MACHINE LEARNING APPROACHES TO PREDICTING AND IMPROVING CUSTOMER SERVICE QUALITY THROUGH TARGETED EDUCATIONAL INTERVENTIONS

Annotation This research investigates applying advanced machine learning methodologies for predicting and optimizing employee performance and customer service quality through targeted educational interventions. The study introduces a comprehensive model integrating multidimensional data sources using sophisticated ensemble machine learning techniques to enhance organizational performance through data-driven personnel development strategies. Scientific Contribution The research provides a groundbreaking interdisciplinary approach at the intersection of machine learning, human resource management, and service quality optimization. The study addresses critical limitations in traditional employee training and performance assessment approaches by developing a methodologically robust framework for personalized educational interventions. Methodology The research analyzed over 50 performance-related variables across five hospitality organizations using advanced machine learning algorithms, including Random Forest, XGBoost, and BERT. The comprehensive study employed systematic bibliometric analysis, empirical research, and rigorous statistical validation, achieving up to 92% predictive accuracy and demonstrating significant improvements in organizational performance. Key Outcomes The research revealed remarkable improvements: 25-30% enhancement in customer interaction effectiveness 40-45% reduction in new employee adaptation time 30-35% improvement in personnel decision-making accuracy The study offers a scalable approach to personalized educational interventions, integrating theoretical frameworks from service-dominant logic, adaptive learning theory, and emotional intelligence assessment models. Theoretical and Practical Significance By bridging technological innovation with human capital development, the research provides a comprehensive framework for data-driven personnel development, offering critical insights for organizations seeking to optimize employee performance and customer service quality through intelligent, personalized learning approaches.