Platform · Mytheresa

Mytheresa Data Scraping

A tighter edit than most luxury sites, with exclusives that exist nowhere else. The unmatched share here is partly the point.

Mytheresa data scraping collects luxury product listings, prices and availability. What makes it distinct: a curated range with a meaningful share of exclusives and capsule collections that exist on no other stockist. So a low cross-retailer matched share here is partly the retailer's strategy, not a data gap — and the exclusive share is reported alongside it so the two are never confused. It shares an owner with Net-a-Porter since April 2025.

On most sites an unmatched product is a matching problem. Here a good share of them are unmatched because that is what the retailer set out to sell.

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mytheresa.jsonl LIVE FEED

{"retailer":"mytheresa","brand_name":"designer-a", "is_exclusive":true,"exclusive_source":"retailer label on page", "price":2100.00,"cross_retailer_matched":false} {"brand_name":"designer-b","is_exclusive":false, "cross_retailer_matched":false, "note":"unmatched but NOT labelled exclusive. we do not assume it is"} {"matched_share_category":0.41,"exclusive_share_category":0.19, "styles_per_brand":23,"styles_per_brand_broad_stockist":61}

3 of 544,110 product rows · globalmatched and exclusive shares, together · schema v1.0

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

How we handle Mytheresa specifically

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

Retailer: Mytheresa — curated luxury
Group: Same owner as Net-a-Porter since April 2025
The point: Exclusives and capsules are part of the range
So: Low matched share is partly strategy
Reported together: matched_share and exclusive_share
Curation: Fewer styles per brand than broader sites
Model: Buys and holds inventory — retailer sets price
Refresh: Daily through markdown season

Platform specifics

Exclusives, curation depth, and what unmatched means

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

Separating strategy from a matching gap

A cross-retailer comparison reports what matched. On this site, a meaningful part of what did not match was never meant to.

Exclusives are sold here and nowhere else.
Capsule collections produced with brands are exclusive for their run.
Curation means fewer styles per brand, so overlap is lower even on shared brands.
So a matched share of 40% means something different here from 40% on a broad stockist.

We report matched_share_category and exclusive_share_category together, with is_exclusive taken from the retailer's own labelling. Exclusivity is not inferred from the absence of a match — an unmatched product might be exclusive, or might be stocked somewhere we are not observing.

Curation depth is a measure

styles_per_brand per batch, against broader stockists, shows how tightly each designer is edited.

Group overlap, markdowns and scope

The sibling storefront

Overlap with Net-a-Porter is measured before commissioning both, as our Net-a-Porter page sets out. Exclusives are the part that cannot overlap, which is one reason product overlap is lower than brand overlap.

Markdowns

Retailer-set, with start dates on a shared schedule across stockists and siblings.

Markets

Prices are presented per delivery market. market_observed_from travels with every price.

What we do not collect

Buying terms, stock quantities, customer data, or an exclusivity status inferred from a failed match.

Scope

What we collect on Mytheresa, 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

is_exclusive from the retailer's own labelling
matched_share_category and exclusive_share_category reported together
Exclusivity never inferred from an absent match
styles_per_brand per batch as a curation measure
Overlap with the sibling storefront measured before commissioning both
Markdown start dates on a shared schedule
market_observed_from on every price
Style-code matching with match_basis
Size availability per size

❌ What we do not, and why

An unmatched product assumed to be exclusive
A low matched share presented as a data gap without the exclusive share
An exclusive matched to another retailer's product
Two group storefronts sold as full panels without measuring overlap
Buying terms, stock quantities or customer data

Core Mytheresa fields

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

Field	What it is on this platform
retailer / group	Storefront and owner
brand_name / brand_style_code / colourway	Match keys
price / currency / price_setter	Retailer sets it
is_exclusive / exclusive_source	From the retailer's labelling
matched_share_category / exclusive_share_category	Reported together
styles_per_brand	Curation depth
product_overlap_with_sibling	Measured in the pilot
markdown_started_at / markdown_observed	Retailer-set
market_observed_from	Where the price was seen from
size_availability	Per size
observed_at	Timestamp

Use cases

What teams do with Mytheresa data

Exclusive strategy tracking

Exclusive share by category over time, as a measure of how much of the range is designed to be unavailable elsewhere.

Curation depth benchmarking

Styles per brand against broader stockists, showing how tightly each designer is edited.

Honest luxury parity

Matched share reported alongside exclusive share, so a low match rate is read correctly.

Group overlap measurement

Product overlap with the sibling storefront before commissioning both.

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

Send us a Mytheresa 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.

Mytheresa is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Mytheresa 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 Mytheresa-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

Mytheresa data scraping: frequently asked questions

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

Partly by design. A meaningful share of the range is exclusives and capsules sold nowhere else, and curation means fewer styles per brand, so overlap is lower even on shared brands.

We report matched share and exclusive share together so the two are not confused.

From the retailer's own labelling. We do not infer exclusivity from a failed match — an unmatched product might be stocked somewhere we are not observing.

Same group since April 2025, different storefront. That page covers the group structure and overlap; this one covers exclusives and curation. Overlap is measured before commissioning both.

Yes — styles per brand per batch, against broader stockists, shows how tightly each designer is edited.

They are presented per delivery market, so we record the market observed from on every price.

We quote individually on categories, markets and refresh, and where the sibling storefront is already collected, the difference is usually the better programme.

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

See real Mytheresa 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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