Platform · Nike

Nike Data Scraping

The brand's own site is the price every stockist is measured against. Some of what it sells is only visible to signed-in members.

Nike data scraping collects product listings, prices, availability and launch information from the brand's own channels. Its role in fashion data is specific: the brand site is the reference price that stockist prices are measured against. Two limits come with that. Member-only product and pricing sit behind an account we do not create, and launch releases are allocated rather than simply sold.

A stockist price only means something against the brand's own. This is where that number comes from, and where it stops being visible.

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nike.jsonl LIVE FEED

{"brand":"nike","country":"US", "brand_style_code":"as published","colourway_code":"as published", "price":140.00,"price_setter":"brand", "panel_schedule_id":"us-footwear-sync"} {"member_only_signposted":true,"price":"null", "gated_reason":"member_signin_required","gated_share":0.07} {"sale_mechanic":"draw","excluded_from_availability":true, "resale_value":"not_estimated"}

3 of 804,110 product rows · multi-marketthe reference price · member content is a gap · schema v1.0

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

How we handle Nike specifically

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

Source: Nike — brand-direct channels
Role: The reference price for stockist comparison
Member-only: Product and pricing behind an account
So: gated_share reported; no accounts created
Launches: Allocated by draw on limited releases
Brand discounting: The brand's own sale is a brand decision
Markets: Priced per country. Market recorded
Refresh: Daily; launch calendar tracked

Platform specifics

The reference price, and its edges

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

Why the brand site anchors every comparison

When a stockist sells a brand's product, the question is always relative: at, above or below the brand's own price?

The brand site gives that price directly, on the brand's own style and colourway codes.
Those codes are the match keys stockists inherit, which makes matching from this side strong.
A brand-site markdown is the brand clearing stock; a stockist markdown on the same product is the stockist's decision. They are different events and are kept apart.
Stockist prices are compared on a shared schedule, since both sides move.

brand_style_code and colourway_code are recorded as the canonical keys, and price_setter is brand throughout — the argument our brand D2C page sets out across brands.

Member-only product, launches and markets

Member-only is a gap, not a workaround

Some product and some pricing is only visible to signed-in members. We do not create accounts or use client credentials — the boundary on our access page. Where member content is signposted publicly we record that it exists, and we report gated_share.

Launches

Limited releases are allocated by draw through the brand's channels. sale_mechanic is recorded and those items are kept out of availability figures, as on our END. page.

Markets

Prices are set per country. country is a dimension with FX stamped per observation.

What we do not collect

Member account data, draw entrants, stock quantities, customer data, or a resale estimate on launch product.

Scope

What we collect on Nike, 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

brand_style_code and colourway_code as canonical match keys
price_setter as brand throughout
Brand markdowns kept separate from stockist markdowns
Shared schedule with stockists where comparison is the aim
gated_share reported for member-only content
sale_mechanic recorded, launch draws kept out of availability
country as a dimension, FX stamped per observation
Size availability per size, size system recorded
Launch history retained

❌ What we do not, and why

Account creation to reach member-only product or pricing
A brand markdown treated as a stockist markdown
A launch draw size run treated as stock
A resale value attached to launch product
Member, entrant or customer data

Core Nike 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	Brand-direct, per market
brand_style_code / colourway_code	Canonical match keys
price / currency / price_setter	The reference price
member_only_signposted / gated_share	The gap, measured
sale_mechanic / launch_date / draw_closes	Draw or standard
on_sale / markdown_started_at	The brand's own clearance
size_label / size_system	Never converted
size_availability	Per size, standard items
fx_rate / fx_observed_at	Stamped
panel_schedule_id	Shared with stockists
observed_at	Timestamp

Use cases

What teams do with Nike data

Stockist price benchmarking

The brand's own price on canonical codes, against which every stockist's price is compared on a shared schedule.

Brand clearance tracking

The brand's own markdowns kept separate from stockists', since they are different decisions.

Launch calendar

Brand-direct releases and their allocation method, retained over time.

Member-content measurement

The share of range signposted as member-only, stated as a gap.

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

Send us a Nike 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.

Nike is usually collected alongside its competitors

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

Nike data scraping: frequently asked questions

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

Because every stockist price is only meaningful relative to the brand's. The brand site gives that price directly, on the codes stockists inherit.

No. We do not create accounts or use client credentials. Where member content is signposted publicly we record that it exists, and we report the gated share.

No. A brand-site markdown is the brand clearing stock; a stockist markdown is the stockist's decision. We keep them apart.

Recorded with their allocation method and kept out of availability figures, since a draw size run is not stock.

Yes — country is a dimension, with FX stamped per observation.

We quote individually on countries, categories and refresh. Most engagements pair it with a stockist panel, which is where the comparison value is.

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

See real Nike 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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