How GenAI Is Changing Retail

Michael Hickins | Content Strategist | May 16, 2024

Leading retailers are experimenting with generative AI in the hopes of solving some of the industry’s biggest problems since Richard Sears stabled his horses and wagons and started opening physical stores. They’re beginning to use GenAI to create product summaries and other website content, generate conversational responses to prompts from customers and employees, personalize their marketing, and even summarize customer feedback to help with merchandising and product innovation.

However, many organizations experimenting with GenAI have seen disappointing results. This isn’t because of flaws in the technology itself, but because when GenAI’s training stops, so does its learning. Often, this results in so-called hallucinations—inaccurate or misleading results generated by GenAI models. Retailers are starting to use techniques such as retrieval-augmented generation (RAG) to give the model more relevant information for each prompt so it can respond more accurately to employee and customer queries.

Read on to learn more about how retailers are overcoming early GenAI obstacles and finding success with pioneering applications.

What Is Generative AI?

Generative AI is a subset of artificial intelligence that’s capable of understanding plain language prompts or questions and responding with text or images. It’s also capable of ingesting large quantities of data and producing summaries of that content, as well as interpreting that data and making suggestions.

Key Takeaways

  • GenAI is helping retailers understand customers better based on their order histories, enabling retailers to serve more helpful responses to customer questions and create personalized shopping lists.
  • Retailers are using GenAI to generate content such as product descriptions for online stores, catalogs, and shelf displays, as well as blog posts and personalized marketing emails.
  • Retailers are using GenAI to take “social listening” to the next level, summarizing customer feedback across a variety of sources including social media, product review sites, and call center transcripts to create the ultimate feedback loop.
  • Retailers are inserting GenAI-powered chatbots into the procurement process, lowering the cost of goods and freeing up personnel.
  • Large retail chains are using GenAI to create more immersive and interactive training videos.

How Will GenAI Change Retail?

GenAI can change the industry by helping retailers maximize sales and profit margins with existing customers. It may even help reverse the decades-long trend of deteriorating customer loyalty by enabling retailers to provide surprisingly great customer service. For example, GenAI can help retailers achieve the following:

  • Optimize displays. Retailers can make physical store displays more effective by combining smart display devices, conventional AI analysis of sales data, and GenAI's conversational capabilities to produce more compelling promotional copy and design elements.
  • Improve in-store customer service. Not all employees have the innate ability to connect with customers on the floor, but anyone with a tablet can access assistance from GenAI. This tool can quickly help employees direct customers to the particular item they’re looking for and suggest related items they might be interested in buying. GenAI could even prompt employees to ask customers about themselves or what they’re looking to accomplish, which can present even more cross-selling opportunities. For example, a customer shopping for a particular interior paint at a hardware store could be led to buy paint brushes suited for that type of paint.
  • Consolidate and summarize feedback. Retailers can use GenAI to aggregate and interpret customer feedback from a variety of internal and third-party sources, including social media sites, call center and chat transcripts, and online reviews, creating succinct summaries from hundreds or thousands of comments and suggestions. These summaries can keep management better informed about the performance of their physical and online stores—notably, customer satisfaction with the quality of service, the breadth and depth of product selection, and whether products meet expectations.
  • Personalize shopping experiences. Retailers can use GenAI to create offers and marketing communications that are tailored to each customer rather than to a demographic group since GenAI can create them rapidly and in any quantity. GenAI can also analyze browsing history, purchasing behavior, and preferences to create highly personalized shopping experiences. By understanding individual customer preferences and behaviors, retailers can tailor their product recommendations, promotions, and marketing campaigns, increasing customer satisfaction and loyalty. They can also use GenAI, in combination with RAG or other means of supplying updated data, to offer more personalized after-sales service. This can mean helping customers get more out of the products they bought, making the returns process smoother, and even collecting more feedback on their experiences and generating interest in future transactions.

Benefits of Generative AI in Retail

GenAI offers retailers a variety of operational, customer service, and other benefits, described in more detail below.

Boost Operational and Cost Efficiency

Retailers tend to operate with very narrow margins, so any improvements in operational efficiency can go a long way toward increasing profitability. For example, retailers can use GenAI to replace or augment customer service agents, both online and over the phone, reducing the time their personnel need to spend helping customers with rote tasks such as returns or exchanges. One retailer has lowered its procurement costs by 3% by using chatbots powered by GenAI and informed by conventional analytics and third-party market data to conduct contract negotiations with equipment suppliers via their online portals. Retailers also claim that they are increasing employees’ productivity and reducing costly turnover by developing their people using training videos with built-in GenAI to walk trainees through a multitude of interactive scenarios.

Increase Customer Loyalty

Retail brand loyalty has been on the wane for decades. Retailers need to work harder than ever to retain their customers.

GenAI can be a valuable tool that helps retailers harness data on each individual customer, allowing them to put out highly targeted emails and other marketing materials at scale—to an extent that wouldn’t be possible with human labor alone. The way it works is that GenAI sorts through aggregated shopping histories, social media posts, and other third-party data to determine which specific marketing messages may appeal to a given shopper. The fact that these messages are personalized rather than mass-produced can help reduce brand fatigue, improves the relevance of content, and increases customer loyalty.

Improve Customer Experiences

Most retailers not only suffer from high employee churn rates, but they also have to bring on seasonal help, which means they’re always managing lots of new employees with little institutional knowledge. GenAI can help by generating summaries of product features and walking directions for these employees to help them direct customers around stores. GenAI can also surface customer histories and product information for call center agents, in addition to powering interactive customer service chatbots.

Retailers can also use GenAI to respond to customer questions or complaints, either directly through an online chatbot or indirectly by providing scripts to store associates. Both take into account the full context of a shopper’s experience as well as relevant product information.

For example, if a customer asks about a store’s returns policy, a response that includes, “Well, that grill you bought last month is still under warranty, and I can make an appointment for someone to pick it up for you,” would be a lot more helpful than a response such as, “it depends” or “usually 30 days.” It would also go a longer way toward establishing a long-term relationship with that customer.

Additionally, retailers can use GenAI to respond to queries about order status, and even suggest language and images for customized goods such as T-shirts and coffee mugs.

Foster Product Development and Innovation

Product lifecycle management has always been a goal for retailers, but it has been honored more often in the breach than in the observance. Until the advent of GenAI, it has simply been too time- and labor-intensive for retailers to sift through reams of customer and end-user feedback, find common complaints about a given product, then communicate those complaints to the product development teams of their suppliers or their own private label manufacturers. Such regular feedback could lead to beneficial product changes—or even entirely new products.

Using GenAI, however, retailers can comb through call center transcripts and audio records, social media posts, and customer reviews on retail and aggregator websites (such as Yelp and Google), synthesize that data, and even distinguish irrational rants from cogent suggestions. And then they can use GenAI to summarize that data in a timely and succinct manner. GenAI models can then make suggestions based on their interpretation of broad generalizations by, for example, translating comments such as “I keep dropping it and it breaks all the time!” into “make it more ergonomic by narrowing the handle by a few centimeters.”

5 Use Cases for Generative AI in Retail

Retailers have begun using GenAI in a variety of clever ways to improve customer service and retention, reduce return rates, increase basket sizes, and grow their margins. Here are five use cases.

1. Enhance the customer experience.

Retailers can use GenAI-based chatbots, supplemented with updated customer data thanks to RAG or similar techniques, to interact conversationally with consumers as they ask questions. These interactions take place over the phone or on retailers’ ecommerce sites, and can cover the products customers are researching, the retailer’s return policy, or its store hours or inventory. In contrast with older generations of chatbots that use conventional AI, which have a limited number of decision trees, modern chatbots powered by GenAI offer consumers an almost unlimited number of conversational avenues and can respond to more complicated customer queries.

For example, a big box hardware store’s GenAI-based chatbot can help customers decide which type of lighting or plumbing fixtures would work best for them by asking questions about house size and location, helping them pick items with the appropriate tensile strength, power profile, and resistance to high temperatures. While conventional AI chatbots are already good at recommendations, GenAI ones are more conversational, and they’re able to respond to online customer requests to, for instance, “shorten the hem” or “let me see that in navy blue.” These GenAI-powered virtual assistants are increasingly able to tell when someone is frustrated or is using sarcasm or other idiomatic expressions that aren’t to be taken literally. They know that a customer who says, “Go jump in a lake!” in frustration isn’t actually issuing that command.

2. Automate content generation.

Retailers can use GenAI to create concise, compelling product summaries for their ecommerce sites and shelf labels. By changing the prompts, marketers can ask GenAI to produce longer pieces, such as blog posts. For example, one national grocery chain is using GenAI to assemble enticing recipes using ingredients for sale in its stores, which it publishes as blog posts. The chatbot can also provide a shopping list based on a question such as “what do I need to make a lasagna?” Retailers can use GenAI to generate personalized shopping lists, such as lists tailored to customers who have a gluten intolerance, are allergic to pistachios, and don’t like miso.

3. Personalize marketing.

Retailers can help address email fatigue by using GenAI to suggest compelling subject lines and create content tailored to individual recipients, as opposed to addressing demographic cohorts or other less-customized versions of “people like you also like…” In combination with classic AI and RAG, GenAI can help produce these individualized emails for tens of thousands of current, former, and potential customers—personalization at a vast scale. GenAI can also produce an infinite number of A/B tests, identifying which content is most effective at driving conversions.

4. Synthesize customer feedback.

Retailers can use GenAI to review and summarize comments from customer reviews, social media feeds, and other sources. That concise feedback can help inform such decisions as which products to stock, where to place them in their stores and on their websites, how to handle returns, where they need to assign more knowledgeable staffers, and even (working with suppliers) how to improve existing products.

5. Augment conventional AI with GenAI.

Retailers are also using GenAI to enhance conventional AI applications. For example, retailers already use conventional AI to help online shoppers search for products by letting them upload a photo. Using GenAI, retailers can now use chatbots to engage in more complex, human-like conversations with shoppers. This allows for a natural give and take: Show me one in green, how about a shorter hem, do you have a blazer to pair with that?

Further, retailers can use GenAI to enhance back-office tools that rely on conventional AI to forecast trends. For example, retailers use conventional AI–based analytics to analyze trends based on data from sources such as weather and economic reports. With GenAI, they can parse and interpret data from more diverse types of sources—such as social media feeds, customer reviews, online fashion magazines, and news sites—to predict trends with greater accuracy.

Likewise, while retail suppliers already use AI to adjust delivery routes in response to supply chain disruptions, GenAI can provide summaries of news reports, social media posts, and other unconventional data sources to augment such analyses.

Examples of GenAI in the Retail Industry

Although GenAI has been available to businesses for only a relatively short time, retailers have been quick to take advantage of its myriad attributes. Here are some examples.

  • One global hypermarket chain is using a GenAI-powered chatbot to help shoppers buy groceries that not only match the recipes they’re trying to cook, but also adhere to their dietary restrictions, all while using a friendly, conversational tone. On the back end, the hypermarket also uses GenAI to write requests for proposals for potential vendors and to analyze and summarize their bids.
  • An online electronics store uses GenAI to encapsulate the gist of customer reviews for different products, giving customers a summary of what other people think about a given item so they no longer have to read through each individual product review.
  • An online apparel and accessories store uses a GenAI-powered chatbot to assist shoppers with buying gifts. The chatbot asks shoppers about the intended recipient’s gender, size, hobbies, and interests, as well as the customer’s preferred price range.
  • A global superstore is using GenAI to negotiate small contracts with vendors and contractors. It does this by using conventional analytics to understand the market for a given item or service, and a GenAI-powered chatbot to interact and negotiate terms with vendors through their online portal. In addition to reducing friction with human interlocutors, GenAI frees retailer procurement teams to focus on more strategic deals.
  • One online mall uses conventional AI-driven analytics to help mom-and-pop retail tenants set prices, and GenAI to help write product descriptions and conversational follow-up messages to customers that have shown interest.

Revolutionize Your Retail Operation with Oracle and GenAI

Retailers are using Oracle Retail AI and analytics solutions to help fine-tune their marketing, make more-informed pricing and inventory decisions, optimize their floor space and store locations, improve their product descriptions, and, more broadly, create more fulfilling shopping experiences and boost their margins.

As generative AI helps provide personalized and interactive shopping experiences, deliver a better understanding of consumer behavior and preferences, optimize inventory management, predict trends, streamline supply chain processes and more, smart retailers are charting a course to use it to drive business growth.

Predict the Unpredictable

Learn more about how Oracle can help you create better shopping experiences and generate higher margins with GenAI and analytics.

Generative AI FAQs

How are big box retailers using GenAI?

Big box retailers are using GenAI to generate product descriptions, summarize long documents, create new types of content, and supply associates with cross-sell recommendations for customers.

How are LLMs used in retail?

Retailers use LLMs and other GenAI applications to power chatbots to provide efficient and friendly customer service that’s often faster and more accurate than what call center agents can offer.