Customer Churn Predictive Analysis in the Telecom Industry Using Random Forest Classifier

The telecom sector, which is distinguished by its vast data generation, is becoming more and more important for contemporary company operations. Identification of client loyalty and churn rates is necessary because managing and keeping consumers is a top priority for telecom companies. Predictive analytics are being used more and more in the telecom sector to manage and keep customers. In terms of predicting customer turnover, models like Random Forest, decision tree, SVM, and Logistic Regression have demonstrated encouraging accuracy rates. With the use of these techniques, telecom firms may increase retention tactics and boost overall business performance while actively managing customer attrition. Using data from an Iranian telecom company, the Random Forest classifier was used to predict customer attrition in the telecom sector. The findings demonstrated that the Random Forest classifier performed better in predicting customer attrition than other methods. The Random Forest classifier is a valuable tool in identifying and retaining loyal customers, contributing to ongoing efforts to enhance customer relationship management and market strategies in the telecom sector.