How Zalando uses AI

Zalando has run an in house Size & Fit team since 2018, now 80 specialists, that combines machine learning, computer vision and 3D technology to give size advice on product pages. Size flags tell a shopper when an item runs small or large, size recommendations use purchase and return history and a customer's size profile, and a body measurement tool predicts measurements from two phone photos or a video.

Zalando reports that its foundational size and fit solutions cover around 70% of the assortment and prevented 8% of size related returns overall in 2025. A Virtual Fitting Room that builds a 3D avatar from the measurements is moving from pilots to a permanent experience, and Zalando says its pilots reduced return rates by up to 40%.

Zalando's own engineering team describes building a content creation copilot that generates product attribute suggestions from product images using a GPT-4o based prompt, integrated into the Content Creation Tool copywriters already use to enrich Zalando's product catalog across 25 markets. Suggested attributes are filled in and marked for the copywriter to confirm under the existing four eyes quality check, rather than publishing automatically. Zalando reports the team achieved an accuracy rate of approximately 75% on the suggested attributes, while enriching around 50,000 attributes on average per week, with a controlled rollout that let the team include or exclude specific products or attributes.

Zalando opened a beta of its assistant to logged in customers in Germany, Austria, the United Kingdom and Ireland by November 2023, after testing it internally and with selected customers. Since October 2024 it gives logged in customers fashion advice in their local language across all 25 Zalando markets, using Zalando's own models and OpenAI's large language models.

It interprets context such as occasion, location and weather ("what should I wear to my dad's 60th birthday in November in Barcelona?"), and an update in March 2025 connected it to customer accounts and shopping history and made it aware of the page the customer is browsing. In a pilot of the personalized version Zalando observed 40% more high value interactions, such as likes and add to cart actions.