Platform · Uniqlo

Uniqlo Data Scraping

A core range that persists across seasons. Which makes this one of the few apparel retailers where a real price series is possible.

Uniqlo data scraping collects product listings, prices, size availability and range. What makes it analytically unusual in apparel: a large core range persists across seasons rather than turning over. So unlike fast fashion, a genuine per-SKU price series is possible here — and price movement on a stable SKU actually means something.

Our Zara page argues that range turnover makes price series thin. This retailer is the counter-example, and it changes what to collect.

Get a free sample See sample data

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

uniqlo.jsonl LIVE FEED

{"retailer":"uniqlo","country":"JP", "sku":"as published","sku_continuity_flag":"core_persisting", "base_price":2990,"currency":"JPY", "price_changed_at":"2026-03-14", "sku_lifespan_days":840} {"limited_price_active":true,"price":1990, "limited_price_ends":"2026-08-28", "limited_price_events_count":6, "note":"it REVERTS. not a markdown toward clearance. a weekly cadence misses it"} {"cross_market_matched_share":0.71, "size_system":"JP","size_converted":false}

3 of 684,110 sku-market rows · multi-marketa REAL per-SKU series · rare in apparel · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Uniqlo or its owners. Uniqlo 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

Uniqlo at a glance

How we handle Uniqlo specifically

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

Retailer: Uniqlo — Japanese origin, global
The point: A core range persists across seasons
Consequence: Per-SKU price series are viable
Which is: Unusual in apparel
Promotions: Time-limited price events rather than markdowns
So: Event windows matter for cadence
Markets: Many, priced independently
Refresh: Daily; promotional events are short and matter

Platform specifics

A stable range, and time-limited pricing

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

Stable SKUs make a real price series possible

A large share of this range persists — core garments carried season after season, frequently in the same colourways.

That permits analysis fast fashion does not:

Genuine per-SKU price history over months and years.
Year-over-year comparison on the same SKU, which is rare in apparel.
Price positioning tracked over time rather than inferred from range composition.
Promotional depth measurable against a stable base price, which most apparel does not have.

So the collection design differs: continuity of the SKU panel matters more than introduction tracking, and panel_version with sku_continuity_flag travel with the data.

We still record first-seen, last-seen and lifespan — but here they identify the seasonal exceptions against a stable core, which is the interesting subset.

And the base price is meaningful

base_price and price_changed_at give a real series. On a fast-fashion retailer the same fields are mostly empty.

Time-limited pricing, sizes and markets

Limited-period price events

Promotions here frequently take the form of time-limited prices on core items rather than end-of-season markdowns. That is a different mechanic with different implications.

A limited price has a published end, so the window is knowable.
It reverts, so it is not a markdown toward clearance.
It recurs, so event frequency per SKU is a measure.

limited_price_active, limited_price_ends and limited_price_events_count per SKU per period. Cadence has to catch the window — a weekly schedule misses short events entirely, which is the timing argument our cadence page makes.

Sizes

Size systems differ by market and this retailer's sizing is frequently cited as differing from Western equivalents. size_system travels with every record and we do not convert.

Markets

country as a dimension with FX per observation. Range overlaps heavily across markets here, which makes cross-market price comparison unusually viable — we report cross_market_matched_share so that is a measured claim rather than an assumption.

Scope

What we collect on Uniqlo, 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

base_price and price_changed_at as a real per-SKU series
panel_version and sku_continuity_flag
First-seen and lifespan used to identify seasonal exceptions against the core
limited_price_active, limited_price_ends and event counts per SKU
Cadence designed to catch short price windows
size_system on every record, never converted
cross_market_matched_share reported
size_availability per size, with counts
country as a dimension, with FX stamped per observation

❌ What we do not, and why

A limited-period price treated as a markdown toward clearance
A weekly cadence where short price events are the question
A size converted between labelling systems
A stock quantity inferred from size availability
A cross-market comparison without its matched share

Core Uniqlo fields

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

Field	What it is on this platform
retailer / country / category_path	The market and where it sits
sku / product_name / colourway	Identity as published
base_price / price_changed_at / currency	A real series here
limited_price_active / limited_price_ends	A window, with an end
limited_price_events_count	Per SKU per period
sku_continuity_flag / panel_version	Continuity matters more than introductions
sku_first_seen / sku_lifespan_days	Identifies the seasonal exceptions
size_label / size_system	Never converted
size_availability / sizes_available_count	Buyable, not a quantity
cross_market_matched_share	So cross-market comparison is a measured claim
fx_rate / fx_observed_at	Stamped at the observation

Use cases

What teams do with Uniqlo data

Genuine apparel price series

Per-SKU price history over months and years on a persisting core range, which most apparel retailers cannot support.

Limited-price event analysis

Event windows with published ends and per-SKU frequency, which is a different mechanic from end-of-season markdown and needs a cadence that catches it.

Cross-market price comparison

Range overlapping heavily across markets with the matched share reported, making this unusually viable compared with most apparel.

Seasonal exception identification

First-seen and lifespan used to find what is seasonal against a stable core, which is the interesting subset here.

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

Send us a Uniqlo 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.

Uniqlo is usually collected alongside its competitors

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

Uniqlo data scraping: frequently asked questions

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

Because a large share of the range persists — core garments carried season after season, frequently in the same colourways.

That permits genuine per-SKU price history and year-over-year comparison on the same SKU, which is rare in apparel.

It has a published end and it reverts, so it is not a step toward clearance. And it recurs, which makes event frequency per SKU a measure in itself.

A markdown-shaped analysis reads a reverting price as a failed clearance, which is not what happened.

Yes. A weekly schedule misses short price events entirely, so where those events are the question we recommend daily or faster.

Frequency has to be set by what you are trying to catch rather than by habit.

More viably than at most apparel retailers, because range overlaps heavily. We report the matched share so it is a measured claim rather than an assumption.

FX is stamped per observation and local currency is retained as the primary figure.

No. Size systems differ by market and conversion tables are approximate. A converted size in a data field looks exact when it is not.

We quote individually on markets, categories and refresh. Refresh is the main variable because limited-price windows are short.

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

See real Uniqlo 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