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 examines the evolution, strengths, and limitations of conventional ML techniques—such as Decision Trees, Random Forests, and boosting algorithms—and advanced DL methods, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, Transformers, and hybrid models. Key emerging trends identified are the increasing adoption of ensemble models, profit-driven frameworks, sophisticated DL architectures, and attention-based mechanisms, coupled with a stronger emphasis on Explainable AI (XAI) and adaptive learning strategies. Despite significant progress, the review highlights persistent challenges like class imbalance, interpretability issues associated with DL's black-box nature, and difficulties addressing concept drift in dynamic customer behaviors. This study categorizes predominant methodologies, compares model performances, and identifies critical gaps such as limited consideration of real-world deployment constraints and business-oriented metrics. By addressing these gaps, the review provides actionable insights to develop robust, interpretable, economically beneficial churn prediction models, emphasizing alignment with business goals and guiding future research toward improved accuracy, adaptability, and practical deployment.
