Feature Adoption Analysis AI Prompt

Analyze the adoption of this product feature and identify opportunities to increase usage.

Feature: {{feature_name}}
Adoption data: {{adoption_data}} (user_id, first_use_date, usage_frequency, user_segment)
Launch date: {{launch_date}}

1. Adoption funnel:
   - Awareness: % of eligible users who have seen or been exposed to the feature
   - Activation: % who have used it at least once
   - Adoption: % who have used it more than {{n_times}} times
   - Habit: % who use it regularly (at least once per {{period}})

2. Time to first use:
   - How long after account creation do users first try the feature?
   - Median and distribution of time-to-first-use
   - What % of eventual adopters used it within: 1 day, 7 days, 30 days of account creation?

3. Adoption by segment:
   - Which user segments have the highest adoption rates? (by plan, role, company size, acquisition channel)
   - Which segments are significantly below average? These are the opportunity segments.

4. Correlation with retention:
   - Do users who adopt this feature have higher 30-day and 90-day retention?
   - Compute retention rates for: adopters vs non-adopters (note: correlation, not causation)
   - If adopters retain significantly better: this feature is a potential activation lever

5. Usage depth:
   - Among adopters: how often do they use it? (sessions per week distribution)
   - Are there power users using it far more than average? What do they have in common?
   - At what usage frequency does the feature become 'sticky' (correlated with long-term retention)?

6. Barriers to adoption:
   - In the adoption funnel, which step has the biggest drop-off?
   - For low-adoption segments: what are 3 likely barriers (awareness, discoverability, complexity, value clarity)?

Return: adoption funnel metrics, time-to-first-use analysis, segment breakdown, retention correlation, and barrier hypothesis.