Second-hand luxury fashion marketplace

I worked 4+ years on Vestiaire Collective, a marketplace with 20M active users.

Collaborated with 2 product squads (Engagement & CRM, Seller) to improve the current user experience within the scopes, do user interviews and testing, explore and design new feature opportunities, help with A/B test analysis.

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Smart Listing: a faster, easier listing experience for sellers

Helping sellers create listings faster by using their photos to pre-fill the listing, while keeping them in control of the final information.

Role
Product Designer
Team
Big squad of 1 PM, 1 EM, 1 Data, 2 devs per platform, 4 backends
Timeline
2-3 months to launch, followed by 3 months of iteration
Platform
iOS, Android and Web
Scope
Discovery, UX strategy, interaction design, prototyping, testing, launch and post-launch improvements
Full case study

The public version leaves out the discovery numbers, the competitor benchmark, the decision rationale and the exact post-launch results.

Context

Current experience

Creating a listing required sellers to move through a long, multi-step experience and manually enter all the information about their item. The process remains crucial for our marketplace supply quality and item discoverability, but for sellers it remains repetitive and hard to correct when something is wrong. KPIs:

  1. Time to list
  2. Completion rate
  3. # of listings

Discovery

We started from what we already knew about listing, then ran a survey to check it with sellers and to see how they would take to AI.

What we already knew

  • The process felt long and complex, especially when sellers had to complete many fields manually.

  • Rigid flow, with too many clicks and an outdated UX.

  • Taxonomy and model selection did not always reflect how sellers described their items.

  • Incomplete descriptions with limited support and missing details could reduce buyer confidence.

The survey

We then surveyed sellers in four markets. The survey had three objectives:

  1. Validate the pain points
  2. Quantify them to prioritize later
  3. Gauge how sellers would react to AI in the listing flow
How might we reduce the effort of creating a listing without taking control away from the seller?

The challenge was not simply to add AI to the existing form. It was to create an experience that balanced speed, accuracy, clarity and seller control.

Design strategy

Five principles shaped every decision in the new flow.

  • Start with photos

    Most sellers already have their photos ready before they start, so the listing begins with them.

  • Let AI do the tedious work

    A lot of the item information can be detected from the photos, so sellers no longer have to enter it by hand.

  • Keep the seller in control

    AI can be wrong, and sellers remain responsible for their item information. They can always review, edit and publish it themselves.

  • Design for inaccuracy

    When the AI is unsure, it suggests rather than prefills, and the seller makes the choice.

  • Keep the MVP focused

    Pricing and descriptions still have room to improve, but we started simple: photos, AI and one recap screen to shorten the flow. The manual flow stays for now, as not every seller wants AI, and we wanted to confirm its accuracy at scale with real sellers, beyond our team's own checks.

Defining the experience

With the strategy set, the experience came down to four steps.

  1. 1Photo selectionThe seller starts from the photos they already have.
  2. 2AI analysisThe system prepares information such as category, brand, model, colour, material and description.
  3. 3Recap and editThe seller reviews everything in one place and edits anything the AI did not get right.
  4. 4PublishThe listing goes live once the seller confirms the information.

Prototyping and validation

  • How we tested

    Moderated interviews with a Figma Make prototype of the full flow.

  • Who we tested with

    Engaged and semi-pro sellers: some excited about AI, others who said they would refuse it.

  • What we tested

    Whether the flow was easy to understand, and whether sellers noticed when the AI got something wrong.

  • Two versions

    Step by step (closer to Leboncoin) against a single recap screen (closer to Vinted).

Before and after

A view per platform

Before Manual listing Step after step, with the seller typing or selecting every attribute.
After Smart Listing Photos, a short analysis, then the recap with everything already in place.

Timeline

  1. 2-3 months Discovery to launch

    The project moved from discovery to launch in approximately 2-3 months.

  2. Progressive Rollout

    First to internal users and beta testers, then gradually to more sellers, so we could learn from real usage.

  3. 3 months After launch

    We monitored the experience, fixed issues from real usage and improved the description, prefill accuracy and edge cases.

Impact

We tracked four product metrics against the manual listing flow. Time to List went down, and Completion Rate and Listings per Seller went up: listing became easier.

  • Lower Median Time to List

    Whether the new flow reduced the time required to create a listing.

  • Higher Completion Rate

    Whether sellers starting a listing were finishing it and publishing the item.

  • Higher Listings per Seller

    Whether the experience increased our marketplace supply.

More work

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