By Jack Schember During the past 12 years I moved four times in Southern California—from Mission Viejo to Lake Forest to Long Beach to Laguna Niguel. My career also took me to new locales, from Santa Ana to Dana Point and Rancho Santa Margarita. You guessed it: I'm like millions of Americans who don't stay in one place for too long. So if you're a marketer trying to keep track of my whereabouts, best of luck! The U.S. Postal Service says 43 million of us pick up and move every year. Many of us will take a few minutes to file a change-of-address notice
To understand how pervasive an issue data quality is within a direct marketing organization, think back to the old TV commercials: "BASF—We make the things you buy better." As Trish Brothers, product marketing specialist at Firstlogic Inc., La Crosse, WI, explains, "Whether it's marketing activities, marketing results or data mining, data quality makes it better." As amazing as it may seem, the simple problem of duplicate records is still the number-one data-quality issue. "But it (a dupe) is really a symptom of how well you can match your existing data," Brothers says. For instance, she says, pointing to this writer's name as an
In a previous article, ("Segmentation Secrets," Target Marketing, Sept. 1999, pp. 58-62) we examined some of the secrets that are typically uncovered when reviewing marketing data. In this article, we will look at the secrets for the more experienced marketer.
by Christine A. Smiley Imagine as you walk by your favorite department store that you impulsively purchase this season's trendiest shoes and wear them home. But you soon realize that shoes are very uncomfortable, and they quickly find a permanent home in the spare closet. You wish you had spent more time going to different stores to compare prices, styles and overall fit. Fortunately, an investment in a pair of worthless shoes may go unnoticed. But companies looking to make a substantial investment in demographic data want to be sure that the purchase will add value to their marketing decisions over time. Even with
by Bob McKim For many years, companies like Claritas, MicroVision, Trans Union and the Polk Company have offered cluster systems, which define customer populations by demographically-defined lifestyle or lifestage descriptions. Developed using statistical analysis of census data and other sources, these systems became quickly popular and widespread in marketing research circles. Terms such as "dinks," "yuppies" and "empty-nesters" became popular marketing lingo. The problem with this method of characterizing a customer base is it ignores the fact that households are not cliches but have their own individual lifestyles. This ZIP+4 clustering, while interesting, does not allow for individual distinctions and therefore households can