Site Selection’s New Look
As a tough economic climate forces retailers to batten down the hatches, wait out the storm or jump ship entirely, the greatest chances for survival will likely come to those who most effectively choose when and where to expand—and what stores to close. Senior editor Katherine Field talked with Rick Erwin of U.K.-based information-services provider Experian, which has its U.S. headquarters in Costa Mesa, Calif. Erwin discussed how technology and up-to-the-minute data have changed the look, and effectiveness, of site selection.
With retailers closing stores and many chains in a stalled expansion mode, how important is pinpointed site selection today?
It’s more important than ever before. As we see retailers limiting their expansion activities or, as you pointed out, sometimes making the difficult decision to close locations, retailers absolutely need to identify the best-performing sites or the future potential of an existing site before they make a closure or expansion decision. So retailers now are trying to optimize their location networks.
What exactly do you mean by “optimizing location networks?”
Having the right retail locations in the right places, meaning that you must improve on the best-performing stores and minimize the effect of the under-performing stores.
How do you do that?
Understanding the population around site locations is important to help drive those decisions, but since the population is continually developing and changing, it’s absolutely critical to have current and fresh information to make good decisions about how to optimize the location network.
It’s common for retailers to make location-optimization decisions based on census data or census household projections. However, that information is not current and therefore may be flawed when used in isolation. For example, if you were to look at a graph of the Dallas/Fort Worth area depicting the 2007 census household projections, you would see an area just north of Fort Worth that appears to be relatively unpopulated in terms of the number of households. But current information would show that same area to actually be a very densely populated part of the Dallas/Fort Worth metroplex, which has experienced a huge influx of new households in the past three or four years.
How would that data impact the reliability of a retailer’s site-selection decision?
A retailer looking at traditional census-based projections would potentially decide it was not important to have a store in a particular area. But using more current information about actual households would likely lead that retailer to make a different decision. The actual population data shows a different population dispersion from the census projections. It’s critically important for retailers to be conducting this sort of analysis right now, which allows them to make better decisions than they would otherwise.
Where does this current data come from?
Experian’s INSOURCE is an up-to-the-minute accurate data source on every consumer and household in the United States. It has information about actual household behavior, compiled from 3,200 public and permission data sources 24 hours a day, seven days a week. The data sources range from publicly available sources such as directories, public record and property/realty data, to proprietary sources that are fully permissioned for use in marketing applications, such as survey data, production registration and mail-order data. Once a month, we take an extract of that database and integrate it into our various information solutions, one of which is Micromarketer Generation 3.
What is Micromarketer Generation 3?
It is a geographical analysis tool that allows retailers to visualize, interact with and make decisions off of all these large and unique data sources that bear on critical location decisions. It gives multiple views of the data that are relevant for decision-making in both the real estate environment and the marketing environment. The tool integrates geographic data, census data, household demographic information, behavioral segmentation data, as well as brand consumption—that is, what household brands each consumer household consumes, as well as what media—television, radio, newspaper, magazine—each household consumes.
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