Pinterest announced this week that it is testing a new AI-powered feature called “Auto-Collages,” created specifically to help advertisers convert their product catalogs into visually engaging, shoppable collages. According to the company, the tool aims to streamline how brands showcase their products while significantly reducing the creative resources and time needed to reach potential customers.
Acknowledging the growing popularity of collages among Gen Z users, the company points out that Pinterest hosts tens of millions of user-generated collages on its platform. Early tests of the Auto-Collages feature have shown promising engagement results, with Pinterest reporting these AI-generated collages are being saved at double the rate compared to traditional product pins.
The Auto-Collages feature leverages AI to intelligently group product images into visually appealing, shoppable arrangements based on various factors such as existing user interactions, trending outfit ideas, and similarity between products. For example, the tool can automatically assemble complementary clothing items into complete outfit suggestions or generate new collages that closely resemble popular existing pins. Additionally, the AI might suggest collages using products similar to those previously saved by users on their personal boards, increasing personalization and relevance.
Julie Towns, Vice President for Product Marketing and Operations at Pinterest, explained that Auto-Collages emerged from Pinterest’s Ads Labs program, launched in the previous year specifically to encourage innovation using generative AI technology. Towns highlighted the feature as an exciting advancement, emphasizing that it harnesses AI technology to transform advertisers’ product catalogs instantly into creative visuals tailored to the tastes and preferences of Gen Z consumers and beyond.
In addition to Auto-Collages, Pinterest also revealed an update to its “Trends” tool. This upgraded functionality will provide advertisers with deeper insights into consumer behavior, predictions on upcoming purchase trends, and a deeper understanding of what users are currently saving, curating, and buying on the platform.
