Platform · Net-a-Porter

Net-a-Porter Data Scraping

Since April 2025 it shares an owner with Mytheresa. Two rows on the hub, one group — so the overlap is measured before anyone pays twice.

Net-a-Porter data scraping collects luxury product listings, prices and availability. The structural fact that shapes commissioning: Mytheresa's parent completed its acquisition of the Yoox Net-a-Porter group from Richemont in April 2025, so this site, Mr Porter, the off-price YOOX and Mytheresa now sit in one group. Because this site buys and holds its inventory, the retailer is the price setter — and brand-level overlap with its sibling is measurable before paying for both.

This is the Goibibo and MakeMyTrip situation in luxury fashion. Same group, separate storefronts, and a question worth answering first.

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

netaporter.jsonl LIVE FEED

{"group":"same owner as mytheresa since 2025-04", "banner":"womenswear","price_setter":"retailer", "brand_style_code":"as published","price":1450.00} {"brand_overlap_with_sibling":0.78, "product_overlap_with_sibling":0.34, "note":"most brands shared, a third of products. the difference is what is worth buying"} {"banner":"off_price","pooled_with_full_price":false, "is_exclusive":true,"cross_retailer_matched":false}

3 of 804,220 product rows · globalone group, several storefronts · overlap first · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Net-a-Porter or its owners. Net-a-Porter 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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Net-a-Porter at a glance

How we handle Net-a-Porter specifically

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

Retailer: Net-a-Porter — womenswear luxury
Group: Same owner as Mytheresa since April 2025
Siblings: Mr Porter (menswear), YOOX (off-price), Mytheresa
Model: Buys and holds inventory — retailer sets price
So: Measure overlap with Mytheresa first
Off-price: YOOX is a separate banner, never pooled
Exclusives: Flagged, and unmatched by construction
Refresh: Daily through markdown season

Platform specifics

One group, several storefronts

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

Measure brand and product overlap before commissioning both

The two womenswear storefronts now share an owner. They were built separately, and their brand rosters and buying differ — but not entirely.

Brand overlap is substantial in the core designer set.
Product overlap is lower, because buying decisions differ by style.
Prices on overlapping products are frequently identical, with markdown timing the place they diverge.
Exclusives and capsules differ by storefront by design.

We report brand_overlap_with_sibling and product_overlap_with_sibling in the pilot, on a shared schedule. Where product overlap is high we recommend collecting the difference — the position our Goibibo and Hotels.com pages take.

And the sibling page makes the other half of the argument

Our Mytheresa page covers exclusives and curation depth. Read together they set out what each storefront adds.

Inventory model, banners and markdowns

The retailer is the price setter

Unlike a marketplace, this storefront buys stock and sets its price. price_setter is retailer throughout, which makes markdowns the retailer's decisions rather than a seller mix — the contrast with our Farfetch page.

Banners are separate

banner separates the womenswear storefront, the menswear storefront and the off-price banner. The off-price banner is never pooled with full price, for the reasons our Nordstrom page sets out.

Markdown timing

Start dates per storefront on a shared schedule, retained across seasons.

What we do not collect

Group financials, buying terms, stock quantities, customer data or personal-shopper content.

Scope

What we collect on Net-a-Porter, 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

brand_overlap_with_sibling and product_overlap_with_sibling in the pilot
A recommendation to collect the difference where product overlap is high
Shared schedule across the group's storefronts
banner separating womenswear, menswear and off-price
price_setter as retailer throughout
is_exclusive flagged, unmatched by construction
Markdown start dates per storefront, retained across seasons
Style-code matching against non-group stockists
Size availability per size

❌ What we do not, and why

Two group storefronts sold as full panels without measuring overlap
The off-price banner pooled with full price
An exclusive matched to another retailer's product
A markdown sequence from unsynchronised observations
Group financials, buying terms or customer data

Core Net-a-Porter fields

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

Field	What it is on this platform
group / banner	Which storefront in the group
brand_name / brand_style_code / colourway	Match keys
price / currency / price_setter	Retailer sets it
brand_overlap_with_sibling / product_overlap_with_sibling	Measured in the pilot
is_exclusive	Unmatched by construction
markdown_started_at / markdown_observed	Per storefront
match_basis / matched_share_category	Against non-group stockists
size_availability	Per size
panel_schedule_id	Shared across storefronts
market_observed_from	Where the price was seen from
observed_at	Timestamp

Use cases

What teams do with Net-a-Porter data

Deciding whether to collect both group storefronts

Brand and product overlap measured on a shared schedule, with a recommendation to collect the difference where product overlap is high.

Group markdown behaviour

Markdown start dates per storefront, showing whether siblings in one group now move together.

Luxury stockist parity

Style-code matching against stockists outside the group.

Off-price versus full-price

The group's off-price banner kept separate, with cross-banner pairs only on style codes.

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

Send us a Net-a-Porter 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.

Net-a-Porter is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Net-a-Porter 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 Net-a-Porter-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

Net-a-Porter data scraping: frequently asked questions

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

Same group. Mytheresa's parent completed its acquisition of the Yoox Net-a-Porter group from Richemont in April 2025. The storefronts continue separately.

That is measurable, and we measure it first — brand overlap and product overlap on a shared schedule. Brand overlap is typically substantial; product overlap is lower because buying differs by style.

Where product overlap is high, collecting the difference is the better programme.

The retailer. This storefront buys and holds inventory, so markdowns are its own decisions rather than a seller mix.

Only if in scope, and always as separate banners. The off-price banner is never pooled with full price.

Flagged, and unmatched by construction — an exclusive has no counterpart elsewhere to match.

We quote individually. Where the sibling storefront is already collected, the right programme is usually the difference rather than a second full panel.

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

See real Net-a-Porter 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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