This study proposes a data-driven clustering methodology to evaluate the service performance of foodservice brand chain branches in Türkiye by analyzing customer-generated content from Google Reviews. The research identifies key performance indicators, total and recent review frequency and ratings and applies k-means clustering to segment 735 branches from five major chains into distinct performance profiles. Cross-analysis with contextual variables such as sector, location, and urbanization level provides insights into the drivers of customer satisfaction. Results reveal significant performance variations among branches, highlighting clusters of high satisfaction and clusters with persistent issues, particularly among mall-based and fast-food locations. The findings underscore the utility of online review data for performance benchmarking and strategic improvement in the foodservice sector. This clustering approach offers both researchers and practitioners a practical tool for assessing service quality and enhancing customer satisfaction.