Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study

In this paper a comparative study is presented on dynamic prediction of customer profitability over time. Customer profitability is measured by Re-cency, Frequency, and Monetary (RFM) model. A real transactional data set collected from a UK-based retail is examined for the analysis, and a monthly RFM time series for each customer of the business has been generated accord-ingly. At each time point, the customers can be segmented by using k-means clustering into high, medium, or low groups based on their RFM values. 12 dif-ferent models have been utilized to predict how a customer’s membership in terms of profitability group could evolve over time, including regression, multi-layer perception, and Naïve Bayesian models in open-loop and closed-loop modes. The experimental results have demonstrated a good, consistent and in-terpretable predictability of the RFM time series of interest.