Customer Churn Prediction: A Review of Recent Advances, Trends, and Challenges in Conventional Machine Learning and Deep Learning

Source abstract

Customer churn poses a significant challenge across various sectors, resulting in considerable revenue losses and increased customer acquisition costs. Machine Learning (ML) and Deep Learning (DL) have emerged as transformative approaches in churn prediction, significantly outperforming traditional statistical methods by effectively analyzing high-dimensional and dynamic customer datasets. This literature review systematically examines recent advancements in churn prediction methodologies based on 240 peer-reviewed studies published between 2020 and 2024 across diverse domains such as telecommunications, retail, banking, healthcare, education, and insurance. It emphasizes the evolution of ML and DL approaches, their practical applications, and ongoing challenges, including model interpretability, class imbalance, and concept drift. The study identifies an increasing preference for advanced techniques, including ensemble models, profit-driven frameworks, hybrid architectures, and sophisticated DL methods like convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention mechanisms. It highlights the growing focus on explainable AI (XAI), profit-oriented modeling, and adaptive learning strategies to accommodate evolving customer behaviors. Despite these advancements, the review underscores persistent challenges such as class imbalance, the black-box nature of complex DL models, difficulties adapting to concept drift, and limited consideration of real-world deployment constraints. This review contributes to the field by comprehensively synthesizing recent methodological trends and identifying gaps related to real-world applicability, interpretability, and business-oriented evaluation metrics. It offers essential insights and practical guidance for data scientists, researchers, and industry practitioners seeking to develop more accurate, robust, and interpretable churn prediction models, enabling more effective customer retention strategies and improved business outcomes.