AI-Powered Mobile Feedback Systems for Real-Time Service Quality Improvement in Hotels

Abstract

The explosion in electronically submitted guest feedback has created new opportunities for managing hotel service quality. With online review platforms, smartphone apps, and messaging applications now generating unprecedented quantities of unstructured textual guest feedback about hotels, manually monitoring and responding to online feedback at scale is not feasible. Forward-thinking hotel operators are instead implementing artificial intelligence (AI)-enabled guest feedback platforms to inform operations in real time. This paper reports a review of academic literature, industry articles, and hotel case studies describing the use of AI to analyze guest feedback within hotels. Thematic analysis was conducted to identify AI techniques that are commonly used to analyze guest feedback, implementation themes, and reported results. Results show that AI is most used in feedback systems to perform sentiment analysis, aspect-based sentiment analysis, topic extraction, or automated/ semi-automated responses. Reported uses include enabling faster service failure detection, targeted service recovery, and increased responsiveness with real-time and mobile feedback channels. Articles also discussed key contextual factors that affect implementation success, such as language, cultural variations in guest expression, and the receiving hotel’s preparedness. This paper contributes to the hospitality literature by centring conversations about AI use in guest feedback analysis on management and operational issues rather than algorithm accuracy. Managers can use these findings to understand how other organizations have leveraged AI-enabled guest feedback systems as decision-support technology rather than automation technology. These findings support the design of mobile feedback systems as operational decision-support tools rather than automation replacements.