Adoption rate is users who used the feature divided by total eligible users. Define "used" consistently (for example, at least once in 30 days) and reuse that same window every time you check.
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Feature adoption rate answers a narrower question than most product dashboards do. It is not asking how active your users are overall, it is asking what share of the people who could use a specific feature actually have. Enter your used and eligible counts above to get the rate, the raw number of holdouts, and a read on whether that number is worth a closer look.
A feature can look busy in your analytics and still have low adoption, because a handful of power users can generate a lot of events while most of your base never touches it. Adoption rate fixes that by counting people, not actions. It asks what fraction of eligible users crossed the "used it" line at least once, which is a much better proxy for how broadly a feature is landing than raw click or session counts.
The single biggest source of misleading feature adoption numbers is an inconsistent definition of "used." One click counts very differently from repeated use over a set period, and comparing a lifetime count this quarter to a 30-day window next quarter will make a flat trend look like growth or decline that never happened. Decide on a definition, such as at least one use in the last 30 days, write it down, and keep it fixed so every reading is comparable to the last.
A low adoption rate is a starting point for questions, not a verdict. Check whether users can actually find the feature, whether it is explained anywhere in onboarding, and whether it solves a problem your eligible users actually have. Segmenting by plan, account age or use case often reveals that adoption is fine for the people the feature was built for and simply low among users it was never meant to serve, which is a very different fix than a feature nobody wants.
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