Exploring Customer Experience in Cafes: Insights from Big Data Analysis

Abstract

This study builds upon existing research and theory related to cafe customer experience and proposes a comprehensive theoretical framework for understanding customer experience. To achieve this, 17,553 valid online reviews about Starbucks in the Seoul area were collected using a big data approach from Google Maps. The collected data was analyzed using the Textom tool, and TF-IDF and N-Gram frequency analysis were performed on the text. The results reveal that customer experience in a cafe is primarily influenced by three main factors: ambience, service, and product. Among these factors, the internal environment was deemed the most mentioned, and six subordinate attributes were identified, including accessibility, convenience, seating comfort, aesthetics, air conditioning, and cleanliness. The findings offer important insights for cafe operators who can leverage these factors to more effectively design and run their businesses. Overall, this study contributes to the growing body of research on customer experience in cafes, providing a theoretical framework for future research in this area. Furthermore, the study demonstrates the value of big data analytics in understanding complex phenomena such as customer experience, underscoring the potential of this approach for further research in this domain.