Content Map
How Can Generative AI Benefit the Retail Industry?
How Can Retailers Take Advantage of Generative AI?
Real-life Examples of Retail Companies Successfully Using Generative AI
Challenges When Retailers Adopting Generative AI
Final Thoughts
FAQs
Retailers have traditionally relied on artificial intelligence (AI) to analyze customer data, detect patterns, forecast demand, and enhance predictive capabilities. This technology has paved the way for innovations in various areas like demand forecasting, pricing strategies, and personalized recommendations. However, the emergence of Generative AI (GenAI) marks a significant leap forward. Unlike traditional AI, GenAI can generate new content from its training data, offering novel opportunities to enhance retail processes.
NVIDIA survey estimated that an overwhelming 98% of retailers plan to invest in Generative AI models within the next 18 months. The appeal is clear: McKinsey evaluates that Generative AI could potentially boost productivity in the retail and consumer packaged goods (CPG) sector by 1.2 to 2.0 percent of annual revenues, translating to an additional $400 billion to $660 billion in value creation.
In this article, we’ll explore 6 significant use cases of Generative AI in retail. We’ll then discuss how retailers are harnessing this breakthrough technology to their advantage and the challenges of adopting it.
Let’s get right into it!
Key Takeaways:
Generative AI tools hold tremendous potential for transforming the retail sector, expected to make a significant $9.2 trillion impact by 2029. They allow retailers to enhance operational efficiency, reduce costs, foster customer loyalty through personalized customer experiences, and so much more.
In retail, even small efficiency gains can significantly boost profits due to the industry’s typically narrow margins. Generative Artificial Intelligence can help by automating content creation and inventory management tasks. It also assists in or replaces customer service agents, reducing the time spent on repetitive tasks like returns and exchanges. Additionally, training videos powered by Generative AI can improve employee productivity and reduce turnover via interactive training scenarios.
Maintaining customer loyalty has become increasingly challenging over the years. GenAI proves valuable for retailers in retaining their customers by using data to create highly targeted marketing materials. By analyzing shopping histories, social media activity, and other data, GenAI can craft personalized messages that resonate with individual customers. This approach makes marketing efforts more relevant and less likely to cause brand fatigue, thus fostering stronger customer loyalty.
Generative AI allows for customer service automation through AI-powered chatbots. They’ll handle customer inquiries, resolve issues, and provide information in real-time 24/7, resulting in higher customer satisfaction. For example, a chatbot could provide detailed and helpful information about a store’s return policy and even offer to schedule a pickup for a returned item. Thus, it helps improve the overall customer experience and foster long-term relationships.
Managing the product lifecycle has always been important for retailers. However, it’s often neglected due to the time and labor required. Generative AI simplifies this process by analyzing extensive customer feedback from various sources. They include call center transcripts, social media, and online reviews. It then even identifies common issues and provides actionable insights to product development teams.
Learn more: Top 8 Generative AI Use Cases Across Industries
Now comes the meat of the matter. Here are 6 ways GenAI is currently supporting retailers and transforming this vital sector of the global economy.
Generative AI is transforming customer interactions, with McKinsey & Co. noting its potential to revolutionize customer operations by improving both the customer experience and agent productivity.
75% of customers use multiple channels for their shopping needs. GenAI ensures consistent and great customer service across all touchpoints, from digital self-service options to support in branches, call centers, and social media. GenAI-powered customer service chatbots, updated with the latest customer data, can handle complex queries over the phone or on e-commerce sites. They surpass the capabilities of traditional AI chatbots that rely on limited decision trees.
Retailers are leveraging Generative AI for conversational commerce, creating virtual stylists that help customers find what they need by offering personalized product recommendations and style inspiration from influencers. GenAI, with large language models, can instantly respond to inquiries using pre-existing information such as policies, FAQs, and knowledge base articles, providing a human-like conversation and personalized answers.
Given that only 10% of consumers find exactly what they’re looking for using traditional search functions on retail websites, these virtual assistants not only answer queries but also guide shopping decisions, addressing issues like search abandonment, which costs retailers over $2 trillion annually.
Another significant application of GenAI in the retail sector is automated content creation, which empowers retailers to create product descriptions, promotional content, blog posts, and more, enhancing SEO and customer engagement.
Generative AI models are particularly adept at crafting detailed product descriptions, titles, and taglines that capture a product’s attributes and brand voice. With minimal input—such as the product name and key features—GenAI can generate initial drafts instantly. These descriptions are tailored in terms of length, style, and tone, ensuring consistency across all products. Beyond product descriptions, Generative AI can generate longer-form content like blog posts based on specific prompts, further diversifying marketing strategies and customer engagement efforts.
Another innovative use of Generative AI is in generating personalized product images based on textual descriptions and historical image data. Traditionally, creating high-quality product images requires extensive resources like photographers and graphic designers. With AI, retailers can automatically generate images tailored to consumer preferences or demographic details, enhancing the personalized shopping experience.
Through its advanced capabilities in predictive analytics, Generative AI can greatly improve inventory management and supply chain optimization. It synthesizes data, detects anomalies, forecasts market trends, and personalizes recommendations to significantly enhance demand forecasting accuracy.
This predictive prowess ensures retailers can anticipate demand fluctuations with precision, leveraging insights from past sales, current market trends, and seasonal factors. GenAI reduces the risk of stockouts and improves overall supply chain efficiency via inventory level optimization and replenishment planning. For instance, ahead of Black Friday events, GenAI automatically adjusts inventory levels to meet heightened customer demand, minimizing disruptions and maximizing profitability.
In addition to demand forecasting, Generative AI plays a crucial role in various supply chain operations, including supplier risk assessment, anomaly detection, and transportation optimization. Unlike traditional AI, which relies on historical sales data to detect patterns, Generative AI can create synthetic training data to simulate market scenarios and stress-test logistic models. This capability allows retailers to optimize logistics operations more effectively, even with limited historical data, thereby enhancing agility and responsiveness across the supply chain.
Virtual try-on technology has evolved significantly with the advent of Generative AI, enhancing the realism of online shopping experiences. Previously, virtual try-ons were limited to basic features like selecting from a small set of body shapes to preview how a garment might look. Now, Generative AI has elevated this capability to new heights.
Google has introduced advanced virtual try-on technology that uses Generative AI to simulate clothing on a diverse range of real models. This AI model can accurately depict how clothing fits and drapes on models ranging from sizes XXS to 4XL, representing various skin tones and ethnicities. Anthropologie and H&M brands have already integrated this feature, offering customers a more realistic preview of their potential purchases.
Looking ahead, Generative AI promises even more personalized experiences. Customers will soon create realistic avatars based on their photos and exact measurements. These avatars will enable them to virtually try on outfits, mix and match different items, and visualize themselves in various settings—from casual outings to formal events. Using Generative AI not only enhances decision-making but also enriches the online shopping journey, helping customers make informed choices about their purchases.
Review analysis is pivotal for businesses to gauge customer sentiment, identify emerging trends, and anticipate market shifts. Traditionally, this process involves labor-intensive manual reading and classification, which can overwhelm brands with data.
Generative AI, however, offers a transformative solution. Large language models (LLMs) excel in processing vast amounts of data from customer reviews, social media, and other sources. They perform sentiment analysis, discerning the emotional tone—whether positive, negative, or neutral—from textual feedback. This capability provides companies with real-time and precise insights that inform decisions such as product stocking, placement strategies in stores and online, customer service improvements, and product enhancements in collaboration with suppliers.
Generative AI streamlines review analysis, enabling brands to swiftly adapt to customer feedback and market dynamics. Effectively leveraging these insights enables businesses to enhance customer satisfaction, optimize operations, and drive competitive advantage in the retail landscape.
Effective product lifecycle management has long been a priority for retailers, yet its implementation has often fallen short due to the arduous nature of manual feedback analysis. Before the emergence of Generative AI, retailers faced significant challenges in sorting through extensive customer feedback to identify common issues and relay them to product development teams. This process was time-consuming and inefficient.
Generative AI, however, revolutionizes the approach. It helps analyze vast datasets from call center transcripts, social media, and customer reviews across platforms like Yelp and Google so retailers can extract valuable insights. This GenAI capability allows businesses to distinguish between meaningful suggestions and irrelevant complaints, summarizing feedback efficiently.
Moreover, Generative AI models interpret broad feedback to generate actionable recommendations. For instance, a comment about a product frequently breaking could prompt AI to suggest ergonomic improvements. This streamlined process empowers retailers to iterate product designs more responsively and potentially introduce entirely new products that better meet customer needs.
Now that we understand Generative AI’s benefits and use cases, let’s see how retail companies are actually putting this transformative technology to work.
Established in 1995 as one of the earliest online marketplaces, eBay has expanded its presence to 190 global markets, achieving approximately $10 billion in revenue in 2023.
Recently, eBay introduced innovative features leveraging Generative AI:
eBay’s implementation of Generative AI has been met with high user satisfaction and adoption rates. Reports indicate that approximately 95% of users who utilized these AI-powered features continued using them, contributing to an impressive 80% increase in customer satisfaction.
Walmart, a leading retail giant in the USA, operates a vast network of hypermarkets, department stores, and grocery outlets across 24 countries.
In its pursuit of innovation, Walmart has embraced Generative AI to enhance customer experiences and operational efficiencies:
These Generative AI features not only improve customer satisfaction online and offline but also boost operational efficiency for Walmart’s associates. Since its implementation, Walmart reports a notable reduction in customer contacts, effectively handling millions of queries regarding returns, order status, and more.
Carrefour, a leading French retail business operating across 30 countries and specializing in wholesale, achieved a remarkable revenue of up to $92 billion in 2023, securing its position as the seventh-largest retailer globally.
In a forward-thinking move last year, Carrefour embraced Generative AI and introduced several new features:
The outcomes of Carrefour’s AI initiatives have been transformative. The Hopla chatbot has significantly enriched online shopping interactions. It offers personalized assistance and enhances convenience with features like anti-waste suggestions tailored to individual preferences. Meanwhile, the enhancement of over 2,000 product details through AI has greatly enriched consumer understanding and satisfaction. Internally, AI-driven procurement has improved efficiency by automating tasks such as tender drafting and quote analysis.
Despite the promising benefits and successful applications of Generative AI discussed, the adoption of Generative AI in retail faces several significant challenges.
In retail, transformation isn’t just necessary—it’s a competitive race.
Today’s consumers expect seamless experiences—real-time recommendations, autonomous shopping options, and personalized interactions across channels. Generative AI stands ready to meet these demands while also enhancing internal efficiencies and employee satisfaction.
Early adopters in retail have already applied GenAI-driven solutions such as personalized shopping advisors and AI-generated product descriptions. Many have started with off-the-shelf models like OpenAI’s GPT-4. Yet the trend is shifting towards custom models trained on proprietary data. This approach ensures a brand-specific tone and delivers personalized outcomes at scale and within budget.
Nevertheless, leveraging Generative AI in business isn’t without challenges. From data preparation to building trust in AI systems, organizations face hurdles that require careful navigation. At Neurond, we specialize in guiding businesses through these complexities. Whether you’re considering virtual shopping assistants or optimizing inventory management, our tailored Generative AI consulting services for businesses are designed to meet your unique needs.
Contact us now and embark on your Generative AI transformation with confidence!
GenAI can be leveraged by retailers in numerous ways: enhanced customer support, automated marketing content generation, inventory management & supply chain optimization, realistic virtual try-on, customer review analysis, and innovative product development.
To implement generative AI solutions in retail, companies must first address several critical questions. These include deciding between open-source, closed-source, or enterprise models, determining strategies for model training and deployment, selecting appropriate hosting options, and ensuring flexibility for seamlessly integrating future innovations and new products. Each decision plays a crucial role in shaping how effectively generative AI enhances operations and customer experiences within retail environment.
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