Macha
CX & Support Metrics

Forecast Accuracy

Definition

Forecast accuracy measures how close a support team's predicted contact volume (or workload) was to the volume that actually arrived, expressed as a percentage that shows how reliable the forecast was.

Also known as: volume forecast accuracydemand forecast accuracy

How to calculate it

A common approach is: accuracy = 100% − |forecast − actual| ÷ actual × 100 (the complement of percentage error). If you forecast 1,000 tickets and 1,100 arrive, the error is 100 ÷ 1,100 ≈ 9%, so accuracy is about 91%.

Teams also track it with error metrics like MAPE (mean absolute percentage error) across many intervals, since a forecast can look accurate in total while being wrong at specific hours of the day.

Why it matters

Everything downstream depends on the forecast. Accurate forecasts let you staff to demand — protecting service levels without overstaffing — while inaccurate ones cause either long queues (understaffed) or wasted payroll (overstaffed). It's the foundation the whole scheduling process rests on.

Frequently asked

What is a good forecast accuracy?

Many workforce management teams aim for 90%+ at the daily or weekly level. Accuracy at finer intervals (per half-hour) is harder and naturally lower, but more useful for scheduling.

Why forecast at the interval level, not just the day?

Because staffing happens by interval. A daily forecast can be perfect while morning is understaffed and afternoon overstaffed — interval-level accuracy is what actually protects service levels.

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