Retailers are as ideally suited to benefit from AI as companies in any other industry, enriching the data analytics they’re already doing to improve inventory management, assortment planning, merchandising, marketing, and other business activities. For example, AI-based applications can suggest the reordering and restocking of merchandise as needs arise, inventory shuffles for grocery store shelves filled with perishable goods, shelf placement down to the individual item level, and personalized marketing offers that are more relevant and timely for consumers and more profitable for the retailer.
AI can also help retailers overcome worker shortages by supplementing human labor with autonomous digital assistants, and it can improve customer service by providing human agents with better and more contextually relevant answers to customer queries than if they had to comb through knowledge bases on their own. Retailers are also starting to use AI to identify ideal store locations and help online customers choose apparel that fits them better, which can improve satisfaction rates while cutting down on expensive returns.
What Is AI?
AI, or artificial intelligence, is a technology that mimics the intellectual capabilities of humans. It handles tasks that would normally require human intelligence. AI can reason, process natural language, and make suggestions much more quickly and analyze a much greater amount of data than humans possibly could in the same amount of time.
For example, generative AI can read and summarize entire manuals in minutes, a task that would take hours or days for humans. AI can also quickly suggest a course of action based on data it has combed through, letting businesses do things like optimize purchase recommendations or delivery routes.
Why Is AI Important in Retail?
AI can help retailers overcome many of the hurdles to profitability, including the unpredictable nature of customer purchasing decisions and the high cost of labor. The industry tends to generate small profit margins—around 2.5% for general retailers, with grocery store margins even lower.
AI helps retailers boost margins by maximizing the effectiveness of marketing campaigns, assortment planning, and other techniques they use to increase sales. For example, while retailers often have clustered products that tend to be bought together, such as peanut butter and jelly, AI can suggest clusters of complementary goods that a human alone might not think of grouping in the same area, and it can predict how promoting one item could affect sales of a related one.
AI can also do a better job than humans of synthesizing product descriptions provided by manufacturers and suggesting language that’s more enticing to consumers. It can also help retailers improve customer satisfaction and loyalty rates by giving human agents relevant information they can use to provide customers with better after-sales service. AI algorithms can also generate personalized product recommendations and tailored marketing messages derived from customer purchase histories, behaviors, and preferences.
AI-enabled robots that scan shelves for out-of-stock items or clean up spills in aisle 5 can also augment or replace workers, letting retailers redeploy store associates to higher-value activities, such as helping customers.
Benefits of AI in Retail
AI can help retailers reduce costs associated with labor, shrinkage, and waste as well as generate incremental revenue by suggesting better assortment mixes in stores, producing more precise recommendations for online shoppers, and helping ensure retailers have enough stock of popular items. Read on for more information on those and other benefits.
- More profitable product mixes. AI can help retailers make better decisions around what inventory to hold, and in what quantities, by analyzing historical sales trends for a given store as well as third-party data, such as weather forecasts and calendars of upcoming local events. Sporting goods retail planners don’t need to be rocket scientists to know they should stock up on Lionel Messi jerseys when Inter Miami comes to town, but AI might suggest they also stock up on jerseys for Messi’s Spanish teammate Jordi Alba because there’s a large Spanish population in that city. AI can also suggest opportunities for retailers to substitute private label or generic alternatives to manufacturers’ brands and help determine when those alternatives would be accretive to earnings rather than cannibalize existing sales.
- Automated item attribution. Retailers can use generative AI to interpret and summarize product attributes from long, detailed descriptions provided by manufacturers, extract the most salient features, and write pithy descriptions that appeal to factors that drive customer buying decisions. This can apply as easily to descriptions of furniture in online catalogs as it can to esoteric new grocery items.
- Better ecommerce recommendations. AI can quickly take into account a customer’s entire ordering history to offer more personalized cross-sells. For example, it can suggest that a female customer shopping for sandals also buy youth-sized crew socks—not to go along with the sandals, of course, but because it detected that she previously bought children’s sneakers.
- Supplemented labor. Retailers can deploy robots with embedded AI capabilities to help with mundane tasks, such as counting items on shelves to ensure they’re properly stocked or monitoring floors for spills and cleaning them up as necessary. The people doing those tasks can be redeployed to help customers find what they’re seeking or even make suggestions about additional items they can buy, with the assistance of AI running on a handheld tablet.
- Reduced shrinkage. US retailers lose more than $110 billion a year to shrinkage, or shrink, according to the National Retail Federation, whereby inventories decrease because of shoplifting, vendor fraud, employee theft, and other non-sales reasons. Retailers can use AI, often in conjunction with sensors and other technologies at the point of sale, to detect when customers take a more expensive item than the one they scan or if a cashier deliberately undercharges a customer, often called sweethearting. Retailers can also use AI to help ensure that products don’t wither on the vine because they’ve suddenly become unpopular and thus unsellable.
- Better location decisions. Location, location, location! Retailers can use AI to run a multitude of different simulations using an almost incalculable number of variables—the location of competing retailers, population density, rent and other costs, climate and demographic information, and many others—to make better decisions about where to place new store locations.
10 Examples of AI in Retail
AI can affect almost every aspect of retail operations, including customer service, inventory management, and even real estate operations.
1. Personalized customer experience.
A department store with online and physical locations uses AI to analyze data from various combined data repositories, including order histories, browsing histories, and its loyalty program, to personalize and improve the relevance of marketing messages. A clothing retailer uses AI-powered chatbots to provide more relevant recommendations to customers online or on the phone by engaging them in a conversation about where and how they plan to wear a new coat.
2. Improved cross-selling opportunities.
Continued supply chain disruptions caused by numerous factors (including crop failures, trucker strikes, and geopolitical upheaval) are forcing grocery retailers to rethink their fulfillment models and product assortments to meet consumer demand. Grocers will increasingly align their planning decisions with demand forecasting, inventory management, and goods receipt flow (checking items received against purchase orders).
3. Automated inventory management.
A small grocery chain uses AI to help determine the right time to shuffle dairy products and other perishable items on store shelves to minimize waste. A large supermarket in Europe, where people tend to shop during their lunch breaks, uses AI to help with multiple replenishments throughout the day.
4. Demand forecasting.
Retailers can use AI to better forecast demand for specific items across geographies by pulling in and analyzing data about other items, data from stores with similar demographics, and third-party data, such as weather and income levels. A national pharmacy recently used AI to track and forecast demand for a particular vaccine based on national trends reported to the federal government.
5. Frictionless shopping.
Retailers couple AI with video and sensor data to eliminate point-of-sale areas, letting customers pull items off store shelves, place them in their shopping baskets, and walk out without ever waiting in line to check out. Removing cashier lanes and point-of-sale equipment means floor space can be used to display more goods for sale. A national supermarket chain uses AI to visually scan and charge for products with an unreadable barcode.
6. Optimized pricing.
Retailers can use AI to analyze data on competitor prices for the same or comparable goods, local demographics, and the impact of advertising and other promotions to help determine the highest price they can charge for an item without turning off shoppers. Given that most customers prefer shopping at only one location, there’s more at stake than just a single purchase. Set a price too high, and certain customers may abandon their entire shopping cart and go to a competitor’s store or website. Pricing items too low cuts into margins and sometimes devalues a product.
7. Dynamic merchandising.
Retailers use AI to recommend products to accompany items online that customers search for or already have placed in their shopping carts, based on the customers’ purchase histories and on what other customers with similar profiles buy together. One cosmetics retail chain uses AI to help customers select makeup colors and shades that suit their complexions. Physical stores can use AI to help ensure they’re offering promotions on slower-moving items, even just for that day, while pulling back on promotions for items selling well on their own—and rapidly changing course as needed. Retailers can also use AI to compare results of these choices from one store location to another (A/B testing) and adjust accordingly.
8. In-store robots.
AI, combined with video cameras and sensors on shelves, lets retailers better understand foot traffic in their stores and improve sales per square foot. The technology does so by identifying products that customers never linger near and recommending the retailer replace them with more appealing goods. AI can also generate targeted promotions for certain items on shoppers’ mobile devices while they’re in the right store location. The technology can also help retailers improve how they cluster goods.
9. Smart stores.
AI, combined with video cameras and sensors on shelves, lets retailers better understand foot traffic in their stores and improve sales per square foot. The technology does so by identifying products that customers never linger near and recommending the retailer replace them with more appealing goods. AI can also generate targeted promotions for certain items on shoppers’ mobile devices while they’re in the right store location. The technology can also help retailers improve how they cluster goods.
10. Supply chain optimization.
As supply chains are disrupted for various weather, geopolitical, labor, health, and other reasons, retailers face even more challenges to ensure popular items remain affordable and in stock. One convenience store chain used machine learning, a form of AI, to make sense of hundreds of factors influencing the supply chain and, hence, the availability of goods, including weather, current events, and influencer posts.
Future of AI in Retail
Retailers committed to continuous innovation will find new ways of using AI to improve their product selections, marketing campaigns, store operations, and other processes and offerings. Instead of looking at the same reports each day, retail business leaders will rely on AI to flag the most relevant and urgent issues. AI, in combination with advanced edge computing and digital signage, will provide shoppers with personalized suggestions as they walk through store aisles. AI will also improve visual search capabilities, letting consumers find items on retail websites simply by showing a photo of those items—for example, an outfit worn by a movie star or other influencer.
Get the Advantages of AI with Oracle
Oracle Retail AI Foundation applications leverage AI and machine learning to help retailers optimize their product assortments and offers, store layouts and locations, volumes and placement of inventory, demand and sales forecasts, marketing campaigns, pricing, product descriptions, and other key operations—helping boost margins while enhancing the customer experience.
The benefits of AI in retail boil down to its ability to analyze large amounts of data quickly and accurately. This allows retailers to better understand consumer behavior, trends, and preferences. Armed with that data, they can personalize marketing efforts, streamline inventory management, and optimize pricing strategies. Retailers that leverage AI can achieve improved customer experiences, increased operational efficiency—and ultimately more sales and greater profitability.
AI in Retail FAQs
How do retailers use AI?
Leading retailers use AI to help shoppers find what they’re seeking, improve inventory positions, reduce waste and shrink, optimize floor space based on anticipated demand, and for a variety of other purposes.
How is AI used at big box superstores?
The world’s largest superstores use AI for a variety of purposes, such as to provide more intuitive search capabilities on mobile apps, replenish shelves more quickly, and promote slow-moving items more aggressively.
How do retailers use ChatGPT?
Retailers use ChatGPT and other generative AI tools to help summarize long product descriptions from manufacturers and to create more personalized marketing emails based on customer purchase histories. They also use GenAI to provide customers with search capabilities to interpret natural language queries better.
