Know what every release shipped - and how it held up

Most teams treat a release as a version number and a green checkmark. That hides the two things leaders actually ask about: what did we ship, and who does it change things for. Release analytics closes that gap by linking each deploy back to the themes, product plans, and pull requests it contains, then forward to the customers and accounts it touches.

FeatureFactory builds this mapping automatically from your Git history and planning artifacts. When a release goes out, you get a grouped view of the features it delivers instead of a flat list of commit hashes. Pair that with how planning connects to delivery and every release becomes a traceable line from intent to production.

The payoff is a shared language between engineering and the rest of the company. Product knows which themes landed, support knows which customers are affected, and engineering keeps a clean audit trail from product plan to deploy without writing a single status update.

Quality does not end when a PR merges - it ends when the release survives contact with production. Release analytics tracks what happens in the hours and days after a deploy: error spikes, rollbacks, hotfix commits, and how quickly you recover. That turns change-failure rate and mean time to restore into living numbers you can act on, not quarterly retros.

These signals sit on top of the same data that powers our engineering metrics and cycle time analytics, so release health is never a siloed dashboard. You can trace a failing release back to the PR that introduced it and the theme it belonged to in a couple of clicks.

Over time the trend view is where the value compounds. Comparing releases side by side surfaces the risky patterns - oversized deploys, recurring hotfixes, creeping rework - before they become incidents. Read the manifesto for why we think measuring what you ship is the whole point of a feature factory.