Revenue Forecast
Generate SaaS revenue forecasts with cohort analysis and scenario modeling
Analyze billing history and churn data to calculate MRR growth rate, net revenue retention, cohort behavior, and ARPU trends, then model base, optimistic, and pessimistic revenue scenarios with month-by-month MRR buildup and identify which growth lever — reducing churn, increasing ARPU, or faster acquisition — has the highest impact
When
You need to see the next 12 months before making a hiring, pricing, or spend decision.
Input
Billing history (MRR by month), churn data, pipeline deals, and growth context
Output
12-month revenue forecast with base/optimistic/pessimistic scenarios, cohort analysis, and growth lever recommendations
Time
~8-12 min.
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When
You need to see the next 12 months before making a hiring, pricing, or spend decision.
How
Analyzes billing history and growth trends, models revenue scenarios, identifies key growth levers
Input
Billing history (MRR by month), churn data, pipeline deals, and growth context
Output
12-month revenue forecast with base/optimistic/pessimistic scenarios, cohort analysis, and growth lever recommendations
Step by step
- 1Parse billing history to calculate MRR trends, retention curves, and cohort-level revenue patterns.
- 2Model three revenue scenarios with month-by-month MRR buildup and sensitivity analysis.
- 3Identify which growth lever moves the needle most and rank actionable recommendations.
- 4Evaluate assumption transparency, mathematical consistency, and scenario realism.
Useful for