Customer Churn Prediction in the Telecommunication Industry Over the Last Decade: A Systematic Review

Abstract

Aims: This study explores the application of machine learning algorithms in predicting customer churn within the telecommunications sector. By analyzing various predictive models, the study identifies key factors influencing churn and assesses how data integration enhances predictive accuracy. Study Design: A systematic literature review was conducted to evaluate existing research on churn prediction models and their effectiveness in the telecommunications industry. Place and Duration of Study: The study reviews published research from various academic and industry sources over the past decade, focusing on global trends in customer churn prediction. Methodology: Relevant studies were systematically selected and analyzed based on predefined inclusion criteria. The review examined different machine learning techniques, predictive variables, and data sources used to improve churn prediction accuracy. Special attention was given to real-time data integration and the impact of external datasets on model performance. Results: Findings indicate that data integration, particularly real-time and external data sources, significantly enhances churn prediction accuracy. Machine learning techniques, including traditional models and emerging deep learning approaches, show promising results in improving customer retention strategies. However, challenges such as data privacy concerns and the need for methodological advancements remain. The study recommends further exploration of deep learning models to refine predictive capabilities and support robust retention strategies in the telecommunications sector.