Privacy-Preserving Based Technique for Customer Churn Prediction in Telecom Industry

In recent years, customer churn has been one of the most prominent topics, especially in the telecom industry. The telecommunications industry is producing massive amounts of data every minute. Thus, the telecom industry is seeking more ways to analyze and predict their potential and churn customers. According to telecom analysis, acquiring a new customer is costlier than keeping a current one. To lessen customer churn, it is very compulsory for industries to detect an increase in customer churn factors. The number of service suppliers is increasing daily, especially in the telecom industry. Phishing attacks and fraud are crucial points in customer churn. The aim of this study is to predict customer churn with profitable churn models for retention campaigns to satisfy the business requirement of profit maximization. The proposed research used the BAT-ANN classification model with the BigML dataset to predict customer churn in the telecom industry. The proposed model achieved 89.2% testing accuracy.