Free DTC diagnostic
A forecast that's consistently wrong in the same direction isn't noise — it's a broken model. Enter the last 6 months of forecast vs. actual revenue and we'll tell you your accuracy, where the bias lives, and how wide your confidence intervals should actually be.
Your forecast history
Enter your forecasted revenue and actual revenue for each of the last 6 months. Use the most recent month first.
Common questions
MAPE (Mean Absolute Percentage Error) is the average absolute deviation of your forecasts from actual results. Under 10% is strong for monthly revenue forecasting. 10–20% is normal for most DTC brands with reasonable models. Above 25% suggests the forecasting inputs, model structure, or assumptions need structural work. See our guide on driver-based ecommerce forecasting.
Forecast bias is a systematic tendency to forecast too high or too low. Bias compounds: a brand that consistently overforecasts by 15% will over-plan inventory, over-hire, and over-commit on fixed costs — then scramble to cut when actuals land below plan. Random error around a correct mean is manageable. Systematic bias in one direction creates structural planning problems.
The suggested confidence interval is calibrated to your historical MAPE. If your MAPE is 18%, a defensible next-month forecast range is roughly ±20–22%. Present your forecasts as ranges in planning documents, not point estimates. A range of "$320K–$390K" is more useful than "$355K" because it forces the organization to plan for both the high and low case.
Enter the revenue number your team formally committed to at the start of each month — ideally from a planning doc, board report, or weekly review. If your process is informal, use whatever top-line projection was circulated before the month started. Don't retrofit better numbers after you saw how the month landed.