Forecast accuracy calculator
Paste your actual and forecast contact volumes to compute WAPE, MAPE, and bias in one click. Supports any interval length — 15-minute, 30-minute, hourly, daily, or weekly.
Format: actual, forecast — one row = one interval (15 min, 30 min, hourly, daily, or weekly).
WAPE
5.8%
Good
MAPE
5.5%
mean absolute %
Bias
-0.5%
balanced
Intervals
10
data points
How to interpret your WAPE of 5.8%
Interval breakdown
| # | Actual | Forecast | Abs error | % error | Direction |
|---|---|---|---|---|---|
| 1 | 120 | 115 | 5 | 4.2% | ↓ under |
| 2 | 145 | 160 | 15 | 10.3% | ↑ over |
| 3 | 98 | 100 | 2 | 2.0% | ↑ over |
| 4 | 210 | 195 | 15 | 7.1% | ↓ under |
| 5 | 175 | 180 | 5 | 2.9% | ↑ over |
| 6 | 130 | 140 | 10 | 7.7% | ↑ over |
| 7 | 88 | 90 | 2 | 2.3% | ↑ over |
| 8 | 220 | 205 | 15 | 6.8% | ↓ under |
| 9 | 165 | 170 | 5 | 3.0% | ↑ over |
| 10 | 140 | 128 | 12 | 8.6% | ↓ under |
| Total | 1,491 | 1,483 | 86 | WAPE 5.8% | |
WAPE, MAPE, and bias — the three forecast accuracy metrics
WAPE
Weighted Absolute Percentage Error
The primary WFM accuracy metric. Weights high-volume intervals more — quiet overnight periods don't distort the score. Use WAPE as your headline number.
MAPE
Mean Absolute Percentage Error
Simple average of interval-level % errors. Sensitive to low-volume intervals where a small absolute error creates a huge percentage error. Use MAPE as a secondary check.
Bias
Mean Forecast Error (direction)
Positive = over-forecast (overstaffing risk). Negative = under-forecast (understaffing risk). A small bias is normal; above ±5% signals a structural problem.
WAPE benchmarks by forecast level
Accuracy benchmarks vary by interval length — daily forecasts are more accurate than 30-minute interval forecasts on the same data, simply because averaging smooths out noise.
Benchmarks are for stable demand patterns. Operations with high volatility (utilities, insurance) will typically have higher WAPEs — compare against your own historical baseline, not just industry norms.
How to improve forecast accuracy
Separate signal from noise
If WAPE is poor on individual intervals but good at the day level, the issue is intraday distribution — the day total is accurate but the pattern is wrong. Check for ACD data integrity issues (login events creating artificial peaks) and verify your intraday template is up to date.
Chase the bias first
A persistent positive bias (over-forecasting) often comes from a baseline that was set during a high-volume period. A persistent negative bias suggests your seasonal index is not capturing a trend. Fix the structural bias before optimising interval-level accuracy — a biased forecast is wrong in the same direction every day.
Account for special events
A single missed special event (bank holiday, product launch, media event) can drag your monthly WAPE from 8% to 15%. Maintain a forward event calendar and apply volume multipliers for each event type. Post-event, recalibrate your multipliers from the actual data.
More history is not always better
Using 3 years of history to forecast a business that changed its channel mix 18 months ago will produce a biased forecast. Weight recent history more heavily, or use a shorter window that matches the current operating model.
Forecast accuracy questions
What is WAPE and how is it calculated?
WAPE stands for Weighted Absolute Percentage Error: WAPE = Σ|Actual − Forecast| / ΣActual × 100. Unlike MAPE, WAPE weights each interval by its actual volume, so high-volume intervals have a larger impact on the score. This prevents quiet overnight intervals with large percentage errors from distorting the overall accuracy measure. WAPE is the standard accuracy metric in workforce management.
What is a good WAPE for a contact centre forecast?
WAPE benchmarks: under 5% is excellent; 5–10% is good (typical for well-run WFM operations); 10–15% is acceptable; 15–20% is poor; above 20% is very poor. These benchmarks apply to 30-minute interval forecasts. Daily-level WAPE is typically 3–5 percentage points better than interval-level WAPE for the same operation.
What is the difference between WAPE and MAPE?
WAPE weights each interval by its actual volume. MAPE is a simple average of percentage errors. For WFM purposes, WAPE is almost always preferred because quiet intervals can have extreme percentage errors (e.g. 50% error on 2 actual contacts) that inflate MAPE dramatically but have no operational impact. WAPE correctly down-weights these low-volume intervals.
What does forecast bias mean in WFM?
Bias = Σ(Forecast − Actual) / ΣActual × 100. Positive bias means you consistently over-forecast (overstaffing risk). Negative bias means you consistently under-forecast (understaffing risk). A small bias under ±3% is normal. A persistent bias above ±5% signals a structural problem — likely an incorrect baseline, missed trend, or mis-calibrated seasonal index.
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Track forecast accuracy over time
The free calculator scores a single forecast period. Turnella tracks WAPE and bias across every forecast period and connects accuracy outcomes to staffing requirements automatically.