document: Turning telecommunications call details to churn prediction : a data mining approach As deregulation , new technologies , and new competitors open up the mobile telecommunications industry , churn prediction and management has become of great concern to mobile service providers . A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts . In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated , we propose , design , and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details . This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period . In addition , the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners . The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction . Furthermore , the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction . Using a previous demographics-based churn-prediction system as a reference , the lift factors attained by our proposed technique appear largely satisfactory
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