Utilizing Data Science and AI for Customer Churn Prediction in Marketing

Abstract

This study explores the application of data science and AI techniques in predicting customer churn within the telecommunications industry, a sector characterized by intense competition and high customer turnover rates. By analyzing historical customer data, including usage patterns and service preferences, the study aims to identify factors contributing to churn and propose targeted retention strategies to mitigate losses. Traditional classification algorithms and ensemble techniques are evaluated using the Telecom-Customer-Churn dataset, with emphasis on the underutilized Stacking ensemble method. The results demonstrate that ensemble learning algorithms, particularly the Stacking model, outperform single algorithms, with CatBoost exhibiting the highest accuracy at 0.8119, followed closely by RandomForest at 0.7902 and XGBoost at 0.7820. These findings underscore CatBoost's superior generalization capabilities, likely attributed to its adept handling of categorical features and missing values, and its ability to model complex data relationships. The study contributes to advancing understanding of ensemble models and offers valuable insights for predicting telecom customer churn, thereby aiding in the development of effective retention strategies and enhancing customer satisfaction and loyalty.