Forecast Accuracy
How close your revenue predictions are to actual results. Most companies hit 70-80% accuracy. The problems: sandbagging (reps under-forecast to beat quota), happy ears (reps over-forecast hoping deals close), and garbage data in the CRM.
Q3 forecast: $2.5M committed, $3.2M best case. Actual: $2.4M closed. Commit accuracy: 96% (excellent). Best case accuracy: 75% (acceptable). If commit had been $3.0M and actual was $2.4M, commit accuracy would be 80% — a sign of happy ears or deal slippage.
Forecast accuracy is one of the highest-stakes metrics in any company because everyone plans around it — hiring, cash flow, board expectations.
Yet most forecasting is still reps guessing. "I feel good about this one" becomes the company's financial plan. That's terrifying.
The three forecast killers: (1) Sandbagging — reps under-forecast deals they know will close to beat quota easily; (2) Happy ears — reps over-forecast because they want the deal to close; (3) Garbage data — deals in wrong stages, stale pipeline, missing close dates.
The RevOps fix: triangulate. Call-level forecast, roll-up forecast, historical pattern forecast. When they diverge, you find the truth.
Define ItOther Definitions
“Forecast accuracy measures the variance between predicted and actual revenue. High-performing organizations achieve 90%+ accuracy through better data, process, and AI-assisted prediction.”
“Sales forecast accuracy is the percentage match between forecasted and actual closed revenue. It indicates the health of sales process, data quality, and management rigor.”
“Forecast accuracy reflects pipeline management quality. It requires accurate deal staging, realistic probability assignments, and consistent application of qualification criteria.”
Forecast accuracy measures prediction vs. reality for revenue. Clari benchmarks top performers at 90%+ with AI assistance. SalesLoft connects accuracy to process and data quality. InsightSquared emphasizes pipeline management fundamentals.
Key accuracy factors: (1) Stage definitions — consistent, verifiable criteria for progression; (2) Data hygiene — accurate close dates, deal sizes, probabilities; (3) Qualification rigor — only real opportunities in forecast; (4) Review cadence — regular pipeline inspection catching issues early; (5) Triangulation — multiple forecast methods cross-validated.
Forecast categories (Commit, Best Case, Pipeline) should have different expected accuracy — commits at 90%+, best case at 60-70%.
MistakesCommon Mistakes
Forecasting based on rep confidence instead of verified data
Not distinguishing commit, best case, and pipeline accuracy
No historical analysis of which reps/deals forecast accurately
Mixing qualified and unqualified pipeline in forecasts
Adjusting forecasts at month-end instead of managing pipeline
Knowing the definition won't fix the leak.
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Experience across
