Feature Adoption is most useful when it helps you make one real decision.
Connect the launch to the behaviour or metric you hoped to change, then let the result teach you what to do next.
Try saying
“Did this change the behaviour we cared about? Show me who improved and who did not.”
Set the baseline
Write down what is true today.
Watch the change
Compare behaviour after the work ships.
Learn forward
Keep, improve, stop, or investigate.
Feature Adoption
What this is for
Decide whether to improve, promote, maintain, or retire a feature based on behaviour.
When this helps
- Feature success is assumed from launch completion.
- Low adoption has no diagnosis.
- Teams keep investing in features that do not create value.
How it works
Feature Adoption analyzes usage events and segments to show reach, activation, repeat use, and drop-off. The view turns those patterns into a recommendation such as graduate, keep, improve, or sunset.
Before you start
- A connected analytics source.
- Feature or event definitions.
- The target audience and expected adoption behaviour.
What you should see
- Adoption and retention patterns.
- Segment-specific drop-off points.
- A recommendation with evidence for the next product decision.
Try it now
- Connect PostHog, Pendo, Mixpanel, Amplitude, or another supported source.
- Open Feature Adoption and choose the feature event or feature area.
- Compare first use, repeat use, and drop-off by meaningful segment.
- Create the follow-up experiment, product decision, or sunset plan and link the evidence.
A real example
A reporting feature has high first use but almost no repeat use among small teams. The team interviews those users, discovers setup complexity, and chooses an onboarding experiment instead of declaring the feature a failure.
Your next move
Pair the result with Outcome Intelligence and Experiment Tracker.
Ready to apply this?
Start with your own product and keep the first read grounded. You can create an account after you see the result.