Abstract

Understanding the reasons behind churn can help businesses develop effective retention strategies, improve customer satisfaction, and sustain a competitive edge in a highly saturated market like the telecommunication sector. This systematic literature review uncovers the implementation of machine learning algorithms to predict customer churn rates in the telecommunications industry. The review identifies key predictive variables and methodologies that enhance churn prediction accuracy by examining a wide range of studies. The findings highlight the significant role of data integration, particularly the inclusion of real-time and external data sources, in improving model performance. Data quality and privacy issues are also discussed, emphasizing the need for ongoing methodological improvements. The study concludes with recommendations for prospective studies, including the adoption of machine learning approaches like deep learning to refine predictive capabilities further and support robust customer retention strategies in the telecommunications sector.