Supporting sellers with enhanced product listings

Mercari, one of Japan’s leading online marketplaces, saw downloads soar after expanding into the U.S. in 2014. By integrating GPT‑4o mini and other models, Mercari is redefining how users buy and sell secondhand goods, making the process more seamless and accessible worldwide.

To simplify listings and boost sales, Mercari incorporated OpenAI’s API into two key features. These enhancements reduce friction, helping users create high-quality product pages that drive more successful transactions.

Optimizing product listings to drive sales with the Mercari AI Assistant

Launched in October 2023, the Mercari AI Assistant helps users optimize their listings. If an item remains unsold for a certain period, the AI evaluates the listing against best practices and provides tailored suggestions.

This feature initially relied on a hybrid model approach using the OpenAI models:

• Offline analysis: GPT‑4 analyzed top listings in each category to identify key attributes.

• Online suggestions: GPT‑3.5 Turbo evaluated active listings of users and provided real-time improvement tips.

The result? Mercari observed a statistically significant increase in average sales per user, demonstrating the feature’s effectiveness in improving listing quality. 

“Today, these features are seamlessly integrated into the listing experience, allowing users to benefit from AI without even being aware of it,” says Yasuo Hishii, Supply Team Director at Mercari. This approach ensures the technology works behind the scenes, enhancing the process without adding complexity.

Streamlining the listing process with AI Listing Support

Taking the AI-driven selling experience further, Mercari introduced AI Listing Support in September 2024. 

With AI Listing Support, users can list items effortlessly by simply uploading photos. Powered by GPT‑4o mini, the feature suggests relevant categories, then generates complete titles and descriptions tailored to the product and its category.

Positive results and user feedback