Ensemble Learning for Churn Analysis: A Comprehensive Evaluation of Methods

In the telecom industry, customer retention is crucial for sustainable growth and profitability. Additionally, a stable customer base ensures a steady business flow and reduces the churn rate. However, the telecom industry is particularly prone to churn, making it essential to implement effective strategies to retain customers and increase loyalty. We conducted an in-depth analysis of 1 lakh prepaid customers’ data, consisting of 226 features over a four-month period to address the challenge of churn in the Telecom industry. Our study yielded valuable insights into the factors that impact churn, as well as accurate predictions about the number of customers likely to churn. We utilized a range of supervised machine learning algorithms, including Logistic Regression, Random Forest, SVM, and XGBoost with the latter achieving the highest accuracy of 92.7%. Additionally, we performed hyperparameter tuning with XGBoost, which resulted in an increase in accuracy to 94.19%. Overall, our study presents a comprehensive evaluation of churn prediction in the Telecom industry, with practical implications for enhancing customer retention.