Exploring the potential of generative AI in ecommerce

Gen AI’s large language models are trained on massive amounts of structured and unstructured data for natural language processing tasks. In ecommerce, for example, this represents a move toward LLM-based recommendation algorithms that can produce highly relevant, personalized real-time recommendations and open up new opportunities for cross-selling and upselling.

Pre-trained on expansive datasets, Gen AI foundation models boast advanced cognitive and content generation capabilities and encapsulate a wide spectrum of knowledge that can be applied to a broad range of specific tasks. They can recognize patterns and objects, classify and summarize, and even perform multi-modal tasks that involve both text and other modalities like audio or image. A practical example could be utilizing foundation models for automating product categorization or enhancing visual search.

Generative Adversarial Networks (GANs) refer to generative modeling that uses two opposing neural networks to create new, synthetic data closely resembling real data. GANs promise to revolutionize ecommerce by enabling virtual try-ons, data augmentation, anomaly detection, product image generation, and automated content creation. A good example is VIRTON-GANs (Virtual Try-On GANs), which make it possible to visualize clothes on virtual mannequins in real time and thus create a highly personalized customer experience.

The integration of generative AI into ecommerce represents a paradigm shift toward more personalized and immersive customer experiences. According to McKinsey research, 71% of customers expect individualized experiences from businesses.

Introducing Gen AI-based chatbots into customer service makes the entire process feel more bespoke. Though chatbots have been essential tools in the world of online shopping for quite a while, they have never had such conversational prowess they’ve acquired due to generative AI. Virtual assistants can generate timely tailored responses, curate relevant product suggestions, give product details, and handle routine customer service tasks. Alongside providing a more satisfying user experience, personalized interactions can catalyze sales growth and minimize operational expenses.

A real-life example is the IKEA Gen-AI assistant designed to personalize furniture and decor suggestions with AI-based visuals. The app enables its users to communicate their unique needs for home design in a conversational manner and get personalized recommendations based on factors such as room dimensions, personal style, sustainability preferences, budget, and functional requirements.

With Generative AI in ecommerce, online retailers can become better equipped to personalize product search and discovery. For example, Gen AI tools can produce personalized product descriptions that resonate with shoppers and assist customers in finding the most relevant products by providing tailored and relevant product recommendations. These are based on external factors, such as current trends and seasonal preferences, and users’ behavior patterns, including purchase history, items viewed, wishlist items, and time spent on product pages.

The online apparel retailer ASOS is experimenting with ChatGPT language models to explore how it can leverage Gen AI to personalize the customer experience by improving its product discovery process. Tested by internal users and a sampling of real customers, the prototype surprised and delighted users with a fully engaging shopping experience, relevant insights, personalization, and naturalism. As of right now, ASOS has refined its initial prototype into a proof of concept that is prepared for real-world user testing.

Visual search engines powered by generative AI enable users to search for things using images rather than text. Instead of searching through product listing pages for, let’s say, a similar or the exact same dress Megan Fox wore at the Grammy Awards, a user can upload a photo of a piece of clothing and ask AI to suggest related items. Generative AI models can spot visually similar items by analyzing image features and similarities, making product discovery a breeze for customers.
Bespoke experiences like this go a long way in enhancing customer engagement and satisfaction.

Virtual try-on is another way generative AI enhances the ecommerce experience. By utilizing computer vision and deep-learning algorithms, generative AI allows customers to visualize how, for example, this trendy leather jacket will look on them before purchasing it. Gen AI apps alter the virtual try-on experience by taking into account the user’s body type, skin tone, hair type, and ethnicity, in contrast to traditional apps, which merely overlay CGI items on top of a user’s photo or camera view.

In the Google survey, 55% of participants said they were disappointed with online purchases they had made because the items didn’t look as good as they had expected. As a result, Google launched a generative AI virtual try-on tool that enables shoppers to select models to virtually try on tops from brands like Anthropologie, Loft, H&M, Loft, and Everlane. The technology allows consumers to visualize how a piece of clothing may look on models ranging from size XXS to 4XL, with different skin tones, body shapes, and hair types.

One of the highest-value generative AI use cases in ecommerce revolves around streamlining content production for product descriptions. Generative AI tools can significantly reduce the time, cost, and effort associated with crafting countless product descriptions. Pre-trained on massive data sets, large language models eliminate the need to enter numerous pieces of specific product data manually, making it possible to craft highly engaging, concise, and consistent product content in just a single step.

For example, Amazon has integrated generative AI to help sellers write effective product descriptions, titles, and listings. It simply takes a few words or sentences to describe the product for sellers to get started and the generative AI tool will take care of the rest, producing compelling content descriptions.

Capturing visually appealing product images is strategically important for ecommerce business. Normally, the process requires big investments and may involve using models, specific lighting techniques, props, photographers, designers, and image editors, to name a few. The time and resources needed for product photography and image editing could be significantly reduced with the use of GANs—a generative model that can create high-quality, realistic product images based on the dataset of images it was trained on.

Shopify added an AI-powered media editor to its Shopify Magic suite of generative AI tools to allow merchants to edit or change their product images’ backgrounds and scenes by giving prompts to the Gen AI tool.

Gen AI tools are finding their way into marketing functions due to their ability to automate and enhance various aspects of marketing and advertising. The technology allows marketers to craft and automate personalized marketing messages for specific customer segments, geographies, and demographics. Besides, it can instantaneously translate mass email campaigns into as many languages as necessary and add distinct images depending on the recipient group. In addition to creating customized messages, Gen AI can also be used to produce drafts of brand advertisements, headlines, banners, and social media postings.

One of the most common generative AI use cases in ecommerce includes forecasting customer demands to avoid stockouts and overstocking and manage inventory. Generative AI can analyze past orders, customer preferences, seasonal patterns, and more to produce insights ecommerce brands can act upon to optimize inventory levels, streamline the supply chain, and thus reduce costs. Gen AI models can also assist ecommerce stores in improving order fulfillment, enhancing last-mile delivery, and optimizing logistic routes.

Generative AI aids ecommerce brands in detecting fraud and enhancing security. According to the research, fraud costs an average firm 5% of its yearly income. Due to their amazing ability to analyze extensive datasets and learn from historical scamming patterns, generative AI models can spot anomalies, dubious activities, and suspicious transactions and provide ecommerce stores with reliable recommendations on what to do next in real time. Taking such a proactive approach can go a long way toward saving a significant amount of money in the long run.

Another ecommerce area where generative AI shines is dynamic pricing. By continuously monitoring market conditions, trends, demand patterns, inventory levels, competitor pricing, and customer behavior, generative AI allows brands to respond quickly to changes and adjust their pricing strategies accordingly in real-time. Furthermore, generative AI algorithms can estimate price elasticity for demand, which measures how demand for a product responds to a change in price. By examining the relationship between price changes and demand fluctuations, ecommerce stores can identify the optimal price points.

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