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When 4.3 Stars Is Average: The Internet's Grade-Inflation Problem by Geoffrey A Fowler Apr 06, 2017 TOPICS: Consumer Behavior, Marketing SUMMARY: E-commerce and online services solicit customer feedback via the use of stan ratings with 5-star as the best possible score. The ratings system presents challenges for both businesses and consumers because ratings are overwhelmingly positive. One study found the average was about 4.3 stars out of 5. Ratings may help week d out bad products or services but it can be hard to extract useful information from them. For example, Yelp says that 46% of ratings we give local business are five-star Using apps to evaluate workers as occurs for Uber has the same problem. Another problem is fake reviews. Airbnb, Amazon and Netflix are aware of the issue and they are trying to create a simple to use system that offers enough detail to make a rating useful. CLASSROOM APPLICATION: Ratings are part of everyday life online as we use services, visit businesses or make online purchases. The problem is that consumers are too nice and average ratings overall are 4.3 out of 5. This can make it difficult to use ratings to make a decision because ratings are hard to interpret Rankings are another option. Yelp creates rankings based on the number of reviews, the distance from the business and how recent the reviews are Replacing the star system presents its own challenges because consumers are used to this method. Attempts to use other scales usually are abandoned Netflix is trying a new system with fewer decision points. The comipan on viewing behavior. Not every company has access to that type of data. Ratings get better when companies track us, but that presents a tradeoff with privacy y will combine thumbs up or down ratings with data it collects QUESTIONS: 1 Are you a tough or easy star rater? Explain. Why do you think so many star ratings are positive? 2. How do you use these ratings (or do you use them. ) in your online shopping and why is having good information important? 3. How does Yelp create ratings and what additional data does it use? 4. Explain the new Netfix system for providing recommendations and list its advantages 5. Discuss how Airbnb increased ratings accuracy Why do you think this strategy worked? End

Explanation / Answer

1. I am a tough star rater and usually provide a rating of five stars only when the product is cent percent as per the specifications as mentioned on the website. The ratings are also influenced by my overall experience, level of convenience and the value added associated with my purchase.

So many star ratings our positive due to the fact that many of the ratings are fake ratings in which the ratings are artificially inflated through the use of fake accounts. The e-commerce retailers do so in order to lure more buyers through the use of fake high ratings.

2. I use these ratings to determine the usefulness of the product, its value, and it’s utility. All in all I use the ratings to determine whether the product that I am looking at is worth the purchase price. Having god information helps me to make informed choice and ensure that I maximize my level of satisfaction and utility.

3. Yelp creates ratings by consolidating the ratings given by the participants and on the basis of its ranking system. The additional data that it uses are number of reviews, distance from the business, and how recent the reviews are.

4. Netflix’s system for providing recommendations is a combination of thumbs up and thumbs down ratings, and has stopped using the star rating system. The advantage of this is that the participants have to make fewer decisions and thus the system has been simplified for them. This increases the level of objectivity as well as accuracy.

5. Airbnb is increasing its rating accuracy by simplifying the system. The system that is being used by them has comprehensive details and this ensures that the ratings are accurate as well as useful. Under the new system the hosts and guests are not able to see each other’s review prior to their posting. This strategy worked well for Airbnb because the level of bias and subjectivity was reduced as participants were not influenced by other reviews.

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