Feature Adoption

Track how users adopt new features — who's using it, how deeply, are they coming back, and what's preventing adoption.

When to Use

• "How many users have tried the new dashboard?"
• "Are users who tried the new search feature coming back to it?"
• "What's blocking adoption of the new onboarding flow?"
• "Compare feature usage between enterprise and free users"
• "Is the new feature replacing the old one, or are users using both?"
• "Show me sessions where users started using the new feature but stopped"

Mental Model

Feature adoption is a funnel:

1. Awareness: Users who saw the feature (visited the page, saw the announcement)
2. Trial: Users who tried it (clicked on it, interacted once)
3. Adoption: Users who used it repeatedly (came back, integrated into workflow)
4. Retention: Users who are still using it N days later

The drop-off between each stage tells you where the problem is.

Workflow

Step 1: Define the feature

Clarify what "using the feature" means:

• A page visit? (e.g., visited /dashboard/new)
• A click event? (e.g., clicked "New Report" button)
• A custom event? (e.g., feature_search_v2_used)
• A sequence? (e.g., visited /search → typed query → clicked result)

Step 2: Measure awareness

How many users encountered the feature?

fullstory:build_metric(
  query="users who visited /dashboard/new",
  output_type="single_number"
)

If this number is low, the problem is discovery, not the feature itself.

Step 3: Measure trial

Of those who were aware, how many tried it?

fullstory:build_metric(
  query="users who interacted with the new dashboard (clicked any element or spent >10 seconds)",
  output_type="single_number"
)

Trial rate = trial users / aware users. Low trial rate = the feature doesn't look compelling or its purpose isn't clear.

Step 4: Measure adoption (repeat usage)

Of those who tried it, how many came back?

fullstory:build_segment("users who used new dashboard at least once")
fullstory:build_metric(query="sessions with new dashboard usage per user in last 30 days", output_type="top_n")
→ "60% used it once, 25% used it 2-3 times, 15% used it 4+ times"

The "used once and never returned" group is the most important to investigate.

Step 5: Segment adoption

Break down by user properties:

By plan:
Enterprise: 42% adoption
Free:        8% adoption ← BIG GAP

By onboarding:
Completed onboarding: 35% adoption
Skipped onboarding:    5% adoption

A big gap between segments tells you where to focus adoption efforts.

Step 6: Investigate blockers

For users who tried but didn't adopt:

fullstory:build_segment("users who used new dashboard exactly once in last 30 days")
fullstory:get_sessions(segment_id, limit=5)
→ session-context agent: "What did the user do on the new dashboard? Did they encounter errors, confusion, or dead clicks? Why didn't they return?"

Look for:

• Errors or crashes during first use
• Rage clicks or dead clicks (confusing UI)
• Users switching back to the old feature immediately after trying the new one
• Users spending very little time (they bounced immediately)

Step 7: Report

## Feature Adoption — New Dashboard (launched Jul 15, 30 days)

### Adoption Funnel
- Aware: 8,200 users (visited /dashboard/new)
- Tried: 3,400 users (41% trial rate)
- Adopted: 1,100 users (32% of trial users, 2+ sessions with feature)
- Retained: 780 users (71% of adopters still using in Week 4)

### Drop-off Analysis
- Biggest drop: Trial → Adoption (68% tried once and never returned)
- These 2,300 users need investigation — run frustration-hunter on /dashboard/new

### By Segment
- Enterprise: 42% adoption | Free: 8% adoption
- Feature isn't reaching free users — consider in-app promotion or gating

### Comparison to Old Dashboard
- Old dashboard still has 5,200 active users
- 1,800 users use both (transitioning slowly)
- 600 users tried the new one, went back exclusively to the old one

### Recommendations
1. Investigate why 68% of trial users don't return (session evidence needed)
2. Promote the new dashboard to free users — they're not discovering it
3. Consider sunset timeline for old dashboard once adoption hits 60%

Feature Lifecycle Stages

Guidelines

• Always compare feature adoption across user segments — the overall number hides important patterns.
• "Tried once, never returned" is the single most important group to investigate. That's where the UX problems live.
• If the feature is gated (enterprise-only, beta), adjust expectations — adoption will be lower and that's normal.
• Old feature usage is a key metric. If nobody switches, the new feature isn't solving a real problem (or migration is too hard).
• Custom events are the most reliable way to track feature usage. If your team fires a feature_X_used event, use that instead of page visits or click patterns.