Forecast Accuracy Calculator for B2B Sales

Calculate Forecast Accuracy From Predicted and Actual Revenue

Use this forecast accuracy calculator for B2B sales to compare a committed prediction with what actually closed. Enter an optional reporting period and revenue values to check sales forecast accuracy without a signup.

Forecast accuracy = (1 - |Predicted revenue - Actual closed revenue| / Actual closed revenue) x 100. A positive variance indicates an overforecast, while a negative variance indicates an underforecast.

This live example compares $500,000 predicted revenue with $450,000 actual closed revenue. For periods with no closed revenue, percentage results are not meaningful, so review the dollar difference instead.

Use the result to improve revenue forecasting, then explore all RevXForge calculators for related no-signup tools.

What Is the Forecast Accuracy Formula?

This forecast accuracy formula for B2B sales measures absolute forecast error relative to revenue actually closed in a specified reporting period.

• Forecast is predicted revenue for the period.
• Actual is revenue actually closed in that same period.
• The vertical bars indicate absolute value, so overforecasting and underforecasting reduce sales forecast accuracy equally.

A result of 100% means the forecast exactly matched actual closed revenue. Using actual closed revenue as the denominator makes forecast variance comparable to the amount ultimately realized. See our methodology for how RevXForge presents transparent derived metrics and limitations.

For example, if forecast revenue is $450,000 and actual revenue is $500,000, the absolute error is $50,000: (1 - $50,000 / $500,000) x 100 = 90% forecast accuracy.

When actual closed revenue is zero, a percentage cannot be calculated because it would require division by zero. In that case, the calculator should show a plain-language notice and report the dollar sales forecast variance instead.

How to Read Forecast Variance and Forecast Direction

Revenue variance equals actual closed revenue minus predicted revenue. A positive sales forecast variance means actual revenue exceeded the forecast, which is favorable for revenue attainment. A negative variance means actual revenue fell below forecast, commonly viewed as unfavorable.

Neither direction is inherently accurate. Both are forecast error: an overforecast happens when predicted revenue is higher than actual revenue, while an underforecast happens when predicted revenue is lower.

• $500,000 forecast, $450,000 actual: $50,000 unfavorable variance, or 88.9% accuracy.
• $400,000 forecast, $450,000 actual: $50,000 favorable variance, or 88.9% accuracy.

The same absolute miss produces the same forecast accuracy, even though the attainment outcome differs. Use both measures to understand whether the forecast was wrong and in which direction.

• 95% to 100%: close alignment.
• 90% to 94.9%: warrants review.
• Below 90%: material variance worth investigating.

These ranges are a starting point, not an industry standard. Compare periods using the same forecast stage, revenue definition, and reporting window. For context by deal size, motion, or industry, review B2B revenue benchmarks rather than relying on one blended target.

Worked B2B Sales Forecast Accuracy Examples

These sales forecast accuracy examples use closed revenue from one comparable reporting period. Do not substitute pipeline value, weighted pipeline, or total opportunity value for actual closed revenue.

1. Overforecasting example: Predicted revenue: $1,200,000. Actual closed revenue: $1,000,000. Absolute error: |$1,200,000 − $1,000,000| = $200,000. Variance direction: overforecast by $200,000. Forecast accuracy: 1 − ($200,000 ÷ $1,000,000) = 80%.
2. Underforecasting example: Predicted revenue: $750,000. Actual closed revenue: $900,000. Absolute error: |$750,000 − $900,000| = $150,000. Variance direction: underforecast by $150,000. Sales forecast accuracy: 1 − ($150,000 ÷ $900,000) = 83.3%.

Calculate forecast variance at a consistent level of aggregation, such as company, segment, region, or rep. Comparing a company forecast with a single rep’s closed revenue produces a misleading result.

Repeated variance by segment can signal issues with pipeline quality, conversion assumptions, deal timing, sales capacity, or ICP fit. Teams seeing persistent patterns can investigate broader revenue engine constraints rather than treating revenue forecasting as an isolated reporting problem.

Use Forecast Accuracy to Find the Source of Revenue Risk

Use this forecast accuracy calculator for B2B sales as a trend measure, not a verdict on a single quarter. One period can be distorted by slipped deal timing, an unusual renewal, or a small number of large opportunities. Track sales forecast accuracy over multiple periods before deciding that a pattern needs intervention.

When there are enough comparable observations, segment forecast variance by:

• sales motion, such as new business, expansion, or partner-led deals;
• deal size, region, forecast category, or individual rep;
• stage and opportunity age, particularly for material late-stage deals.

Persistent overforecasting is a reason to examine stage conversion assumptions, close-date discipline, and the criteria used for each forecast category. Persistent underforecasting can warrant a review of upside qualification, deal inspection, and reporting cadence. These are investigation paths, not automatic explanations.

For example, three consecutive quarters with negative revenue variance concentrated in late-stage enterprise opportunities is a useful hypothesis to investigate. It may point to how enterprise close dates, approvals, or forecast categories are handled, but the pattern alone does not prove any one cause.

Forecast accuracy is one revenue engine signal alongside pipeline coverage, conversion, sales capacity, and GTM economics. Explore evidence-led forecasting research on B2B pipeline movement, or visit RevXForge for the broader free revenue research library.

Calculation Methodology and Limitations

This forecast accuracy calculator for B2B sales measures point-in-time revenue forecast accuracy from two inputs: one predicted revenue figure and one actual closed revenue figure for the same reporting period. It shows the size of the gap between forecast and outcome, not whether the business performed well overall.

For a valid sales forecast accuracy result, keep the comparison like for like:

• Use the same revenue basis in both fields, such as bookings, recognized revenue, or closed-won ARR.
• Use amounts in the same currency.
• Match the reporting period, such as the same month, quarter, or fiscal year.
• Include or exclude renewals, expansion, taxes, and adjustments consistently.

For example, do not compare a quarterly bookings forecast of $600,000 with $550,000 in recognized revenue. Those measures are not equivalent, so the resulting forecast variance would not represent a meaningful revenue forecasting error.

This calculation does not assess forecast timing, forecast confidence, pipeline coverage, deal-level probability calibration, or the quality of underlying CRM data. A strong or weak result can signal where to investigate, but it cannot identify the cause by itself.

Any interpretation thresholds are practical review cues, not universal B2B revenue benchmarks. Forecasting motions, deal sizes, sales cycles, and revenue definitions differ. See our editorial standards for how RevXForge distinguishes sourced findings, derived calculations, methodology, and limitations.

Conclusion

A forecast accuracy calculator for B2B sales is most useful when it turns forecast variance into a repeatable measure of planning reliability, not just a scorecard for a single quarter. Track accuracy consistently, examine the assumptions behind misses, and distinguish forecasting discipline from underlying pipeline, conversion, capacity, or economic constraints.

RevXForge's Revenue Engine Diagnostic helps B2B revenue teams assess those connected factors to identify what may be driving recurring forecast gaps.