- Amazon offers a star ranking for every item on its website, which takes customer ratings of the product into account.
- The ratings are not a straight average of the product like they may appear to be.
- Amazon instead uses a machine-learning model that takes into account when the ratings were made and whether they were made by a reviewer with a verified purchase.
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Every Amazon customer is likely familiar with the little star ratings that every product has, a composite of all of the sometimes thousands of reviews customers have left. They aim to be helpful, a sort of chorus of previous buyers that could convince customers to either press the checkout button or stay far away.
But many Amazon customers may be surprised to learn that the ratings, which are stars on a scale of one to five including one-tenths, are not a straight average of the product like some may assume.
How they are calculated, however, remains something of a mystery.
Amazon doesn’t like to talk about how those ratings are calculated. The last time it did was in 2015, when it announced it was switching away from an unweighted average.
At that time, it began giving greater weight in its calculations to ratings that were more recent, that were made by verified purchasers, and that were rated as more helpful by other customers.
“Amazon is enhancing the customer reviews system, adding a few changes we hope will help make product feedback even more useful to customers,” an Amazon spokesperson told GeekWire in 2015. “The enhanced system will use a machine-learned model to give more weight to newer, more helpful reviews from Amazon customers. The system will continue to learn which reviews are most helpful to customers and improve the experience over time.”
A spokesperson for the company declined to comment on the record for this story.
Amazon’s star system is important for more reasons than just consumer confidence. It now has a growing store concept, called Amazon 4-star, that stocks only items that customers have rated four stars or more.