Brand forecasting: How to improve your forecasting skills

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Most marketers are not very good at brand forecasting. They either over-think or quite frankly, under-think the forecast.  You have to know your business: I do believe that writing a Monthly Report is smart practice. It helps keep your finger on the pulse of the business.  To be good at brand forecasting, you need to know the underlying key performance indicators, comparing sales trends against future demand. Stay close to your sales team to hear the collection of details that will impact your forecast.  And as the leader of the team, you have to steady the ship and avoid creating your fluctuations. An excellent question I always ask is “So what has changed since last month that makes us change our number?” You will find that many times, the number has changed, yet not very little on your business has changed. That makes no sense. Why would you change the forecast? Avoid panic or over-reaction. Communicate with the supply chain your high/medium/low thinking so they can decide on inventory to avoid missed sales versus excess inventory.  Help them manage the risk.

Here are the ten laws of brand forecasting:  

1. Your forecast will always be wrong

Knowing 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 accurate

It 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 end

No 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 dangerous

Simple 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 degree

Like 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 forecasting

Data 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 accidentally

It 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 strategy

Create 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 worse

If 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 ones

With 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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