A Review of Customer Churn Prediction in Telecommunications and the Medical Industry Using Machine Learning Classification Models

Abstract

In today’s competitive business landscape, understanding and mitigating customer churn is paramount, particularly in telecommunications and the medical industry. This review offers a comprehensive look into the application of machine learning classification models in predicting customer churn within these two sectors. The telecommunications industry, characterised by its fast-paced environment, has adopted machine learning models, ranging from decision trees to neural networks, to retain its vast customer base. On the other hand, the medical industry emphasises the importance of patient retention for operational sustainability and ensuring consistent and continuous patient care. Through a comparative analysis, this review highlights the methodologies employed, the unique challenges each industry faces, and the effectiveness of machine learning models in addressing these challenges. Key findings suggest that while both industries employ similar foundational algorithms, the customisation and application significantly differ based on industry-specific needs. Furthermore, the review sheds light on potential areas for future research, emphasising the necessity for enhanced data privacy measures, especially in the medical sector, and the continuous evolution of machine learning models to cater to changing customer behaviours. By amalgamating insights from both sectors, this review provides a holistic understanding of the current landscape of churn prediction and sets the stage for future innovations in the domain.