Galileo is our machine learning layer that sits on top of the LogRocket platform. It combines information about how users react to problems with traditional error reporting and analytics to uncover the technical and usability issues holding back key metrics such as conversion, adoption, retention, or performance.
Galileo's models have been trained on billions of data points to predict whether identified issues and friction points are important, automating the analytics work that humans already do in LogRocket. Importance is based on vectors such as impact, frequency, and years of user feedback around what matters most.
Galileo learns via user feedback, so its recommendations are constantly improving. Activities such as triaging issues as "high impact", "low impact", or "ignored" help Galileo better understand what matters most and make more accurate and relevant recommendations in the future.
Galileo provides a severity score for every issue, based on how likely it is that the issue is associated with user frustration. The higher a severity score, the more likely it is to be associated with frustration and a poor user experience.
Severity scores can be viewed both within the Issues tab, as well as in individual session replays. They appear in the Logs and Network panes of the Developer view, and in the Event Timeline of the Playback view.
Issues without severity scores
Issues need to occur a minimum of 10 times before LogRocket will assign a severity score.
Within the Issues tab is a toggle to "Show recommended issues only." Turning this toggle on will filter issues to show only the most-severe issues detected by Galileo
Slack users can integrate LogRocket with their Slack workspace to receive weekly updates on the top recommended issues as identified by Galileo. Issues Digest will send the top 10 issues each week so that you can be efficient with your time and focus your efforts on the issues with the greatest impact.
Updated about 2 months ago