Source:Intel

AI is transforming various industries including retail. We have identified several common artificial intelligence use cases in retail. We grouped these applications by the business unit served by the use case. Please note that this does not include AI applications that are applicable to all industries, we covered those in detail and retail companies would also benefit from them.

Store operations

Layout optimization

Computer vision provides advanced security and plans store layout for better customer engagement. Businesses perform analysis on in-store data to create heatmaps of their customers. With these heatmaps, organizations can optimize their store layout and can decide on their upselling and cross-selling strategies. MallIQ is an in-store analytics vendor that aims to increase foot traffic and sales of retail brands with targeted actions.

Merchandising automation

As an increasing number of IoT sensors and beacons become part of the retail experience, more and more data will become available for key decisions. With this data, a growing number of tasks can then become automated, ensuring that each customer always receives the right promotional efforts and that their related products don’t go out of stock.

Dynamic staffing

Real-time customer analytics solutions help schedule future staffing needs. For example. this can help reduce POS queue times.

Fraud detection

Beacons have enabled retailers to reduce theft since the early 2000s. However, they require setup of smart detectors and additional labor as beacons are added to merchandise. However, with machine learning, video analytics powered by in-store security cameras can help catch odd behavioral patterns that may suggest the likelihood of theft or fraud.

Supply chain

Real-time inventory management

Retailers may be able to use historical purchase data to predict inventory needs in real-time. This could lead to a dashboard or an automated system ordering more of an item before it runs out. It could also prove helpful in finding the causes of anomalies in sales and product volumes to help determine better ways to manage them.

Digital sales

Share of wallet analytics

Vendors such as Dataweawe help companies track sales of their products with a category, estimating their share of wallet. While this is a relatively simple analytics capability, it allows companies to focus their efforts on products in categories with increasing interest.

Customer journey maps

These help businesses learn common customer pain points, how they can improve the customer experience, and define what customers, and prospective customers, need to complete a purchase. For example, organizations may compare the success of different customer experiences by showing various designs to different customers as Netflix does with movie covers.

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