From pendo-analytics
Analyze feature adoption rates, identify power users vs laggards, and track adoption trends using Pendo analytics. Use this skill whenever someone asks about feature adoption, feature usage, who's using a feature, adoption rates, feature rollout progress, or wants to understand how a specific feature is performing. Also trigger when users mention tracking a feature launch, finding champions or power users of a feature, identifying accounts that haven't adopted a feature, comparing adoption across segments, or analyzing usage trends over time — even if they don't say "adoption" explicitly. If someone asks "who's using X" or "how is feature Y doing", this is the right skill.
How this skill is triggered — by the user, by Claude, or both
Slash command
/pendo-analytics:feature-adoptionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze how features are being adopted across your user base, identify champions and laggards, and track adoption trends over time.
Analyze how features are being adopted across your user base, identify champions and laggards, and track adoption trends over time.
This skill uses Pendo MCP tools exclusively. All tool references below (e.g., searchEntities, activityQuery, segmentList, visitorQuery) refer to the Pendo connector tools (prefixed Pendo: in the tool list).
Before starting, call Pendo:list_all_applications to get the available subscription IDs and app IDs. Every subsequent Pendo tool call requires a subId (subscription ID) and most require an appId. If the user hasn't specified which app or subscription to use and there are multiple options, ask them to confirm before proceeding.
Users often provide partial or ambiguous feature names. Never silently pick a feature — always confirm when there's any ambiguity.
Use Pendo:searchEntities with itemType: ["Feature"] and the user's search term to find matching features.
If the search returns exactly one result, confirm with the user: show the feature name, ID, and any relevant metadata, and ask "Is this the right feature?"
If the search returns multiple results, present a disambiguation list so the user can pick the right one. For each result, show:
Mark the most likely feature with a ⭐ Recommended tag based on:
Example:
I found several features matching "dashboard". Which one are you looking for?
1. ⭐ **Dashboard Main View** (ID: abc123) — Recommended
- App: Web App | Last active: Feb 2026
→ *Closest name match with recent activity*
2. **Dashboard Settings Panel** (ID: def456)
- App: Web App | Last active: Jan 2026
3. **Admin Dashboard** (ID: ghi789)
- App: Admin Portal | Last active: Feb 2026
If no results are found, let the user know and suggest alternative search terms or ask them to double-check the name.
Do not proceed to the adoption report until the user has confirmed which feature to analyze.
Once the feature is confirmed, gather core adoption data. Run these queries together to save time:
Tools: Pendo:activityQuery
When constructing activity queries, filter by the confirmed feature ID and use the appropriate time period. Request both numVisitors and numEvents where possible.
Understand who is and isn't using the feature:
Tools: Pendo:activityQuery, Pendo:visitorQuery
For each power user, try to include their account name so the data is actionable. The Pendo:activityQuery grouped by visitorId may not always return a clean account name — if needed, cross-reference by running a separate Pendo:activityQuery grouped by accountId for the same feature to get account-level context, or infer the account from the visitor's email domain.
Compare feature users against the total active user base to get a meaningful adoption percentage:
Tools: Pendo:activityQuery, Pendo:segmentList
(feature users / total active visitors) × 100Pendo:segmentList), calculate adoption rate per segment for additional insight — this often reveals that adoption is strong in one segment but weak in another. Note: some subscriptions have thousands of segments, which will cause Pendo:segmentList to fail without a substring filter. If this happens, note in the report that segment-level breakdown is available if the user specifies a segment of interest, and move on — don't let this block the rest of the report.The adoption rate relative to active users is more useful than against all visitors, since inactive users aren't a realistic adoption target.
Look at how adoption is changing over time:
Tools: Pendo:activityQuery
If the timeframe is 14 days or less, use daily granularity. For longer periods, use weekly.
Generate a structured feature adoption report:
## Feature Adoption Report: {feature_name}
**Feature ID**: {feature_id}
**Period**: {timeframe}
### Adoption Overview
- **Total Users**: {unique_visitors} visitors across {unique_accounts} accounts
- **Adoption Rate**: {adoption_rate}% of active users ({feature_users} / {total_active_users})
- **Total Events**: {event_count} interactions
- **Trend**: {trend_direction} ({percent_change}% vs previous period)
### Power Users (Champions)
| Rank | Visitor | Account | Events |
|------|---------|---------|--------|
| 1 | {visitor_1} | {account_1} | {events} |
| 2 | {visitor_2} | {account_2} | {events} |
| ... | ... | ... | ... |
### Top Accounts by Adoption
| Rank | Account | Users | Events |
|------|---------|-------|--------|
| 1 | {account_1} | {user_count} | {events} |
| 2 | {account_2} | {user_count} | {events} |
| ... | ... | ... | ... |
### Adoption Trend
{weekly_or_daily_trend_summary — describe the trajectory in words, noting any inflection points}
### Segment Adoption (if available)
| Segment | Adoption Rate | Users |
|---------|--------------|-------|
| {segment_1} | {rate}% | {count} |
| ... | ... | ... |
### Insights & Recommendations
- {insight based on the data — e.g., "Adoption is concentrated in 3 accounts, suggesting broad rollout hasn't happened yet"}
- {actionable recommendation — e.g., "Consider targeting accounts with high overall activity but zero feature usage for outreach"}
- {trend insight — e.g., "Week-over-week growth has been steady at ~5%, indicating organic discovery"}
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