A rule-based method for customer churn prediction in telecommunication services

Authors:
Ying Huang;Bingquan Huang;M.-T. Kechadi
Affiliations:
School of Computer Science and Informatics, University College Dublin, Belfield Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield Dublin, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield Dublin, Ireland
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Year:
2011

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Abstract

Rule-based classification methods, which provide the interpretation of a classification, are very useful in churn prediction. However, most of the rule-based methods are not able to provide the prediction probability which is helpful for evaluating customers. This paper proposes a rule induction based classification algorithm, called CRL. CRL applies several heuristic methods to learn a set of rules, and then uses them to predict the customer potential behaviours. The experiments were carried out to evaluate the proposed method, based on 4 datasets of University of California, Irvine(UCI) and one dataset of telecoms. The experimental results show that CRL can achieve high classification accuracy and outperforms the existing rule-based methods in churn prediction.