When this topic matters
Every month or quarter: how much revenue will we bring? Hiring, budget, investor expectations depend on forecast.
Bad forecast is not just inaccuracy — it causes wrong decisions across the company.
What happens in practice
Typical problems: 1) Pipeline full of "maybe" deals that never close. 2) Probabilities set subjectively, not based on historical data. 3) Deals stay in pipeline for months without movement.
Result: forecast says 1M €, reality is 400k €. Every month again.
Why it fails
Wishful thinking: salespeople are optimists. 30% probability is often 10%.
Missing exit criteria: deals never "die", just accumulate. And distort forecast.
Uncalibrated probabilities: 50% in pipeline does not mean 50% close chance if history shows 20%.
How to think about it
Calibrate probabilities on historical data: what is actual close rate from each stage? Use that, not subjective estimate.
Create exit criteria: deal without movement 30+ days = evaluate. 60+ days = probably dead. Clean pipeline.
Split forecast into: committed (high certainty), best case (optimistic), pipeline (everything).
- Calibration: historical close rate per stage
- Exit: 30 days = review, 60 days = clean
- Committed: high certainty, short close
- Best case: realistic optimism
What you gain and what you lose
Strict pipeline hygiene: more accurate forecast, but smaller pipeline. May look "worse" in CRM, but more realistic.
Loose pipeline hygiene: larger pipeline, but inaccurate forecast. Looks better, but leads to wrong decisions.
When to apply
Always when forecasting matters — which is from the moment you have more than 3-5 deals.
Especially important during: scaling, fundraising, strategic decisions. Bad forecast in these moments is expensive.
Calibrate probabilities on history, clean pipeline (exit criteria), and split forecast into committed vs best case. Wishful thinking is your enemy.