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This is a Literature Review study.

October 2, 2025

Machine Learning Applications in Customer Relationship Management: A Comprehensive Review

Key Points

Machine learning techniques significantly enhance customer relationship management strategies and operations.
Key application areas include churn prediction and customer lifetime value estimation, addressing business needs.
This review analyzes various machine learning methods, from classification to advanced deep learning approaches.
Emerging trends indicate a growing focus on ethical AI and model interpretability within CRM applications.

Abstract

Customer Relationship Management (CRM) systems have evolved significantly by integrating machine learning (ML) techniques, transforming how businesses understand, predict, and respond to customer behavior. This comprehensive review examines the current landscape of ML applications in CRM, analyzing key techniques, application areas, challenges, and future directions based on recent literature from 2019 to 2025. Our analysis reveals that ML techniques in CRM span from traditional classification and clustering methods to advanced deep learning, natural language processing, and reinforcement learning approaches. Key application areas include churn prediction, customer lifetime value estimation, personalization, sentiment analysis, and customer segmentation. While significant progress has been made, challenges remain in model interpretability, bias mitigation, and production scalability. This review provides insights into emerging trends such as domain-aware language models, graph neural networks, and ethical AI considerations in CRM implementations

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