Customer churn prediction using predictive analytics in Telecommunication Market: A Review

Abstract

In the face of extreme competitive telecommunication market, the cost of acquiring new customer is much more expensive than to retain the existing customer. Therefore, it has become imperative to pay much attention towards retaining the existing customers in order to get stabilize in market comprised of vibrant service providers. In current market, a number of prevailing statistical techniques for customer churn management are replaced by more machine learning and predictive analysis techniques. This article reviews the customer churn prediction problem, factors escalating the phenomena, prediction through predictive analytics, steps for processing of predictive analytics and evaluation of performance metrics for various churn prediction models are surveyed. Moreover, the CRM data from Pakistan Telecommunication Company limited as case study to discuss the process of data mining and predictive analytics for customer churn prediction.

Predictive Analytics in Marketing: Contribution to Marketing Performance

“…In the telecommunications sector, the use of predictive analytics has significantly improved customer churn prediction, leading to enhanced predictive modeling performance (Khan, 2019). Moreover, the integration of churn prediction and customer segmentation frameworks in telco businesses has been highlighted as a comprehensive approach to customer analytics, ensuring a holistic understanding of customer behavior for targeted marketing strategies (Shu-li et al, 2021).…”