Enter your forecasted and actual revenue for up to 4 quarters. We will calculate your accuracy and show where variance concentrates.
How to read your score
Forecast accuracy is calculated as: 1 - |Forecast - Actual| / Forecast, expressed as a percentage. A score of 95% means your forecast was within 5% of actual revenue.
Here is how your accuracy stacks up against published B2B SaaS benchmarks:
| Accuracy Range | Assessment | Context |
|---|---|---|
| 95%+ | Excellent | ORM's target range for client engagements. Top decile. Gartner reports only 7% of companies achieve 90%+ consistently. At this level, your board can trust the number and plan around it. |
| 85-95% | Strong | Top quartile. Forecast is reliable enough for revenue planning but typically lacks the prescriptive layer that turns the gap into action. |
| 75-85% | Average | Typical for B2B SaaS companies using CRM-based forecasting without predictive models. |
| Below 75% | Needs work | Forecast misses at this level create board-level credibility problems and make resource planning unreliable. |
What the bias tells you
Forecast bias is as important as accuracy. Consistent over-forecasting (actuals come in below forecast) usually signals one of three problems: reps sandbagging pipeline quality, conversion assumptions that are too aggressive, or deals slipping that are not being flagged early enough.
Consistent under-forecasting (actuals beat forecast) is less common but signals conservative assumptions or an acceleration in deal velocity that the model is not capturing.
At ORM, we decompose variance by segment, deal type, rep, and pipeline stage. That granularity reveals whether your accuracy problem is structural (the model is wrong) or behavioral (reps are not updating pipeline accurately). The fix is different in each case.
ORM's take: accuracy is the output, not the goal
Most companies treat forecast accuracy as a metric to improve. We treat it as a diagnostic of the revenue engine. When accuracy is low, the question is not "how do we get the number closer to actual." The question is "what is the underlying pipeline dynamic that the forecast is not capturing."
A custom model built on your data, calibrated to your conversion rates, and adjusted for your specific seasonal patterns will consistently outperform any spreadsheet, CRM roll-up, or generic platform algorithm. That is not because the math is more complex. It is because the model is specific to your business.
Common questions
How do you measure forecast accuracy?
Compare the forecast submitted at the start of a period against what actually closed, as a percentage. The standard formula is 100 minus the absolute percentage error, so a forecast of $1M against $900K actual scores 90%.
What is a good forecast accuracy percentage?
Most B2B SaaS teams should target 90% or better at the quarter level. Only about 7% of companies achieve it. Below 80% the forecast stops being useful for planning, because the error is larger than the decisions it informs.
What is the difference between forecast accuracy and forecast bias?
Accuracy measures how far the forecast lands from actual. Bias measures which direction it misses, consistently. A forecast can be accurate on average while carrying a strong bias that cancels out, which is why both need measuring.
How can you improve forecast accuracy quickly?
Track pipeline weekly rather than sporadically. Teams that do report 87% accuracy against 52% for irregular tracking. The next largest gain comes from measuring each rep bias and correcting for it rather than asking them to try harder.
Should forecast accuracy be measured per rep or per team?
Both. Team accuracy tells you whether the number is safe to commit. Per-rep accuracy tells you where the error comes from, and because individual bias is consistent quarter to quarter it can be subtracted rather than argued about.
Get to 95%+ accuracy
ORM builds custom forecast models that deliver board-ready accuracy. No dashboards to interpret. Just the number and what to do about it.
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