Let's say you're on a journey to improve your forecast accuracy, and you want greater insights into what drives your demand. The process and methodology that you use will be critical to achieving this goal.
Ideally, you will want a solution that meets your immediate needs, and will also provide flexibility for continued improvement. Some solutions will appear to deliver what you need, but their fundamental design means that they limit your ability to continually improve.
There are a few things to consider ;
Don’t rely solely on your sales history
The vast majority of forecasting solutions are founded on an approach dependent only on your demand history and typically use a variety of algorithms that determine patterns and trends. This could be as simple as using a moving average or sometimes a more advanced approach - such as using Fourier transformations (a series of curves) to replicate seasonality.
However, there may be other factors that you may want to consider – such as the effect of things under your control (price, store ranging, promotions and advertising for example) as well as external factors such as weather, economic conditions, and market indicators. Unfortunately, the algorithms used in the vast majority of tools can’t use this additional information, resulting in forecasting becoming less analytical and more subjective. The user will need to “guestimate” what the effect of a discount will be, and then manually override the statistical forecast.
With Cauself you don’t have this problem. The application has a powerful algorithm that identifies trends, seasonality and recurring patterns, but also allows additional drivers (for example price) to be used to develop a more accurate forecast. The user can see that uplift is being driven by a sharp promotional discount, and they can easily modify, delete or move the promotion and see the effects immediately. There is more science, greater efficiency and improved visibility to the forecasting process as a result.
Establish a baseline + incremental approach ;
Your business most likely has events that drive demand – such as running a sales drive or a promotion. When the event is being run the demand can respond very differently. For example – when a product is not on promotion it may be very seasonal, but the product is very price sensitive when on promotion.
To represent this its best two have two complementary models ; a baseline model that forms the baseline foundation, and an incremental model that recognizes the uplift caused by the event.
This is possible within Cauself and means that you can align the models to how sales organizations operate. You can make changes to an event - and straight away see the results.
Unfortunately, the majority of applications don’t have this ability and it means that there is more reliance on data management and the related process.
Develop Meaningful insights;
The majority of forecasting solutions simply output a forecast – and there isn’t a lot of information on how it arrived at that forecast. Cauself's unique design means that it can identify the top drivers of both your baseline demand and the event-based demand. This can help to focus on maintaining these specific causal factors and also help in developing greater insights. Through the solution you could identify that Product A seems to be more driven by price, whereas Product B is more driven by display type. The user can also simulate changes to data and see the results immediately.
This capability not only helps to improve accuracy it can also help in shaping how the business operates. The insights gathered could mean that the business does less of a certain sales mechanism because it doesn’t have a strong effect on increasing demand.
Whether you want to improve your existing process or tools, or you’d like to create a new process from scratch, Cauself may be the answer.
Want to learn more - visit www.cauself.com
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