How AI and ML Enhance Demand Forecasting In Retail

Over the coming weeks, we will be delving into each of the main applications of AI in retail to demystify new technologies and explain, in simple terms, what they entail. In this post, we will look at demand forecasting and how it is enhanced with AI technologies. Demand forecasting is not a new concept for retailers, in fact, every retailer performs some kind of demand forecasting as functioning without it would simply be impossible.

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What is demand forecasting?

Demand forecasting is a process of using internal and/or external data to predict how much of a specific product or service customers will want to purchase during a certain period of time. It is especially relevant in the retail industry as it highly depends on demand generation and stock availability.

Why is demand forecasting important?

Demand forecasting is an important part of retailing as it can drive an organization towards high cost-efficiency and improved customer experience. Which in turn, both contribute to higher profits. While the importance is clear – the biggest issue remains to be the accuracy of the forecasts. If you haven’t been tracking all the necessary data properly, or on the other hand, have been tracking lots of data which now results in an immeasurable amount – chances of having accurate demand forecasts are pretty low. It is also difficult to account for external changes in the market, not just the weather, but also new products coming to market, competitors adjusting their strategies, new stores opening and what is trending on social media. These are also important factors to consider when looking for high accuracy of results.

How does AI enhance demand forecasting?

AI helps with the above-mentioned issues as it can replicate human knowledge of consumers and their behavior. Without requiring the help of a human, AI technologies like machine learning and natural language processing can inform retailers on what the customers will buy, when and why, all the while considering how outside events like new trends, economics and weather can affect it.

AI technologies can build highly accurate demand forecasting predictions based on heterogeneous data that can seem unstructured and unrelated. It finds hidden patterns and complex relationships between factors that influence product demand. Using AI technologies, a large number of models and hypotheses can be tested allowing retailers to save time on doing manual analyses and cut down costs. One of the greatest advantages of AI-powered demand forecasting is that these models become more and more precise with time as they adapt to new information and changes.

Benefits of demand forecasting for retailers:

  • Determine optimal stock levels: see how much of each product you need to have to avoid deficits and out-of-stocks, reduce the likelihood of product going bad and effectively assign amounts by channel.

  • Increase sales: add in-demand products to your range and make sure your customers are consistently having their needs met.

  • Become aware of upcoming trends: as AI and ML algorithms analyze external data as well as internal – they are capable of spotting new trends quicker than a human would.

  • Reduce costs: by making sure you only purchase the needed amounts of inventory you will avoid the costs associated with holding products.

  • Increase customer loyalty: by providing your customers with great experiences (by having their desired products on hand for example), you will increase their loyalty and make them choose you over the competition.

As you can see, demand forecasting is a necessary process for retailers and the emergence of new technologies makes it even better. By leveraging AI and ML, retailers have the opportunity to significantly cut down costs, increase sales and boost customer loyalty.


If you are not sure how to get started – reach out for a consultation and our experts will be happy to answer any questions you may have.

Or, check out our latest eBook on how AI & ML help retailers boost profits!

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