Pam and Susan's: Locating New Stores

PAM AND SUSAN'S: LOCATING NEW STORES

Pam and Susan's is a chain of discount department stores. (Note: this is a fictionalized account based upon a real department store chain). 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. However, fundamentally, 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. There is interest in developing better methods for estimating sales potential. One group (you) has been assigned the responsibility to explore the possibility of using the wealth of 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, the following 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):

Variables:

Var 2: population: % Black

Var 3: population: % Spanish speaking

Vars 4 - 10: % in each of the following family income categories (000s): 0-10; 10+-14; 14+-20; 20+-30; 30+-50; 50+-100; >100

Var 11: median yearly family income

Var 12: median rent per month

Var 13: median home value

Var 14: % home owners

Var 15: % with no cars

Var 16: % with one car

Var 17: % households with TV

Var 18: % households with washer

Var 19: % households with dryer

Var 20: % households with dishwasher

Var 21: % households with air conditioner

Var 22: % households with freezer

Var 23: % households with second home

Vars 24 - 27: % adults (over age 25) with the following years of education: 0-8; 9-11; 12, 12+

Var 28: total population

Var 29: average family size

The following data were collected on each store:

Var 30: square feet of selling area (000s)

Var 31: annual sales (000s of $)

Var 32: % hard goods

Var 33: Competitive type, a value from 1-7 as indicated in attachment A.

ASSIGNMENT:

1. Using the variables listed at the top of page 2 (i.e., NOT using comtype, but all others), use stepwise regression to develop the best model you can to predict store sales (Var 31). How good is this model? Based on this model, how would you characterize/describe the nature of location sites that are likely to have higher sales? (Note: one of the 7 categories of the income variable and one of the 4 categories of the education variable must be omitted, since they add to a fixed total (100%); so that we all do it the same way, let the highest category be the category omitted for each set of variables; i.e., omit Var 10 and Var 27)

2. A group within the planning department has developed a more subjective approach in which potential sites are classified according to an assessment of the "competitive type" of the trading zone. In "Attachment A," the 7 "competitive types" are defined. How does the model in question 1 compare to how well you can predict sales based (solely) on the "competitive type" classifications? (Note: dummy variables need to be created for "competitive type" classifications; so we all do it the same way, let category 7 be the base [i.e., omitted]).

3. In the model developed in question 2, interpret very precisely the coefficient of the "competitive type 4" variable. Based on the model in question 2, which is the worst competitive environment for (higher) sales?

4. Using as eligible the "winners" (i.e., the variables that were in the final equation) determined in question 1, combined

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