Sales forecasting predicts future revenue based on current pipeline data, historical win rates, and rep judgment. The best forecasts combine multiple methods and stay auditable — anyone should be able to trace a number back to the deals behind it.
The main forecasting methods
Pipeline stage forecasting weights each open deal by the historical win rate of its current stage. It's simple but assumes every deal in a stage behaves the same, which isn't always true.
Rep-level forecasting asks reps to categorize deals (commit, best case, pipeline) based on their own judgment. It captures nuance stage-based math misses, but is only as reliable as the rep's honesty and calibration.
Historical/statistical forecasting uses past performance trends and deal velocity to project forward, useful for stable, high-volume sales motions but less responsive to sudden market shifts.
Why forecasts go wrong
The most common cause of forecast error isn't a bad model — it's stale data. A deal marked 'commit' that hasn't had a logged interaction in three weeks is a forecast risk hiding behind an optimistic label.
The fix isn't a more complex formula; it's better underlying data. A forecast built on automatically logged activity, rather than self-reported stage updates, tends to be more honest because it isn't filtered through a rep's optimism.