Predicting customer profitability over time based on RFM time series
by Daqing Chen; Kun Guo; George Ubakanma
International Journal of Business Forecasting and Marketing Intelligence (IJBFMI), Vol. 2, No. 1, 2015

Abstract: 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.

Online publication date: Tue, 15-Mar-2016

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