Forecast bias

A rep's mean signed forecast miss across quarters, measured at the same week each quarter. Distinct from error, which ignores direction. A rep with low error and high bias is predictably wrong and can be adjusted; one with high error and low bias cannot.

DefinitionA rep's mean signed forecast miss across quarters, measured at the same week each quarter. Distinct from error, which ignores direction. A rep with low error and high bias is predictably wrong and can be adjusted; one with high error and low bias cannot.

Same week

Week-twelve forecasts are always right. Measure at a fixed week, say six of thirteen.

Shown, not applied

Raw forecast, per-rep adjustment and adjusted total are three lines on one page. The sales leader commits one.

How it is computed

The mean of signed forecast error for a rep over the last four quarters, measured at the same week each quarter.

Example

A rep missed by minus 12, minus 9, minus 15 and minus 11 percent: bias of minus 12. A colleague at plus 6, minus 8, plus 4, minus 5 has the same typical error and no bias.

Where it goes wrong

Only accuracy reported. The sandbagger and the random misser look identical and need opposite conversations.

Articles that use this term

Data quality and reconciliation

Baseline and norm: what this account usually does, and what accounts like it do

The difference between a baseline, an account's own typical level from its own history, and a norm, what similar accounts do when the relationship is full, why the two answer different questions, has this account changed and how far is it from where it could be, which measures use which, surge and dormancy against baseline, gap and share against norm, the case where an account is at its baseline and far from the norm, and the rule that both are stated on the line that uses them.

16 Sept 20263 min read Forecast and pipeline · Supply chain

Demand forecast bias by SKU and site: the plan that is always high in one place

How a supply chain team measures demand forecast accuracy and bias per SKU per site from the forecast snapshots and the actual demand: error and signed bias, the SKU-site pairs that are consistently over-forecast and carry the excess stock, the ones consistently under-forecast and carry the stock-outs, why bias at one site is usually a planner's override, and the adjustment the team's own history supports.

16 Sept 20263 min read Forecast and pipeline

Forecast bias by rep: the direction of the miss matters more than its size

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.

16 Sept 20263 min read Forecast and pipeline

Four sales forecasting methods compared, and the bridge that shows where they disagree

The four common ways to forecast a quarter, rep roll-up, stage-weighted pipeline, run rate and historical conversion, what each needs, what each is good and bad at, a worked quarter where they give four different numbers, and why the right output is not one of them but the bridge between them, with the deals that explain the gaps.

16 Sept 20263 min read How-to guides

How often should sales data be refreshed? A cadence per measure, not one for the tool

Why 'real-time' is the wrong answer to how often sales data should be refreshed, the cadence each measure actually needs, weekly for coverage and pipeline, monthly for share of wallet and concentration, quarterly for norms and segments, annually for definitions, what a refresh more frequent than the measure's cadence does to the reader, and the export schedule that follows.

16 Sept 20262 min read Data quality and reconciliation

Outliers: the one-off order that breaks the norm, and what to do with it

How a single large order distorts every measure built on it, the norm it inflates, the run rate it doubles, the concentration it spikes, the share of wallet above 100 percent it produces, how outliers are detected from the account's own history and the segment's distribution, the rule that they are flagged and shown rather than removed, the medians that make norms robust to them, and the two kinds of outlier that are not errors at all.

16 Sept 20263 min read