Product Reviews Are Ubiquitous, But Not All Are Equal


With the growing availability of product and service opinions from experts and users, companies can no longer unconditionally rely on mature, well-regarded brand names to ensure success. Product and service retailers proactively solicit product feedback at unprecedented levels, posting post-purchase consumer reviews to their websites. Additionally, third-party sites like Yelp and TripAdvisor allow consumers to post about their experiences with hotels, doctors, restaurants, and a myriad of other service providers.

The increasing role of technology in consumer purchases is well documented. Thirty percent of US consumers say they begin their online purchase research by going to Amazon for product info and reviews. Additionally, studies commissioned by Google have found that shoppers consult 10.4 sources of info, on average, before making a purchase. It is clear that companies need to understand how online user reviews influence consumers and their purchasing decisions. With this in mind, brands must understand how consumers seek out and use reviews.


To study consumers’ use of reviews, the researchers conducted an experiment in which consumers reviewed information, and real reviews pulled from the web, on one of two product categories, television sets or luggage sets. Consumers saw specifications of three different television sets or luggage sets, and indicated which one they preferred. The researchers then asked half the consumers, ‘If you could read a positive review about one of the products, which would it be?’ The other group was asked the same question, except they were asked about a negative review.

When consumers were given a choice of which product to see a positive review of, 89% wanted to see a review of the product they preferred. When the review was negative, 57% of consumers wanted to see a review of the product they preferred. A random selection would assume 33% of consumers would elect to see a review of their chosen product. While consumers universally sought out more information (positive or negative) about their favored product, the 89% preference when asked about a positive review indicates a bias toward confirmatory information in consumers’ product research process. Consumers were next randomly shown a 2 or 4 star review of the product they identified for purchase.

The researchers asked them various questions about the competency of the reviewer and how helpful, relevant, informative, and credible they found the review to be.

Analysis revealed that two star reviews were perceived to be less helpful than four star reviews. This was explained by the fact that four star reviews were perceived to be more credible, and the four star reviewers more competent, than the two star reviews and reviewers. Therefore, credibility and competence influence the helpfulness of a review.

Additionally, analysis of the research revealed that the relevance of a review was predictive of its helpfulness. While this is intuitive, it is important to note that this is independent of the star rating, unlike credibility and competence mentioned above.





Brands can use these results when determining how to supply consumers with review information. The results suggest consumers want to see reviews for the product they are interested in and are not likely to be swayed by negative reviews. In fact, they may actively avoid this information. Brands can benefi t from this insight by providing positive reviews of a product after they have inferred consumers’ developing preferences. Further, when choosing which reviews to highlight, companies should pick those that appear to be the most credible with competent reviewers because these will be most helpful to consumers across star ratings.

The results also indicate that consumers perceive negative reviews to be less helpful than positive reviews. This is good news for companies with poor product ratings; the negative reviews may be dismissed as not helpful and future positive reviews could outweigh a negative past.

Lastly, brands could try to use available data to tailor the reviews that their consumers view first based on the relevance of that review to the consumer. For example, if the consumer searched for ‘high quality luggage sets’, a relevant review would speak to the quality of the luggage set (positive or negative), and the consumer would deem it helpful.


Christina Bianchi


Christina Bianchi is a third year Evening Program MBA student and a Program Analyst at the Transportation Security Administration. In her job, she analyzes airport-level data to help airports meet their operational goals. She hopes to work in a data analysis or market research role in the entertainment industry after graduation.

Julie Thompson


Julie Thompson is a third-year MBA candidate in Georgetown’s Evening Program. By day, she is a Strategic Market Analyst at PulteGroup, where she collects and analyzes data to inform the company’s land acquisition, product development and pricing strategies.  After starting with the company in 2005, Julie held a variety of sales and marketing roles before discovering her passion for data analysis and market research. Julie earned dual undergraduate degrees in finance and German and an international business minor from Miami University.

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