trScore is an algorithm that calculates a product’s scores based on a weighted average of reviews and ratings, rather than a simple average.

The problem: Simple averages can be misleading

Most review sites use simple averages to determine a product’s score or overall star rating. At first, TrustRadius did as well, until we noticed a couple of problems with simple averages:

  1. Over time, simple averages don’t always tell the most up-to-date story. Products can change quickly, and for products that have a large review base with a significant number of older reviews, a score based on a simple average won’t reflect current customer sentiment.

  2. Simple averages allow vendors to artificially inflate their product’s score by inviting only known promoters to write a review. Happy customers are valid users and reviewers, but when they are over-represented in a product’s review base, a score based on a simple average will be biased.

The solution: A weighted average called trScore

Our trScore algorithm weights ratings and reviews differently in the following ways, to provide a more reliable score than a simple average:

  1. More recent reviews and ratings are weighted more heavily than older ones.

  2. Reviews from a random, representative sample of customers are weighted more heavily than biased samples.

  3. Ratings given as part of an in-depth review are weighted more heavily than single-click star ratings, since they are more considered and include more context.

All average scores on TrustRadius are calculated using the trScore algorithm, including the overall Likelihood to Recommend score, scores for attributes like usability and support, and scores for specific features like dashboarding and reporting.

The trScore algorithm allows us to present the most accurate and objective picture of customer sentiment to buyers, as well as a level playing field for vendors.

If you’d like to understand how to source reviews from a random, representative sample of customers, please reach out to to ask for a call with our research team.