How a sales leader measures forecast accuracy per rep from the weekly forecast snapshots and the closed results: error, bias and the split between them, why a rep who is always 20 percent high is more useful than one who is randomly 10 percent out, the per-rep adjustment it produces, and the identity that ties the adjusted forecast to the raw one.
A sales leader has two reps who both missed by 15 percent last quarter. One was 15 percent high, as they were the quarter before and the one before that. The other was 15 percent low after being 20 percent high. The first is calibratable. The second is noise. Accuracy alone cannot tell them apart. This guide sets out error and bias per rep from forecast snapshots, and the adjustment they produce.
Per rep, per quarter, at a fixed week:
Miss = forecast at week w − closed result Error = |miss| ÷ closed result Bias = mean of signed miss ÷ closed result, across quarters
And the split of error into bias and noise:
Noise = error not explained by bias
Rep identifiers only. The snapshot at the same week every quarter is the key; a forecast tool that overwrites its history cannot produce this, and a weekly export of the forecast to a file can.
| Error | Bias | Reading | Use of the forecast |
|---|---|---|---|
| Low | Low | Accurate | As given |
| Low | High | Predictably wrong | Adjust by bias |
| High | Low | Unpredictable | Weight down; coach on inspection |
| High | High | Predictably very wrong | Adjust, and coach |
Measured at week six, last five quarters.
| Rep | Mean error | Bias | Quarters | Adjustment | This quarter's raw | Adjusted |
|---|---|---|---|---|---|---|
| R-04 | 18% | +17% | 5 | −15% | $1.20m | $1.02m |
| R-11 | 16% | −2% | 5 | none | $0.90m | $0.90m |
| R-17 | 9% | +8% | 3 | shown, not applied | $0.75m | $0.75m |
| R-22 | 22% | +21% | 5 | −18% | $1.10m | $0.90m |
Rep R-04 has forecast high five quarters running by a consistent amount; the adjustment is a haircut of most of the bias, shown with its range. Rep R-11 misses as often high as low, and nothing systematic can be done except improve the inspection. Rep R-17 has only three quarters and is shown unadjusted.
adjusted total = raw total + Σ per-rep adjustments
Every dollar of adjustment is attributed to a rep and shown. A forecast that is "haircut 10 percent at the top" has no such identity and cannot be defended deal by deal.
Bias measured at different weeks. Week-twelve forecasts are always accurate. Fix the week.
Adjustment applied silently. The rep's number is changed and nobody knows by whom. Show all three lines.
Fewer than four quarters. A bias from two quarters is a coincidence with a sign. Show the count and the range.
Error read without bias. Two reps at 15 percent, one calibratable and one not, treated the same.
Mapped once, the weekly forecast exports and the closed results produce error, bias, the adjustments and the identity every quarter. Covirage builds this from the exports as they are. The forecast analysis solution describes the setup, and the forecast bridge guide covers the deal-level view beneath the rep-level bias.
Four is the floor for a stable bias figure per rep; two gives a hint with a wide range. The report shows the number of quarters behind each rep's bias and the range, and a rep with fewer than four is shown unadjusted.
It is computed and shown, never silently applied. The raw forecast, the per-rep adjustment and the adjusted total are three lines on the same page, and the sales leader decides which to commit.
At a fixed week, say week six of thirteen, so every quarter's bias is measured from the same distance to the end. A forecast made in week twelve is nearly always right and tells you nothing about the rep.