Quick Answer
A good revenue forecast should be within 5-10% of actuals for the current quarter and within 10-15% for the next quarter. Full-year forecasts should target plus or minus 10% accuracy. The acceptable range depends on your business maturity, revenue predictability, and how far ahead you are forecasting. Track accuracy over time using MAPE or forecast bias metrics to improve continuously.
Forecast accuracy measures how close your predicted financial results were to actual outcomes. It is typically expressed as a percentage deviation: a forecast of £1M against actuals of £950K has 5% variance and 95% accuracy.
Current month: 95-98% accuracy. You have most of the data already.
Current quarter: 90-95% accuracy. Achievable with good pipeline visibility and cost discipline.
Next quarter: 85-90% accuracy. Requires solid leading indicators and driver-based models.
Full year: 85-95% accuracy. Wider range depending on business predictability.
Beyond 12 months: 70-85% accuracy. Uncertainty increases significantly; focus on ranges rather than point estimates.
Subscription/SaaS businesses: Should achieve tighter accuracy (within 5%) because recurring revenue is predictable. The main unknowns are new business, churn, and expansion.
Project-based businesses: Wider accuracy ranges (10-15%) because revenue depends on winning specific contracts with uncertain timing.
Seasonal businesses: Can be very accurate if seasonal patterns are well understood, but may show significant monthly variance even if the annual forecast is close.
MAPE (Mean Absolute Percentage Error): The average of absolute percentage differences between forecast and actual. Good for overall accuracy measurement.
Forecast bias: Whether you consistently over-forecast or under-forecast. A bias indicates systematic optimism or conservatism that should be corrected.
Tracking signal: Cumulative forecast error divided by mean absolute deviation. Flags when forecasts are drifting in one direction over time.
1. Use driver-based models rather than top-line estimates 2. Track accuracy at the line-item level to find your biggest error sources 3. Shorten the feedback loop — monthly reforecasts catch issues faster than quarterly 4. Incorporate leading indicators (pipeline, bookings, usage data) 5. Review past accuracy before setting new targets — build on what you know about your error patterns
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FAQ
Neither is ideal. Consistent over-forecasting (optimistic bias) leads to overspending and missed targets. Consistent under-forecasting (sandbagging) misallocates resources and understates potential. Aim for unbiased forecasts that are equally likely to be above or below actuals.
Use pipeline-weighted forecasting for new business, track leading indicators (MQLs, SQLs, demos), analyse historical conversion rates by stage, and segment your forecast by revenue type (new, expansion, renewal). The more granular your model, the more accurate it tends to be.
Costs are more controllable than revenue, so accuracy should be tighter: within 3-5% for fixed costs (rent, salaries) and 5-10% for variable costs. Total cost forecasts should be within 5% for the current quarter.
Yes. Tracking and reporting forecast accuracy builds credibility. Show the trend over time — improving accuracy demonstrates a maturing finance function. Include accuracy metrics in your monthly finance report.
Grove FP automatically tracks forecast vs actual at every level. It calculates MAPE, bias, and variance trends over time, helping you identify your biggest error sources and improve accuracy systematically.
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