should be studied to determine the optimal store location. Third, fairly high costs are associated with the decision where to locate a new store. Once a new store has been located, the disadvantages of a wrong location may be difficult to overcome. Therefore, costs of mistakes are high. Finally, the dynamics of the retail environment force retailers to carefully evaluate the deployment options of new stores. Given the dynamics of today’s retail environment, retailers need to rely on statistical models that give consistent results rather than solely their own experience.

Situation: Chain of Stores with Many Outlets Important issues: Performance of current outlets Site selection for new outlets ? Imagine a retailer that operates several stores throughout the Netherlands. If this retailer wants to build an additional store, two sources of information may be of interest to him. First, the retailer may want to evaluate the performance of existing stores over time. Therefore, he would like to have a certain benchmark value that accounts for (exogenous) differences across stores. Second, given that a new store should be located, the retailer wants to know which location has the highest sales potential, given the current spatial configuration of stores. The model that will be proposed in the next few slides deals with these issues. The model is aimed at: (1) explaining performance of existing stores and (2) identifying opportunities for the location of new stores.

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. To elaborate somewhat more on our modeling approach, the modeling framework is as follows. First, we determine the relative impact of different drivers of store performance on the performance of existing stores. Once we have have established this relationship, we copy the relationship found to determine sales potential of possible new store locations. This means that we use parameter estimates of stage one to predict sales potential of new locations. If we do this for more than one location, we can compare their sales potential estimates and select the one that has highest profit (not sales!) potential.

The Conceptual Framework Store Characteristics, including: Location Composition Consumer Characteristics, including: Geodemographics Number of households Market Characteristics, including: Number of competitors Retail activity Store Performance Existing stores New stores Main and Interaction effects As I explained, we first establish a relationship between different drivers of store performance and performance of existing stores. So this figure refers to stage 1. As appears from this framework, we distinguish between variables measuring store, market, and consumer characteristics. We empirically assess the relative size of main and interaction effects of the various factors explaining performance.

Which Consumers? Our approach: We use a distance measure to include all zip code areas that are within a 10 miles driving distance to the store Store A critical issue in establishing this relationship, is the decision which consumers to consider. Since we want to estimate the relationship for new stores, we need an exogenous variable to determine a store’s trade area; this is the geographical area from which the store attracts most of its customers. As proposed in some popular textbooks (e.g., the book of Levy and Weitz), we use travel distance to establish trade area boundaries. Levy and Weitz say that a store’s primary and secondary trade area typically cover 85% of total sales. Therfore, we identified those zip code areas that together constitute 85% of total sales. Next, we computed travel distances from the store to these zip code areas and took the average of these values. This appeared to be 10 miles. We therefore include zip codes that are within a 10 miles travel distance from the store. = Trade area

Van Heerde & Bijmolt (JMR 2005): The Model (1) Van Heerde & 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 As I said, we use a decomposition framework to get detailed insights in the impact of drivers of store performance. We first distinguish between sales to loyalty card holders and sales to other customers. In this equation, i refers to the store, whereas t refers to the time period.

The Model (2) Sales to loyalty card holders can be further i: Store j: Zip code t: Time period Sales to loyalty card holders 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 A store’s trade area typically consists of small zip code areas with specific socio-demographic and lifestyle characteristics. For loyalty card holders we know their residence locations. Therefore, we further decompose sales to loyalty card holders into sales to loyalty card holders for each zip code area (denoted by j). For each zip code area, we have information about the number of loyalty card holders living there, the number of times they visit the store, and their average expenditures. To obtain insights in drivers of store performance we –among others- model these criterion variables.

+ + + + + 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 + + + Here a graphical representation of our decomposion framework. Again, total revenues can be decomposed into sales to members and sales to non-members. For members, we know their residence location. So we can further decompose sales to members into sales from different zip code areas. For each zip code area, we have information on the number of members living there, the number of times they visit the store and their average expenditures. 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

Penetration of loyalty card (Logit) Average number of visits (Ln) Dependent Variables Per Zip code: Penetration of loyalty card (Logit) Average number of visits (Ln) Average purchase amount (Ln) (Percentage) sales to other customers (Percentage) sales to LC holders outside trade area In total we model five criterion variables. For each zip code area, we model the penetration rate of the loyalty card, the average number of visits, and average expenditures. In addition, we model (the percentage of) sales to non-members and (the percentage of) sales to members who live outside the store’s trade area since we (also) want to know total sales for new locations.

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 To obtain insights in the drivers of store performance, we specify a so-called multilevel or hierarchical model because zip code areas are nested within stores. In the models, the criterion variable is explained by two types of explanatory variables. The level-one variables (denoted by Z in this equation) are measured at the zip code level, whereas the X-variables are store specific predictors. Such an equation is specified for the three criterion variables: the penetration rate of the loyalty card, the average number of visits and average amount spent. It should be mentioned that the set of predictor variables is not necessarily the same for each criterion variable. In this model, we have a random intercept. This means that the intercept (beta) is allowed to differ between stores. We have an error term at the store level (indicated by U) as well as at the zip code level (denoted by R).

The Spatial Model Relation between zip codes that are close to each other. Spatial error model: weight matrix in the error term accounts for spatial autocorrelation. Here: Consumer preferences and choice behaviour in different zip code areas cannot be considered to be independent. Therefore, if we assume that consumers with similar characteristics and spending patterns cluster together, we need to account for this unobserved spatial dependencies. This can be done by adopting a spatial error model. In such a model, spatial dependencies across zip codes are accounted for by assuming spatial autocorrelation in the error terms. The spatial autoregressive process in the error terms can be expressed by the following equation. Here W refers to the spatial weight matrix, which specifies for each observation which locations belong to its neighborhood; lambda is the spatial autoregressive parameter, and the last term (xi) is a spatially uncorrelated error term. the spatial autoregressive coefficient for the error lag W; a spatially uncorrelated and homoskedastic error term.

Empirical Study Dutch chain of clothing retailer 28 stores throughout The Netherlands Trade area: about 60 to 200 zip code areas per store. 3 years (2002-2004) We have data for each store as well as data about characteristics of their market areas. Hierarchical model: ZIP codes nested within stores.

Cross-level interactions Random slopes Multivariate model Further Research Model improvements: Cross-level interactions Random slopes Multivariate model Spatial weight matrix Predictive validity: Predict sales for potential new locations Comparison to benchmark models A logical extension of the current model is that we allow for interdependencies between the different components of the sales equation. Therefore, we have to treat them as a system of equations (estimated by 3SLS) that allows for the error terms to be related. Another improvement may be the specification of the spatial weight matrix. We now used contiguity weigths (zeros and ones) to indicate which zip code areas are neighbors. However, it is also possible to use a continuous (inverse of distance) measure to allow for spatial dependencies. Since model outcomes are influenced by the specification of the W matrix, we have to compare model estimates for alternative specifications of W. We now only focused on explaining performance of existing stores. The next step is to use modeling results to predict sales potential for new locations. It would be of interest to investigate how the model performs against some benchmark models. For example, it would be useful to compare model outcomes with those of the widely used Huff model.

Independent Variables (1) STORE M2TOT Total selling space (in m2) %FEMASS, %KIDSASS Percentage of selling space attributed to female and kids assortment respectively ESTABLISH Number of years the store has been established after the first store PAYMENT Total salaries paid per year MARKET/COMPETITION COMPETITION Number of local competitors

Independent Variables (2) CONSUMER HHCHILD % of HH with children COUPLE % of couples without children DOUBLEINC % of double-income families HPROS, >AVGPROS, AVGPROS, LPROS % of families with high, above average, average, low, and minimum prosperity respectively DAVGHIGH, D>AVGHIGH, DAVGSEC, D>AVGSEC, DAVGELEM, D>AVGELEM Dummy variables indicating average and above average number of people with higher, secondary, and elementary education DISTANCE Travel distance to the store