Enhancing Customer Satisfaction in the Hospitality Industry: A Machine Learning Approach to Analyzing Customer Complaints in Seoul's Hotels

This study employs machine learning algorithms to explore the determinants of customer dissatisfaction in the hotel sector, focusing on the Seoul hospitality market. Utilizing a data- set comprising 800 online complaint reviews across 30 distinct hotel categories, we conducted a comprehensive analysis to identify the primary drivers of customer complaints. Our findings indicate that service-related issues are predominant, with a significant impact on luxury hotel ratings due to perceived service quality gaps. Additionally, hardware concerns are a secondary factor, highlighting the importance of infrastructure and amenities to customer satisfaction. We also observed that anomalies in service delivery and staff attitudes significantly influence overall satisfaction levels. Leveraging natural language processing techniques for text classification, we categorized complaints into actionable insights, enabling targeted recommendations for hotel management. These include strategies to enhance service quality, upgrade hardware facilities, streamline additional charges and disposable item services, and emphasize employee training in service excellence and cross-cultural communication skills. The proposed interventions aim to bolster the competitiveness of South Korean hotels, cater to the evolving needs of a global clientele, and foster sustainable growth within the industry. Our approach demonstrates the utility of data-driven methodologies in optimizing customer satisfaction and operational efficiency in the hospitality sector.