Applying Data Mining to Telecom Churn Management
نویسندگان
چکیده
Taiwan deregulated its wireless telecommunication services in 1997. Fierce competition followed, and churn management becomes a major focus of mobile operators to retain subscribers via satisfying their needs under resource constraints. One of the challenges is churner prediction. Through empirical evaluation, this study compares various data mining techniques that can assign a “propensity-to-churn” score periodically to each and every subscribers of a mobile operator. The results indicate that both decision tree and neural network techniques can deliver accurate churn prediction models by using customer demographic, billing information, contract/service status, call detail records, and service change log.
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