Direct Marketing Performance Modeling Using Genetic Algorithms

Authors:
Siddhartha Bhattacharyya
Affiliations:
-
Venue:
INFORMS Journal on Computing
Year:
1999

Citing 0
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Abstract

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 criterion can be used to assess model performance, including response to or revenue generated from 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.