Revenue Model Builder AI Prompt

Build a bottom-up revenue model for this business.

Business type: {{business_type}}
Revenue streams: {{revenue_streams}}
Historical data available: {{historical_data}}
Forecast horizon: {{horizon}} years

1. Revenue disaggregation:
   Break total revenue into its most granular meaningful components:
   - For SaaS: ARR = Customers x Average Revenue Per Account (ARPA)
     - New ARR (new logos), Expansion ARR (upsell), Churn ARR (lost customers)
     - Net Revenue Retention (NRR) = (Beginning ARR + Expansion - Churn) / Beginning ARR
   - For transactional: Revenue = Transactions x Average Order Value
   - For subscription: Revenue = Subscribers x Monthly Fee x (1 - Churn Rate)
   - For services: Revenue = Headcount x Utilization Rate x Billing Rate

2. Forecast each driver separately:
   For each revenue driver:
   - Historical trend (last 3 years CAGR)
   - Management guidance or market growth rate
   - Internal capacity constraints
   - Derive the forecast assumption with a clear rationale

3. Bridge from current year to forecast:
   Revenue(year N+1) = Revenue(year N) + New Business + Expansion - Churn + Price Effect
   Show each component explicitly so the forecast is auditable.

4. Scenario analysis:
   - Base case: management guidance or consensus growth rates
   - Bull case: top quartile of peer growth rates
   - Bear case: mean reversion or macro headwind scenario
   - Show the revenue range at each forecast year

5. Sanity checks:
   - Implied market share: does the forecast require unrealistic market share gains?
   - Revenue per employee: does it stay within a reasonable range for the industry?
   - Cohort math: does the model's churn assumption agree with the cohort retention data?

Return: disaggregated revenue model, driver-by-driver assumptions, three-scenario table, and sanity check results.