Artificial Intelligence in Retail

To compete today, retailers must respond to their customers like never before, all while eliminating waste and inefficiencies from their operations. Data can you get there, but making sense of the sheer volume of it takes serious intelligence.

Digital transformation in retail is about more than connecting things. It’s about converting data into insights, which inform actions that drive better business outcomes. AI in retail—including machine learning and deep learning—are key to generating these insights. For retailers, that leads to incredible customer experiences, opportunities to grow revenue, fast innovation, and smart operations—all of which help differentiate you from your competitors.

Plenty of retailers are already using AI in some part of their operations. You might use AI in CRM software to automate marketing activities, or predictive analytics to identify which customers are likely to buy certain products. The cloud enables AI workloads that require volumes of data from many different sources to be stored and processed. Some examples of cloud retail workloads are demand forecasting machine learning and online product recommendations.

But running AI in the store itself offers advantages. Edge computing in retail acts as a catalyst of insight, aggregating and transforming massive volumes of raw data into valuable, actionable intelligence. Imagine inventory robots that automatically restock shelves; digital signage that adapts to the audience; and sensors that track customer traffic patterns to inform cross-selling and upselling opportunities.

A special type of AI deep learning in retail known as computer vision is gaining traction at brick and mortar. Computer vision “sees” and interprets visual data, giving you eyes where you need them. And it’s opening the door for new retail use cases across customer experience, demand forecasting, inventory management, and more.

Customer Experiences that Are Convenient and Personal

Whether it’s a small boutique or a multinational superstore, retailers work hard to create shopping experiences that are convenient, personalized, and enjoyable. Customers should be able to quickly find what they’re looking for, get help when they need it, and check out fast. AI streamlines these activities to help create more satisfying customer experiences.

For example, shoppers may be concerned about picking up germs from point of sale (POS) systems. But what if they could check out without touching anything? Computer vision makes it possible to accurately “see” items in a customer’s cart.

Digital signage embedded with computer vision can also measure customer engagement and serve up real-time advertising that speaks to the audience.

From the retail edge to the cloud, AI means more opportunities to personalize experiences. A POS system captures data about what was purchased that is used to generate new product recommendations for a given customer. Digital signage collects data about which types of customers are shopping and when, so that merchandising can make better decisions about product promotions. All this leads to more accurate segmentation and experiences that are tailored to a customer’s patterns and preferences.

Demand Forecasting and Merchandising

The more you understand customer behaviors and trends, the better you can meet demand and present the best possible products. AI helps retailers improve demand forecasting, make pricing decisions, and optimize product placement. As a result, customers connect with the right products, in the right place, at the right time. Predictive analytics can help you order the right amount of stock so that stores won’t end up with too much or too little. AI can also track data from online channels, informing better e-commerce strategies.

New types of AI at the retail edge help you recognize customer intent and optimize the shopper’s journey accordingly. One example is heat mapping in the store. The combination of cameras and computer vision reveals which products are picked up, which are returned, and where the customer goes after leaving the shelf. You can use this intelligence to create experiences that promote engagement with products and help shoppers learn more.

Retail sales revenue is a key performance metric, but in-depth analysis of poor sales performance is rare. By combining vision analytics with transaction data, you can gain insights into sales performance during periods of high and low traffic for each store.

Automated Inventory Management

Maintaining an accurate inventory is a major challenge for retailers. By connecting more parts of their operations and applying AI, retailers gain a comprehensive view of stores, shoppers, and products to help with inventory management.

Intel’s responsive retail technologies make it possible to collect and process information from sensors, cameras, and other sources. Designed to bridge islands of technology and eliminate data silos, this platform supports sensors and software from a variety of third parties.

Another type of AI inventory management uses smart shelves to quickly identify out-of-stock items and pricing errors. Inventory robots can alert staff to low stock or misplaced items for more-up-to-date inventories. And computer vision‒enabled checkout systems can help mitigate product loss in real time. As a result, retailers can run stores more efficiently and free up associates’ time to focus on improving the shopping experience.

Intel® Technologies and Platforms for AI in Retail

Intel® technologies enable a number of exciting and emerging use cases for analytics and AI in retail:

• Intelligent display ads use AI inferencing to understand customer engagement and interest. Content can be adapted to the audience in real time.
• Smart shelves instantly check product availability so that items can be quickly replenished.
• Endless aisle kiosks let customers see more products available at other locations. They also offer cross-selling and upselling opportunities.
• Smart self-checkout systems accept loyalty cards, coupons, and transactions via mobile phone. Integrated video analytics identify products when a barcode is missing or unreadable.
• A new class of retail solutions, like touchless kiosks that recognize speech and gestures and service robots that interact with customers, help shoppers minimize contact with people and objects.

At Intel, we work with innovators in the retail ecosystem to deliver integrated, AI-powered solutions that can be deployed quickly and cost-effectively. These partner solutions apply our wide range of AI capabilities, from computer vision at the edge to machine learning in the cloud. To find prevalidated solutions through Intel’s partner ecosystem, explore the Intel® Solutions Marketplace.

In addition, our Edge Insights for Retail platform brings together data from across your retail edge, setting the stage for hyperconvenient, engaging experiences and improved operations.

In addition, Intel is leading the Open Retail Initiative (ORI) to encourage an ecosystem of interchangeable components and accessible retail solutions. Our goal is to accelerate the deployment of retail solutions through a common, open framework and make it easier for retailers to use AI and analytics.

Retail Thrives on AI

By adding AI capabilities to both the edge and the cloud, Intel and our partner ecosystem are helping retailers turn their data into powerful new insights. These data-centric solutions result in highly personalized experiences and product recommendations, accurate forecasts, inventory efficiencies, and smarter business overall.