Data-Driven Customer Relationship Management and Service Quality Outcomes: A Machine Learning Perspective on Competitive Advantage in the Hotel Industry

Abstract

This study investigates machine learning (ML) integration into hotel Customer Relationship Management (CRM) systems to elevate service quality and competitive advantage in saturated hospitality markets, where predicting guest preferences drives differentiation. A dataset of 484 guest interaction logs, feedback ratings, and transaction records was analyzed using Python tools—Scikit-Learn for Random Forest and clustering algorithms, and Pandas for data processing—to compare algorithmic predictions against traditional manual segmentation. ML outperformed manual methods, generating data-driven insights that significantly reduced response times, enhanced guest loyalty, and improved service quality, establishing predictive personalization as essential for market relevance. Hotels can implement this ML pipeline—from raw data collection to proactive service enhancements—empowering managers to optimize operations, anticipate needs, and deliver superior guest experiences for sustained loyalty. The study provides empirical evidence and a practical framework for ML-driven CRM in hospitality, transforming data overload into strategic personalization amid hyper-competition.