Artificial intelligence (AI) in retail is one of the more prevalent examples of how this technology can truly transform an entire industry.
Retailers are using AI and technology like machine learning (ML) to power decisions about inventory and the customer sales journey and behind-the-scenes tasks that impact profit margins and efficiency. From better virtual retailer recommendations to theft prevention in physical stores, retail is an industry that has fully embraced the promises of AI.
Retail as a field is notoriously competitive, and AI can give companies an edge when it comes to responding to customer desires and reducing inefficiency. Often, AI in retail focuses on data retailers already own but haven’t fully explored. Making sense of massive stores of data is a virtually impossible task human workers, but AI can autonomously apply data insights for better predictions and recommendations.
Here, we examine some ways AI is being applied throughout the retail sector:
See more: Artificial Intelligence Market
5 examples of AI in retail
1. AI is helping brick-and-mortar customers forego checkout lines
If there’s anything retail customers universally dislike, it’s standing in long checkout lines. It’s a major factor in the shift to virtual shopping. According to a survey conducted by Quidini (pre-COVID-19), a virtual wait line and appointment booking software provider, 14% of respondents said they avoid entering or wind up walking out of stores without making purchases because of lines. Since the COVID-19 era, long lines have become an even bigger issue for consumers.
One approach to the long line issue is AI-powered checkout-free stores. Forget long lines. How about no line at all? Checkout-free stores like Amazon Go are powered by AI video surveillance, Internet of Things (IoT) sensors, and data analysis. Customers walk in, grab the items they want to purchase and walk back out. Purchases are automatically charged to the customer’s account.
AI-enhanced automation also powers the Mashgin self-checkout station used in company cafeterias. Here, lunch-goers place their selected food and drink products on the Mashgin device, which uses deep learning and AI recognition software to identify each item and display a bill on a credit card-based payment portal.
2. AI-enhanced security is improving theft prevention
Similar to the AI-driven surveillance used in checkout-free stores, AI-enhanced theft-prevention platforms help to prevent shoplifting by monitoring in-person shoppers through CCTV footage. These systems are intelligent enough to identify potentially suspicious behavior and alert security guards, a significant improvement over CCTV systems that must be monitored by humans.
Platforms like SmartDepart, from Indyme Solutions, use AI to detect shopping baskets as they approach exits and identify any that bypassed the store’s checkout system. AI algorithms can also detect merchandise in carts and track it through the store, checkout, and exit. If the system detects someone trying to bypass the checkout line, it flashes a lightbar above the store’s exit along with a customizable voice message aimed directly at the offender. From there, the store’s human loss-prevention workers can further investigate.
3. AI-enhanced recommendations are improving virtual shopper satisfaction and profitability
By now, e-commerce customers are accustomed to “guided” shopping experiences online, where stores suggest products as they browse and subtly, or not-so-subtly, guide them through the entire shopping experience. The product recommendations that pop up for virtual customers are powered by AI that analyzes customer behavior in real-time or over longer periods of time for returning customers.
AI platforms like Zeta analyze customer data from multiple customer interactions with mobile apps, email campaigns, and website clicks to identify patterns related to online shopping behaviors. These insights empower digital retailers to make more effective product recommendations and improve the online shopping experience according to each customer’s specific preferences and behaviors.
4. Inventory management decisions powered by AI are more reliable than human inventory management
Figuring out the right amount of inventory to keep on hand and order is a challenging endeavor for human retail professionals, but AI can make easy work of this task. AI-enabled inventory management platforms analyze data related to previous sales trends, projected changes in consumer demands, and outside factors like potential supply-chain slowdowns.
In an e-commerce warehouse or a brick-and-mortar store’s retail floor space, AI-powered robots can pull down stock from inventory and restock it or prepare it for shipping. Unlike human employees, robots can function 24/7.
Platforms like Oracle’s NetSuite are being used by clothing manufacturing brands such as Ralph Lauren to optimize inventory management with machine learning and predictive analytics. The platform can generate models of future customer behaviors and deliver reports related to purchasing patterns, improving inventory management over time.
5. AI is allowing consumers to “try on” virtual outfits from the comfort of home
AI-powered “virtual mirrors” are giving consumers the opportunity to see how they would look in clothes available through e-commerce retailers. These systems use augmented reality (AR) to display merchandise like clothes and accessories directly on a live video image of the customer. In the future, we may see this technology merge with 3D printing capabilities, allowing customers to walk out the door in a new outfit right after they purchase it online.
Also, as evidenced by this YouTube video, virtual, or “digital,” fashion is an emerging trend. Consumers are paying money — sometimes a lot of money — to buy virtual fashions they can display on social media. Companies like DRESSX add virtual fashions to customer-uploaded images, so they can “wear” the outfits on Instagram, Facebook, and other social platforms.