Marketers are increasingly combining several variables in an effort to identify smaller, better defined target groups. Thus, a bank may not only identify a group of wealthy retired adults, but within that group may distinguish several segments depending on current income, assets, savings, and risk preferences.
One of the most promising developments in multi-attribute segmentation is geoclustering, which yields richer descriptions of consumers and neighborhoods than does traditional demographics. Geoclustering can help a firm answer such questions as: Which clusters (neighborhoods or zip codes) contain our most valuable customers? How deeply have we already penetrated these segments? Which markets provide the best opportunities for growth?
Claritas Inc. has developed a geoclustering approach called PRIZM (Potential Rating Index by Zip Markets), classifying over half a million U.S. residential neighborhoods into 62 lifestyle groupings called PRIZM Clusters.24 The groupings take into consideration 39 factors in five broad categories: (1) education and affluence, (2) family life cycle, (3) urbanization, (4) race and ethnicity, and (5) mobility, and cover specific geographic areas defined by Zip code, Zip + 4, census tract, and block group.
Each cluster has a descriptive title, such as American Dreams and Rural Industria. Within each cluster, members tend to lead similar lives, drive similar cars, have similar jobs, and read similar magazines. The American Dreams cluster, for example, is upscale and ethnic—a big-city mosaic of people likely to buy imported cars, Elle magazine, Mueslix cereal, tennis weekends, and designer jeans. In contrast, Rural Industria contains young families in heartland offices and factories whose lifestyle is typified by trucks, True Story magazine, Shake n' Bake, fishing trips, and tropical fish.25
Geoclustering is an especially valuable segmentation tool because it captures the increasing diversity of the American population. Moreover, it can help even smaller firms identify microsegments that are economically feasible because of lower database costs, more sophisticated software, increased data integration, and wider use of the Internet.26
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