Method and assumptions
This deterministic forecast is a scenario tool, not a statistical prediction. Run conservative, base and upside cases with separate assumptions.
Each month: ending customers = prior customers + new customers - prior customers × churn rate; MRR = ending customers × ARPA.
Worked scenario
Begin with current recurring revenue and model new, expansion, contraction and churn movements month by month. Use separate assumptions for committed backlog and uncommitted pipeline. Build base, downside and upside cases, and reconcile each completed month before extending the remaining forecast.
How to interpret the result
The forecast is most useful as a transparent driver model. A single growth percentage hides whether revenue depends on sales capacity, conversion, retention or expansion. Compare actual movement against each driver, not only total revenue, so forecast error changes the relevant operating assumption.
Input reference
- Currency
- Example default: USD
- Starting customers
- Example default: 250
- New customers per month
- Example default: 35
- Monthly churn rate
- Example default: 2%
- Average revenue per account
- Example default: 120
- Forecast months
- Example default: 12
Common mistakes
- Applying one compound growth rate to every month.
- Treating pipeline as contracted recurring revenue.
- Ignoring capacity, seasonality and implementation delay.
Before using the result
- Forecast recurring movements as separate drivers.
- Maintain base, downside and upside assumptions.
- Reforecast after every closed reporting period.
Questions to check before deciding
Does this model expansion revenue?
No. Use a higher ARPA scenario or MRR model when expansion needs to be explicit.
How should seasonality be handled?
Run different new-customer assumptions by month in a spreadsheet when seasonality matters.
Independent planning calculator. Not financial, tax, legal or investment advice.