Platform · Vinted

Vinted Data Scraping Services

Every listing is one item, so when it sells it is gone for good — which makes sell-through definitive here.

Vinted data scraping is the automated collection of publicly visible Vinted data across European markets — where each listing is a single unique item, so a sold listing never restocks and sell-through is a definitive event rather than an inference from availability changes.

Every other retail page here wrestles with the same problem: an item goes out of stock, comes back, and you cannot tell a reorder from a return. Peer resale removes that ambiguity entirely, and that changes what the data can prove.

Get a free sample See sample data

Free pilot on your own Vinted list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

vinted_outcomes.jsonl LIVE FEED

{"vinted_listing_id":"vt-7712049", "market":"FR","currency":"EUR", "brand_normalised":"Example Label", "asking_price":24.00, "price_revisions":[{"price":32.00,"date":"2026-07-14"}, {"price":24.00,"date":"2026-08-01"}], "listed_at":"2026-07-14", "sold_at":"2026-08-06", "days_to_sale":23, "outcome":"sold", "outcome_note":"unique item — will not restock", "condition_declared":"Very good", "condition_assessed_by":"seller", "size_label":"38"} {"vinted_listing_id":"vt-7719981", "outcome":"unconfirmed", "sold_at":"null", "note":"vanished without a sold marker — not counted as sold"}

2 of 9,412,880 listing rows · markets: 8outcome confirmed on 91.2% · schema v1.7

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Vinted or its owners. Vinted and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Vinted at a glance

How we handle Vinted specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Vinted across European markets
Structural fact: Unique-item inventory — one listing is one physical item
Consequence: A sold listing never returns, so sell-through is definitive
Unit: The listing, since there is no product catalogue behind it
Condition: Seller-declared, flagged as such
Market: A dimension on every record, with currency
Refresh: Daily standard; more frequent where time-to-sale precision matters
Region: Europe

Platform specifics

What makes peer resale data different from retail data

These are the reasons a Vinted dataset needs its own handling rather than a shared retail schema.

Unique inventory makes sell-through definitive

On a retailer, a size going out of stock is ambiguous: it may have sold, or it may be a returns cycle, or a replenishment gap. Our Zalando, Otto and Myntra pages all carry that caveat.

Here there is no ambiguity. One listing is one garment. When it is marked sold, it sold. It will never reappear.

What that enables

Time to sale is a real measurement, not a proxy. Listed date to sold date, exactly.
Sell-through rate by attribute is computable: what share of listings with a given brand, size or price band actually sold.
Price-to-sale relationships are observable, since asking price and outcome are both known.
Delisting without sale is separable from selling, where the platform distinguishes them.

We capture listed_at, sold_at, days_to_sale and outcome as sold, delisted_unsold or still_listed. Where the platform does not distinguish a sale from a withdrawal, we record outcome_unconfirmed rather than assuming a sale — the same discipline as our property pages, applied to a market where the confirmed cases are genuinely definitive.

There is no product catalogue, so the listing is the unit

Like Etsy, there is no shared product identity here — but for a different reason. On Etsy items are individually made; here they are individually owned second-hand goods, so two listings of the same original product are still different items in different condition.

What we therefore deliver

Brand and category level price distributions, conditioned on declared condition and size where structured.
Sell-through and time-to-sale by attribute band, which is the strongest signal available.
Brand-level supply volume — how many listings of a brand exist and enter per week.
Not product-to-product matching against retail, since condition and wear make that comparison unsound.

Where a brand wants a resale-to-retail relationship, we can compute it at brand and category band level, not per item. That is an honest aggregate and we label it as one. Per-item resale-to-retail on unauthenticated second-hand clothing would imply a comparability that the condition variance does not support.

Condition is seller-declared, and that is the ceiling

Condition here is declared by a private seller with no verification step. That is a lower evidential standard than the retailer-graded open-box tiers on Best Buy and even than platform-assessed resale grading.

We capture the declared condition exactly and set condition_assessed_by to seller.
We do not normalise across sellers, since a private seller's "very good" carries no common definition.
We do not assess authenticity, for the same reason as on our luxury pages: it requires physical examination.
Defect text is captured where present, as free text rather than structured.

What this means practically: condition is useful as a segmentation variable within the platform, and unsafe as a basis for cross-platform value comparison. We say so rather than delivering a normalised grade that would invite exactly that misuse.

Scope

What we collect on Vinted, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Listed and sold dates with days to sale computed
Outcome as sold, delisted_unsold, still_listed or unconfirmed
Sell-through rate and time to sale by brand, category, size and price band
Brand-level supply volume and weekly listing entry rate
Asking price with price change history where the seller revised it
Declared condition captured exactly, with assessed-by set to seller
Market and currency on every record
Resale-to-retail relationships at brand and category band level, labelled as aggregate
Listing images count and defect text where present

❌ What we do not, and why

A normalised condition grade across sellers, which would invite cross-platform misuse
Any authenticity assessment
Per-item resale-to-retail comparison against a retail product
Seller personal identity as distinct from seller activity metrics
Outcome classified as a sale where the platform does not distinguish it

Core Vinted fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
vinted_listing_id	Listing identifier, which is the unit of record
market / currency	Storefront country and currency, both mandatory
brand_normalised / category_path	Brand mapped to a consistent identity, plus category
asking_price / price_revisions	Current asking price and any seller revisions with dates
listed_at / sold_at / days_to_sale	Lifecycle dates and computed duration
outcome	sold, delisted_unsold, still_listed or unconfirmed
condition_declared / condition_assessed_by	Declared condition and that a seller declared it
size_label	Size as published, without cross-brand normalisation
defect_text	Free-text defect description where present
seller_listing_count / seller_sold_count	Seller activity metrics, not personal identity
images_count	Number of listing images, a listing-quality signal

Use cases

What teams do with Vinted data

Definitive sell-through measurement

Unique inventory means a sold listing never returns, so sell-through by brand, size and price band is measured rather than inferred from availability changes.

Price-to-sale relationship analysis

Asking price with revisions and confirmed outcomes shows which price bands actually clear and how quickly, which retail availability data cannot establish.

Brand resale supply monitoring

Brand-level listing volume and weekly entry rate show how much second-hand supply of a brand exists and how fast it grows.

Aggregate resale-to-retail positioning

Resale-to-retail is computed at brand and category band level and labelled as aggregate, avoiding a per-item comparison the condition variance cannot support.

The 24-hour sample — run on your sources, not ours

Send us a Vinted item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Vinted is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Vinted data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what luxury & resale data covers, and a Vinted-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Vinted data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because each listing is one physical garment. When it is marked sold, it sold, and it will never reappear.

On a retailer, a size going out of stock could be a sale, a returns cycle or a replenishment gap — a caveat that runs through our Zalando, Otto and Myntra pages. That ambiguity does not exist here, which makes time to sale a measurement rather than a proxy.

At brand and category band level, yes, labelled as an aggregate. Per item, no.

Two listings of the same original product are different physical items in different condition, and condition here is seller-declared without verification. A per-item resale-to-retail figure would imply a comparability the condition variance does not support.

No. A private seller's "very good" carries no common definition, so a normalised grade would look usable and invite cross-platform value comparison it cannot support.

We capture the declared condition exactly with condition_assessed_by set to seller. It is useful as a segmentation variable within the platform and unsafe as a cross-platform basis, and we say so rather than hiding it behind a clean field.

Where the platform distinguishes a sale from a withdrawal, we record which. Where it does not, we set outcome_unconfirmed rather than assuming a sale.

Assuming would inflate sell-through rates — and sell-through is the headline metric this dataset exists for, so an inflated version would be worse than useless.

No, for the same reason as on our luxury pages: authentication requires physical examination by qualified specialists, and no data signal substitutes for it.

What we can flag are observable anomalies such as unusually low asking prices for a stated brand and condition, as starting points for your own team rather than conclusions from us.

We quote individually. Drivers are market count, brand or category scope, and refresh frequency — which here is largely a time-to-sale precision question, since daily collection bounds days-to-sale to within a day.

One scoping call, a free pilot on your own brands within 24 hours, then a fixed monthly quote. Request a quote.

See real Vinted data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner