Three Ways to Transform Your Merchandising with Generative AI

Generative AI (GenAI) in commerce may still be in its early stages, but its impact can’t be ignored. AI-powered commerce solutions are expected to form a market worth $16.8 billion by 2030. According to Future Commerce’s GenAI x Commerce Report, 65% of respondents expect their GenAI budgets to increase over the next six months. Merchandisers, specifically, can use GenAI to move inventory, increase average order volume (AOV), and drive competitive advantage.

A few of the most powerful use cases for GenAI involve the creation of content and imagery, as well as the ability to understand trends in customer data without technical analytics skills. Let’s explore these use cases in more depth.

1. SEO-optimized, shoppable landing pages

Search traffic dominates how many merchandisers optimize their eCommerce sites. Product categories with the highest search volume typically get the most attention when it comes to dedicated landing pages and promotions. But, with GenAI, it takes far fewer resources to create branded content for a shoppable landing page. In theory, a retailer could create hundreds of shoppable landing pages based on frequently searched keywords in a matter of hours, increasing the likelihood of their content ranking for high intent shoppers.

In the past, only the Walmarts and Amazons of the world have been able to capture this search volume — simply because they have more resources to do so. Today, any retailer can quickly create variations of their content based on on-site search volume and general search trends, competing more effectively with retail giants.

2. AI-generated and edited images

The possibilities for AI-generated content don’t stop with text. Tools like Midjourney and others enable merchandisers to create more branded content for their product detail pages — whether this content is based on third-party images or their own.

For example, many shoppers want to see eCommerce sites showcase clothing on a variety of body types. More and more brands are using plus-size models in their photo shoots, but it’s nearly impossible to shoot every item of clothing in every size available for shoppers. Some brands, like Levis, are augmenting their use of live models with AI-generated models, so more shoppers can see how the clothing would look on someone with similar measurements. Finally, brands are using GenAI to create consistent presentation of product images when they come from a multitude of vendors, so the images look consistent and match the brands aesthetic. Whether its white backgrounds, moonscapes, or something else, GenAI can smooth out the rough edges, enabling shoppers to focus on the products themselves.

3. Analyzing customer data and turning it into action

Effectively analyzing trends in customer data is a challenge for many merchandisers. For many, the data simply isn’t accessible — whether it’s stored in silos among a variety of tools, or it requires the involvement of a data engineer to analyze.

With GenAI, merchandisers can get the capability to ask questions of their data in natural language, giving them the ability to understand all sorts of trends. Using a prompt-based interface, a merchandiser could ask, which products drove the biggest Black Friday sales? Which items would work most effectively in a bundle to drive AOV? Or, which search terms drove the highest conversion rates?

Search on the commerce site itself may change to a prompt-based interface for shoppers. For example, prebuilt prompts can give merchandisers more control over the promotions experience, keeping shoppers on the site and reducing the use of external deal-hunting sites and apps. GenAI could even suggest further items to add to the cart to maximize discounts, dynamically bundling merchandise and increasing AOV on the fly.

While there are many other potential uses for GenAI in commerce, these represent some of the most powerful opportunities for merchandisers within the next one to two years. It will be exciting to see how commerce technology evolves to support GenAI, enabling merchandising teams to achieve their desired level of AI maturity, without the extra effort.