Academic journal article ABA Banking Journal

Clusters' Last Stand: Demographic Clustering Methods Give Way to Household-Specific Demographic Targeting

Academic journal article ABA Banking Journal

Clusters' Last Stand: Demographic Clustering Methods Give Way to Household-Specific Demographic Targeting

Article excerpt

When Michael J. Weiss's seminal work, "The Clustering of America," appeared nearly ten years ago, it had a profound effect on the retail banking industry. At a time when direct marketing concepts were just beginning to filter in, his work helped shape and define the way financial institutions conducted direct marketing campaigns. Ten years later, banks are still regularly applying his principles. But are they the right principles for modern times?

Important new techniques are now emerging that promise to bring unprecedented precision to database marketing. No longer do advanced bank marketers rely on generalized demographic profiles to determine their best prospects. Nor do they market to whole zip codes at a time. Instead, using new databases and software tools, they zero in on prospects house by house, creating demographically precise profiles that exactly match characteristics of their own existing best customers.

Using these new approaches, banks are already enjoying reduced printing and mailing costs, increased response rates and higher rates of return. Longer term, they are building clearer pictures of their customers, their competitive positions and their own profitability.

Psychographic and Geographic Clustering

To understand these new techniques, we need to review the old models that still dominate the direct marketing landscape. Typically, when prospecting for new customers, bank direct marketers first determine key characteristics they believe future customers will share. They may develop profiles using marketing software, by gleaning ideas from nationally available marketing sources, or just by gut feel. Often, lifestyle factors such as the kind of car potential customers drive, what kinds of food they eat, how much they travel, etc., are key considerations. Such psychographic data elements are seen as important predictors of future buying behavior.

Next, marketers locate neighborhoods where residents match the characteristics of targeted prospects. Called geographic clustering, this technique is based on an important assumption: people of the same demographic profile share the same neighborhoods. Using traditional marketing software tools, marketers section neighborhoods down to the level of zip code or zip + 4. The software -- and the marketer -- assume that everyone living in a particular zip code (or zip + 4) share similar characteristics and, hence, similar purchasing behaviors.

With a list of zip codes in hand, they complete the process by purchasing a mailing list of all the households in the target clusters.

There are obvious limitations to this technique. Say, for example, the marketer seeks all local neighborhoods where household incomes are above $100,000. Using zip + 4 as the basic neighborhood unit, typical clustering software tools will produce a list of all neighborhoods matching the profile. But in a given zip there may be widely varying income ranges. For example, 30 percent of the households may have incomes between $50,000 and $75,000; 34 percent between $75,000 and $100,000, and 36 percent above $100,000. The software program identifies the entire zip code as a $100,000 plus neighborhood because such households are the largest category.

If the marketer now mails to all households in the zip code, 64 percent of the mailings will go to the wrong targets. That means 64 percent of the mailings were a complete waste of printing and postage.

Suppose in the same example cluster, the marketer seeks households with incomes between $50,000 and $75,000. This time the software produces zero targets because the income range is in the minority. The marketer completely misses these prospects.

Marketers often compensate for this deficiency by mailing to any cluster where a significant percentage matches the criteria, even if in the minority. But, in the example zip, it means 70 percent (34 plus 36) would get mail that, to them, is junk. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed


An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.