Platform · ASOS

ASOS Data Scraping Services

Q: Do you separate outlet from the main range?

Yes, via is_outlet. Merging them makes main-range markdown depth look worse than it is and hides outlet clearance as its own mechanic. Both views are then available: main-range markdown behaviour, and outlet clearance depth and velocity separately. Most clients want the first for benchmarking and the second for understanding how ASOS exits stock.

Q: Can you track how fast articles get marked down?

Yes, via days_to_first_markdown derived from first observation. It is one of the more informative fields at ASOS given the continuous drop model. The honest caveat: for articles that launched before our collection began, first-seen is when we first saw it rather than the true launch date. We report the archive start date so weeks-on-site and time-to-markdown are not misread as full-lifecycle figures.

With ASOS own-brand labels separated from stocked brands, because they follow completely different pricing logic.

ASOS data scraping is the automated collection of publicly visible ASOS data — own-brand labels classified separately from stocked third-party brands, drop cadence and newness share, per-size availability, markdown depth including outlet, and per-market pricing across ASOS storefronts.

ASOS runs its own labels alongside hundreds of stocked brands on the same site. The own-brand lines are priced, dropped and marked down on entirely different logic. Treating them as one catalogue produces an average that describes neither.

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asos_lines.jsonl LIVE FEED

{"asos_product_id":"208841027", "name":"Example Linen Shirt", "brand_type":"acquired_own_brand", "size_run":"main", "first_seen":"2026-06-11", "weeks_on_site":8, "is_newness":false, "full_price":38.00,"current_price":26.60, "markdown_events":2, "days_to_first_markdown":34, "size_curve":[{"size":"S","in_stock":false}, {"size":"M","in_stock":false}, {"size":"XL","in_stock":true}], "core_sizes_oos":true,"is_outlet":false} {"asos_product_id":"208841027", "size_run":"curve", "core_sizes_oos":false, "note":"separate run — not merged with main"}

2 of 1,884,200 product-size rowsnew lines this week: 4,180 · schema v4.3

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

How we handle ASOS specifically

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

Platform: ASOS, including outlet, across UK, EU, US and RoW storefronts
The core split: ASOS own-brand labels classified separately from stocked brands
Own-brand labels: ASOS DESIGN, Collusion, Topshop, Topman, Miss Selfridge and others
Drop cadence: Newness share and introduction rate, since ASOS ranges refresh continuously
Size level: Per-size availability with broken curve and core-size flags
Outlet: Outlet markdown depth captured as a distinct state
Refresh: Daily standard; sub-daily during sale and drop launches
Region: United Kingdom primarily; EU, US and RoW storefronts on request

Platform specifics

What makes ASOS data different

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

Own-brand and stocked brands are two businesses

ASOS sits in an unusual position: a large-scale multi-brand retailer that is also one of the biggest own-label fashion businesses in the market. Both live on the same site.

Own-brand lines — ASOS DESIGN, Collusion, the acquired heritage labels — are ASOS's own margin, own markdown decisions and own drop calendar. Their pricing reflects internal strategy.
Stocked brands are wholesale relationships. Pricing reflects brand RRP and whatever markdown ASOS has agreed or taken.
Analysis needs them apart. A category price index blending both measures a mixture of ASOS's own-label positioning and hundreds of brands' RRPs.

We classify brand_type using maintained label mappings rather than name matching, because several own-brand labels do not carry the ASOS name at all. For anyone benchmarking against ASOS or competing with its own-label ranges, this is the field the whole analysis depends on.

Drop cadence is the metric, not range size

ASOS refreshes its range continuously rather than in seasonal drops. That makes static range breadth a weak metric and newness rate the informative one.

Newness share — the proportion of the range introduced recently — shows how aggressively a category is being refreshed.
Introduction rate over time reveals where ASOS is investing and where it is coasting.
Time to first markdown from launch is the clearest signal of whether newness is working.
Delisting rate alongside introduction rate gives net range direction, which a snapshot count cannot.

We record first_seen on every article and derive newness share, introduction rate and time-to-first-markdown per category. The honest constraint: for articles that launched before our collection began, first-seen is when we first observed it, not the true launch date, and we report the archive start so weeks-on-site is not misread.

Outlet as a distinct state, and size curves

ASOS operates an outlet section carrying deeper markdowns on end-of-life lines. Merging outlet records into the main catalogue makes main-range markdown depth look worse than it is, and makes outlet clearance invisible as a separate mechanic.

We flag is_outlet so both views are available: main-range markdown behaviour, and outlet clearance depth and velocity as their own analysis.

Size curves work as they do across our fashion coverage — per-size availability with broken-curve and core-size-stockout flags, sizes normalised with originals retained, footwear on its own scales, and markdown events joined to size curve state at each step so tail clearance is distinguishable from a genuine slow seller.

ASOS also runs extended, petite, tall and maternity ranges as separate size runs. We treat them separately rather than merging into one curve, because a broken curve in the main run should not be masked by availability in an extended run.

Scope

What we collect on ASOS, 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

Own-brand label classification via maintained mappings, separate from stocked brands
Newness share, introduction rate and time to first markdown per category
Outlet flagged as a distinct state rather than merged into the main range
Full per-size availability with broken curve and core-size flags
Extended, petite, tall and maternity treated as separate size runs
Markdown event history with size curve state at each step
Per-storefront pricing in local currency with tax basis recorded
Article first-seen, weeks on site and delisting detection
Published composition, fit and care attributes

❌ What we do not, and why

ASOS supplier or brand partner portals of any kind
Unit sales, inventory depth or return rates, none of which is published
ASOS Premier member pricing requiring a logged-in session
Availability revealed only by adding items to a basket
Reviewer names, profiles or review histories

Core ASOS fields

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

Field	What it is on this platform
asos_product_id	The platform identifier, used as the join key
brand / brand_type	Brand name, and whether asos_own_brand or stocked_brand via maintained mappings
own_brand_label	Which own-brand label, where applicable
price / full_price / markdown_pct	Current price, original price and markdown depth
is_outlet	Whether the record is from the outlet range, kept as a distinct state
first_seen / weeks_on_site	Launch observation and elapsed weeks
is_newness	Whether ASOS currently classifies the article as new in
days_to_first_markdown	Derived from launch, the clearest signal of whether newness worked
size_curve / size_run	Per-size availability, and which run: main, petite, tall, plus, maternity
size_curve_broken / core_sizes_oos	Derived flags for gaps and core-size stockouts
storefront / currency / tax_basis	Which storefront, its currency and the tax basis of the price

Use cases

What teams do with ASOS data

Own-label competitive benchmarking

Own-brand labels are classified separately from stocked brands, so a category index reflects ASOS's own-label positioning rather than a blend of its labels and hundreds of brand RRPs.

Drop cadence and newness benchmarking

Newness share, introduction rate and time to first markdown are derived per category, showing where ASOS is investing and whether its newness is selling.

Outlet clearance analysis

Outlet is flagged as a distinct state, so main-range markdown behaviour and outlet clearance depth and velocity can each be analysed without contaminating the other.

Sell-through inference from size curves

Per-size availability across separate size runs shows core-size stockouts at full price as strong sellers and full curves entering markdown as genuine slow movers.

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

Send us a ASOS 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 inside two business days
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.

ASOS is usually collected alongside its competitors

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

ASOS data scraping: frequently asked questions

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

Because they are two different businesses on one site. Own-brand lines are ASOS's own margin, markdown decisions and drop calendar. Stocked brands are wholesale relationships priced against brand RRP.

A category index blending both measures a mixture of ASOS's own-label strategy and hundreds of brands' RRPs, which answers no question. We classify via maintained label mappings because several own-brand labels do not carry the ASOS name.

The proportion of a category's range introduced recently. It matters at ASOS specifically because the range refreshes continuously rather than in seasonal drops, which makes static range breadth a weak metric.

We derive newness share, introduction rate and time to first markdown per category. That last one is the clearest signal of whether newness is actually selling — fast markdown from launch means it is not.

Yes, via is_outlet. Merging them makes main-range markdown depth look worse than it is and hides outlet clearance as its own mechanic.

Both views are then available: main-range markdown behaviour, and outlet clearance depth and velocity separately. Most clients want the first for benchmarking and the second for understanding how ASOS exits stock.

As separate size runs rather than one merged curve. A broken curve in the main run should not be masked by availability in an extended run, and vice versa.

Each record carries size_run so you can analyse any run independently. Merging them is a common error that makes size curve analysis unreliable in exactly the categories where extended ranges are largest.

Yes, via days_to_first_markdown derived from first observation. It is one of the more informative fields at ASOS given the continuous drop model.

The honest caveat: for articles that launched before our collection began, first-seen is when we first saw it rather than the true launch date. We report the archive start date so weeks-on-site and time-to-markdown are not misread as full-lifecycle figures.

We quote individually. Drivers are category scope, storefront count, refresh frequency and whether size-level collection is required — size curves multiply volume six to twelve times over article-level.

A defined category on the UK storefront at daily size-level refresh sits at the lighter end. Full catalogue across multiple storefronts with outlet and sub-daily sale collection sits higher. One scoping call, a free pilot on your own categories within 24 hours, then a fixed monthly quote. Request a quote.

See real ASOS 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.

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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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