Sign up to receive the Vogue Business newsletter for the latest Vestiaire Collective and resale news and insights, plus exclusive membership discounts. To become a Vogue Business Member and receive the Technology Edit newsletter, click here.
Fashion resale platforms have the unique challenge of broad inventory catalogues that span sizes, styles and seasons, coupled with incomplete product descriptions and customers who are often looking for very specific finds.
Vestiaire Collective thinks AI can help. The peer-to-peer fashion resale platform is adding AI tools to help with a range of functions, including product search (on the buyer side) and product pricing (for sellers). It hopes that AI will decrease the time and increase the accuracy of product searches from its catalogue of 5 million items across 10,000 brands of new and vintage styles. This was partly in response to the finding that 22 per cent of one-time buyers and 17 per cent of repeat buyers struggled to find the items they wanted.
“Every day, more than 30,000 new items are coming on the platform. We have passionate consumers, but even the most passionate cannot be expected to filter through 30,000 new items every day,” says Vestiaire Collective CEO Maximilian Bittner. “Optimising our search capabilities and personalisation, and the way we rank products to make sure the right people find the right products, is among the top challenges and opportunities we see with the business.”
Bittner recruited two tech experts to help lead this charge. Stacia Carr, formerly of Zalando and Soundcloud, has joined as the company’s new chief technology and product officer, while Jim Freeman, who was CTO at Zalando and VP of Prime Video and Alexa at Amazon, to Vestiaire Collective’s board of directors.
Bittner praised Carr’s background at Zalando, where she led projects in fit technology and beyond, and at music streaming platform Soundcloud, which, similarly to fashion, has a challenge of matching personal taste with available inventory. “She worked on the most complicated topic you see in fashion platforms, which is around size and fit … and she has seen the balance between curation and inspiration.” Matching personalisation with curated suggestions will be an ongoing goal of her work. “If you think about big platforms and why many have not fully succeeded in fashion — with the important one being Amazon — it’s often because they are strong on the one side and need to balance that.”
Vestiaire’s new search engine has computer vision under the hood, meaning it can translate keyword searches into images to return more relevant and specific results. In other words, someone previously searching for a floral Gucci Ophidia bag might have had to wade through numerous listings unless the listing seller had added those specific details to the product description pages. Now, the AI can connect the dots between words (“floral” or “cowboy”) and corresponding product images. Since adding visual similarity to the algorithm, it found that items sold via a “similar items” widget doubled.
These types of specific product searches are common among secondhand shoppers. While someone shopping at a traditional retailer might be looking for a dress, bag or shoe to solve a specific occasion or function, the Vestiaire shopper is more likely looking to find a pivotal piece from fashion history. Forty per cent of all orders on Vestiaire Collective come from text searches, the company reports, and customer searches often include very specific designers and categories; nearly 45 per cent of these are more complex than simply the combination of a brand and category, Vestiaire reports.
The “hunt” for a specific piece is a top way in which people shop Vestiaire Collective, says Fanny Moizant, Vestiaire Collective co-founder and president. “People come on to the platform with a precise vision of the item they’re looking for, but often a seller’s descriptions may be too brief or lack the key words to help them find it.”
Other resale platforms are looking to AI and technology to help connect buyers with product listings. Poshmark CEO Manish Chandra recently told Vogue Business that parent company Navar’s advanced AI capabilities, including search and recommendations, will be particularly useful. (It also just partnered with Coach’s Coachtopia on an “instant resale” tech that autofills more complete product listings.) Ebay recently added AI-generated product listings, recommended prices, shipping costs and product backgrounds. Hardly Ever Worn It is using an AI chatbot from Sociate to guide customers to desired products.
More AI upgrades are in development. Later this year, Vestiaire will add image search, enabling people to upload images to find matching or similar items. This is the same type of technology used by Pinterest and Google to find shoppable items that match uploaded pictures, and it’s especially helpful in fashion and style, where people will often use different words to describe the same aesthetic. Its forthcoming price recommendation system for people listing items will incorporate data not just on the age or wear and tear of a product but on how many are available and how hot a specific brand or designer is at the time, Bittner says.
There’s also an opportunity to further personalise recommendations, based both on past shopping history and current behaviour, Bittner adds, and pulling from more than 10 years of data. “We are sitting on so much data to educate algorithms. I’m not saying it’s easy, but we have a huge amount of historical pieces that at some point entered our platform, and that is the big driver of educating our algorithms.”
Comments, questions or feedback? Email us at feedback@voguebusiness.com.
