Knowledge discovery on customer churn prediction

Authors:
Li-Shang Yang;Chaochang Chiu
Affiliations:
Department of Business Administration, St. John's University, Tamsui, Taiwan, ROC;Department of Information Management, Yuan Z University, Chungli, Taiwan, ROC
Venue:
MATH'06 Proceedings of the 10th WSEAS International Conference on APPLIED MATHEMATICS
Year:
2006

Citing 3
Cited 0

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IEEE Transactions on Neural Networks

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Abstract

As customer churn has become a critical business issue for most mobile telecommunications providers, predictive modeling based on knowledge discovery of data mining is an approach being used to facilitate customer retention more effectively and proactively. This paper reports a preliminary study of applying data mining to solve a real-world customer attrition problem. It uses customer information to make predictions about the likelihood of churn. The target rate is very small, around 0.5% monthly churn rate. Nevertheless, by scrutinizing data to extract a large number of independent variables and by using a large training data set, the predictive model delivers excellent performance in field test. Issues in the practical application of data mining are also discussed.