Use of Artificial Intelligence in Retail Business in 2021
People in the retail business is gradually increasing the use of artificial intelligence
Artificial intelligence (AI) is reinventing the retail landscape. From using computer vision to customize promotions in real time to applying machine learning for inventory management, retailers can harness Artificial intelligence to connect with their customers and operate more efficiently. Intel® technologies power Artificial intelligence in retail at every step of the way, from the brick-and-mortar edge to the cloud. 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. Artificial intelligence 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 Artificial intelligence in some parts of their operations. You might use Artificial intelligence 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 Artificial intelligence 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.
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. Artificial intelligence 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, Artificial intelligence 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.
The more you understand customer behaviors and trends, the better you can meet demand and present the best possible products. Artificial intelligence 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. Artificial intelligence 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.