Churn Prediction Framework for Telco Using Machine Learning

Abstract

"The telecommunication industry faces a significant challenge in retaining customers due to the increasing competition and customer choice. This research addresses this issue by developing an innovative hybrid machine learning model for predicting customer churn, specifically personalized to the Sri Lankan telecommunication sector. The model integrates synthetic data generation using Conditional Generative Adversarial Networks (CTGAN) to tackle the common problem of class imbalance which often undermines the accuracy of churn predictions. By combining advanced machine learning algorithms such as XGBoost and Random Forest with customer segmentation techniques, the research provides a comprehensive solution that improves predictive accuracy and offers actionable insights for customer retention strategies. The model’s performance was evaluated using real-world data, demonstrating significant improvements in accuracy and precision compared to traditional churn prediction approaches.

Despite its success, the research identified several limitations, including model complexity, high computational requirements, and limited applicability to real-time data scenarios. To overcome these challenges, future work will focus on optimizing model efficiency, expanding datasets to include diverse customer bases, and incorporating real-time data processing capabilities. These enhancements aim to further improve the model's scalability, adaptability, and relevance across various telecommunication markets. The findings of this research contribute to the ongoing development of more robust and effective churn prediction models, providing valuable insights for telecom operators seeking to reduce churn rates and enhance customer loyalty."