Platform · adidas

adidas Data Scraping

The brand sells full price and clearance on the same site. Which means the brand itself is one of the discounters stockists compete with.

adidas data scraping collects product listings, prices, availability and launch information from the brand's own channels. What distinguishes it: the brand runs an outlet section on its own site alongside full-price product. So a discount on the brand site is the brand clearing stock, and for stockists carrying the same product the brand is a competitor as well as the reference price.

Our Nike page treats the brand site as the reference. Here the reference also runs a clearance channel, and that has to be separated before either role is usable.

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adidas.jsonl LIVE FEED

{"brand":"adidas","country":"DE", "surface":"main_range","brand_style_code":"as published", "price":110.00,"currency":"EUR"} {"surface":"outlet","brand_style_code":"same code", "price":66.00,"moved_to_outlet_at":"2026-09-09", "move_observed":true, "note":"the brand clearing its own stock. that date is the signal"} {"surface":"outlet","was_price_displayed":90.00, "ever_listed_main_range":false, "caution":"never on the main range here. the was-price is a reference"}

3 of 904,220 product rows · multi-marketmain range and outlet separated · schema v1.0

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

How we handle adidas specifically

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

Source: adidas — brand-direct channels
The point: Full price and outlet on one site
So: surface separates main range from outlet
Consequence: The brand is reference and competitor
Outlet product: Some was never full price on this site
Members: Member-only product and pricing. Gated share reported
Launches: Limited releases allocated by draw
Refresh: Daily; outlet changes fast

Platform specifics

Reference and competitor on one site

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

Separate the outlet before using either number

A brand site with an outlet section plays two roles at once: it sets the reference price, and it discounts in competition with its own stockists.

Main-range prices are the reference against which stockists are compared.
Outlet prices are the brand's clearance — a competitive price, not a reference.
Some outlet product was never listed at full price on this site, so its "was" figure is a reference rather than a history.
Pooling the two makes the brand's reference price look lower than it is.

So surface is main_range or outlet on every record, and was_price_displayed is labelled as the brand's figure. Where a product moved from main range to outlet within our series, moved_to_outlet_at is recorded — that transition is the clearance signal.

Stockist comparison, members and launches

Stockist comparison

Main-range prices on canonical style and colourway codes, against stockists on a shared schedule, as our Nike page sets out. Outlet prices are compared separately, as a clearance competitor.

Members

Member-only product and pricing are not collected; gated_share is reported. No accounts.

Launches

Draw releases recorded with their mechanic and kept out of availability figures.

Markets

Prices per country, FX stamped per observation, size systems recorded and never converted.

What we do not collect

Member data, draw entrants, stock quantities or customer data.

Scope

What we collect on adidas, 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

surface as main_range or outlet on every record
was_price_displayed labelled as the brand's figure
moved_to_outlet_at where our series contains the transition
Main-range prices used as the stockist reference
Outlet prices compared separately as a clearance competitor
gated_share reported for member-only content
sale_mechanic recorded, launch draws out of availability
country as a dimension, FX stamped per observation
size_system recorded, never converted

❌ What we do not, and why

Outlet and main range pooled into one reference price
An outlet was-price treated as price history
Account creation to reach member-only content
A launch draw size run treated as stock
Member, entrant or customer data

Core adidas fields

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

Field	What it is on this platform
brand / country / surface	main_range or outlet
brand_style_code / colourway_code	Canonical match keys
price / currency / price_setter	Brand-set
was_price_displayed	The brand's figure, labelled
moved_to_outlet_at / move_observed	The clearance signal
member_only_signposted / gated_share	The gap
sale_mechanic / launch_date	Draw or standard
size_label / size_system	Never converted
size_availability	Per size
panel_schedule_id	Shared with stockists
observed_at	Timestamp

Use cases

What teams do with adidas data

Clean brand reference price

Main-range prices only, so the reference for stockist comparison is not dragged down by clearance.

Brand clearance tracking

Products moving from main range to outlet, which dates when the brand starts clearing a style.

Brand as competitor

Outlet prices against stockists' markdowns on the same product, showing where the brand undercuts its own channel.

Launch calendar

Brand-direct releases and their allocation method.

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

Send us a adidas 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.

adidas is usually collected alongside its competitors

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

adidas data scraping: frequently asked questions

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

Because it plays a different role. Main-range prices are the reference for stockist comparison; outlet prices are the brand's own clearance, competing with stockists. Pooling them makes the reference look lower than it is.

Not necessarily. Some outlet product was never listed at full price on this site, so the figure is labelled as the brand's reference rather than treated as history.

When a product moves from the main range to the outlet. That transition dates when the brand starts clearing a style.

No accounts are created. We report the gated share of member-signposted content.

Both are brand-direct references. This one adds the outlet section, which makes the brand a competitor on the same site.

We quote individually on countries, categories and refresh. Daily suits the outlet section, which changes fast.

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

See real adidas 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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