AI is redefining retail for good, bringing in the kind of automation and professionalism once implemented in the manufacturing industry. In this case, it’s mostly revolving around data-driven marketing decisions and in-store retail media capabilities. As shown by Axians, a VINCI group company, AI isn’t a mere toy for undergraduate students who are failing their tests and need better inspiration. It’s a robust, state of the art high-tech engine for growth and better in-store management. Yet, as often with technology, there are two sides of the same coin. The other one is more ominous, though, depicting a future of retail where layoffs will continue to rise, mostly for those retailers who missed that boat of AI-driven customisation. Here is the account of our discussion with Hugo Rocha Gonçalves, Axians’ head of Smart RetAIl, at Tech for Retail 2024. AI in retail: shrinking queuing times today, headcount tomorrow You’re in charge of the smart retail solution at Axians. What is it? Hugo Gonçalves. We developed the Smart retAIl concept to address the main challenges that the retail industry is facing today. There is a strong need to better understand in-store consumer behaviour, profile and shopping habits. We provide this knowledge to improve store efficiency, and to enable data-driven decision-making. Can you describe the process of Smart RetAIl? H.G. We are using AI and computer vision to accomplish this. * The first step is to understand how the stores are organised, what the shop floor looks like, and also how we can capture this data anonymously — for obvious GDPR compliance reasons — to fuel a data-driven decision process. * After capturing this anonymised data through computer vision, there are a couple of things we need to understand. Such as footfall, who are the buyers, when they are buying, and their paths through the store. We need to map, with the help of AI, the hot and cold zones within the store. Within these zones, we can understand if people are proper shoppers or if they are merely passers-by, and how much time they spend doing their purchases. In a sense, this is some sort of heat map within the store H.G. This is precisely what it is. And with this heat map, we can also understand what products people are looking at, how much time they spend. With AI we are taking this to a new level. This new level includes product tasting and testing. Two good examples are chocolate tasting, where we need to understand through computer vision when a customer is tasting something, which is very important in chocolate stores, and perfume stores. With this technology we can detect if the customer is testing the perfume and then understand if he or she will buy it or not afterwards. This means you are automating the work of market researchers who used to observe in-store consumer behaviour H.G. Indeed. It used to be very tedious work to have someone watching hours and hours of video, trying to understand customer behaviour, customisation, and buying habits. Now we have AI that can process 24 hours of video, covering all the opening hours of a given store. We can process all this data and obtain valuable insights as well as data enriched by AI and computer vision. So you are capturing a flow of images through in-store cameras, how is it working? H.G. This entire process demonstrates the beauty of machine learning and AI. No need to resort to supplementary intrusive devices in the stores. We are using existing in-store CCTV cameras. We subsequently apply AI image processing, frame by frame, on the existing footage. The data is recognised and categorised by the AI automaticall...
