Here are the ten laws of brand forecasting:
1. Your forecast will always be wrongKnowing your forecast is wrong the second you release it, will focus you on finding midpoints, not on exactness. The only question that matters is “how wrong is your forecast?” Get the forecast accurate enough that it doesn’t hurt the business too much when it is within a reasonable variation.
2. Correct predictions are not proof that the forecast method is accurateIt could have been luck. Don’t just look at the results; look at your methodology. An excellent, reliable method produces consistent forecasts, which month after month will be more important than nailing one period. Process matters.
3. All trends eventually endNo matter how accurately the trend is forecasted, at some point in the future, it will be wrong. Consider what might cause a trend to change (seasonality, new competition, saturated market, etc.) when evaluating a forecasted trend.
4. Complicated forecast methodologies can be dangerousSimple forecasting methods are easy to explain, understand, analyze and debug. Complicated methods tend to obscure key assumptions built into the forecast, which can lead to unexpected failures. It’s ok if your supply chain experts use complicated formulas, but balance that with your instincts. Once you let go of your instincts, your forecast will get worse.
5. The underlying data in the forecast are nearly always wrong to some degreeLike forecasts being wrong, so too is the data that you are basing it on. You can have better data. But you will never have perfect data. It is just a question of how far off it is. Therefore, the more data in the forecasting process, the more likely some critical error will be missed.
6. Data that has not been regularly used is almost useless for forecastingData quality is usually directly proportional to the number of times it has been used on your business. Without regular usage, data errors remain undetected, and inconsistencies develop. It’s better to use reliable data in a forecast even if additional assumptions have to be made in order to use it.
7. Most forecasts are biased in some way — usually accidentallyIt is challenging to eliminate all bias in a forecast since the forecaster always has to make certain assumptions about which factors to include, how strongly to weigh them, and which to ignore. And sometimes the bias is intentional.
8. Technology will not make up for a bad forecasting strategyCreate an appropriate strategy first, then use the technology to make it better. Everyone always thinks the technology will help with forecasting, but if you don’t use your brain and think, the better system will just get you a bad forecast faster.
9. Adding sophisticated technology to a bad model makes it worseIf the model is bad, anything you add to it–statistical methods, time-series methods, neural networks–will make your forecast worse. And now, it will be harder to figure out what is going wrong.
10. Large numbers are easier to forecast than small onesWith forecasting, everything gets easier as the numbers get bigger. A forecast of unit sales where there is an average of 1,000 units sold per month is a lot easier to get right than one where average sales are 2 per month. It is more about the variability than the size itself.
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