Direct marketing decision support through predictive customer response modeling

Decision support techniques and models for marketing decisions are critical to retail success. Among different marketing domains, customer segmentation or profiling is recognized as an important area in research and industry practice. Various data mining techniques can be useful for efficient customer segmentation and targeted marketing. One such technique is the RFM method. Recency, frequency, and monetary methods provide a simple means to categorize retail customers. We identify two sets of data involving catalog sales and donor contributions. Variants of RFM‐based predictive models are constructed and compared to classical data mining techniques of logistic regression, decision trees, and neural networks. The spectrum of tradeoffs is analyzed. RFM methods are simpler, but less accurate. The effect of balancing cells, of the value function, and classical data mining algorithms (decision tree, logistic regression, neural networks) are also applied to the data. Both balancing expected cell densities and compressing RFM variables into a value function were found to provide models similar in accuracy to the basic RFM model, with slight improvement obtained by increasing the cutoff rate for classification. Classical data mining algorithms were found to yield better prediction, as expected, in terms of both prediction accuracy and cumulative gains. Relative tradeoffs among these data mining algorithms in the context of customer segmentation are presented. Finally we discuss practical implications based on the empirical results.