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: Mon, 14-Mar-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Forecasting and Marketing Intelligence (IJBFMI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com