An enhanced ensemble classifier for telecom churn prediction using cost based uplift modelling

Abstract

Telecom, being a dynamic and competitive industry which contains an inherently high potential for customer churn, necessitating of accurate churn prediction models. Regular classification approaches fail to effectively predict churn due to low correlation levels between conventional performance metrics and business goals. This work presents an ensemble stacking incorporated with uplifting-based strategies for telecom churn prediction model. Evaluations have been performed based on conventional performance and a cost heuristic, with a major focus upon the cost heuristic. This mode of operation exhibits a high correlation levels between performance indicators and business goals, thus enabling the algorithm suitable for most cost-sensitive applications. A heterogeneous ensemble is created by using multiple algorithms to provide first level predictions. Those predictions with discrepancies are processed at the secondary level using a heuristic based combiner to provide the final predictions. Combination heuristics are fine-tuned based on the cost to predict more accurately concentrating on business goals. Subsequently, Customer uplifting is performed on final predictions, thus making the proposed model 50% more cost efficient than the state-of-the-art ensemble models.