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According to the report, reviews typically have a distribution of opinion that is “highly polarized, with many extreme positive and/or negative reviews, and few moderate opinions.”
Indeed, it is not unusual to encounter reviews written by customers who loved their experience at a business so much that they gave it a 5-star rating, or by those so disappointed by, even sometimes angry at, a business that they decided to leave the lowest-rated review (and harshest feedback) possible.
But what of the oft-silent majority of middle-of-the-road voices?
According to HBR, their opinion might not always be accurately represented in reviews. “If you had a moderate view, you’re likely to have left no review at all, finding it not worth the time and effort.” Unless a person really loved or absolutely hated the customer experience, she or he is simply not likely to review the business. This kind of polarization can then lead to others having misleading views of opinion about businesses.
Previous research by Sinan Aral for MIT also suggested that some reviews could be systematically biased or easily manipulated. Among the factors that cause this to happen: social proof, a psychological and social phenomenon where people assume the actions of others in an attempt to reflect correct behavior in a given situation.
Wrote Aral: “Our herd instincts — natural human impulses characterized by a lack of individual decision-making — cause us to think and act in the same way as other people around us.”
As a powerful form of social proof, reviews can influence what people think they should say. So a restaurant-goer’s written review of, say, a Yelp- or TripAdvisor-listed Japanese restaurant with 100 reviews and a 4.7-star rating is less likely to go against the grain and offer a counterpoint to the majority’s positive opinion. She or he will probably decide to say the same as other diners did: best sushi ever.
“When we see that other people have appreciated a certain book, enjoyed a hotel or restaurant or liked a particular doctor — and rewarded them with a high online rating — this can cause us to feel the same positive feelings about the book, hotel, restaurant or doctor and to likewise provide a similarly high online rating,” Aral added.
Situations where extreme views are over-represented — or where social proof pushes consumers to follow their yes-herd instincts — may negatively affect the quality of data found in online reviews, potentially resulting in information inaccuracies, ratings bubbles, undeserved low scores, and reputation crises.
Meanwhile, for consumers, the usefulness of the reviews they read may end up being disproportionate to the amount of trust they put in these reviews.
Improving the Quality of Data in Online Reviews
In today’s age where reviews have a powerful effect on consumer behavior and decision-making, it is critical for marketers and firms to introduce mechanisms that improve the quality of their customers’ review data, as well as help reduce bias and manipulation in online reviews.
Proactively ask for feedback. Facilitating as many authentic positive reviews as possible, particularly in the early stages of the ratings process, can help reduce review bias and balance review sentiment. You can do this with customer feedback surveys, review request handouts, post-transactional SMS messages, review request emails, and similar campaigns for review generation.
Remind customers that their opinion helps others. When asking for feedback, social reinforcement goes a long way. “Online reviews are a fundamentally social endeavor,” the HBR reporters wrote. “People are more likely to leave online reviews when they’re reminded that doing so helps others. Simple pro-social incentives also led the distribution of reviews to be less biased, creating a more normal bell-curve distribution of reviews.”
Provide motivational incentives. We’re not necessarily talking about rewarding reviewers with cash, freebies, or discounts. Non-monetary economic incentives based on the concept of “give to get” can push less vocal middle-of-the-road customers to share their opinions online.
One of the classic examples of this kind of “give to get” mechanism is Airbnb’s policy of not making guest reviews public until the host leaves theirs — or the other way around.
In a study, employer review site Glassdoor asked its users to submit content in exchange for free access to valuable information on its website. The policy helped Glassdoor draw reviews from a much broader base of users, as well as achieve a more balanced and representative picture of the distribution of online opinions about given products, services and companies.