Build a Sales or Revenue Forecast
Create a structured revenue forecast model with scenarios, assumptions, and confidence ranges.
The Prompt
Build a revenue forecast model for the following business. Include: 1. Forecasting methodology: bottom-up from pipeline or top-down from market 2. Key input assumptions with confidence levels 3. Three scenarios: bear / base / bull case 4. Monthly forecast for next 12 months 5. Seasonality adjustments if applicable 6. Sensitivity analysis: which assumptions matter most 7. Forecast accuracy metric: how to track forecast vs. actuals Output as tables I can implement in a spreadsheet. Business context: - Revenue model: [SUBSCRIPTION / TRANSACTIONAL / SERVICES] - Current run rate: [CURRENT ARR OR MONTHLY REVENUE] - Historical growth: [PAST GROWTH RATE IF KNOWN] - Pipeline: [SALES PIPELINE VALUE IF B2B] - Key uncertainties: [WHAT COULD CHANGE THE FORECAST MOST]
Example Output
12-month forecast: base case $1.2M ARR (48% growth) with assumptions of 12% monthly new ARR, 3% monthly churn, 110% net revenue retention. Bear case: 8% monthly new ARR + 5% churn = $840K ARR. Bull: 18% new ARR + 2% churn = $1.8M. Tornado chart shows churn rate is 2x more sensitive than growth rate.
FAQ
Which AI model is best for Build a Sales or Revenue Forecast?
Claude Sonnet 4 — careful about forecasting assumptions and building range estimates.
How do I use the Build a Sales or Revenue Forecast prompt?
Copy the prompt, replace the [BRACKETED] placeholders with your specific information, and paste into your preferred AI assistant (ChatGPT, Claude, Gemini, etc.). 12-month forecast: base case $1.2M ARR (48% growth) with assumptions of 12% monthly new ARR, 3% monthly churn, 110% net revenue retention. Bear case: 8% monthly new ARR + 5% churn = $840K ARR. Bull: 18% new ARR + 2% churn = $1.8M. Tornado chart shows churn rate is 2x more sensitive than growth rate.
Model Recommendation
Claude Sonnet 4 — careful about forecasting assumptions and building range estimates.