Predicting Inactiveness in Telecom (Prepaid) Sector: A Complex Bigdata Application

A business company especially into telecom operation, suffers from high acquisition cost on new customer rather retaining the in-house customers. As a consequence larger business groups are now spending on retaining those customer who are at the verge of moving out of the service. Even retention activity also accounts for larger portion of the expenditure. In response to these issue, this paper oriented towards finding the ways and means to deriving higher accuracy model along with precision and recall measure of actual inactivity individuals with help of derived KPI's (feature engineering). Various Churn model techniques have been evolved in recent past for the above requirements. The focus of this paper is to manifesting new techniques on feature deriving to unearth hidden pattern on customer behavior, which in-turn helps to determine the Inactive/Churn customer at the higher precision rate.