Determinants of Coffee Shop Preference Through Online Review Data Analytics

The rapid expansion of the coffee shop industry, driven by digital consumerism, necessitates a deeper understanding of the factors influencing customer preference for business sustainability. This study aims to identify the primary determinants of customer choice by analyzing online review data. Focusing on Kanoko Coffee Shop in Bandung, Indonesia as a representative Micro, Small, and Medium-sized Enterprise (MSME), this research applies a data analytics methodology to 289 user-generated reviews collected from various online platforms between 2019 and 2024. The approach integrates keyword extraction and thematic analysis with a Naive Bayes algorithm to perform sentiment classification on unstructured text data. Key findings reveal that "Comfortable & Green Atmosphere" and "Good Services" are the most significant drivers of positive customer sentiment, followed by "Strategic Location". In contrast, technical amenities such as Wi-Fi and price were less frequently highlighted, indicating a preference for experiential and environmental qualities over purely functional attributes. This study concludes that leveraging online review analytics provides actionable strategic insights for coffee shop entrepreneurs. The results underscore the importance of prioritizing ambiance and service quality to enhance customer satisfaction and ensure long-term market competitiveness, offering a data-driven model for other MSMEs in the hospitality sector.