What is Machine Learning and how is it used in Retail Demand Forecasting?
Nearly everyone takes advantage of some form of artificial intelligence (AI) and machine learning in their daily lives.
However, as the definition of machine learning evolves, it is often misunderstood. In particular, retailers need to understand that machine learning is more than just a buzzy term—it is a tool that can be used to drive specific business benefits.
Imagine a retailer that operates at airports, providing fresh food products. Their demand fluctuates not only from day to day, but also throughout the day depending on footfall numbers at the airport. Manual entry for this sort of data would be wildly time consuming, not to mention that it would almost guarantee an error-prone forecast. Travel restrictions from the current COVID-19 pandemic surely have not made things any easier.
A machine learning algorithm with access to airport data, though, could automatically recognize the relevant footfall patterns and apply those trends toward the retailer’s demand forecasting, all without the need for any human programming. This is exactly how RELEX helped WHSmith, one of Britain’s leading retailers, drive significant improvement in its forecast accuracy. It’s a straightforward solution, but a powerful one.
In RELEX’s on-demand Industry Talk webinar, RELEX’s data scientists and retail planning experts explain what machine learning is, what kinds of challenges it solves, and why so many retailers today are transitioning toward machine learning-based demand forecasting.
Key takeaways:
- What is machine learning?
- What retail demand forecasting challenges does it solve?
- Case examples of how machine learning is used in retail demand forecasting
- How to make machine learning work for your retail demand planning
Watch the on-demand webinar here.
Don’t forget to also get your free copy of The Complete Guide to Machine Learning in Retail Demand Forecasting, which discusses the benefits of machine learning, how machine learning can improve the accuracy of your forecasts, and best practices for using machine learning in your retail business.
