What is revenue forecasting?

Revenue forecasting is the process of predicting revenue for a future period using current pipeline, historical conversion rates, contracted recurring revenue and expected churn.

How do I build a revenue forecast?

Clean the pipeline, calculate historical close rates by stage, apply them to open deals, add contracted recurring revenue, subtract expected churn, then reconcile the result against the rep roll-up.

How do I forecast sales?

Use stage-weighted pipeline for most teams, historical run rate for high-volume short-cycle businesses, and rep commit roll-ups for complex enterprise deals. Comparing two methods is the fastest way to spot optimism.

How can AI improve sales forecasting?

AI weights each deal individually using age, engagement, stakeholder count and slip history instead of applying one average close rate per stage, which materially improves accuracy for teams with enough deal history.

Revenue forecasting and how to build an accurate sales forecast

Revenue forecasting predicts how much revenue will close in a future period based on current pipeline, historical conversion and known commitments. A forecast is not a target — it is your best honest estimate, and its value depends entirely on how consistently you measure its accuracy.

All answers

Three ways to build a revenue forecast

Method	How it works	Best for
Stage-weighted	Multiply each open deal by its stage's historical close rate	Teams with clean stage data and consistent process
Historical run rate	Project from trailing revenue and growth rate	Steady, high-volume, short-cycle businesses
Rep commit and roll-up	Reps commit deals; managers adjust with judgement	Complex enterprise deals where context beats averages

How to build the forecast step by step

Clean the pipeline first: every open deal needs an amount, an owner and a realistic close date.
Calculate the historical close rate for each stage over the last two to four quarters.
Apply those rates to current open pipeline to get the weighted number.
Add signed but unbilled revenue and recurring revenue already contracted.
Subtract expected churn or downgrade for the period.
Compare the result against the rep roll-up and investigate any gap larger than ten percent.

How accurate is my sales forecast?

Measure accuracy as actual revenue divided by the forecast made at the start of the period, and track it every period. Mature teams land within five to ten percent. If your accuracy swings wildly, the cause is almost always close dates that slip rather than deals that die — check how many deals moved their close date more than once.

How AI improves forecasting

Stage weighting assumes every deal in a stage behaves the same way. In reality, deal age, engagement, stakeholder count and slip history all shift the odds. AI forecasting learns those patterns from your own history and adjusts each deal individually, then explains which signals moved the number.

Alegria RevOps generates scenario-based forecasts with written narratives so finance can see the assumptions behind the number, not just the total.

Frequently asked questions

What is revenue forecasting?: Revenue forecasting is the process of predicting revenue for a future period using current pipeline, historical conversion rates, contracted recurring revenue and expected churn.
How do I build a revenue forecast?: Clean the pipeline, calculate historical close rates by stage, apply them to open deals, add contracted recurring revenue, subtract expected churn, then reconcile the result against the rep roll-up.
How do I forecast sales?: Use stage-weighted pipeline for most teams, historical run rate for high-volume short-cycle businesses, and rep commit roll-ups for complex enterprise deals. Comparing two methods is the fastest way to spot optimism.
How accurate is my sales forecast?: Measure actual revenue against the forecast made at period start, every period. Within five to ten percent is strong. Persistent misses usually come from close dates slipping rather than deals being lost.
How can AI improve sales forecasting?: AI weights each deal individually using age, engagement, stakeholder count and slip history instead of applying one average close rate per stage, which materially improves accuracy for teams with enough deal history.