Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews

Analyzing data from user-generated websites with data mining practices can suggest meaningful insights into services performances and help identifying the precursors of service satisfiers and dis-satisfiers of restaurants.

Abstract

Analyzing data from user-generated websites with data mining practices can suggest meaningful insights into services performances. Such techniques may help identifying the precursors of service satisfiers and dis-satisfiers of restaurants. The aim of this research is to analyze online customer reviews for restaurants and underpin the restaurant satisfiers and nature of online restaurant reviews. After retrieving 2214 usable customer comments on Yelp.com, using a coded web spider, a series of both qualitative (e.g., tag clouds, word networks, and word trees were created using data visualization techniques) and quantitative (e.g., MANOVA) data analysis techniques were deployed to detect the satisfiers and dis-satisfiers of different types of restaurants. Three distinct types of clues – functional, mechanic, and humanic –explain a customer's overall perception of a dining experience that lead to satisfaction or dissatisfaction. A focus on food, menu offerings, ambiance, and service create buzz in social media. Furthermore, several implications inform customer researchers about effective ways to investigate and serve the restaurant industry.