Quick Answer
MAPE (Mean Absolute Percentage Error) is the most widely used metric for measuring forecast accuracy. Calculate it by taking the absolute percentage difference between forecast and actual for each period, then averaging across all periods. The formula is: MAPE = (1/n) x SUM(|Actual - Forecast| / |Actual|) x 100. A MAPE of 5% means your forecasts are, on average, 5% away from actual results — lower is better.
MAPE = (1/n) x SUM from i=1 to n of (|Actual_i - Forecast_i| / |Actual_i|) x 100%
Step-by-step example:
| Month | Forecast | Actual | Abs Error | % Error | |-------|----------|--------|-----------|---------| | Jan | £420K | £400K | £20K | 5.0% | | Feb | £440K | £450K | £10K | 2.2% | | Mar | £460K | £430K | £30K | 7.0% | | Apr | £480K | £500K | £20K | 4.0% |
MAPE = (5.0 + 2.2 + 7.0 + 4.0) / 4 = 4.55%
| MAPE Range | Interpretation | |------------|----------------| | < 5% | Excellent accuracy | | 5-10% | Good accuracy | | 10-20% | Reasonable accuracy | | 20-50% | Poor accuracy | | > 50% | Inaccurate forecasting |
These benchmarks vary by industry and what is being forecast. Revenue forecasts for subscription businesses should be under 5%. Cost forecasts for project-based businesses might accept 10-15%.
Division by zero: MAPE is undefined when actual values are zero. This is a problem for seasonal businesses with months of zero activity.
Asymmetric penalty: MAPE penalises over-forecasting and under-forecasting differently in percentage terms. Forecasting 100 when actual is 50 gives 100% error; forecasting 50 when actual is 100 gives 50% error.
Small denominator problem: When actual values are very small, percentage errors become enormous and skew the average.
WMAPE (Weighted MAPE): Weights each period by its actual value. Better for time series with significant volume variation.
MAE (Mean Absolute Error): Uses absolute values instead of percentages. Better when comparing forecasts at the same scale.
RMSE (Root Mean Square Error): Penalises large errors more heavily. Useful when big misses are particularly costly.
Forecast bias: Complements MAPE by showing the direction of errors, not just the magnitude.
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FAQ
MAPE expresses errors as percentages, making it scale-independent. MAE expresses errors in absolute units (e.g., pounds). Use MAPE to compare accuracy across different metrics; use MAE when you want to know the typical error in absolute terms.
Calculate MAPE monthly when actuals are available. Report trailing 3-month and 12-month MAPE to show both recent performance and longer-term trends. Include MAPE in your monthly finance report.
Yes. MAPE works well for cost forecasting, particularly for variable costs. For fixed costs, the percentage errors are usually very small (since the costs are predictable), so MAE may be more informative.
Weighted MAPE weights each period by its actual value: WMAPE = SUM(|Actual - Forecast|) / SUM(|Actual|) x 100. Use it when periods have very different magnitudes — it prevents small-value periods from disproportionately affecting the score.
Yes. Grove FP calculates MAPE, WMAPE, and bias automatically when actuals are loaded. Results are available at every level — company, department, and line item — with trend charts showing accuracy over time.
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