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Predicting customer profitability over time based on RFM time series

Published Online:pp 1-18

Predicting consumer profitability dynamically over time plays a vital role in today's customer-centric business. In this paper, we adopt a dynamic systems approach to address the dynamic prediction problem of customer profitability. Based on customer transaction records, RFM score-based time series are generated using cluster analysis. These time series are used to measure and describe customer profitability. Furthermore, multilayer feed-forward neural network models are trained to capture the dynamics of the evolving customer profitability. A set of real transactions from a UK-based online retailer is used in this study. Relevant experimental results have shown good performance of the proposed approach.


dynamic prediction, consumer profitability, RFM-based customer segmentation, recency frequency monetary model, temporal data mining, k-means clustering, multilayer feed-forward neural networks, dynamic forecasting, cluster analysis, UK, United Kingdom, online retailers, e-tailing, electronic retailing