Feature Adoption AI Prompts

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.

Assess the business impact of this recently launched feature. Feature: {{feature_name}} Launch date: {{launch_date}} Primary success metric: {{primary_metric}} Data available: {{data}} 1. Pre/post comparison: - Define the pre-period (same length as post, ending at launch date) - Compare primary metric: pre-period average vs post-period average - Absolute change and % change - Account for trends: was the metric already trending up/down before launch? 2. Confound check: - What else changed during the post-period? (Seasonality, marketing campaigns, other feature launches) - How might these confounds explain the observed change? - Can any confounds be controlled for or isolated? 3. Adoption-outcome correlation: - Segment users by adoption level: non-adopters, light adopters, heavy adopters - Compare primary metric across adoption segments - Does heavier feature usage correlate with better outcomes? 4. Counterfactual estimation: - If possible: use a holdout group (users who did not have access to the feature) as a control - Difference-in-differences: compare the change in metric for treatment vs control groups - If no holdout: use synthetic control (similar product/market as proxy) 5. Secondary effects: - Did the feature have any unintended effects on other metrics? - Check: session length, support tickets, error rates, other feature usage 6. ROI estimate: - Translate the metric impact into business value: what is the estimated annual impact in revenue or cost? - How does this compare to the development cost of the feature? Return: pre/post comparison, confound analysis, adoption-outcome correlation, counterfactual estimate, and ROI.