Forecast snapshot

The forecast per rep saved every week, so accuracy can be measured at a stated horizon rather than on the day the period closes.

DefinitionThe forecast per rep saved every week, so accuracy can be measured at a stated horizon rather than on the day the period closes.

A forecast snapshot is the number each rep was forecasting on a given date, kept. Most CRMs overwrite the forecast, so without snapshots the only measurable accuracy is the final week's. With weekly snapshots, error is computed at twelve, eight, four and one weeks before period end, the horizon curve becomes the team's own benchmark, and bias per rep can be shown across quarters.

How it is used

Each week the forecast per rep and period is saved with its date and never overwritten. Accuracy, bias and category close rates are all computed from the snapshots.

Example

Twelve weekly rows per rep per quarter. At quarter end, the row from week four is compared with the actual to give the eight-week error.

Where it goes wrong

The CRM holds only the current forecast. Without snapshots, history cannot be recovered and nothing about forecast quality can be measured.

Articles that use this term

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 Alternatives and comparisons · Sales teams

How to choose analytics software for sales teams: questions, data and traps

How sales teams should choose analytics software: start from the questions, check the data you hold, ask vendors ten questions, avoid the traps.

24 Sept 20264 min read Alternatives and comparisons · Supply chain

How to choose analytics software for supply chain: questions, data and traps

How supply chain teams should choose analytics software: start from the questions, check the data you hold, ask vendors ten questions, avoid the traps.

24 Sept 20264 min read How-to guides

How to measure forecast accuracy and bias in Excel, per rep, from weekly snapshots

A step-by-step guide to measuring sales forecast accuracy and bias in Excel: keeping a weekly snapshot table, pulling the forecast each rep made at a fixed horizon, signed error and absolute error against actuals, bias per rep across four quarters with AVERAGEIFS, mean absolute error, the team figure against the per-rep spread, and the horizon curve. Includes the exact formulas and the point at which the spreadsheet stops being enough.

17 Sept 20263 min read Board and management reporting · Supply chain

KPIs for supply chain teams: ten measures that matter, each with its formula and the export it comes from

The ten KPIs a supply chain function should run on, each with its formula, the export it comes from and what it tells you: supplier OTIF against each supplier's own baseline, lead time variability, customer OTIF under each customer's rule, forecast bias by SKU and site, inventory ageing, single-source exposure, days of cover against target, expedite cost, supplier deterioration watch, and cause of service misses. Also the three measures most teams miss, the figures to drop, the identities, and who owns what.

17 Sept 20264 min read