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Glossary:
Sales Performance Metrics
Master the essential revenue and financial metrics that drive B2B SaaS success. From ARR and MRR to retention metrics and customer economics, these terms are critical for understanding pipeline health, forecasting growth, and making data-driven decisions.
Forecast Accuracy
Short Definition
Definition
Forecast Accuracy quantifies the reliability of sales forecasts by comparing predicted revenue (by forecast category) to actual closed-won revenue. In B2B SaaS, it's essential for cash flow planning, investor reporting, and executive decision-making. High accuracy (>85–90%) indicates disciplined forecasting, strong pipeline health, and predictable execution.
CROs track it by category (commit, best case, upside) and aggregate to assess overall process maturity. Low accuracy signals issues like optimistic forecasting, poor qualification, or slippage.
How to Calculate Forecast Accuracy
Forecast Accuracy Formula
Forecast Accuracy (%) = Actual Revenue ÷ Forecasted Revenue × 100
Commit Accuracy Formula
Commit Accuracy = Actual Closed-Won from Commit Deals ÷ Commit Forecast Value × 100
Best Case Accuracy Formula
Best Case Accuracy = Actual Closed-Won from Best Case Deals ÷ Best Case Forecast Value × 100
Step-by-Step Calculation
- At period close (EOM/EOQ), pull all deals forecasted in each category.
- Identify which actually closed-won in that period.
- Sum actual revenue from those deals.
- Divide by the forecasted value for that category.
- Repeat for each category; average for overall accuracy.
Example
- Q1 Forecasts: Commit $800K, Best Case $1.2M (total $2M)
- Actual Q1 closes: $750K from Commit, $300K from Best Case
Commit Accuracy = 750K/800K = 94%
Best Case Accuracy = 300K/1.2M = 25%
Overall Accuracy = 1.05M/2M = 52.5%
Why Forecast Accuracy Matters
Forecast Accuracy determines how much leadership can trust sales predictions for…
- Cash flow planning (accurate ARR timing)
- Investor reporting (credible guidance)
- Capacity decisions (hiring, spend based on real ramps)
- GTM adjustments (pipeline generation targets)
85% commit accuracy + predictable best case = boardroom credibility. <70% accuracy signals process breakdown.
Industry Benchmarks
Real-World Examples
- A Series B SaaS team improves Commit Accuracy from 72% to 91% by requiring signed MAPs for the Commit category.
- An enterprise sales team achieves 95% overall accuracy, but only 22% Best Case; they focus coaching on realistic upside sizing.
- RevOps implements a "forecast only deals >60% probability" policy, which lifts overall accuracy from 68% to 87%.
Common Mistakes
- Over-forecasting commit (too many deals in top category).
- Category gaming (reps push "sure things" to next period).
- No MAP requirement for commit forecasts.
- Ignoring slippage patterns (same deals slip repeatedly).
- Small sample bias (3 deals vs. 30 deals accuracy meaningless).
The Fix: require MAPs for commit and use clear category definitions (60/80/95% thresholds) and slippage thresholds (re-forecast if >14 days late). Run monthly accuracy reviews.
Frequently Asked Questions
What's considered good forecast accuracy?
Commit: >90%.
Overall: >85%.
Should Forecast Accuracy include expansion revenue?
Typically, they should be separate. New logo and expansion have different predictability and motions.
How often to measure Forecast Accuracy?
Monthly for trends, quarterly for major reviews. Run weekly pipeline reviews to prevent end-of-period surprises.
Does ramping rep pipeline count in accuracy?
Exclude it or track it separately. Ramping deals have lower predictability.
Can Forecast Accuracy be >100%?
Yes, from early closes and upside hits. Track "forecast bias" (whether you’re consistently over/under) separately.
Last Updated: December 18, 2025
Reviewed by: Ben Hale