Title: Predicting customer profitability over time based on RFM time series

Authors: Daqing Chen; Kun Guo; George Ubakanma

Addresses: Department of Informatics, Faculty of Business, London South Bank University, London, UK ' Department of Urban Engineering, Faculty of Engineering, Science, and the Built Environment, London South Bank University, London, UK ' Department of Informatics, Faculty of Business, London South Bank University, London, UK

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.

Keywords: 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.

DOI: 10.1504/IJBFMI.2015.075325

International Journal of Business Forecasting and Marketing Intelligence, 2015 Vol.2 No.1, pp.1 - 18

Available online: 14 Mar 2016 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article