Free online revenue projection calculator to forecast future 12-60 month revenue trends based on monthly growth rate and starting MRR. Supports MoM and YoY modes. | No Signup · Data Never Leaves Your Device
Zero Dependencies · Works OfflineFree online revenue projection calculator to forecast future 12-60 month revenue trends based on monthly growth rate and starting MRR. Supports MoM and YoY modes.
SaaS Fundraising Deck：Generate 3-5 year revenue projection tables showing the path to $100M ARR under different growth assumptions, presenting a clear growth roadmap to investors.
Team Hiring Planning：Calculate affordable team size from projected revenue — SaaS companies typically spend 40-60% of revenue on personnel. Ensure hiring pace matches revenue growth.
Annual Goal Decomposition：Work backwards from annual revenue targets to required monthly growth rates, breaking them into quarterly milestones and new customer acquisition targets.
Early-stage SaaS startups can see 10-20% MoM (3-9x annualized). Growth-stage settles to 3-7%. Mature companies see 1-3% MoM. Reference peer public data and your own historical numbers. Conservative estimates are usually wiser.
MoM (Month-over-Month) compares adjacent months, showing short-term trends. YoY (Year-over-Year) compares the same month a year ago, eliminating seasonality. This calculator supports both modes.
CAGR = (Ending Value / Starting Value)^(1/n) - 1, where n = number of years. Example: $10K → $100K over 5 years, CAGR = (100/10)^(1/5)-1 ≈ 58.5%. CAGR smooths out volatility to show the long-term trend.
① Linear extrapolation ignoring market saturation and competition; ② Ignoring seasonality — Dec/Jan revenue often below average; ③ Not separating MRR from one-time revenue — SaaS should focus on recurring revenue; ④ Not accounting for churn — new customer adds must net of churn.
Investors want replicable growth models. Show growth sources (new customers/price increases/new markets), present optimistic/realistic/pessimistic scenarios, and back projections with actual retention data rather than pure linear assumptions.