Build a growth model to forecast and diagnose product growth

Description

Your growth feels like a black box — new users come in, some churn, revenue goes up or down, but nobody can explain why or predict what happens next. This builds a bottom-up growth model that decomposes your growth into its component levers so you can forecast, diagnose, and intervene.

Build a bottom-up growth model for {{product_name}} that explains current growth and predicts future trajectories.

## Context
- Product: {{product_name}}
- Business model: {{business_model}}
- Monthly revenue: {{mrr}}
- Monthly new users: {{new_users_per_month}}
- Monthly churn rate: {{churn_rate}}
- Average revenue per user: {{arpu}}
- Primary acquisition channels: {{channels}}
- Historical data available: {{data_period}} (e.g., last 12 months)

## Step 1: Decompose the Growth Equation
Break growth into its fundamental components:

**For SaaS:**
Revenue(t) = Revenue(t-1) + New MRR + Expansion MRR - Churned MRR - Contraction MRR

**For consumer/marketplace:**
Active Users(t) = Active Users(t-1) + New Users + Resurrected Users - Churned Users

For each component, list:
1. Current value (last 3 months average)
2. Trend direction (growing, stable, declining)
3. What drives this component? (which levers are within your control?)

## Step 2: Map the Acquisition Funnel
For each acquisition channel:
1. Top-of-funnel volume (visitors, impressions, leads)
2. Conversion rate at each stage
3. CAC by channel
4. Payback period by channel
5. Which channels are saturating? Which have room to scale?

## Step 3: Map the Retention Curve
1. Plot cohort retention: Day 1, Day 7, Day 30, Day 90
2. Does the curve flatten (good) or continue declining (bad)?
3. What's the terminal retention rate? (the floor)
4. Compare retention by: acquisition channel, user segment, activation status
5. Identify the "aha moment" that separates retained users from churned users

## Step 4: Build the Model
Create a spreadsheet model with:
- Monthly cohort inputs (new users by channel)
- Retention curve applied to each cohort
- Revenue assumptions (ARPU by segment, expansion rate, contraction rate)
- 12-month forecast under three scenarios: pessimistic, baseline, optimistic

## Step 5: Sensitivity Analysis
1. Which single lever has the highest impact on 12-month revenue?
2. If you improve retention by 5 percentage points, what's the revenue impact?
3. If you double acquisition from the best channel, what's the revenue impact?
4. At what churn rate does growth stall regardless of acquisition?

## Step 6: Growth Diagnosis
Based on the model:
- Is growth limited by acquisition, activation, retention, or monetization?
- What's the one lever that would change the trajectory most?
- What experiments should you run to test that lever?
- What's the minimum growth rate needed to hit {{target_metric}} by {{target_date}}?

Customize This Prompt