Platform · JD Sports

JD Sports Data Scraping

Big brands, but many colourways made only for this retailer. Branded does not mean matchable here.

JD Sports data scraping collects sportswear and footwear listings, prices, availability and range across markets. The handling that decides the analysis: a meaningful share of the branded range is retailer-exclusive colourways and product made by major brands for this retailer only. So branded does not mean matchable — an exclusive has the brand's name and no counterpart at any other stockist.

The John Lewis page argues branded stock matches across stockists. Here that rule has a large, deliberate exception.

Get a free sample See sample data

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

jdsports.jsonl LIVE FEED

{"retailer":"jd_sports","country":"GB", "brand_name":"brand-a","brand_style_code":"as published", "colourway_code":"retailer-exclusive","is_exclusive":true, "price":120.00,"cross_retailer_matched":false} {"retailer":"stockist-b","brand_style_code":"same style", "colourway_code":"general release", "note":"same style code, different colourway. NOT a match"} {"matched_share_category":0.52,"exclusive_share_category":0.21, "size_system":"UK","size_converted":false}

3 of 1,604,220 product rows · multi-marketbranded but exclusive · two codes to match · schema v1.0

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

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

JD Sports at a glance

How we handle JD Sports specifically

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

Retailer: JD Sports — multi-market sportswear
The point: Retailer-exclusive colourways from major brands
Consequence: Branded but unmatched by construction
So: is_exclusive from the retailer's labelling
Reported with: matched_share and exclusive_share together
Markets: Several fascias and countries, priced independently
Launches: Some releases allocated by draw
Refresh: Daily; launch calendar tracked

Platform specifics

Branded, exclusive, unmatched

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

Why a big-brand product can have no match

Major sportswear brands produce colourways and styles exclusively for particular retailers. They carry the brand name, sit in the brand's product family, and are sold nowhere else.

A style code may be shared with the general release while the colourway is exclusive.
So matching on style code alone pairs the exclusive with a different colourway at another stockist.
Matching needs style code and colourway code, both, to hold.
Exclusive share is itself a measure of how much of the range is protected from direct price comparison.

We record is_exclusive from the retailer's own labelling, require brand_style_code and colourway_code for a cross-stockist match, and report matched_share_category alongside exclusive_share_category — the same pairing our Mytheresa page uses in luxury.

Markets, fascias and launches

Markets and fascias

The group trades across many countries and several store fascias, and prices are set by market. country and fascia are dimensions, FX is stamped per observation, and a group figure is a computed rollup with the mix stated — the argument our country dimension page makes.

Launches

Some releases are allocated by draw. sale_mechanic is recorded and draw items are excluded from availability figures by default, as on our END. page.

Size availability

Per size, with footwear size system recorded and never converted.

What we do not collect

Brand supply terms, stock quantities, draw entrants or customer data.

Scope

What we collect on JD Sports, 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
Cross-stockist match only on style code AND colourway code
matched_share_category and exclusive_share_category together
country and fascia as dimensions
FX stamped per observation, local currency primary
sale_mechanic recorded, draw items excluded from availability
size_system recorded, never converted
Size availability per size
Group figures as rollups with the mix stated

❌ What we do not, and why

A match on style code alone where the colourway differs
An exclusive paired with a general-release colourway
A group average without its country and fascia mix
A footwear size converted between systems
Brand supply terms, draw entrants or customer data

Core JD Sports fields

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

Field	What it is on this platform
retailer / fascia / country	Where, and under which fascia
brand_name / brand_style_code / colourway_code	Both codes required to match
is_exclusive / exclusive_source	From the retailer's labelling
price / currency / fx_observed_at	As displayed, stamped
matched_share_category / exclusive_share_category	Reported together
sale_mechanic / draw_closes	Standard or draw
size_label / size_system	Never converted
size_availability	Per size
on_sale / discount_pct_displayed	As displayed
country_mix	On any group rollup
observed_at	Timestamp

Use cases

What teams do with JD Sports data

Exclusive range measurement

Exclusive share by category, showing how much of a big-brand range is protected from direct price comparison.

Correct sportswear matching

Matches only on style and colourway code together, so an exclusive is never paired with a general release.

Multi-market sportswear pricing

Country and fascia as dimensions, with FX per observation.

Launch tracking

Draw releases recorded separately from ordinary stock.

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

Send us a JD Sports 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.

JD Sports is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. JD Sports 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 JD Sports-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

JD Sports data scraping: frequently asked questions

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

Because brands make colourways and styles exclusively for particular retailers. They carry the brand name and are sold nowhere else.

A style code may be shared with the general release while the colourway is exclusive, so style code alone is not enough to match.

On style code and colourway code together. Where either is missing, we do not assert a match.

From the retailer's own labelling. We do not infer it from a failed match.

Yes, and by fascia. Both are dimensions, and a group figure is a rollup with the mix stated.

Recorded as draws and excluded from availability figures by default, since a draw size run is not stock.

We quote individually on countries, fascias and refresh. Country count is the main driver.

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

See real JD Sports 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.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner