Most Brand Leaders are not very good at 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 good practice. As the leader you need a finger on the pulse of the business. You should know the underlying key performance indicators, match those up to the in-market realities of customer market orders on the surface and in the near future. Stay close to your sales team. Know what your competitors are doing and how it can impact. You have to be aware of how seasonality can drive your brand.
The impact of the three types of seasonality:
- Real seasonality driven by customer needs (allergies, xmas, spring/fall)
- Driven by financials (Year end, new budgets etc)
- Driven by business habits (this is when we always do it)
And as the leader on the team, you have to steady the ship and avoid creating your own fluctuations. A great question is “so what’s changed since last month”. You’ll find that many times, less things have changed even though everyone in your group has changed the forecast. Finally, be a good communicator of your thinking behind the forecast. Avoid the panic or reactions by communicating upwards so that your boss can understand your logic behind your thinking, helping make a decision. Communicate with supply chain your high/medium/low thinking so they can make a decision on inventory to avoid missed sales vs excess inventory. Help them manage the risk.
The 10 laws of brand forecasting:
Forecasts are always wrong.
Knowing they are wrong might enable you to focus on finding mid points not exactness because the only question that matters is “how wrong is it?” The goal is to get it accurate enough that it doesn’t hurt the business too much when it is within its normal variation. That can usually be accomplished either through better forecasting methods or changes to the business to make it less sensitive to a bad forecast.
Correct forecasts are not proof that the forecast method is correct.
It could have been luck. Don’t just look at the results, look at the methodology. A good solid method will make you consistent, which month after month will be more important than nailing one period. Process matters.
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.
Complicated forecast methodologies can be dangerous.
Simple forecast 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 complex formulas, but balance that with your instincts.
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. There is no perfect data. It is just a question of how far off it is. Therefore, the more data that is in the forecast, the more likely some important error will be missed.
Data that has not been regularly used is almost useless for forecasting.
Data quality is usually directly proportional to the amount it has been used. Without regular usage, errors remain undetected and inconsistencies develop. It’s better to use solid data in a forecast even if additional assumptions have to be made in order to use it.
Most forecasts are biased in some way — usually accidentally.
It is very difficult to eliminate all bias in a forecast, since the forecaster always has to make certain assumptions about which factors to include, how strongly to weight them, and which to ignore. And sometimes the bias is intentional.
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 you get better forecasts, but if you don’t think, it will just get you bad forecasts faster.
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, etc. — will make it worse. And it will be more difficult to figure out what is going wrong.
Large numbers are easier to forecast than small ones.
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.
To read more on how to do forecasting, follow this presentation
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