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Feature adoption report

Weekly automated feature adoption report. AI tracks usage of every new feature, surfaces power users, and flags features that aren't catching on.

Runs weekly Scheduled Delivered to Slack, Email
Feature adoption report
Active

Trigger

Runs weekly

Instructions

Generate a weekly feature adoption report.

Delivery

Slack · Email

Example output

A realistic sample of what this automation delivers — your version draws on your data.

Feature adoption report Runs weekly · Sent to Slack, Email
Sample output
Adoption %, top features % of paying customers
Multi-source Faster retries Chart hover Dashboards v2 SQL export
This week Previous week

Features shipped in the past 180 days — 11 in scope

FeatureAdoptionTrendPower users
Multi-source automations38%+9 pts WoW22
Faster chat retries71% (auto)n/a
Chart hover insights24%+414
Dashboards v219%flat8
Sleeper: SQL export4%flat3

Worth interviewing. Top 5 power users of multi-source automations all upgraded to Growth in the same week they hit 5+ automations. Worth a CSM hello.

Sleeper alert. SQL export hasn’t crossed 5% adoption in 12 weeks — either re-discover or quietly deprecate in Q3.

Generated by Basedash AI from your connected data sources Product
The prompt

Copy, paste, customize.

Drop this into a Basedash automation. AI fills in the numbers from every source you've connected.

Instructions
Generate a weekly feature adoption report.

For each feature shipped in the past 180 days, compute:

- Unique users last 7 days
- % of weekly active users who used it at least once
- Time-to-first-use for users who signed up in the past 30 days
- Trend vs. previous 7 days

Output:

**Headline.** Number of features tracked. Net new adoption this week.

**Top movers.** Five features with biggest gains or drops.

**Power users.** Up to three users who used the most distinct features. Useful for design partner outreach.

**Sleepers.** Features with < 5% adoption that haven't grown in 4 weeks — candidates to redesign, repromote, or retire.

End with one named experiment per sleeper feature.
Data sources

Powered by these sources.

Basedash automations read across every connected source in a single run. These are the ones this template tends to use most.

Frequently asked questions

What is feature adoption?
What counts as low feature adoption?

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