What is forecast accuracy?
Forecast accuracy is how close the forecast came to the actual result, usually shown as a percentage. The simplest version compares the forecast submitted at a fixed point, such as week two of the quarter, with the final number. Teams also track it by seller, manager and forecast category to see whose numbers hold up.
Why it matters to a revenue leader
Accuracy is how a CEO, board or sponsor judges a revenue leader's grip on the business. A miss in either direction costs something: under-forecasting hides capacity and cash, and over-forecasting breaks hiring and purchasing plans. In public companies the forecast feeds guidance. In PE-backed companies it feeds the lender model and covenant tests. Forrester argues that forecasts built on opinion are prone to bias, and that models built on deal and trend data improve accuracy.
How to calculate it
Forecast accuracy = 1 minus (absolute value of actual minus forecast ÷ actual). Across many periods, analysts often use mean absolute percentage error (MAPE), the average of the absolute percentage errors.
Common mistake: measuring against the final-week forecast, which by then is mostly closed deals and flatters everyone.
Example
Fictitious numbersA mechanical services company forecast $4.0M at week two of the quarter and closed $3.6M. The error is $0.4M, or 11% of actual, so accuracy is 89%. The prior quarter it forecast $3.5M and closed $3.9M, an error of about 10% in the other direction.
"How accurate was our week-two forecast in each of the last four quarters, and in which direction did we miss?"
Sources
- Mean absolute percentage error checked 9 Oct 2026
- Careful With That Axe, Sales Team: AI And The Future Of Forecasting (Forrester, 16 Mar 2021) checked 17 Oct 2026