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A New Data Mining Method for
 Identifying Extreme Response Segments
Bruce Ratner, Ph.D.

Data analysts use the decile analysis - based on the scores of the response model at hand - for creating a solicitation list of the most likely individuals to obtain an advantage over a random selection of individuals. The decile analysis involves a brute division of a database into ten equal-sized contiguous groups (deciles) without regard for the shape of the distribution of model scores. The assumption of the decile analysis - individuals within a decile have equivalent model scores, which are different from the model scores of the above-and-below neighboring deciles - is not always tenable, as the distribution of model scores is not always "smooth" but often characterized by "clumps" or "gaps". Deciles with these characteristics lodge extreme response segments, which reflect what the model is doing and how to implement the model to obtain a greater advantage over a random selection. The purpose of this article is to present a data mining method, which performs a "smart" division of a database, for identifying extreme response segments to aid in understanding what the response model is doing and how to best implement the response model.

Related Article:
1. "Dumb” Decile Analysis versus “Smart” Decile Analysis: Identifying Extreme Response Segments

For more information about this article, call Bruce Ratner at 516.791.3544,
1 800 DM STAT-1, or e-mail at