Finding the Best Variables for
Direct Marketing Models
Bruce Ratner, Ph.D.
Finding the best possible subset of variables to put in a model has been a frustrating exercise. Many methods of variable selection exist, but none of them is perfect. Furthermore, none use a criterion that addresses the specific needs of direct marketing models. I have developed a new methodology - the GenIQ Model© - that uses genetic modelling to isolate the variables. The GenIQ Model determines the best set of predictors based on a simultaneous and virtually unbiased assessment of all variables, an achievement not possible with current methods. Most significantly, genetic modelling can be used to address the specific requirements of direct marketers. Moreover, GenIQ offers exceptional predictions with minimal error variance, and a unique feature accommodating dirty and incomplete data. GenIQ can handle both classification (e.g., target yes-no response variable) and regression (e.g., target continuous sales variable) problems with categorical, ordinal and continuous candidate predictor variables. Case studies are reported showing the potential power, and future prominence of GenIQ in the data analyst's toolkit.
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