Forecast Accuracy

Forecast accuracy is the gap between what the CRO promised the board and what actually landed in the bank. It's calculated as the variance between predicted bookings and closed revenue at the end of a forecast period — usually a quarter — and it's the single number that defines a sales leader's credibility. A forecast that misses by 4% is a rounding error. A forecast that misses by 25% gets people fired.

How Forecast Accuracy Is Calculated

The standard formula is |Actual − Forecast| / Forecast, expressed as a percentage. Some orgs use a signed version (Actual / Forecast) to surface whether the team consistently overcalls or undercalls. Best-in-class B2B SaaS teams land within ±5%. Most teams hover at ±15%. Anything past 20% means the forecast was a guess wearing a spreadsheet.

Two variants matter. Commit accuracy asks whether the deals tagged "commit" actually closed. Call accuracy asks whether the CRO's number to the board matched the print. They drift apart when reps sandbag the commit category but the CRO inflates the rolled-up call.

Worked Example: A Quarter That Missed

Q3 forecast: $4.2M bookings. Closed: $3.65M. Variance: 13.1%. That's a $550k miss on a number the board treated as a rounding-to-zero certainty, and it triggers a four-hour call with the CFO running spreadsheets line by line. The post-mortem reveals six "commit" deals slipped to Q4 and one renewal churn was missed in modeling. The CRO's next two forecasts get audited by RevOps before they leave the building.

When Sales Teams Use Forecast Accuracy

The CFO uses it for cash planning and hiring decisions. The CEO uses it to set guidance with investors. The board uses it to decide whether the CRO survives another quarter. RevOps uses historical accuracy to weight deal scoring models — a rep with 95% commit accuracy gets their deals taken at face value, while a rep at 60% gets discounted by the algorithm before the number rolls up to the executive call.

Recruiters care too. A VP Sales candidate who quotes a beat-by-3% track record over six quarters is a fundamentally different hire than one who quotes "we hit number" without the variance math.

Common Forecast Accuracy Gaming Patterns

Sandbagging is the classic — reps lowball the commit so they can beat it, which makes their personal accuracy look heroic but ruins the rolled-up call when the CRO believes the soft number. The opposite is "happy ears," where reps mark deals as commit on a verbal nod from a champion who can't actually sign. Both kill accuracy in opposite directions.

Pulling deals forward (closing a Q4 deal in late Q3 with a discount) makes the current quarter but starves the next one — the variance shows up one period later as a negative miss. Sandbagging next quarter's pipeline to seed an easy beat is the inverse exploit. "Submarining" — keeping a deal off the forecast entirely until it closes — produces a positive variance that looks like a beat but is actually forecast malpractice; the CRO didn't predict the future, they hid it.

The metric also doesn't tell you why the forecast was wrong. A team can hit ±5% accuracy by closing the wrong deals at the wrong prices — see deal slippage and no-decision rate for what hides underneath a clean variance number. Accuracy is necessary but not sufficient.