Note, this post was originally posted in December of 2023, and has been updated in December 2025

Trustpilot is a wide-spread consumer review platform. Founded in 2007, they have over 1m reviews posted every month from consumers. Reviewers share their experiences with a company, giving a score, from one to five and a free-text comment.

Companies proudly display the scores on emails, websites, and other mediums as a form of social proof. As such, it’s important to understand how Trustpilot works and how you can influence the scores.

What is Trustpilot’s TrustScore?

Trustpilot’s public review forum is most similar to that of the Net Promoter Score (NPS) - but in the public eye.

When consumers are leaving a review, they have to leave a numerical star rating, and are encouraged to leave written feedback in the free-text box. This free form text is visible to other consumers on the company’s website.

To aggregate these scores, Trustpilot distils this information into a relevant score called TrustScore.

How is Trustpilot’s TrustScore calculated?

TrustScore is a measure of overall satisfaction, and can be seen in a similar light to Net Promoter Score, NPS. Unlike NPS which is ranked from -100 to 100, TrustScore is bounded between 1 to 5 stars.

Trustpilot are very open in terms of how the TrustScore is calculated, saying there are three factors that contribute towards a company’s TrustScore:

  • Recency of reviews
  • Frequency of reviews
  • Bayesian Averaging.

Let’s now explore each aspect and how it affects your business’s score.

Recency of reviews

Trustpilot states that they give more weight to newer reviews than older reviews. This is likely done by an exponential decay function that downweights reviews values as time goes on.

This is a common extension of review systems, and is more helpful for consumers as they want a review that reflects the current state of the business.

Frequency of reviews

This second point is tied in closely to that of the first. The more frequent your reviews are, the more accurate the score will be. For businesses with less than 10,000 reviews the TrustScore will be re-calculated after every review. For those with over 10,000 reviews, the score will be re-calculated on a daily basis.

Bayesian Averaging

The most important statement is that they use Bayesian averaging - a technique not uncommon for rating products, forum posts, and in this case - companies.

To best understand bayesian averaging, let’s illustrate two businesses:

  • Business A has 9,700 reviews and a TrustScore of 4.4
  • Business B has 2 reviews and a TrustScore of 5.

Which business would you trust more? Despite Business B having a higher score, you might lean toward trusting Business A because the volume of reviews is much higher, and the TrustScore of Business 2 feels unrealistically high.

This concept is central to bayesian averaging, of which Wikipedia defines as: “A Bayesian average is a method of estimating the mean of a population using outside information, especially a pre-existing belief, that is factored into the calculation.”

Bayesian averaging really shines for consumers evaluating businesses with few reviews. Trustpilot says that all new businesses start off with a review score of 7 reviews worth 3.5 stars - a very neutral view of a business. This is the pre-existing belief, that the business is neither bad nor great.

Through bayesian averaging it’s implied that given a 5 star review, we’ll update the prior belief (7 reviews of 3.5 stars) to reflect this new review. As a result, the TrustScore will increase, but will not go straight to 5 stars - the company will need to work harder than that.

Bayesian averaging results in a much smoother movement over time, with scores not whipsawing between 1 and 5 stars. It also means that for a business’ score to go up (or down), there needs to be a consistent period of good (or bad) reviews as scores aren’t affected largely by one off reviews.

How frequency and recency tie into bayesian averaging

By now, you’ll probably notice the best way to influence a TrustScore is to gather reviews - this is exactly the behaviour that bayesian averaging has and the exponential decay has.

Businesses, wanting to improve or even maintain their score need to work hard to continually get frequent reviews to maintain recency in their reviews.

This also has an added benefit for businesses, in that recent reviews have more of an effect on your current score than old ones. You can learn more about how to improve your Trustscore in this follow-up post.

From Trustpilot’s view, it’s a fantastic measure, as it encourages businesses to actively engage with their platform to help drive frequent reviews to maintain a good score. And for consumers, scores are often more representative and more meaningful than a simple average.

Does Trustpilot ‘delete’ reviews?

This is quite a loaded question as Trustpilot does delete reviews under certain circumstances if the review breaks their guidelines. In the context of how your Trustscore is calculated however, old reviews are effectively not influencing the Trustscore - effectively removing them from your Trustscore.

Why did my score drop when I got a 5-star review?

This certainly feels like a glitch in the calculation, but it’s actually the Recency Decay algorithm working exactly as intended. As we’ve found, Trustpilot’s TrustScore is a bayesian average with time components down-weighting old reviews. Every time a new review is posted, it triggers a full recalculation of your entire history. If you received a flurry of 5-star reviews 6 or 12 months ago, those reviews were positively influencing your TrustScore. When you get a new review today, the algorithm updates the “age” of those older reviews, downweighting them. If the total weight lost from those older reviews is greater than the weight gained from your one new 5-star review, your score will drop. Essentially, you aren’t just competing against bad reviews; you are competing against the “aging out” of your own past success