Machine learning for direct marketing response models : Bayesian networks with evolutionary programming
Document Type
Journal article
Source Publication
Management Science
Publication Date
4-1-2006
Volume
52
Issue
4
First Page
597
Last Page
612
Keywords
Bayesian networks, Data mining, Direct marketing, Evolutionary programming, Machine learning
Abstract
Machine learning methods are powerful tools for data mining with large noisy databases and give researchers the opportunity to gain new insights into consumer behavior and to improve the performance of marketing operations. To model consumer responses to direct marketing, this study proposes Bayesian networks learned by evolutionary programming. Using a large direct marketing data set, we tested the endogeneity bias in the recency, frequency, monetary value (RFM) variables using the control function approach; compared the results of Bayesian networks with those of neural networks, classification and regression tree (CART), and latent class regression; and applied a tenfold cross-validation. The results suggest that Bayesian networks have distinct advantages over the other methods in accuracy of prediction, transparency of procedures, interpretability of results, and explanatory insight. Our findings lend strong support to Bayesian networks as a robust tool for modeling consumer response and other marketing problems and for assisting management decision making.
DOI
10.1287/mnsc.1060.0514
Print ISSN
00251909
E-ISSN
15265501
Publisher Statement
Copyright © INFORMS
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Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Cui, G., Wong, M. L., & Lui, H.-K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597-612. doi: 10.1287/mnsc.1060.0514