Direct marketing performance modeling using genetic algorithms

Data analysts in direct marketing seek models to identify the most promising individuals to mail to and thus maximize returns from solicitations. A variety of criteria can be used to assess model performance, including response to or revenue generated form earlier solicitations. Given budgetary limitations, typically a fraction of the total customer database is selected for mailing. This depth-of-file that is to be mailed to provides potentially useful information that should be considered in model determination. This article presents a genetic algorithm-based approach for obtaining models in explicit consideration of this mailing depth. Issues related to overfitting, common in application of machine learning techniques, are examined, and experiments are based on a real-life data set.