Growth Analytics AI Prompts

Apply a growth accounting framework to decompose MAU growth into its constituent components. User activity data: {{activity_data}} (user_id, active_month) Time period: {{period}} 1. User state classification: For each user in each month, classify their state: - New: first month of activity - Retained: active this month AND last month - Resurrected: active this month but NOT last month (but active at some prior point) - Churned: active last month but NOT this month (not visible in current month counts) 2. Growth accounting equation: MAU(t) = MAU(t-1) + New(t) + Resurrected(t) - Churned(t) - Verify this equation balances in the data 3. Monthly trend of each component: - Plot New, Retained, Resurrected, and Churned users over time - Quick ratio = (New + Resurrected) / Churned Quick ratio > 1: growing. < 1: shrinking. = 1: flat. - What is the trend in the quick ratio? 4. Component deep dive: - New users: growing or declining? What is driving acquisition? - Churn: is the churn count growing as MAU grows? (Structural churn problem if yes) - Resurrection: what brings users back? Is resurrection a meaningful growth driver? - Retention: what % of users are retained month over month? Is it improving? 5. Diagnosis: - Is this a new user problem (top of funnel), a retention problem, or both? - If the quick ratio < 1: which component needs improvement most? - If the quick ratio > 1 but slowing: is churn keeping pace with new user growth? Return: monthly growth accounting table, quick ratio trend, component analysis, and growth diagnosis.

Decompose the North Star Metric into its input metrics and build a measurement tree. North Star Metric: {{nsm}} (e.g. 'Weekly Active Engaged Users' or 'Messages Sent per Month') Product context: {{product_description}} 1. Level 1 decomposition: Break the NSM into 2-3 multiplicative or additive components. Example: Weekly Active Engaged Users = Weekly Active Users x Engagement Rate Example: Revenue = Users x Conversion Rate x Average Order Value 2. Level 2 decomposition: Break each Level 1 component further. Example: Weekly Active Users = New Users + Retained Users + Resurrected Users Example: Engagement Rate = % Users Completing Core Action 3. Level 3 decomposition (where meaningful): Continue decomposing into actionable leaf metrics that specific teams own. 4. For each leaf metric: - Current value - Owner: which team or squad controls this metric? - Lever: what specific action moves this metric? - Effort to improve by 10%: Low / Medium / High 5. Sensitivity analysis: - If each leaf metric improves by 10%, which has the largest impact on the NSM? - This identifies the highest-leverage improvement opportunity 6. Metric tree dashboard spec: - Top level: NSM with trend - Second level: Level 1 components with trend - Third level: Level 2 components with owner labeled - Color coding: green = above target, yellow = near target, red = below target Return: metric tree (all three levels), owner assignment, sensitivity analysis, and dashboard specification.