NEW YORK — Mom and pop never had issues like these to deal with.
This story first appeared in the April 10, 2003 issue of WWD. Subscribe Today.
Once upon a time, local retailers opened stores downtown and served their communities, of which they had an intimate knowledge. But in an era of national and international chains, malls and megamalls, retailers want to know as much as possible about an area before they take the plunge of opening a unit. For example, are area residents affluent? Do they have a propensity for spending money on apparel and accessories? Is there enough population density to support a new store?
Aiding retailers in their search for nirvanas populated with acquisitive consumers are predictive analytics demographers. Market research firms such as Claritas, MediaMark Research and MapInfo take population and income information from the U.S. Census Bureau and layer on data about the behaviors and characteristics of a population. Using sophisticated computer programs, they can identify and define potential markets and untapped areas within existing markets for retail clients.
“Retailers license a lot of our information to pinpoint opportunities by total market or within a market,” said Tom Spencer, vice president and senior practice leader at Claritas Inc. “We help them understand the potential based on the demographic makeup of the market.”
For the WWDLIST, Claritas ranked the top 15 metropolitan statistical areas in the U.S. in terms of wealth per capita. The company used behavioral data to determine the likelihood of residents to shop at 10 stores: Sears, Eddie Bauer, Gap, J.C. Penney, Kmart, Limited, Nordstrom, Target, T.J. Maxx and Wal-Mart. The number next to the store name indicates the likelihood to shop, where a score of 100 equals average.
In 12 out of 15 MSAs, Nordstrom received the highest score. This may not be too surprising, since the MSAs on the list are affluent, with 2002 per capita wealth of between $92,791 and $120,496. Spencer called the potential to shop at Nordstrom based on the demographic makeup of the markets “strong.” (Some MSAs may already have a Nordstrom or other store used by Claritas in its study.)
Wealth per capita for Wal-Mart’s top-scoring MSAs, which include Johnstown, Pa.; Danville, Va.; Cumberland, Md., and Jamestown, N.Y., was considerably lower in 2002 — around $59,000. Wal-Mart scored between 119 and 123 in the above four MSAs. Conversely, Nordstrom scored between 34 and 40.
Wal-Mart also scored high among the affluent MSAs. By now, it’s been well established that wealthy consumers cross-shop at mass merchants. Target seemed to be the mass retailer of choice for the 15 MSAs with the highest per capita wealth, followed by Wal-Mart.
“Wal-Mart tends to appeal to everyone,” said Spencer. “A lot of different households shop at Wal-Mart.”
MapInfo, which recently updated its PSYTE U.S. program, found that the wealthy seem to have some common characteristics. For example, they tend to peruse Forbes, Gourmet and Golf magazines, fly business class, listen to classical music, have country club memberships, contribute to PBS, have a home equity line of credit, visit dermatologists and shop at Bloomingdale’s and Macy’s.
PSYTE uses a combination of location-enhanced lifestyle and consumer demographics and clustering techniques to help retailers make decisions about store placement, product potential and target marketing. It will be available this summer.
“It allows for precise and profitable real estate site selection and brings target marketing to new levels of accuracy and performance,” said Jon Winslow, market director of predictive analytics at MapInfo.
For now, retailers are using predictive analytics demography to locate potential new markets and reduce risk.
“They say, ‘Based on the way we’ve done business in the past, where can we replicate that success in the future?’” said Spencer, noting that retail executives aren’t a particularly adventurous breed.
“The reason why there aren’t more retailers going into minority areas is because they basically have no experience there, so they don’t know how well they could do,” he added. “It’s not that the households don’t have the disposable income.”
Spencer would like to see retailers use the data to develop “stores of the community” that address the needs of local residents, through specific clothing or groceries.
“That’s one of the tricks of using this information,” he said.