Machine Learning-Driven Framework for Enhancing Customer Retention and Reducing Churn

Customer retention remains a pivotal factor for organizational sustainability, with customer churn posing significant challenges to revenue stability across industries. Despite extensive advancements in machine learning techniques for churn prediction, the practical integration of these methods into cohesive, actionable frameworks that effectively translate predictive insights into retention strategies is insufficiently addressed. This study aims to develop and validate a comprehensive machine learning-driven framework explicitly designed to enhance customer retention and reduce churn by encompassing data preprocessing, model selection, predictive analytics, and intervention planning. Employing a mixed-methods approach, the research synthesizes findings from a systematic literature review and incorporates qualitative expert interviews with practitioners in customer relationship management and machine learning to ensure the framework’s practical relevance and operational feasibility. The outcomes demonstrate the critical importance of integrating human expertise for model validation and ethical considerations, further highlighting organizational and technological factors influencing successful implementation. The resultant framework bridges theoretical machine learning advancements and pragmatic retention applications by providing a robust, modular system that supports real-world decisionmaking. This contribution not only advances scholarly discourse on AIenabled customer lifecycle management but also offers actionable guidance for businesses aiming to leverage data-driven strategies to mitigate churn and foster long-term customer loyalty.