Opinions matter, depending on whose they are. Some 88% of respondents to a Yelp-commissioned survey said it is important to understand how online review platforms determine the reliability of reviews, and 85% said they trust written reviews versus only a star rating.
The Yelp survey conducted by Material, which focuses on customer relationships, polled 2,000 U.S. adults to learn how they handle potentially fake reviews, as well as which factors about online reviews or review platforms they do not trust.
It analyzes what makes a review trustworthy to consumers and how to build on that relationship. For example, 85% of respondents trust reviews with written text compared with those with only a star rating.
Consumers value transparency. It may seem like a simple task for brands to build relationships with consumers, but it’s not. It takes work and trust, as demonstrated in the findings released Wednesday.
Some 49% said when spotting a fake online review, they will read other reviews to gather additional opinions, whereas 34% will ignore the potentially fake review, 27% will find another business, and 24% will report the review.
About 67% said they research and consider online reviews more for restaurants than other businesses, followed by 57% for household repairs and work, 55% for car repair and services, 51% for medical needs, and 42% for professional services.
Only 28% of respondents look for incentivized reviews, but 71% say they would no longer visit a business if they learned the business has fake or compensated online reviews.
And 79% of respondents prefer to see all reviews for a business or product, including those that the review platform believes are fake or non-trustworthy.
Yelp’s automated system makes recommendations based on hundreds of signals of quality, reliability, and user activity on Yelp, but is still accessible to anyone via a link at the bottom of a business’s Yelp page.
These do not factor into the business’s overall star rating or review count, and allows consumers to see all reviews -- even those the recommendation software determines less useful or trustworthy.