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.

Why this matters

This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.

Practical checklist

  • Write a 1-line definition for "Forecast Accuracy" that your team will use consistently.
  • Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
  • Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
  • Sanity-check with a related calculator from the same category on MetricKit.
  • Read the related guide (e.g., Sales ops metrics hub: quota, pipeline, win rate, and capacity planning) for context and common pitfalls.

Where to use this on MetricKit

Calculators

Guides