Prediction of Customer Churn Incline in Mobile Communication

In order to solve the problems of retention time delay caused by the direct prediction for users' off-grid state and insufficient information mining caused by the too rough granularity of the monthly statistical features in the past, this paper presents the approach that applies serialized data analysis method to users' records serialized with Day as its granularity. Users' data would be sampled and analyzed by using the sliding windows. Then the churn incline among the users would be predicted in advance by collecting and sorting out their antecedent behavior occurring before their churn. The related experiments were conducted with real mobile communication records of social users and reached 98.22% recall and 0.98 F1(F1-measure) when it reached its highest level. Along with comparing with the traditional modeling which takes month as the granularity for its statistical item, the timeliness and predicting competency of the method presented in this article has been verified both theoretically and by the result of the test.