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Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms

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  • YongSeog Kim

    (Business Information Systems Department, Utah State University, Logan, Utah 84322)

  • W. Nick Street

    (Management Sciences Department, University of Iowa, Iowa City, Iowa 52242)

  • Gary J. Russell

    (Marketing Department, University of Iowa, Iowa City, Iowa 52242)

  • Filippo Menczer

    (School of Informatics, Indiana University, Bloomington, Indiana 47408)

Abstract

One of the key problems in database marketing is the identification and profiling of households that are most likely to be interested in a particular product or service. Principal component analysis (PCA) of customer background information followed by logistic regression analysis of response behavior is commonly used by database marketers. In this paper, we propose a new approach that uses artificial neural networks (ANNs) guided by genetic algorithms (GAs) to target households. We show that the resulting selection rule is more accurate and more parsimonious than the PCA/logit rule when the manager has a clear decision criterion. Under vague decision criteria, the new procedure loses its advantage in interpretability, but is still more accurate than PCA/logit in targeting households.

Suggested Citation

  • YongSeog Kim & W. Nick Street & Gary J. Russell & Filippo Menczer, 2005. "Customer Targeting: A Neural Network Approach Guided by Genetic Algorithms," Management Science, INFORMS, vol. 51(2), pages 264-276, February.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:2:p:264-276
    DOI: 10.1287/mnsc.1040.0296
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    References listed on IDEAS

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