The rise of Generative AI has transformed many industries, including e-commerce. With the ability to generate new content such as product descriptions, images, and even new products, Generative AI has the potential to revolutionize the e-commerce industry. A few dates to put it in perspective: By 2030, the generative AI market alone is projected to reach $110.8 billion USD. And Generative AI will account in 2025 for 10% of all data produced, up from less than 1% in 2021 (Gartner).
In this article, we explore several examples of how Generative AI is being used in e-commerce and the benefits it brings to brands and retailers.
Generative AI refers to a type of artificial intelligence (AI) that can create new content, such as text, images, or videos, similar to what a human might produce. In the context of e-commerce, Generative AI can be used to create product descriptions, generate product images, and even design new products.
Generative AI uses deep learning techniques such as neural networks, variational autoencoders (VAEs), and generative adversarial networks (GANs) to learn patterns in existing data and then generate new content based on those patterns. Some of the known examples of Generative AI are Google Bard, Dall-E, Bing, and ChatGPT.
A survey conducted in 2023 among marketing and advertising professionals (e-commerce is part of it) in the United States revealed that 37% of respondents had utilized generative AI to support their work-related tasks. The graph below shows the adoption rate of generative AI in the workplace in the US in 2023, by industry:
One of the most significant uses of Generative AI in e-commerce is generating product descriptions. Natural Language Generation (NLG) algorithms analyze product data and generate descriptions that can be used on e-commerce websites. For example, such a tool can analyze a product’s features, benefits, and specifications and generate a compelling product description that can enhance the customer experience.
One concrete example of using Generative AI for product descriptions is the platform Phrasee. For instance, it can analyze a product’s features (of headphones for example) and generate a description like “These noise-canceling headphones feature advanced noise reduction technology that blocks out ambient noise, providing clear and immersive sound quality.” It can also create email subject lines or push notifications. This approach has helped e-commerce brands save time and increase customer engagement with automated and personalized content. Domino’s Pizza and eBay are examples of using such “AI-empowered content”.
Generative Adversarial Networks (GANs) are another form of Generative AI being used in e-commerce, this time to generate new product images. By training GANs on a dataset of existing product images, the generator network can learn to create new, realistic-looking product images that can be used for e-commerce or advertising. This approach can save brands and merchants time and resources spent on product photography and image editing.
Image-generation tools like DALL-E 2 are already being employed in advertising. Heinz, for instance, used an image of a ketchup bottle with a label resembling their own to illustrate how AI perceives ketchup. However, this was simply because the model was trained on a substantial number of Heinz ketchup bottle photos. Similarly, Nestle utilized an AI-enhanced version of a Vermeer painting to promote one of its yogurt brands, and Mattel is using the technology to generate images for toy design and marketing purposes.
The technology can also be used to generate personalized product recommendations for customers. By analyzing customer data, such as browsing history and purchase behavior, Generative AI algorithms can create product recommendations that are tailored to the individual customer’s preferences. This approach can help companies increase customer loyalty and drive sales.
Stitch Fix, a San Francisco-based clothing company and online personal styling service, has disrupted the fashion retail industry. By combining the expertise of personal stylists with the efficiency of artificial intelligence, Stitch Fix delivers personalized clothing recommendations to its customers’ doorsteps on a regular basis. The company’s AI analyzes data on style trends, body measurements, customer feedback, and preferences to provide stylists with a curated selection of recommendations that fit their customers’ lifestyle and budgets.
Similarly, generative AI can analyze vast amounts of customer data to identify patterns and trends, allowing businesses to create highly targeted marketing campaigns and personalized product suggestions. Amazon also uses generative AI algorithms to deliver highly personalized product recommendations that have contributed to its success. In 2021, Forbes reported that 35% of what consumers purchased on Amazon was a result of product recommendations.
Using Generative AI, companies can leverage GANs to design new products based on existing ones, allowing them to quickly and efficiently create new and innovative products. This approach can help brands stay competitive and meet customer demand for new and improved products.
Generative design has been used in industries that prioritize both aesthetics and structural performance. For example, New Balance utilized generative design to create shoe sole geometries with proprietary software developed by Nervous System, a company based in Boston. This software enables customization of soles that cater to individual users’ foot support needs and aesthetic preferences.
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