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

By Byrne, Thomas C. | ABA Banking Journal, November 1997 | Go to article overview

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


Byrne, Thomas C., ABA Banking Journal


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. …

The rest of this article is only available to active members of Questia

Sign up now for a free, 1-day trial and receive full access to:

  • Questia's entire collection
  • Automatic bibliography creation
  • More helpful research tools like notes, citations, and highlights
  • Ad-free environment

Already a member? Log in now.

Notes for this article

Add a new note
If you are trying to select text to create highlights or citations, remember that you must now click or tap on the first word, and then click or tap on the last word.
One moment ...
Default project is now your active project.
Project items

Items saved from this article

This article has been saved
Highlights (0)
Some of your highlights are legacy items.

Highlights saved before July 30, 2012 will not be displayed on their respective source pages.

You can easily re-create the highlights by opening the book page or article, selecting the text, and clicking “Highlight.”

Citations (0)
Some of your citations are legacy items.

Any citation created before July 30, 2012 will labeled as a “Cited page.” New citations will be saved as cited passages, pages or articles.

We also added the ability to view new citations from your projects or the book or article where you created them.

Notes (0)
Bookmarks (0)

You have no saved items from this article

Project items include:
  • Saved book/article
  • Highlights
  • Quotes/citations
  • Notes
  • Bookmarks
Notes
Cite this article

Cited article

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

(Einhorn, 1992, p. 25)

(Einhorn 25)

1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited article

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

Settings

Typeface
Text size Smaller Larger Reset View mode
Search within

Search within this article

Look up

Look up a word

  • Dictionary
  • Thesaurus
Please submit a word or phrase above.
Print this page

Print this page

Why can't I print more than one page at a time?

Full screen

matching results for page

Cited passage

Style
Citations are available only to our active members.
Sign up now to cite pages or passages in MLA, APA and Chicago citation styles.

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn, 1992, p. 25).

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences." (Einhorn 25)

"Portraying himself as an honest, ordinary person helped Lincoln identify with his audiences."1

1. Lois J. Einhorn, Abraham Lincoln, the Orator: Penetrating the Lincoln Legend (Westport, CT: Greenwood Press, 1992), 25, http://www.questia.com/read/27419298.

Cited passage

Thanks for trying Questia!

Please continue trying out our research tools, but please note, full functionality is available only to our active members.

Your work will be lost once you leave this Web page.

For full access in an ad-free environment, sign up now for a FREE, 1-day trial.

Already a member? Log in now.