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What are the most common forecasting mistakes small businesses make?
Common mistakes include skipping a baseline, judging models on the data they were trained on, ignoring stock-outs in history, not recording promotions, forecasting at too fine a level, reporting only a single number, and never tracking accuracy or bias. Many also add complexity before cleaning their data.
Further pitfalls:
- Letting targets or hopes shape the forecast.
- Using accuracy metrics that hide bias.
- Extrapolating a short-lived trend.
- Forgetting that the forecast should drive a specific decision.
- Never revisiting the model after launch.
Most fixes are about process, not algorithms: record events, measure error honestly, review regularly, and keep methods as simple as the decision allows.
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