Specialized AI and digital innovations in retail: Insights from NRF 2024
The NRF 2024: Retail’s Big Show offered insight into the latest retail developments, reportedly attracting nearly 40,000 attendees, up from 35,000 last year. This year’s conference offered several intriguing trends for brands and retailers to consider when planning their strategies for the year ahead. Here are some of the top takeaways:
Experimenting with new shopping formats
Retailers are continuing to explore digitally-enabled shopping formats to keep up with consumer expectations for seamless services and experiences. Think end-to-end virtual shopping through livestream channels where consumers can browse products, consult with influencers in real time, complete transactions and arrange delivery – all without leaving the livestream. Other solutions offer augmented reality for virtual product trials and use QR codes on packaging to unlock detailed product information or digital rewards. As these concepts gain traction, brands may have opportunities to sponsor shoppable livestreams or collaborate on AR shopping tools to increase product discovery and brand visibility in immersive new formats.
The specialization of generative AI
The topic that generated the most buzz was, of course, artificial intelligence. It was impossible to miss discussions—or signage—about AI and generative AI. While 2023 was about broad applications of generative AI tools, the focus has shifted to specialized, vertical generative applications. Expect to see more industry-specific generative models emerge for sectors like retail, marketing and beyond. These specialized tools are being developed with a deeper understanding of nuanced domains, industry terminology and other unique considerations. Examples include: - AI-generated product descriptions and creatives for an ecommerce site - Personalized product recommendations based on customer data - Packaging designs customized for different consumer preferences - Data simulation for demand planning, pricing optimization, store analytics or loss prevention
QR codes get an intelligent upgrade
The QR code has become ubiquitous in recent years. The pandemic accelerated adoption as restaurants, retail stores and event venues turned to contactless QR menu and payment options. Now AI is taking these pixelated squares into new territory when it comes to functionality. Legacy QR codes simply encode a link, image, phone number or text blurb. They serve as quick gateways to static information. However, powered by machine learning, QR codes are evolving into dynamic tools for personalized, interactive experiences between businesses and consumers. Tech vendors, for example, are embedding product recognition AI into QR readers. Now when you scan a code, the app can identify exact products and pull tailored content on the spot. Imagine getting care instructions specific to a plant you scanned or recipe ideas based on the ingredients in a packaged food item. Generative AI can open even more possibilities for info generation. QR content can be customized via natural language processing to match shopper profiles and context. A health and beauty company could produce a unique skincare routine for each customer or a snacks company could add seasonal information for a limited time to products that are scanned in the grocery aisle. Even the visual appearance of codes can change dynamically. Companies can create styled, branded QR designs that embed changing data. Accessible QR codes that are not only larger but can be scanned from a greater distance than conventional codes enable visually impaired people to activate audio product descriptions. As connectivity expands into the physical world, QR codes are emerging as gateways to digital interactions powered by data. For businesses, they provide greater options for dynamic content delivery and consumer engagement in both digital and physical environments.
The growing use of AI and personalization in retail signals the importance of consumer data behind the scenes. To enable hyper-personalized recommendations and experiences, retailers and brands need to ingest even more consumer data across channels, integrate it into robust profiles and develop AI algorithms to generate insights. For data science teams, it means developing expertise in these cutting-edge algorithms and other necessary resources. Companies without strong data science teams will likely struggle to keep pace.
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