We keep hearing about "big data," but in reality, a lot of businesses don't have the overwhelming amounts of data that industry experts claim is available. For many businesses, the types and quantities of data that are readily available haven't changed all that much over the last 10 years. Sure, a business could buy additional data (at a cost), but most businesses will have their own ex-DC data, and for a segment of their range, they may have some scan or sell-through data.
However, there may be data that you can use, and it comes in different forms. These can be used to improve your market understanding and accuracy.
The first type of data is the characteristics of your existing historical demand, which can be used to develop a powerful forecast. In addition to seasonality and trend, there are myriad other factors such as a recurring yearly profile, a long-term business cycle, or a daily purchasing profile. Traditional demand planning tools often overlook these factors because of the algorithms they use. By leveraging this information, you will have a more information-rich forecast compared to common old-style methodologies.
It doesn't end there. Importantly, with a flexible forecasting algorithm, you can easily build on this by adding the data you have, such as price, to further improve accuracy. A good solution will help you understand the quality of the data and improve it when developing the models, adopting a more agile approach.
You can achieve additional improvements by using data that is freely available. Something as simple as the number of school days per week or when public holidays occur can improve accuracy. There may be information available from government and industry bodies, such as housing approvals, tourist numbers, and weather information. At CauSelf, we are making it easier for companies to collect and use this information.
Once these causal drivers have been collected, variations of them could be used to deliver further benefits. For example, if you sold building products, you probably see a delayed effect from housing approvals, and this can be reflected by having a causal offset. For a product affected by temperature, rather than a weekly average temperature, a better predictor may be the count of the number of days over 30 degrees. Aligning existing causal factors with how consumers actually react can help deliver insights and improve accuracy.
A traditional demand planning solution may let you view the causal factors in the forecast screen, but they don't change how the forecasts are calculated.
This is where CauSelf differs. The solution can analyse up to 20 different factors, determine the biggest drivers, and use these to establish an advanced statistical model specific to that product. It could identify that baseline demand is driven by an economic factor like market sentiment, while an event like a promotion is price-driven.
These capabilities provide real insights and break down the barriers to improving accuracy — regardless of how much or how little data you think you have.
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