# Sales Forecast Accuracy Calculator

> Enter last quarter's forecast vs. actuals to get your forecast-accuracy percentage plus which direction the bias is running, over-forecasting (happy ears) or under-forecasting (sandbagging).

Free calculator/tool for sales leaders. Topic: [Pipeline, Deal Management & Forecasting](https://www.peerlab.ai/library/topic/pipeline-deal-management-forecasting/). Published by PeerLab in the [Sales Enablement Resource Library](https://www.peerlab.ai/library/). Source: https://www.peerlab.ai/library/sales-forecast-accuracy-calculator/

## How to use it

Drop last quarter's committed forecast and actual closed-won into the worksheet at the start of every QBR to get your accuracy score and bias direction, then track it quarter over quarter to see if the bias is a person issue or a process issue.

## What's inside

- Forecast Accuracy % formula: 100% − (|Actual − Forecast| ÷ Forecast × 100)
- Bias-direction formula distinguishing over-forecasting from under-forecasting
- Fully worked example calculation ($1M forecast vs $760K actual = 76%, 24% over-forecast)
- Fill-in worksheet for your own numbers
- Per-rep table so team rollups don't hide individual bias
- Score-reading bands: 90-100% Excellent through Below 70% Systemic
- Diagnostic guide for what consistent over- vs under-forecasting each imply about your process
- Quarter-over-quarter tracking table to separate a one-off miss from a pattern

## The formula
```
Forecast Accuracy % = 100% − ( |Actual − Forecast| ÷ Forecast × 100 )
```

**Bias Direction:**
```
If Actual < Forecast → Over-forecasting (optimism / "happy ears" bias)
If Actual > Forecast → Under-forecasting (sandbagging bias)
If Actual = Forecast → No directional bias
```

**Bias Magnitude %** = (Actual − Forecast) ÷ Forecast × 100: negative = over-forecast, positive = under-forecast (sandbag).

## Worksheet: fill in your numbers

| Field | Your Number |
|---|---|
| Forecast committed at start of quarter ($) | ___________ |
| Actual closed-won at end of quarter ($) | ___________ |
| Variance ($) = Actual − Forecast | ___________ |
| Variance (%) = Variance ÷ Forecast × 100 | ___________ |
| Forecast Accuracy % (formula above) | ___________ |
| Bias direction (over-forecast / under-forecast / none) | ___________ |

## Worked example
- Forecast: $1,000,000
- Actual: $760,000
- Variance: −$240,000 (−24%)
- Forecast Accuracy % = 100 − (240,000/1,000,000×100) = **76%**
- Bias direction: **Over-forecasting by 24%** (optimism bias / happy ears)

## Do this per rep, then roll up
Repeat the worksheet for every rep, then for the team total. A team accuracy score can look fine while masking two reps who wildly over-forecast and two who sandbag, the rollup hides it. Always calculate per-rep before you calculate for the team.

| Rep | Forecast | Actual | Accuracy % | Bias |
|---|---|---|---|---|
| | | | | |
| | | | | |
| | | | | |
| **Team Total** | | | | |

## Reading the score
- **90–100%:** Excellent. Forecast is a reliable planning input.
- **80–89%:** Good. Minor noise, not a systemic issue, spot-check the worst-variance rep.
- **70–79%:** Caution. Something in the qualification or deal-review process is letting unverified deals into the number (see the companion Deal Review Checklist).
- **Below 70%:** Systemic. The forecasting process itself, not any one rep, needs to change. Look for a pattern: is it concentrated in one segment, one rep, one stage?

## Reading the direction (this matters more than the score)
- **Consistent over-forecasting (Actual < Forecast, repeatedly):** Deals are being called Commit/Best Case without verified economic-buyer access or a compelling event. Fix at the deal-review layer, not the number.
- **Consistent under-forecasting (Actual > Forecast, repeatedly):** Reps are sandbagging, holding deals out of the forecast to guarantee they "beat the number." This inflates confidence in the wrong direction and causes leadership to under-resource. Fix by rewarding forecast accuracy, not just overachievement.
- **Bias flips quarter to quarter with no pattern:** Deal timing/close-date discipline is the issue, not confidence calibration.

## Track it over time
| Quarter | Forecast | Actual | Accuracy % | Bias Direction |
|---|---|---|---|---|
| Q1 | | | | |
| Q2 | | | | |
| Q3 | | | | |
| Q4 | | | | |

A single quarter tells you the score. Four quarters tell you whether it's a person problem or a process problem.