Multiple Regression Analysis: Locating New Pam and Susan`s Store

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Multiple Regression Analysis: Locating New Pam and Susan`s Store

Case Study
Pam and Susan`s is a chain of discount department stores.* The original store was opened in the South in the mid-1950s by Pam and Susan`s father. About 10 years ago, Pam and Susan took over operational control of the stores, working together under a joint power sharing arrangement. The unusual management arrangement and consensus decision making by the two women, for which they have received a great deal of publicity, have contributed in part to sales growth and to the recent upsurge in new store openings. Fundamentally, however, their success is based on an uncanny ability to appropriately stock stores and underprice competitors. State-of-the-art business processes are at the core of their low price structure.

There are currently 250 Pam and Susan`s stores, mostly located throughout the South. Expansion has been incremental, growing from its Southern base into the Border States and increasingly into the Southwest. Identification of the most appropriate sites for new stores is becoming an issue of increasing strategic importance.

Store location decisions are based upon estimates of sales potential. The traditional process leading to estimates of sales potential starts with demographic analyses, site visits, and studies by the company`s real estate experts (augmented by input from local experts). The demographic data judged relevant for a given store location is that for people within a store`s estimated "trading zone," usually operationalized as consisting of those census tracts within a 15 minute drive of the store. Planners in the real estate department consider current and expected future competition, ease of highway access, costs of the site, planned square footage of the store, and estimates of average sales per square foot, based on data from all existing stores. They judgmentally combine the demographic information, site information and overall sales rates to come up with an estimate of sales for a new store. Pam and Susan`s stores have primarily targeted lower-middle class to poorer neighborhoods/trading zones.

Increasingly, actual store sales at new locations have deviated from estimates provided by the real estate department. Pam and Susan want to develop better methods for estimating sales potential. A consultant (you) has been hired to explore the possibility of using the census data in stores` trading zones, along with data on individual stores, to construct regression models to help make the location decisions..

To explore this option, a number of variables derived from the most recent census were compiled for the trading zone of each of the 250 stores (there is no overlap in the trading zones of the 250 stores). For each store there is data gathered on demographics and economics of the trading zones, as well as size, composition and sales of the store.

This data is in the file pamsue.xls (an excerpt is in Table 12. 13.


1. As a possible alternative to the subjective "competitive type" classifications, how well can you forecast sales using the demographic variables (along with the store size and the percentage of hard goods)? What does your model reveal about the nature of location sites that are likely to have higher sales?
2. How good is the "competitive type" classification method (along with using the store size and the percentage of hard goods) at predicting sales? What recommendations do you have for simplifying the competitive type categories?
3. Two sites, A and B, are currently under consideration for the next new store opening. Characteristics of the two sites are provided below in Table B. Which site would you recommend? What sales forecasting approach would you recommend?
4. Two of the variables in the data base are under managerial control, the size of the store (square feet of selling area) and the percentage hard goods stocked in the store. Margins on hard goods (clothing, for example). What impact do these variables have on sales?
5. TECHNICAL: For your recommended model, check to make sure the technical assumptions are satisfied. Comment on any points that would concern you based on the diagnostic.

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