Calculator Inputs
Use the responsive grid below. It shows three columns on large screens, two on smaller screens, and one on mobile.
Example Data Table
| Period | Forecast Revenue | Actual Revenue | Weighted Forecast | Closed Deals | Coverage Ratio |
|---|---|---|---|---|---|
| Jan | 95,000 | 90,500 | 92,300 | 24 | 3.1 |
| Feb | 108,000 | 111,900 | 110,000 | 28 | 3.5 |
| Mar | 120,000 | 118,400 | 119,500 | 31 | 3.4 |
Formula Used
Revenue Variance = Actual Revenue − Forecast Revenue
Absolute Error = |Actual Revenue − Forecast Revenue|
Forecast Accuracy = (1 − Absolute Error ÷ Actual Revenue) × 100
Weighted Accuracy = (1 − |Actual Revenue − Weighted Forecast| ÷ Actual Revenue) × 100
Deal Count Accuracy = (1 − |Forecast Deals − Closed Deals| ÷ Closed Deals) × 100
Average Deal Size Accuracy = (1 − |Actual Deal Size − Forecast Deal Size| ÷ Actual Deal Size) × 100
Composite Accuracy = 40% Core Accuracy + 20% Weighted Accuracy + 20% Deal Accuracy + 20% Deal Size Accuracy
These formulas help sales leaders see forecast precision, bias direction, and whether pipeline quality supports a reliable revenue call.
How to Use This Calculator
- Enter the forecast period name, such as a month or quarter.
- Input forecast revenue, actual revenue, weighted forecast, commit forecast, and best case forecast.
- Add forecasted deals, closed deals, expected deal size, actual deal size, pipeline coverage, and target revenue.
- Press Calculate Accuracy to show results below the header and above the form.
- Review accuracy, bias, error, and attainment metrics to improve pipeline inspection and rep coaching.
- Use the CSV or PDF buttons to export the displayed result summary.
FAQs
1. What does sales forecast accuracy measure?
It measures how closely projected sales match actual closed revenue. High accuracy means forecasting discipline is strong, while lower accuracy signals bias, weak deal qualification, or unstable pipeline movement.
2. Why include weighted, commit, and best case forecasts?
These views show planning confidence at different levels. Weighted forecasts reflect probability, commit forecasts show expected close confidence, and best case forecasts reveal upside assumptions that may or may not materialize.
3. What is forecast bias?
Forecast bias shows whether your team consistently over-forecasts or under-forecasts. Direction matters because repeated bias can distort hiring, spending, territory planning, and executive confidence in pipeline reporting.
4. Why track deal count accuracy?
Deal count accuracy checks whether reps predict the correct number of wins. It helps separate a revenue miss caused by too few deals from one caused by smaller contract values.
5. How does pipeline coverage affect forecast quality?
Coverage compares open pipeline value with quota or target. Low coverage can pressure teams into optimistic forecasting, while healthy coverage usually supports stronger forecast consistency and better risk visibility.
6. What is a good forecast accuracy percentage?
Targets vary by sales motion, but many teams aim for 90% or higher on commit forecasts. Early-stage or volatile businesses may accept lower accuracy while processes mature.
7. Should this calculator be used monthly or quarterly?
Both work well. Monthly reviews help coaching and correction speed, while quarterly reviews support strategic planning. Many revenue teams use both cadences to monitor near-term execution and longer trend reliability.
8. Can this tool support CRM forecast reviews?
Yes. It gives leaders a structured way to compare forecast layers, actual performance, coverage, and deal quality. That makes pipeline review conversations more objective and easier to document.