SaaS Metrics

Forecast Accuracy

Forecast accuracy measures how close your forecast was to the actual outcome (bookings/revenue) for a period.

Updated 2026-01-24

Definition

Forecast accuracy measures how close your forecast was to the actual outcome (bookings/revenue) for a period.

Example

If you forecast $1.0M and book $900k, accuracy is about 90%.

How to use it

  • Track accuracy by segment and by stage source to identify systemic bias.
  • Use accuracy to improve process, not to punish teams (or you get sandbagging).
  • Measure both over-forecast and under-forecast to see direction of bias.
  • Review forecast accuracy alongside slippage to separate timing vs quality.
  • Report accuracy by horizon (30, 60, 90 days) to see where signal breaks.

Common mistakes

  • Comparing accuracy across periods with different forecast definitions.
  • Ignoring slippage rates that shift deals out of the period.
  • Changing stage definitions without re-baselining accuracy.
  • Measuring accuracy without excluding pulled-in deals from future periods.

Measured as

Measure Forecast Accuracy on the same customer segment, time window, and revenue basis each time you review it.

Misused when

  • Comparing accuracy across periods with different forecast definitions.
  • Ignoring slippage rates that shift deals out of the period.
  • Changing stage definitions without re-baselining accuracy.
  • Measuring accuracy without excluding pulled-in deals from future periods.

Operator takeaway

  • Track accuracy by segment and by stage source to identify systemic bias.
  • Use accuracy to improve process, not to punish teams (or you get sandbagging).
  • Measure both over-forecast and under-forecast to see direction of bias.
  • Keep Forecast Accuracy consistent by cohort, segment, and period before you use it as a decision signal in planning or reporting.
  • Interpret the metric alongside retention, margin, or payback so one ratio does not hide the real operating trade-off.

Next decision

  • Read Sales ops metrics hub: quota, pipeline, win rate, and capacity planning if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
  • Decide whether Forecast Accuracy is a growth, retention, or efficiency signal before you set targets around it.

Where to use this on MetricKit

Guides