Platform · M&S

M&S Data Scraping

One garment in petite, regular and tall, each with its own size run. The product is a family of records, and availability lives at that level.

M&S data scraping collects clothing listings, prices, size and fit availability and range changes. The structural point: garments are offered across fit ranges — petite, regular, tall and in places extended sizes — each with its own size run. So one product is a family of variant records, availability is per fit and size, and the same site sells food and home, which a fashion panel has to scope out explicitly.

Most apparel pages in this set treat size as the variant. Here fit is a second axis, and it changes what sold out means.

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mands.jsonl LIVE FEED

{"retailer":"m&s","department":"clothing", "product_id":"ms-44120","fit_range":"regular", "size_label":"12","variant_available":true, "price":39.50,"currency":"GBP"} {"product_id":"ms-44120","fit_range":"petite", "size_label":"12","variant_available":false, "note":"same garment, same size. sold out in petite only. a product flag says in stock"} {"fit_coverage_category":0.37,"category":"trousers", "department_excluded":"food, home", "stock_quantity":"not_collected"}

3 of 884,220 variant rows · UKfit and size are two axes · food out of scope · schema v1.0

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

How we handle M&S specifically

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

Retailer: M&S — UK, clothing, food and home
The point: Fit ranges are a second variant axis
Consequence: One garment is many records
Availability: Per fit and size, not per product
Third-party brands: Sold alongside. A separate price setter
Food and home: Same site, outside fashion scope
Member offers: Loyalty pricing. Gated share reported
Refresh: Daily for fashion; sale events are concentrated

Platform specifics

Fit as a second axis

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

Fit multiplies the variants, and changes availability

A single garment can be offered in petite, regular and tall fits, each with its own size run. That is a second variant axis most apparel data ignores.

A product is a family of fit × size records.
Price is usually shared across fits, but not always — extended ranges can price differently.
A garment "in stock" can be sold out in petite, which a product-level flag hides.
Range depth by fit is itself a finding — which categories carry extended fits and which do not.

So fit_range and size_label are separate fields, availability is recorded at that level, and fit_coverage_category reports what share of each category offers extended fits.

A product-level availability flag is computed from the variant records rather than collected, and never substituted for them.

Scope, third-party brands and member pricing

Food and home are out of scope unless named

The site sells food and home alongside clothing. department is on every record and departments are named in the scoping document — the position our Target page takes. A fashion panel that crawls category pages without this picks up products it was never meant to price.

Third-party brands

Other brands are sold alongside the own range and are priced by the brand. price_setter is on every record, as on our Next page.

Member offers

Loyalty offers where publicly displayed, null with a reason where gated, and gated_share reported. No accounts are created — the boundary on our access page.

What we do not collect

Stock quantities, customer or member data, or a product-level availability flag in place of the variant records.

Scope

What we collect on M&S, 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

fit_range and size_label as separate fields
Availability at fit and size level
fit_coverage_category per batch
Product-level availability computed from variant records
department on every record, named at scoping
price_setter distinguishing own range from third-party brands
Member offers where public, with gated_share reported
SKU lifespan with introduction and delisting rates
on_sale with sale_event_id where identifiable

❌ What we do not, and why

A product-level in-stock flag substituted for variant availability
Food or home products in a fashion panel by accident
Third-party brands pooled with the own range
A stock quantity inferred from availability
Member identities or account data

Core M&S fields

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

Field	What it is on this platform
retailer / department / price_setter	Scope, and who priced it
product_id / product_name / colourway	The garment
fit_range / size_label	Two axes, both recorded
variant_available	Per fit and size
fit_coverage_category	Share offering extended fits
price / currency	As displayed, per variant where it differs
price_member / gated_reason / gated_share	Member pricing and the gap
on_sale / sale_event_id	As displayed
sku_first_seen / sku_lifespan_days	The range cycle
is_third_party / brand_name	Whose product
observed_at	Timestamp

Use cases

What teams do with M&S data

Fit range coverage analysis

Share of each category offering extended fits, which shows where a retailer serves petite and tall customers and where it does not.

Variant-level availability

Availability per fit and size, so a garment sold out in petite is not reported as in stock.

Own-range pricing with scope controlled

Department and price setter on every record, so a fashion series excludes food, home and third-party brands.

Sale event depth

Concentrated sale windows with collection aligned, so promotional depth measures the event rather than the schedule.

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

Send us a M&S 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.

M&S is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. M&S 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 M&S-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

M&S data scraping: frequently asked questions

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

Because a garment can be offered in petite, regular and tall, each with its own size run. That is a second variant axis.

A garment in stock overall can be sold out in petite, and a product-level flag hides it.

Usually not, but extended ranges sometimes do. We record price per variant where it differs rather than assuming it is shared.

Not if scope is set properly. Department is on every record and we name the departments in the scoping document.

Only if you want them, and always separated. They are priced by the brand, so pooling them with the own range measures a catalogue mix rather than a pricing strategy.

Where publicly displayed. Where gated, the field is null with a reason and we report the share. No accounts are created.

We quote individually on departments, categories and refresh. Fit ranges multiply rows, so extended-fit categories cost more to cover.

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

See real M&S 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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