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Churn prevention is a necessary business activity to make informed actions towards customers that are likely to get churn in near time period. Identifying unhappy customers early on gives you a chance to offer them incentives and retain them.
The high level interpretation of churn management can be understood broadly under primary and secondary goals. The primary objective is to identify voluntary churn based on historical data mining. Secondary objective is to create churn profile around every customer highlighting other parameters like life-time value, key reasons for possible churn etc.
Machine learning algorithms have been extremely helpful when implemented on historical data to mine churn support profile of each individual churn prone case and grading a risk score. Furthermore, trends at parametric level for individual clients highlight major churn reason.
Customer behavior patterns before their churn period help to identify characters of churners compared to non churners. Ensemble methods have been very successful to grade risk score and build a full scale churn profile around every case.