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Market Segment Classification Modeling with Machine Learning 
Bruce Ratner, Ph.D.

Multinomial Logistic Regression (MLR) analysis is a recognized technique for classifying individuals into three or more groups. The purpose of this article is to present a machine learning approach using genetic programming as a multi-group classification technique. I start the discussion by defining the (two-group) genetic logistic regression model. After introducing necessary notation for expanding the genetic logistic regression model, I define the genetic MLR (g-MLR) model. For readers uncomfortable with such notation, the g-MLR model provides several equations for classifying individuals into one of many groups. The number of genetic equations is one less than the number of groups. Each equation looks like the genetic logistic regression model.

After reviewing the estimation and modeling processes of g-MLR, I discuss the advantages and weaknesses of the two classification models. Then, I illustrate g-MLR using a cellular phone market segmentation study to build a market segment classification model as part of a customer relationship management (CRM).

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