Dashboard Templates
See which features and flows your users actually use, with adoption rates for every custom event you track.
Feature adoption measures the share of users who try a specific feature or flow, out of everyone active in a given period. It’s usually expressed as a percentage: the number of users who triggered a feature’s event divided by total active users. Teams track it to see which parts of a product are pulling their weight and which ones are being built but ignored.
In Web3 products, feature adoption often means custom actions like swapping, bridging, staking, or claiming rather than generic clicks, so adoption is measured directly from the onchain or app events a team already tracks, not from page views alone.
What’s included
Top event KPI cards: number tiles automatically mapped to the top custom events by usage, showing what percentage of active users triggered each one.
Feature / multi-feature users: a table showing what percentage of active users use each of your custom events.
Feature expansion (cross-adoption): a table showing, for users who did one event, what percentage also did each other event, revealing which features get adopted together.
Core intent signals: a table of the most common next event after each signal, filtered to signals with 20 or more transitions, to surface real usage patterns rather than noise.
Use this dashboard to see which parts of the product are getting used, which features get adopted together, and what typically happens right after a user tries something new.
Frequently Asked Questions
1. What is feature adoption?
Feature adoption analytics measures what share of active users try and keep using a specific feature or flow, usually expressed as the percentage of active users who triggered a given custom event.
2. How is feature adoption rate calculated?
Feature adoption rate is the number of unique users who triggered a feature’s event divided by the total number of active users in the same period, usually shown as a percentage.
3. What is a good feature adoption rate?
There’s no universal benchmark. Adoption rates vary widely by feature type and product maturity, but consistently low adoption for a core feature, below roughly 10 to 15 percent of active users, is usually a signal to investigate onboarding, discoverability, or product-market fit for that feature.
4. What is the difference between feature adoption and user activation?
User activation typically measures a single first meaningful action that predicts retention, while feature adoption measures ongoing usage of a specific feature across the whole active user base, whether or not it was part of onboarding.
5. Why track feature adoption instead of just total users?
Total users shows reach, but feature adoption shows depth: whether the people already using a product are getting value from its specific capabilities, which is often a stronger predictor of retention and expansion than raw user counts.
6. What charts are included in the Feature Adoption Dashboard?
Top-event KPI cards, a feature / multi-feature users table, a feature expansion (cross-adoption) table, and a core intent signals table.
7. Does the Feature Adoption Dashboard require custom events?
Yes. This dashboard is built entirely on custom (track) events, such as Swap, Bridge, or Stake, so at least one custom event needs to be tracked before the charts have data to show.
8. What does cross-adoption mean?
It shows, for users who triggered one event, what percentage also triggered each other event, making it possible to see which features tend to get adopted together.
9. What does the Core Intent Signals chart show?
It surfaces the most common next event after each signal event, filtered to signals with 20 or more transitions, showing the typical path users take right after trying a feature.
10. Who should use the Feature Adoption Dashboard?
Product and growth teams at Web3 apps that want to know which features drive engagement, and which ones aren’t getting traction, without writing custom SQL queries.