Data defines the model by dint of genetic programming, producing the best decile table.

Maximizing the Lift in Database Marketing
Bruce Ratner, Ph.D.

Database marketers use predictive models to identify the top 5% - 30% of their most-likely-to-respond customers for a marketing campaign, as contacting all customers is either too expensive, rr not practical. The standard statistical models used for finding the “top X%” customers are: ordinary regression for a continuous response variable, and logistic regression for a binary response variable. However, these models do not explicitly maximize the lift – the measure indicating the at-hand model’s percentage gain of identifying the top X% customers over the chance model (i.e., the random selection of X% of the customers from the marketer’s database). In effect, the statistical models are not optimal in maximizing lift. The statistical models work well in practice, but a model that explicitly maximizes lift should outperform them. The purpose of this article is to discuss and illustrate the predictive power the GenIQ Model©, as it explicitly maximizes lift, for any top X% depth-of-file.

For more information about this article, call me at 516.791.3544, or e-mail,
My publisher owns the copyright of the article, about which this abstract addresses. The article will appear in my forthcoming book.
My publisher has granted me permission to discuss orally the article's content, but by no means provide an outline, draft or proof-ready of the article.

Sign-up for a free GenIQ webcast: Click here.