The Bayesian Evidence Framework for Database Marketing Modeling using both RFM and Non-RFM Predictors

摘要

We focus on purchase incidence modeling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted. The evidence framework of MacKay is used as an example implementation of Bayesian neural network learning, a method that is fairly robust with respect to problems typically encountered when implementing neural networks. The basic response models use operationalizations of the traditionally discussed Recency, Frequency and Monetary (RFM) predictor categories. In a second experiment, the RFM response framework is enriched by the inclusion of other (non-RFM) customer profiling predictors. We contribute to the literature by providing experimental evidence that: (1) Bayesian learning neural networks offer a viable alternative for purchase incidence modeling; (2) the inclusion of non-RFM variables allows to significantly augment the predictive power of the constructed RFM classifiers.