What is Forecast Haircut?

A forecast haircut is the manager's calibration tool: a deliberate discount that turns an optimistic rep number into a realistic one.

Done well, it is grounded in the rep's track record; done badly, it is arbitrary pessimism that erodes trust in both directions.

Short answer

A forecast haircut is a downward adjustment a manager applies to a rep's or the raw forecast to correct for known optimism, bias, or historical over-calling. If a rep consistently closes 80 percent of their commit, a manager might haircut their number by 20 percent. Applied transparently and based on history, haircuts calibrate the roll-up into a number leadership can trust.

Key takeaways

  • A downward adjustment to correct optimism.
  • Based on a rep's historical over-calling.
  • Calibrates the roll-up toward realism.
  • Should be transparent, not arbitrary.

Why it matters

Raw roll-ups inherit every rep's bias. A haircut grounded in historical accuracy corrects for known optimism, producing a committed number leadership can actually plan on rather than a sum of hopeful calls.

How Ardovo handles it

Ardovo shows each rep's historical accuracy so haircuts are evidence-based, and Rook suggests a calibration factor per rep, turning biased calls into a trustworthy roll-up without guesswork.

Frequently asked questions

What is a forecast haircut?

It is a deliberate downward adjustment a manager applies to a rep's or the raw forecast to correct for known optimism or bias. If a rep consistently closes 80 percent of commit, the manager might haircut their number by 20 percent.

When should a manager apply a haircut?

When a rep or team has a track record of over-calling. The haircut should be grounded in historical accuracy and applied transparently, so it calibrates the number rather than looking like arbitrary pessimism.

Can haircuts hurt forecast accuracy?

Yes, if arbitrary. A haircut not based on history can overcorrect and introduce downward bias. The best practice is to base adjustments on each caller's measured accuracy so the calibration is evidence-driven and transparent.

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