Customer churn prediction by hybrid model

Authors:
Jae Sik Lee;Jin Chun Lee
Affiliations:
Dept. of Business Administration, Graduate School, Ajou University, Suwon, Korea;Dept. of Business Administration, Graduate School, Ajou University, Suwon, Korea
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

In order to improve the performance of a data mining model, many researchers have employed a hybrid model approach in solving a problem. There are two types of approach to build a hybrid model, i.e., the whole data approach and the segmented data approach. In this research, we present a new structure of the latter type of hybrid model, which we shall call SePI. In the SePI, input data is segmented using the performance information of the models tried in the training phase. We applied the SePI to a real customer churn problem of a Korean company that provides streaming digital music services through Internet. The result shows that the SePI outperformed any model that employed only one data mining technique such as artificial neural network, decision tree and logistic regression.