Forecasting has been a key part of the retail industry for decades, but for a long time, it was a difficult discipline that required the collection of a truly arcane set of skills. The entire point of it is to analyze past trends and data to better understand the chances of a demand spike or drop on the part of different goods, after all. That means it has been a vital part of seasonal change-over for big box stores, as well as an important part of other businesses adapting to the changing demand landscape as new products and designs roll out across the goods they offer. The ability to make accurate predictions about the rise and fall of consumer demand trends has become even more important as companies strive to minimize the inventory they are holding, to take advantage of lower operational costs and taxes. It’s no wonder machine learning software that can make the process more precise and more responsive has been so big for so many companies.
Inventory and Forecasting
Companies use forecasting to decide what to buy and when allowing them to provide what customers need when they need it. When it is neglected or executed poorly, that means you wind up with either too much of something the customer base doesn’t want right now or else too little of something they do. On top of that, since forecasting is complex and has to be used to adjust inventory purchases across all the goods you carry, it tends to create both issues at once when something goes awry. Using algorithmic software capable of identifying and isolating trends across a large collection of data points means getting a bigger, more accurate data set and a perfectly consistent method of evaluating it. That alone doesn’t make the predictions more accurate, but it does help.
Analyzing the Right Data, the Right Way
The reason machine learning is so big in the world of forecasting is that it is the point where the software is capable of learning for itself, making new choices about how things work based on changes to the data. That means you don’t have to shift to software meant to run on a new set of assumptions if customer motivations change. Instead, changing behavior patterns are observed in process, and changing recommendations come out as a result. That’s a profoundly powerful tool to have when you need to understand what products will be in demand in 30, 60, or even 90 days.