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IITM Journal of Management and IT

IITM Journal of Management and IT is a Bi-Annual Research Publication of Institute of Information Technology and Management.

P-ISSN: 2349-9826 English Since 2018
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Vol. 17 No. 1 (2026)

Articles Volume 17 Issue 1 Jan - June 2026
DOI 10.65301/iitm.2026.17.1.10

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

Authors
PA
Paras Pandey
Data Engineer II, Amazon
6 Views
1 Downloads
Published 2026-07-22
Pages 161-187
Abstract

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 decision making. This contribution not only advances scholarly discourse on AI enabled 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.

Keywords
Machine Learning Customer Retention Customer Churn Prediction Churn Reduction Framework Predictive Analytics Customer Relationship Management (CRM) Data-Driven Decision Making Human-in-the-Loop Systems AI Adoption Ethical AI Data Governance Prescriptive Analytics Customer Segmentation Business Intelligence Socio-Technical Systems
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Paras Pandey . (2026). Machine Learning-Driven Framework for Enhancing Customer Retention and Reducing Churn. IITM Journal of Management and IT, 17(1), 161-187.
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P-ISSN 2349-9826
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