Segmentation-Based Modeling for Advanced Targeted Marketing

Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior, and it constantly strives to improve its decision-making processes and tools. Fingerhut has found that predictive models can be much more effective when the target audience is split into subpopulations (i.e., customer segments) and individually tailored predictive models are developed for each segment. Historically, Fingerhut BI has used decision trees or simply domain expertise for creating customer segments. Even though these approaches work well, they are “sub-optimal” because effectiveness (i.e., predictive strength) of the segment models is not considered when defining the segments. Given their mailing volumes, Fingerhut is sensitive to the fact that increasing the predictive power of their models means millions of dollars in new revenue. Fingerhut BI approached IBM Research with the problem of how to build segmentation-based models more effectively so as to maximize predictive accuracy. The IBM Advanced Targeted Marketing – Single EventsÔ (IBM ATM-SEÔ) solution is the result of IBM Research and Fingerhut BI directing their efforts jointly towards solving this problem. This paper presents an evaluation of ATM-SE’s modeling capabilities using data from Fingerhut’s catalog mailings.