``` WAPE = ( sum of |Actual - Forecast| ) / ( sum of Actual ) x 100 ```
Why the two scores diverge
Take one quarter split across three segments.
| Segment | Forecast | Actual | Absolute error | Line % error |
|---|---|---|---|---|
| Enterprise | $4.0M | $4.2M | $200K | 4.8% |
| Mid-market | $1.2M | $1.0M | $200K | 20.0% |
| SMB | $180K | $120K | $60K | 50.0% |
| Total | $5.38M | $5.32M | $460K |
Both numbers are correct. The SMB line was off by half, and the SMB line was 2% of the quarter. MAPE says the forecasting process is broken. WAPE says the revenue number a finance team would act on landed within 9%.
When to use each score
Use WAPE when the forecast rolls up to a single number that drives hiring, spend, or a board commitment. The dollars are what get committed, so the score should weight by dollars.
Use MAPE when you are grading forecasting behavior rather than the revenue outcome. A rep running a $400K territory and a rep running a $4M territory should both be held to the same percentage standard, and MAPE does that while WAPE lets the large territory dominate.
The concentration trap
WAPE hides small-segment failures by design. A forecast can post a strong WAPE for four straight quarters while the SMB motion is systematically broken, because SMB never moves the total. Read the line-level errors underneath the score before declaring the forecast healthy.
Concentration cuts the other way too. If two enterprise deals decide a quarter, WAPE is mostly grading whether those two deals were called correctly, and a single slipped deal will swing it. Deal amount error compounds this. ORM's position is that most deals close for less than the value they carry in the CRM, illustrated by a pipeline averaging $80,000 per deal against closed-won deals averaging $40,000. That gap puts a standing overstatement into every segment before the first close date moves.
Report WAPE next to a signed bias number and a line-level breakdown. That combination shows how large the total miss was and which part of the book produced it. For the underlying metric definition, see forecast accuracy, and for the process that produces the number being graded, see sales forecasting.
Frequently Asked Questions
What is the difference between WAPE and MAPE?
MAPE averages the percentage error of each line, so a tiny segment counts as much as the largest one. WAPE sums the dollar error across all lines and divides by total actual dollars, so each line counts in proportion to its size. On the same data the two scores can differ by a factor of three.
How do you calculate WAPE?
Add up the absolute dollar error across every segment or period, then divide that total by the sum of the actuals and multiply by 100. There is no averaging of percentages, which is why the denominator size problem in MAPE disappears.
Which forecast accuracy metric should a RevOps team report to the board?
WAPE, because the board acts on the total revenue number and WAPE grades that total. Keep MAPE for internal grading of forecasting behavior, where every rep should be held to the same percentage standard regardless of territory size.
Does WAPE work when a segment closes zero revenue?
Yes. A zero actual makes MAPE undefined because it divides by zero. WAPE adds that segment's full forecast to the numerator and keeps working, since the denominator is the total across all segments.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like weighted absolute percentage error (wape) into prescriptive action for your team.
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