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

Market Segment Classification Modeling with Logistic Regression and CHAID
Bruce Ratner, Ph.D.
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Logistic regression analysis is a recognized technique for classifying individuals into two groups. Perhaps less known but equally important, polychotomous logistic regression (PLR) analysis is another method for performing classification. The purpose of this article is to present PLR analysis as a multi-group classification technique. I illustrate the technique using a cellular phone market segmentation study to build a market segment classification model as part of a customer relationship management strategy better known as CRM.

I start the discussion by defining the typical two-group (or binary) logistic regression model. After introducing necessary notation for expanding the binary logistic regression model, I define the PLR model. For readers uncomfortable with such notation, the PLR model provides several equations for classifying individuals into one of many groups. The number of equations is one less than the number of groups. Each equation looks like the binary logistic regression model.

After a brief review of the estimation and modeling processes used in polychotomous logistic regression, I illustrate PLR analysis as a multi-group classification technique along with CHAID in a case study based on a survey of cellular phone users. The survey data was used initially to segment the cellular phone market into four groups. I use PLR analysis to build a model for classifying cellular users into one of the four groups.

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