Data Mining Techniques, vol. 114-27 (2002) p. 114–27
Conventional statistical methods (e.g. logistics regression, decision tree, and etc.) are very successful in predicting customer churn. However, these methods could hardly predict when customers will churn, or how long the customers will stay with. The goal of this study is to apply survival analysis techniques to predict customer churn by using data from a telecommunications company. This study will help telecommunications companies understand customer churn risk and customer churn hazard in a timing manner by predicting which customer will churn and when they will churn. The findings from this study are helpful for telecommunications companies to optimize their customer retention and/or treatment resources in their churn reduction efforts.
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