Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study

Conference paper

Chen, D., Guo, K. and Li, B. (2019). Predicting Customer Profitability Dynamically over Time: An Experimental Comparative Study. 24th Iberoamerican Congress on Pattern Recognition (CIARP 2019). Havana, Cuba 28 - 31 Oct 2019 https://doi.org/10.1007/978-3-030-33904-3_16
AuthorsChen, D., Guo, K. and Li, B.
TypeConference paper
Abstract

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.

Year2019
Digital Object Identifier (DOI)https://doi.org/10.1007/978-3-030-33904-3_16
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Print22 Oct 2019
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Accepted05 Aug 2019
Deposited15 Aug 2019
Web address (URL) of conference proceedingshttp://ciarp.uci.cu/
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