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Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study

  • Daqing ChenEmail author
  • Kun Guo
  • Bo Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11896)

Abstract

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

Keywords

Time series analysis RFM model CRM Predictive modelling 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of EngineeringLondon South Bank UniversityLondonUK
  2. 2.School of Electronics and InformationNorthwestern Polytechnical UniversityXi’anChina

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