Generative AI applications such as ChatGPT, Stable Diffusion, Claude, GitHub Copilot, and others have gained widespread attention for their phenomenal ability to engage in human-like conversations and their broad applicability. Generative AI can potentially assist in various use cases across all sectors and industries. E-commerce is no exception.
The capabilities of generative AI are advancing at a dizzying pace, making it difficult for stakeholders to grasp the technology, its power, and its impact on business, industries, and society. Consider this: GPT-4 was launched by OpenAI less than four months after its ChatGPT sparked the Gen AI revolution.
Similarly, Anthropic launched its LLM Claude just a year ago and quickly followed with Claude 2 in July and Claude 3 in March 2024. The pace of innovation is making life difficult for industry leaders. That's where generative AI consultants can help.
In this article, we will discuss the potential value of generative AI in e-commerce, considering its numerous functions, and examine the specific business challenges generative AI can address.
Generative AI in E-commerce: What Is Its Transformative Power?
Generative AI describes deep learning algorithms capable of writing text in natural language, composing music, and even creating digital art in response to prompts. Applying these algorithms to various use cases has significant potential to transform different aspects of e-commerce, such as customer operations, sales and marketing, supply chain and inventory management, and more. In the e-commerce industry, this could mean using generative AI to support customer interactions, create original content for marketing and sales, and customize products, among many other tasks.
To assess the potential value of using generative AI for e-commerce functions, one must deeply understand the groundbreaking technological capabilities underlying generative AI and how they are shifting the industry landscape.
Large language models (LLMs) of Gen AI are trained on vast amounts of structured and unstructured data for natural language processing tasks. In e-commerce, for example, this represents a shift toward recommendation algorithms based on LLMs that can deliver highly relevant personalized recommendations in real-time, opening up new opportunities for cross-selling and upselling.
Pre-trained on massive datasets, Gen AI Foundation models possess advanced cognitive and content-generating capabilities, encapsulating a wide range of knowledge that can be applied to various specific tasks. They can recognize patterns and objects, classify and summarize data, and even perform multimodal tasks that involve both text and other modalities, such as audio or images. A practical example might be using Foundation models to automate product categorization or enhance visual search.
Generative adversarial networks (GANs) involve generative modeling that uses two opposing neural networks to generate new, synthetic data that is very similar to real data. GANs promise to revolutionize e-commerce, enabling virtual try-ons, data augmentation, anomaly detection, product image generation, and automated content creation. A great example is Virtual Try-On GAN (VIRTON-GAN), which allows users to visualize clothing on virtual mannequins in real-time, creating a highly personalized customer experience.
Key Use Cases for Generative AI in E-commerce
Here, we will dive into key Gen AI applications in e-commerce to illustrate areas where generative AI can be used most effectively and bring the most value. Below are the main examples of generative AI use cases in e-commerce, demonstrating the potential benefits that this evolving technology brings.
Enhancing Customer Service
Personalized Chatbots for Customer Support
Integrating generative AI into e-commerce marks a paradigm shift towards a more personalized and immersive customer experience. According to a McKinsey report, 71% of customers expect personalized experiences from companies.
Implementing Gen AI-based chatbots for customer service makes the entire process more personalized. While chatbots have long been important tools in the world of online shopping, they’ve never had the conversational power they’ve gained through generative AI. Virtual assistants can generate timely, personalized responses, suggest relevant products, provide product insights, and perform routine customer service tasks. In addition to providing a more satisfying user experience, personalized interactions can catalyze sales growth and minimize operational costs.
A real-life example is IKEA’s Gen-AI assistant, designed to personalize furniture and décor suggestions using AI-driven visuals. The app allows users to convey their unique home design needs conversationally and receive personalized recommendations based on factors such as room dimensions, personal style, sustainability preferences, budget, and functional requirements.
Personalized Product Search and Recommendations
Thanks to generative AI in e-commerce, online stores can be better equipped to personalize product search and discovery. For example, Gen AI tools can create personalized product descriptions that resonate with shoppers and help customers find the most relevant products by providing tailored and relevant product recommendations. These recommendations are based on external factors, such as current trends and seasonal preferences, and user behavior patterns, including purchase history, viewed items, wishlist items, and time spent on product pages.
The clothing retailer ASOS is experimenting with ChatGPT language models to explore how Gen AI can be used to personalize customer experiences by improving the product discovery process. A prototype tested by internal users and a sample of real customers surprised and delighted users with a fully immersive shopping experience, relevant ideas, personalization, and naturalism. Currently, ASOS has refined its initial prototype into a proof of concept ready for real-user testing.
Visual Search
Generative AI-powered visual search systems allow users to search for items using images rather than text. Instead of searching through product listing pages for, say, a similar or identical dress that Megan Fox wore at the Grammy Awards, users can upload a photo of the garment and ask the AI to suggest similar products. Generative AI models can find visually similar items by analyzing features and similarities in images, making product discovery effortless for customers.
Such individualized experiences make a significant impact on boosting customer engagement and satisfaction.
Virtual Try-On
Virtual try-on is another way generative AI enhances the e-commerce experience. Using computer vision and deep learning algorithms, generative AI allows customers to visualize how, for instance, that stylish leather jacket will look on them before purchasing. Gen AI applications transform virtual try-on experiences by taking into account a user's body type, skin tone, hair type, and ethnicity—unlike traditional applications, which simply overlay CGI elements on a user's photo or camera image.
In a Google survey, 55% of participants said they were disappointed with online purchases because the items didn’t look as expected. In response, Google launched a generative AI-based virtual try-on tool, allowing shoppers to select models for virtual try-ons of tops from brands like Anthropologie, Loft, H&M, Loft, and Everlane. The technology enables consumers to visualize how a piece of clothing might look on models sized from XXS to 4XL, with different skin tones, body shapes, and hair types.
Boosting Productivity and Efficiency
Product Description Content Creation
One of the most valuable use cases of generative AI in e-commerce revolves around optimizing content production for product descriptions. Generative AI tools can significantly reduce the time, cost, and effort associated with creating countless product descriptions. Pre-trained on large datasets, large language models eliminate the need to manually input numerous fragments of specific product data, enabling the creation of highly engaging, concise, and consistent product content in just one step.
For example, Amazon has integrated generative AI to help sellers write effective descriptions, titles, and product listings. Sellers need to provide just a few words or sentences to describe the product, and the generative AI tool takes care of the rest, creating compelling content descriptions.
Product Image Creation
Generating visually appealing product images is strategically important for e-commerce businesses. Typically, this process requires significant investments and may involve models, special lighting techniques, props, photographers, designers, and image editors, to name a few. The time and resources needed for product photography and image editing can be significantly reduced using GANs—a generative model that can create high-quality, realistic product images based on a dataset of images it has been trained on.
Shopify has added an AI-powered media editor to its suite of generative AI tools called Shopify Magic, allowing sellers to edit or change the backgrounds and scenes of their product images by submitting prompts to the Gen AI tool.
Marketing Effort Automation
Gen AI tools are making their way into marketing functions thanks to their ability to automate and improve various aspects of marketing and advertising. The technology allows marketers to create and automate personalized marketing messages for specific customer segments, geographic areas, and demographic groups. Moreover, it can instantly translate mass email campaigns into as many languages as necessary and add different images depending on the recipient group. In addition to creating customizable messages, Gen AI can also be used to draft brand advertisements, headlines, banners, and social media posts.
Expanding Data-Driven Decision-Making
Inventory Management
One of the most common uses of generative AI in e-commerce is predicting customer demand to prevent stock shortages and overstocking and manage inventory. Generative AI can analyze past orders, customer preferences, seasonal patterns, and more to provide actionable insights that e-commerce brands can use to optimize stock levels, streamline supply chains, and reduce costs. Generative AI models can also help e-commerce stores improve order fulfillment, enhance last-mile delivery, and optimize logistics routes.
Fraud Detection Enhancement
Generative AI helps e-commerce brands detect fraud and improve security. According to research, fraud costs the average company 5% of its annual revenue. With its remarkable ability to analyze vast datasets and learn from historical fraud patterns, generative AI models can detect anomalies, suspicious activities, and fraudulent transactions, providing e-commerce stores with real-time recommendations on what to do next. Adopting this proactive approach can save significant sums of money in the long run.
Pricing Optimization
Another area where generative AI shines in e-commerce is dynamic pricing. Constantly tracking market conditions, trends, demand patterns, stock levels, competitor prices, and customer behavior, generative AI enables brands to respond quickly to changes and adjust their pricing strategies accordingly in real-time. Additionally, generative AI algorithms can assess price elasticity of demand, which measures how product demand responds to price changes. By studying the relationship between price changes and demand fluctuations, e-commerce stores can determine optimal price points.
Final Thoughts
As the e-commerce sector continues to expand, consumers increasingly demand more personalized experiences and convenience. A McKinsey report shows that 78% of consumers are more likely to make repeat purchases from brands that personalize their services. These heightened expectations are driving e-commerce companies to embrace emerging technologies, with generative AI leading the charge as the most promising and versatile tool that has the potential to become a general-purpose technology.
Generative AI and e-commerce form a powerful combination that can revolutionize how e-commerce businesses operate and how customers experience online shopping. As you can see from the examples above, the possibilities for generative AI in e-commerce are endless. Generative AI systems are becoming a key competitive advantage for e-commerce businesses, pushing the boundaries of what is achievable with traditional methods. With 78% of consumers ready to recommend brands that personalize their services, now is the perfect time for e-commerce businesses to implement generative AI technologies to stand out through superior customer experiences that resonate on a personal level.
Implementing generative AI solutions into online shopping requires developing a solid AI-powered e-commerce strategy. Partnering with an experienced provider of AI solutions for e-commerce is a surefire way to build a strategic roadmap for AI implementation that best suits your e-commerce business.
To help you better understand what generative AI is and how it works, we have written a series of articles dedicated to generative AI: