MAPE measures the size of the miss
MAPE stands for Mean Absolute Percentage Error. For each period you take the absolute difference between forecast and actual, divide by the actual, and average those percentages.
MAPE = average of |Actual - Forecast| / |Actual|, expressed as a percent.A MAPE of 8% means the forecast is off by about 8% in a typical period, high or low. Because it uses absolute values, MAPE ignores the direction of the miss. It answers one question: how accurate is the forecast on average. Lower is better. ORM sees manual forecasts on new and expansion business land near 90% accuracy, roughly 10% error, at a real cost in analyst time.
Bias measures the direction of the miss
Bias keeps the sign. Instead of absolute values, you average the signed gap between forecast and actual. Positive bias means the forecast runs high and actuals come in under. Negative bias means the forecast runs low.
Bias is what MAPE hides. A rep who sandbags every quarter and a rep who calls every deal a lock can post the same MAPE while their bias points in opposite directions. Bias is also the more fixable number. If your forecast runs 7% high every quarter, you can correct for it. Random error gives you nothing to grab.
Why a trustworthy forecast needs both
Either number alone will fool you. A forecast that lands 15% high one month and 15% low the next has near zero bias, which looks flawless, while its MAPE is poor. A forecast that misses 6% high every month has a clean MAPE and a standing bias you should have corrected quarters ago. Read them together. Low MAPE with low bias is the combination that earns trust.
What makes forecast error grow
Error climbs when the business changes and the forecast keeps running on old assumptions. A new competitor pushes deal sizes down, or a territory change distracts reps while the pipeline still looks full. The model has to catch those shifts fast. ORM targets 95% forecast accuracy and holds it from day one to day 90 of the quarter, updating as conditions move instead of waiting on manual rework.
Frequently Asked Questions
What is a good forecast error to target?
It depends on what you forecast, but a MAPE under 10% is a reasonable bar for quarterly revenue. ORM sees manual forecasts on new and expansion business land near 90% accuracy, about 10% error, and targets 95% accuracy, or 5% error, with a model that updates through the quarter.
What is the difference between MAPE and forecast bias?
MAPE measures the size of the error and ignores direction. Bias keeps the sign and tells you whether the forecast runs consistently high or low. A forecast can post a low MAPE and still carry a bias worth correcting, so read both numbers together.
Can you fix forecast bias?
Yes, because bias is systematic. If a forecast runs 7% high quarter after quarter, you can adjust the number down or retrain the model to remove the tilt. A high MAPE from random swings is harder to fix and usually points to stale inputs or a model that is not tracking current conditions.
Why does forecast error grow during the quarter?
A forecast built on day-one assumptions drifts as conditions change. Reps push close dates and deals slip, or new pricing pressure pulls deal sizes below what the forecast assumed. A forecast that does not update to those signals gets less accurate the longer the quarter runs.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like forecast error into prescriptive action for your team.
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