Supercharging retail with AI: Blending commerce and lifestyle

Global events and changing consumer behaviors in the digital era are causing retailers to look toward the future with concern. In 2024, these pressures will drive businesses increasingly to ask: How can I increase sales without raising overhead? How can I create year-round stability for sourcing, inventory, restocking, and order management? And how can I build better omnichannel brand connections in a competitive market where customer loyalty is increasingly tied to consumer values like sustainability? Yet, with AI-enabled advances opening a world of possibilities for operational efficiencies and hyper-personalized, real-time, omnichannel customer experiences, the future of retail is anything but bleak! 

AI is set to level up the retail landscape. Retailers that embrace AI will be the first to achieve higher sales and better brand connections, now and in the future. Much of this adoption will be driven by the need to resonate with savvy consumers who want to buy from brands that share their values and offer experiences that blend seamlessly into their online and IRL (in real life) lifestyles. The strategic use of AI, infused with operational, customer, and third-party data, can help retailers deliver experiences that genuinely emanate from and fit snugly into their customers’ everyday lifestyles.  

At SAP, we use business AI to help companies optimize business processes, drive profitability, and boost customer loyalty. How, you might ask, can AI help retailers accomplish all that? Here are a few challenges we’re helping retailers solve at NRF ‘24.  

Driving Profitability with AI-powered Operations 

It goes without saying, if the customer can’t find the right product, there is no sale. Poor product discovery can result from the complexity of managing product catalogs, which leads to inaccuracies, poor recommendations, and reduced conversion rates. Inconsistent product tagging and inaccurate descriptions (often handled manually by key account or merchandising managers) can ensure products are hidden beyond customers’ reach.  

AI can alleviate these headaches and streamline product catalog management, improving product data accuracy and delivering a more personalized shopping experience to boot. The SAP CX AI Toolkit is a single Generative AI layer powered by data from across SAP products. Built on proprietary AI models and fine-tuned large language models (LLMs), it surfaces in SAP Commerce Cloud’s eCommerce features. It renders catalog management challenges painless with: 

• AI Product Tagging — The CX AI Toolkit analyzes catalog images and text to tag products within a catalog automatically. The tagging system ensures that each product is appropriately categorized and labeled, reducing errors and inconsistencies. 
• AI Product Descriptions — The toolkit leverages AI to generate personalized and compelling product descriptions. By analyzing product attributes, it can automatically create product descriptions that resonate with customers and provide the details for more informed purchasing decisions, enhancing customer experience and increasing sales. 
• Bulk Editing — Bulk editing of product tags and descriptions allows the quick and efficient update of large catalogs, ensuring that customers are presented with relevant and complementary products. Automating these time-consuming tasks frees catalog managers for higher-value work. 

Optimizing Operations with AI-Informed Logistics 

Getting products to customers necessitates having the right products in the right amounts at the right time. Issues like cost-to-ship, delivery timelines, pick-and-pack expenses, and capacity management can turn into perennial roadblocks that lower efficiency for inventory and sourcing management. Big challenges include consistently managing stock levels for cost efficiency, simulating sourcing strategies for sustainability and margin improvement, and delivering the best solutions to enhance customer satisfaction. One example of this difficult balancing act is the need for retailers to optimize the return process while proactively managing orders.  

AI can play an invaluable role in efficiently managing inventory and sourcing by tailoring sourcing strategies. In SAP Order Management Services, AI is integrated to do just that, using Key Performance Indicators (KPIs) such as cost-to-ship, delivery timelines, and pick-and-pack expenses. With target value, significance weight, and optional constraint for each KPI, retailers can uncover the most effective sourcing strategy and simulate single orders based on these strategies.  

Beyond inventory and sourcing, AI can improve predictive order management and streamline the return process, enhancing overall efficiency and customer satisfaction. 

Meanwhile, the proliferation of channels makes mastering omnichannel advertising and sales a daunting challenge, but if retailers aren’t advertising where customers are, there’s no chance of making a brand connection. Fortunately, retailers can increase their omnichannel excellence with targeted ad solutions such as TikTok and LinkedIn Integrations for Digital Ads from SAP Emarsys Customer Engagement.  

Yet, however strong your omnichannel game is, the opportunity to strengthen brand loyalty and drive repeat purchases drops severely when distribution problems prevent timely delivery. And if, for example, a customer can’t get those shoes from their FYP (For You Page) in time for a big event, there will be no sale.  

For distribution centers, balancing conflict goals such as reducing logistic costs and managing supply chain constraints and supplier restrictions can make achieving efficient fulfillment with optimal order quantities and minimal manual intervention feel out of reach. Enter the power of AI to solve complex constraints at multiple levels and ensure retailers can get products to customers with sustainable business practices and high profit margins. 

AI-infused SAP Predictive Replenishment enhances distribution center ordering by automating and optimizing order quantities. This includes analyzing demands from all channels, supply chain constraints, and business goals to determine the most cost-effective order quantities. The solution consumes machine learning-based demand forecasts of related SAP solutions. These AI capabilities help manage demand volatility and are integrated with existing SAP solutions for improved fulfillment efficiency. SAP Predictive Replenishment integrates with Industry Cloud solutions such as SAP Order and Delivery Scheduling, the future SAP Predictive Demand Planning, and SAP S/4HANA. 

Boosting Loyalty with Sustainable, AI-infused Recommerce  

Increasingly, consumers are seeking to minimize their environmental impact and connect with conscientious brands. The need to build this brand awareness and consumer goodwill has many retailers looking to recommerce as a new business model that drives revenue and customer loyalty. With recommerce, consumers can quickly and easily connect the dots between buying secondhand products from brands and saving money, reducing waste, and improving their overall carbon footprint. Trade-in programs that allow consumers to return their used goods in exchange for incentives also enable retailers to create closer, long-term customer relationships.  

Such a unique opportunity, of course, comes with challenges. How do you ensure used products are inspected, matched to catalog items, cleaned, repaired, and priced based on their condition? And what about the potential necessity of manual data entry to capture the details of unique items, which can introduce errors? And crucially, how can you increase sales and reduce overhead costs while running a recommerce business?  

In some ways, these are challenges reminiscent of a typical supply chain process and its challenges that SAP’s business AI is uniquely qualified to solve thanks to SAP Recommerce. At NRF, SAP Recommerce will launch the first in a set of embedded AI services that reduce used item processing times, costs, and errors while increasing sales.  

The first of these services, our Recommerce pricing module, evaluates a product’s condition, taking into account secondhand market prices, inventory availability, and more, to assign and update product prices to drive conversion intelligently. These prices will also drive the incentives offered to consumers for their used products, propelling inventory acquisition for high-demand items, all while keeping costs in line with potential profit. The next frontier we’re exploring is image-based pricing automation for further efficiency.  

At NRF ’24, SAP will show how AI is becoming the competitive edge retailers need to grow intelligently. AI-driven insights for more efficient omnichannel fulfillment decisions, optimized assortments, personalized recommendations, and resilient and sustainable supply chains will empower organizations that adopt AI to make the right decisions and seamlessly scale. I’m proud to say that SAP has been offering AI-infused capabilities for years, with more industry-leading innovation on the way. I invite you to find us at NRF ’24, where we’ll be sharing how embracing relevant, reliable, and responsible AI helps businesses look toward the future with confidence.