Dining out, going for a movie, or enjoying a bottle of fine wine are all ‘experience goods’, that is, they have to be consumed or experienced in order for them to be assessed and evaluated. Online customer reviews are valuable as they allow potential consumers to get in-depth information from others for a relatively low cost and little effort. With one click, they can solve an information asymmetry problem in which service providers are better informed than customers. The internet allows customers to share their views and feedback cheaply and efficiently with a large audience.
Websites such as TripAdvisor and Yelp offer user-generated content in the form of electronic word-of-mouth (WoM) services, which are often considered more reliable and trustworthy than adverts from retailers. Online customer reviews have also become significant in swaying consumer decisions. Academic evidence suggests that product information is more valuable for services than goods as they appear riskier. Therefore, restaurant customers are more likely to seek external information sources when they have not experienced it themselves.
According to Parikh et al. there are four main reasons why customers read reviews on these platforms:
Restaurant reviews offer opportunities and risks. One benefit is that restaurants can track consumer opinions in a way which would not be entirely feasible with classic WOM communication. However, unlike classic WOM, reviews are available to everyone at all times.
Restaurant managers need to be mindful that these reviews are often trusted by customers and may result in them trying an outlet due to hype or out of curiosity. We recommend that restaurateurs try to ensure that customers post reviews. They can do this by organizing events for influential consumers or more simply by handing out business cards encouraging customers to review the restaurant online.
Restaurateurs should also build a community to promote a positive image of their businesses through, for example, regular updates. They should also engage with, and respond to, both positive and negative online customer reviews. This can help users feel more connected to the business and if this community engagement brings in a considerable amount of business, it can be viewed as a worthwhile investment of resources.
In a recent study, Anderson and Magruder show that higher Yelp scores help restaurants sell tables 19 percent more frequently during peak periods. This impact is strongest for restaurants for which information is the scarcest. Restaurants that are not accredited by experts sell out 27 percent more often if they earn an extra star on Yelp.
According to the study, the closer the link between restaurants and customers through reviews, the greater the positive effects on business as a whole. User reviews may redirect consumers to higher quality restaurants, with the result that lower grade restaurants close or have to improve their quality to meet changes in consumer demand.
In a related study, Luca found that a restaurant's average rating has a strong influence on sales.
One additional Yelp star leads to sales growth of 5-9 percent for independent restaurants. His findings show that the strong influence of Yelp for independent restaurants turns close to zero for chains, for which more information is available.
Finally, Luca examines whether the observed influence differs, depending on the information provided. First, if each review represents a quality signal, ratings with more reviews will contain more information. He shows that the market responds strongest to changes in restaurant ratings when a restaurant has many reviews. Second, restaurant reviews can be written by high quality (named ‘elite’ by Yelp) or low quality reviewers. Reviews written by elite members are almost twice as effective as other reviews.
As user-generated content becomes increasingly popular, concerns are growing that some businesses may be rigging the system. Luca and Zervas note that almost one in five reviews is marked as fake according to Yelp’s algorithm. These reviews tend to be more extreme than the average review and are written by lower-rated reviewers. In addition, they suggest that restaurants are more willing to rig the system when faced with increased competition or having a poor reputation.
It is, therefore, important for platforms to develop mechanisms that reduce the possibility of fraud. While there is no perfect mechanism to remove fraudulent reviews, Luca and Zervas propose three possible approaches:
Credits: Vice
As discussed above, restaurant reviews are not flawless but they have nevertheless contributed to reshape the restaurant industry and will certainly continue to have a profound impact on it in the coming years. Thanks to more transparent competition and an emulation effect, the quality of the food and service offered by most restaurants has improved, especially in tourist areas.
Reviews have also given customers greater autonomy in their decision-making. They can now easily find information, not only about fine dining venues but also casual dining, ethnic and even fast food restaurants.
Finally, social media enables restaurateurs to build a community around their businesses and thereby compensate for the lack of loyalty shown by customers today, who tend to be more cost-conscious than a few decades ago.
In the future, the importance of reviews is unlikely to decline. However, the fragmentation of information sources and the exponential increase in the number of reviews will pose challenges to both restaurateurs and customers.
Websites analyzing and aggregating reviews should therefore be in an ideal position to exploit this situation. This is, to some extent, what the French website “La Liste” tries to achieve through the aggregation of data from more than 550 different sources. But as it only focuses on high-end gastronomic restaurants, it neither fully addresses customers’ needs nor exploits the potential offered by the torrent of available information and data.
Anderson & Magruder, 2012, Learning from the crowd: Regression discontinuity estimates of the effects of an online review database, The Economic Journal, 122 (563).
Luca, 2016, Reviews, reputation, and revenue: The case of Yelp.com, Harvard Business School NOM Unit Working Paper, No. 12-016.
Luca & Zervas, 2016, Fake it till you make it: Reputation, competition, and Yelp review fraud, Management Science, 62 (12).
Parikh et al., 2014, Motives for reading and articulating user-generated restaurant reviews on Yelp.com, Journal of Hospitality and Tourism Technology, 5(2).