feature-adoption-tracker

name	feature-adoption-tracker
description	Monitor adoption of key features across your user base, identify non-adopters of sticky features, and generate targeted in-product + email nudges. Use when the user says "track feature adoption", "nudge users to try X", "which features drive retention", or is rolling out a new feature.
status	new

Feature Adoption Tracker (Retention Squad)

Continuous loop: measure which features predict retention, find users not using them, nudge, measure lift.

Triggers

• "track feature adoption"
• "why isn't anyone using "
• "nudge users who haven't tried X"
• "which features drive retention"
• "rolling out — how do we drive adoption"

Inputs required

1. Feature inventory — list of trackable features with the event name for each
2. Event data — 60+ days of feature-use events
3. Retention cohorts — from Cohort & Churn Analyzer
4. User attributes — plan, tenure, activation status

Process

Step 1 — Rank features by retention impact

For each feature:

• % of active users who have used it at least once
• Retention lift of users who used it vs. users who haven't (matched on tenure + plan)
• Confidence interval on that lift

Split features into four quadrants:

                   High adoption       Low adoption
High lift     ✅ Sticky core        🟡 Hidden gem
Low lift      ⚪️ Table stakes      🔴 Feature bloat

Focus attention on Hidden gems — high lift, low adoption.

Step 2 — Identify non-adopter segments

For each Hidden Gem feature, build a segment:

• Active (used product in last 14 days)
• Has NOT used feature in last 30 days
• Matches the profile of users who normally use it (plan / tenure)

This is the nudge queue.

Step 3 — Design the nudge

For each feature, write:

• In-app banner — shown once, dismissible
• Empty-state nudge — if there's a relevant empty state in the product
• Email — sent only if in-app didn't trigger adoption within 7 days

Copy rules:

• Lead with outcome, not feature name
• Include a 1-click path to try it
• Show proof ("teams who use this get to W4 14% more often")

Step 4 — Instrument the loop

• <feature>_nudge_shown
• <feature>_nudge_clicked
• <feature>_first_use

Measure conversion from nudge → first use → second use (adoption sticks) → retention lift vs. a held-out control.

Step 5 — Output

Produce:

• Feature heatmap — quadrant view of adoption vs. retention impact
• Nudge queue — per-feature, per-user list (CSV)
• Copy spec — in-app + email for each feature
• Measurement plan

Output artifacts

• feature-adoption-heatmap.html
• nudge-queue.csv
• nudge-copy.md

Handoff

• Nudges → product (in-app) + Lifecycle Email Designer (email)
• Adoption lift results → Performance Reporter
• Features in the "bloat" quadrant → consider deprecating (feed to product PM)

Example call

"Analyze our feature usage in events.csv. Find which features are 'hidden gems' — low adoption but strongly predict retention — then give me a nudge queue for Pro-plan users who haven't used our integrations feature."