Churn Prediction and Prevention for OTT/Tele- Communication

Abstract

Customers are becoming more drawn to the quality of service (QoS) offered by businesses in the present. However, the present day shows greater rivalry in offering clients technologically cutting-edge QoS. However, effective customer relationship management systems may help the organization attract new clients, preserve client connections, and enhance client retention by generating more revenue for the company's operations. Churners have always been a major problem for every business that offers services. Churning drives up a company's expenses while also lowering its profit margin. However, it is possible to forecast if a consumer wishes to cancel service using predictive analysis based on historical service usage, service performance, expenditure, and other behavioral patterns. The problem with churn analysis is that it can reveal unnecessary information when used on databases that are combined by a company that owns confidential information. Keywords—customer relationship management, customer retention, machine learning, churn analysis