You built 20 features. Users use 3.
You built 20 features but users only use 3. You don't know which features are underused, whether it's a discovery problem or a usability problem, or which features to promote in your onboarding flow.
How Feature Adoption works
Automatic detection
Onboardics identifies features from your page paths and click targets using path normalization — UUIDs and IDs collapsed to readable labels. No manual tagging or configuration required.
Engagement table
See each feature's usage rate, trend over time, and comparison to your project averages. Features ranked by adoption so the most underused rise to the top.
AI recommendations
Contextual AI diagnosis identifies underused features, distinguishes discovery gaps from usability issues, and suggests specific in-app flows — tooltips, tours, or checklists — to boost adoption.
What you get
- Automatic feature detection from page views and click events — no tagging required
- Engagement rate per feature with trend direction (up, down, flat)
- Underused feature identification with comparison to project average
- AI-powered adoption recommendations: which features need promotion, which need redesigns, which are fine. Overlay-able fixes get a "Create flow →" one-click on Deploy and above. Structural fixes (the feature itself needs work, not a tooltip) are flagged code-only.
- Customizable via AI-defined feature adoption metrics — describe your key actions in plain English
- CSV export for reporting and external analysis
Pricing
Feature Adoption Tracking is available on Diagnose ($149/mo) and above — the entire AI analytics suite ships at the Diagnose tier. AI-defined custom feature adoption metrics are also included starting on Diagnose, so you can customize what counts as “adopting” a feature.