Platform · Farfetch

Farfetch Data Scraping

A marketplace of boutiques. One product, several sellers behind it, and a price that changes with the country you are shipping to.

Farfetch data scraping collects luxury product listings, prices, seller attribution and availability. The structure that decides the data: it is a marketplace of boutiques, department stores and brands, so one product can be offered by several sellers, and the displayed price depends on the delivery market. A price without its seller and its market is not a reproducible observation. The business has been owned by Coupang since January 2024.

Most retailers on this hub are one price setter. This is dozens, behind a single product page.

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farfetch.jsonl LIVE FEED

{"platform":"farfetch","brand_style_code":"as published", "seller_name":"boutique-a","seller_type":"boutique", "delivery_market":"GB","price":1120.00, "currency_displayed":"GBP","offers_visible":3} {"seller_name":"boutique-b","delivery_market":"GB", "price":1185.00, "note":"same product, same market, different seller, 65 apart"} {"delivery_market":"AE","duties_included_displayed":"as shown on page", "landed_cost_computed":"not_produced", "caution":"a price without its delivery market is not a reproducible observation"}

3 of 2,884,110 offer rows · globalseller and delivery market on every price · schema v1.0

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

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Farfetch at a glance

How we handle Farfetch specifically

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

Platform: Farfetch — luxury marketplace
Ownership: Coupang, since January 2024
The structure: Boutiques, stores and brands sell here
Consequence: One product, several sellers
And: Price depends on the delivery market
So: seller and market on every price
Brand-only shops: Some brands sell direct through the platform
Refresh: Daily; seller offers change independently

Platform specifics

Several sellers, several markets

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

The seller is the price setter

A product page aggregates offers from the sellers who hold that item. The displayed price is typically one of those offers, and the others exist behind it.

Sellers set their own prices, within whatever the platform permits.
Which offer is shown first is a platform decision we do not model.
Size availability differs by seller — one boutique has the 38, another the 40.
So a product-level price is one seller's offer presented as the product's price.

Where seller offers are exposed, we record each as its own record with seller_name and seller_type (boutique, department store, brand). Where only one is displayed, offers_visible is 1 and we say so rather than implying the product has one price.

Delivery market changes the price

Prices are presented for a delivery destination, and duties and taxes treatment differs by destination. delivery_market and duties_included_displayed travel with every price. We do not compute a landed cost where the page does not display one.

Matching, markdowns and what we do not collect

Matching

Luxury style codes survive well across marketplaces and stockists. match_basis on every pair and matched_share_category before quoting, as on our Bloomingdale's page.

Markdowns

Seller markdowns start at different times on the same product, which makes markdown timing within one product page observable — unusual, and worth having where offers are exposed.

Cross-market price gaps

The same product priced for different delivery markets on a shared schedule gives a genuine cross-market gap. It is computed from paired records with both markets named.

What we do not collect

Seller commercial terms, commission, stock quantities, customer data, or a landed cost the page did not display.

Scope

What we collect on Farfetch, 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

seller_name and seller_type on every offer where exposed
offers_visible recorded, so a single displayed offer is not read as the only price
delivery_market and duties_included_displayed on every price
No landed cost computed where the page does not display one
Size availability per seller where exposed
Style-code matching with matched_share_category
Seller markdown timing within one product
Cross-market gaps from paired records with both markets named
FX stamped per observation, local currency primary

❌ What we do not, and why

A product-level price presented as the product's only price
A price without its delivery market
A landed cost computed from assumed duty rates
A model of which offer the platform displays first
Seller terms, commission, stock or customer data

Core Farfetch fields

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

Field	What it is on this platform
platform / product_id / brand_style_code	The product and its match key
seller_name / seller_type	Boutique, department store or brand
offers_visible	How many offers the page exposed
price / currency_displayed	One seller's offer
delivery_market / duties_included_displayed	The market it is priced for
fx_rate / fx_observed_at	Stamped at observation
size_availability_seller	Per seller where exposed
markdown_started_at_seller	Per seller, within one product
cross_market_gap_pct	From paired records, both markets named
match_basis / matched_share_category	Cross-stockist
observed_at	Timestamp

Use cases

What teams do with Farfetch data

Seller-level luxury pricing

Offers attributed to boutiques, stores and brands, so a product's price is understood as several sellers' decisions rather than one.

Cross-market luxury price gaps

The same product priced for different delivery markets on a shared schedule, with both markets named.

Within-product markdown timing

Sellers marking down the same product at different times, which is observable on one page where offers are exposed.

Distribution mapping

Which boutiques and stores hold which designers, as a luxury distribution view.

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

Send us a Farfetch 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.

Farfetch is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Farfetch 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 fashion & apparel data covers, and a Farfetch-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

Farfetch data scraping: frequently asked questions

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

Because this is a marketplace of boutiques, department stores and brands. One product can have several sellers, each setting its own price, and size availability differs between them.

A product-level price is one seller's offer presented as the product's price.

Because prices are presented for a delivery destination and duties and taxes treatment differs by destination. A price without its market cannot be reproduced or compared.

Only where the page displays it. We do not compute one from assumed duty rates — that would put an assumption about a customs treatment into a price field.

Coupang, which acquired the business and assets in January 2024. The marketplace continues to operate.

No. That is a platform decision we do not model. We record what is displayed and how many offers were visible.

We quote individually on categories, delivery markets and refresh. Each delivery market multiplies price rows, so market scope is the main driver.

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

See real Farfetch 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.

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