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Optimize your retail activities with AI

From offering personalized experiences and recommendations to your customers to easing inventory, pricing, promotions, assortment, merchandising and logistics among your teams, AI can make your retail processes and decisions even more effective. Our scientific advisor, Maxime Cohen, talks about the future of retail and the three maturity levels of retail analytics. This video was recorded during the ALL IN 2023 event.

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Key points of the video

  • Retail analytics has three levels of maturity: descriptive AI, predictive AI and prescriptive AI. The first level is descriptive AI (or descriptive analytics), which uses historical data to learn patterns in behavior. In the retail sector, this can be used for marketing activities such as customer segmentation and personalized recommendations. It also allows retailers to be more proactive and react to new trends that emerge in the data. Insights are commonly viewed via dashboards, which helps democratize access to analytics.


  • The second level of maturity is predictive AI (or predictive analytics). In this case, the goal is much more ambitious. “We’re trying to answer the question of what is likely to happen in the future. So you look at the data from the past, but now you’re trying to make some inference about what is likely to happen in the future,” says Maxime. One of the biggest use cases of predictive AI is demand prediction, which can be used for inventory management and supply chain management.


  • The third level of maturity is prescriptive AI (or prescriptive analytics)—and this is even more ambitious. The goal is to use mathematical modelling to answer the question: “Which actions should I take in my business?” And here, “the sky is the limit,” says Maxime. Using pattern recognition to anticipate opportunities, prescriptive AI can be used to make decisions about promotions, pricing optimization and planogram assortment, as well as personalizing the customer experience. Recent work in retail uses prescriptive AI with A/B testing to understand which actions to take with which customers.


  • Retail analytics can be used to gain deeper insights into consumer preferences around personalized promotions and digital marketing efforts. It can also help with decisions related to inventory and the supply chain, as well as the optimization of planograms. For example, it can help retailers make data-driven decisions about where to locate certain items in a store, how often to replenish stock and how to make decisions around new product introductions (where historical data may not be available). It can even be used for tactical decisions, such as where to open a new store. For customers, retail analytics can be used to provide a hyper-personalized experience.


  • The Retail Innovation Lab: Maxime highlighted this initiative led by McGill’s Bensadoun School of Retail Management and Couche-Tard. The lab blends multidisciplinary research with the latest technologies to study and practice responsible innovation for the future of retail. “We like to call it the store of the future,” says Maxime. It’s also “the first frictionless store in Canada.” Customers don’t have to wait in a checkout line or make any type of payment. They do everything through an app, without any friction.


  • In the store lab, McGill’s Bensadoun School and Couche-Tard are conducting research to better understand how people shop. “We don’t collect any private information, but we can track customer trajectories to understand how long people are spending in each part of the store in order to optimize the layout of the store and planogram decisions,” says Maxime. For example, one research project looked at how they could incentivize customers to improve their eating habits by ‘nudging’ them toward healthy food choices.


  • They’re also experimenting with the use of digital twins. By taking thousands of photos of the physical store, they’ve been able to reproduce a virtual store in the metaverse that looks exactly like the store in real life. The idea is that, in a few years, customers could be at home wearing a pair of VR goggles and have a completely immersive experience of shopping—picking up products, flipping them over and placing orders—and an hour later the product they ordered in the metaverse would be delivered to their doorstep.

Taking retail to the next level

At IVADO Labs, we’re helping retailers successfully apply AI to solve core business challenges—whether they’re looking to optimize shelf space, improve inventory management or offer personalized customer experiences.

Rather than taking a one-size-fits-all approach, a customized AI-powered solution can be tailored to a retailer’s unique product assortment and forecasted sales demand. Customization, scalable solutions and agile adaptability can refine retail activities and make decisions even more effective. 

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