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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.
Key words: direct marketing; Bayesian networks; evolutionary programming; machine learning; data mining
History: Accepted by Jagmohan S. Raju, marketing; received May 12, 2004. This paper was with the authors 6 months for 4 revisions.
1. Introduction
Machine learning is an innovative method that can potentially improve forecasting models and assist management decision making. Direct marketing, which relies on building accurate predictive models from databases, is one of the areas that can benefit from such applications. As more companies adopt direct marketing as a distribution strategy, spending in this channel has grown in recent years, making consumer response modeling a top priority for direct marketers to increase sales, reduce costs, and improve profitability. In addition to the conventional statistical approach to forecasting consumer purchases, researchers have recently applied machine learning methods, which have several distinctive advantages for data mining with large noisy databases. In this study, we adopt an innovative machine learning method-Bayesian networks (BNs) learned by evolutionary programming (EP)-to model responses to direct marketing. We compare the results of BNs with other benchmark methods, including neural networks, classification and regression tree (CART), and latent class regression, in a tenfold cross-validation with a large data set. The results suggest that BNs have distinctive advantages, including accurate prediction, transparent procedures, interprétable results, and greater explanatory power.
1.1. The Statistical Methods
Because of budget constraints, most...