document: 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 . The models constructed are typically segmentation-based models in which the target audience is split into subpopulations ( i.e. , customer segments ) and individually tailored statistical models are then developed for each segment . Such models are commonly employed in the direct-mail industry ; however , segmentation is often performed on an ad-hoc basis without directly considering how segmentation affects the accuracy of the resulting segment models . 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 EventsTM ( IBM ATM-SETM ) 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 .
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