Feature adoption

Feature adoption tracks how many of the users who could use a given feature actually start using it, and, separately, how many keep using it rather than trying it once and abandoning it. It is tracked per feature, so a product typically has many adoption curves running at once rather than a single adoption number.

A basic adoption rate is users who used the feature ÷ users eligible to use it, over a chosen window, often broken down further into trial (used once) versus repeat use. This differs from feature flag rollout percentage, which is an engineering control over who can see a feature, not a measure of whether they chose to use it. Reliable adoption tracking depends on a clear event taxonomy so that "used the feature" is defined the same way everywhere it's measured.

Feature adoption matters for prioritizing roadmap investment: a feature with low adoption may need better discoverability, a better first-use experience, or may simply not address a real need. It also feeds product-led growth motions, where usage of certain features signals expansion or upsell readiness. A common pitfall is measuring adoption only at launch and not tracking whether early adopters keep using the feature, which can hide a feature that attracts trials but fails to retain users.