DM Stat-1 Articles
Link to Home

Link to Articles

Link to Consulting

Link to Seminar

Link to Stat-Chat

Link to Software

Link to Clients

A Genetic Logistic Regression Model:
A Model-free Approach to Identifying Responders to a CRM Solicitation

Bruce Ratner, Ph.D.

Logistic regression is a popular technique for classifying individuals into two mutually exclusive and exhaustive categories, for example: buy-not buy or responder-non-responder. It is the workhorse of response modeling as its results are considered the gold standard. Moreover, it is used as the benchmark for assessing the superiority of newer techniques, such as a Genetic Logistic Regression Model, also known as the GenIQ Model. In database marketing, response to a prior solicitation is the binary class variable (defined by responder and non-responder), and the logistic regression model is built to classify an individual as either most likely or least likely to respond to a future solicitation. The purpose of this article is to present the Genetic Logistic Regression Model as an assumption-free, nonparametric methodology, i.e., model-free, based on Darwin's Principle of Survival of the Fittest, and natural genetic operations - namely, genetic programming. The genetic-based logistic regression offers a clear advantage over the statistical logistic regression method, whose performance is dependent on theoretical assumptions and data restrictions. The Genetic Logistic Regression determines the best set of predictors based on a simultaneous and virtually unbiased assessment of all variables, an achievement not possible with current statistical logistic regression.

Go Back to Article Market Segment Classification Modeling with Machine Learning

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