Forecast vs Actual

Forecast vs actual is the comparison between the revenue a team predicted for a period and the revenue it actually closed, plus the variance analysis that explains the gap between the two. The forecast is the prediction. The actual is the revenue that landed. Subtract one from the other and you get the variance, stated in dollars or as a percentage of the forecast.

Most teams read the result as a verdict. A stronger practice reads it as data. Every gap between forecast and actual says something about which assumptions were wrong, and a disciplined review turns that signal into a more accurate model for the quarter ahead.

How to calculate forecast variance

Variance = Actual Revenue - Forecasted Revenue. A negative figure is a shortfall, a positive figure is overperformance. Variance percentage divides that gap by the forecast, which lets you compare a $5M quarter against a $50M one on the same scale.

The headline number tells you how far off you were. It says nothing about why. The value sits in the decomposition, not the total.

What drives the gap

Four causes explain most misses.

Deals close below their pipeline value. ORM sees pipelines carrying an $80,000 average deal size where closed-won deals average $40,000. A forecast that trusts the CRM amount overstates every quarter. Deals slip. The clearest slippage signal is a rep pushing the close date. Of the pipeline dated to close in a quarter on day one, roughly 20% closes inside that quarter, so 80% of the visible value moves out or dies. Conditions shift under old assumptions. A new competitor pressures pricing and deal sizes shrink. Rising rates slow buyers and win rates fall. A model built in January cannot see a March that no longer matches it. Seasonality gets ignored. Q2 and Q4 run stronger than Q1 and Q3, and the third month of a quarter beats the first two. A flat model misreads a normal seasonal dip as a shortfall.

Turn the miss into a calibration input

The point of forecast vs actual is the feedback loop, not the scorecard. Once you attribute the variance to a specific cause, you correct the assumption that produced it and carry that correction into the next forecast. If deals closed at half their pipeline value, lower the expected amount. If close dates kept sliding, discount the deals reps keep pushing.

ORM builds this loop into the model. It trains on your historical sales performance over 4 to 6 weeks, then updates through the quarter as deals move, so the forecast recalibrates instead of sitting static. Manual forecasts on new and expansion revenue commonly reach about 90% accuracy and cost heavy effort to maintain. ORM targets 95% and holds it from day 1 to day 90 without manual adjustment.