Most forecast accuracy conversations treat a miss as a miss. Off by 10% is treated as off by 10%, regardless of direction. That framing hides the single most important fact about forecast error in a staffed operation: which direction you miss in matters enormously more than how big the miss is.
The benchmark numbers WFM teams actually track
Industry practice generally treats daily-level forecast accuracy of 90 to 95% as solid performance for an established operation, meaning daily volume predictions land within 5 to 10% of actuals on average. At the interval level, the number that actually drives staffing, 80 to 90% accuracy is considered good for a stable operation, and accuracy below 75% at the interval level tends to produce visible service level failures even when the schedule itself was built correctly. The gap between daily and interval accuracy is not a rounding difference, it is the entire reason a forecast can look accurate in a weekly report while the floor experiences a rough day: strong daily accuracy can still hide a badly distributed interval pattern underneath it.
What actually happens when you miss, in either direction
Take a staffing plan built for 500 calls in a 30 minute interval, AHT of 300 seconds, staffed at 91 agents to hit an 80% service level target. Now hold that staffing fixed and see what happens to service level as actual volume comes in above or below what was forecast:
Look at how lopsided this is. Forecast 20% too high, meaning actual volume comes in lower than predicted, and service level barely moves, it actually improves to 99.9%, because you staffed for more demand than showed up. It is wasteful, you paid for headcount you did not need that day, but nothing breaks. Forecast just 10% too low, meaning actual volume comes in higher than predicted, and service level does not degrade gracefully, it collapses to effectively zero, because your staffed headcount can no longer keep pace with the queue building faster than agents can clear it.
Why the collapse is so sudden
This is the same non-linear behaviour behind the Erlang C staffing curve: service level does not degrade in a straight line as the gap between required and available agents grows, it falls off a cliff once available capacity drops close to or below the traffic intensity the queue is generating. A small under-forecast can be the difference between staffing comfortably above that threshold and staffing right at the edge of it, and the edge is exactly where small errors produce enormous service level consequences.
What this means for how you should actually manage forecast risk
Treating forecast accuracy as a single symmetric percentage misses the point entirely. The real question is not "how accurate was the forecast" in the abstract, it is "which direction is this forecast more likely to be wrong in, and have I protected against the expensive direction." A few practical implications follow directly from this asymmetry:
When you are uncertain about a forecast, a slight upward bias in your volume assumption is far cheaper than a slight downward one, since the cost of the two errors is not remotely equal.
Interval-level accuracy deserves more scrutiny than daily-level accuracy, since a forecast that is right on average across the day can still contain the exact kind of interval under-forecast that produces the collapse shown above.
Real-time monitoring exists precisely because forecasts will sometimes be wrong in the expensive direction, and the only real defence against a forecast that under-called volume is catching it early enough in the day to react, not preventing it from ever happening.
You can test this asymmetry against your own numbers using the Forecasting Toolkit to check historical accuracy by method, and the Erlang C Staffing Calculator to see exactly how sensitive your own service level is to a volume miss at your specific staffing level.