Platform · Boohoo

Boohoo Data Scraping

Discounting is near-continuous here. Which means a displayed discount percentage is marketing copy, not a measurement.

Boohoo data scraping collects product listings, prices, size availability and range across the group's brands. The handling that decides whether the data is usable: discounting is close to continuous, so a displayed "was" price is a reference figure rather than a price the product traded at — and a discount series built from displayed percentages measures marketing.

Our Zara page describes concentrated sale windows. This is the opposite end, and it needs the opposite handling.

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boohoo.jsonl LIVE FEED

{"group":"boohoo_group","brand":"brand-a", "sku":"as published","price":12.00,"currency":"GBP", "reference_price_displayed":40.00, "discount_pct_displayed":70.0, "label":"RETAILER FIGURE. not a measurement"} {"price_high_observed":16.00,"price_low_observed":10.00, "observation_window_days":94, "discount_vs_observed_high":25.0, "note":"70% displayed. 25% real, over 94 observed days. the gap IS the finding"} {"sku_lifespan_days":31,"introduction_rate":0.51, "caution":"the observed-high figure cannot be backfilled. it needs prior observation"}

3 of 2,884,110 brand-sku rows · multi-marketdiscount computed from OUR series · cannot be backfilled · schema v1.0

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

How we handle Boohoo specifically

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

Group: Boohoo group — several brands
The point: Discounting is near-continuous
Consequence: A displayed 'was' price is a reference
So: Discount computed from OUR series, never from their percentage
SKU velocity: Very high. Short lifespans
Brands: Several, at different positions
Markets: Many, priced independently
Refresh: Daily minimum; price movement is constant

Platform specifics

Continuous discounting, and what a percentage means

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

A displayed discount is not a measurement

Where a product is on offer almost all the time, the "was" price against which the discount is shown is not a price the product traded at for any meaningful period.

The reference figure is marketing copy.
A discount percentage built from it describes the marketing, not a price change.
Comparing that percentage to a concentrated-sale retailer's compares a permanent state with an event.
And a "deepest discount" analysis ranks by reference price, not by value.

So we capture reference_price_displayed and discount_pct_displayed as displayed values, clearly labelled as the retailer's own figures, and compute discount_vs_observed_high from our own price series alongside.

The second is the one that means something, and it only exists if you have been observing — which is the cadence argument in its sharpest form. It cannot be backfilled.

Which makes the observed price history the product

price_high_observed, price_low_observed and observation_window_days travel with every record, so any discount figure states the window it was computed over.

SKU velocity, brands and markets

Very high SKU velocity

New SKUs arrive constantly and many have short lives. So as on our Zara page: first-seen, last-seen, lifespan, introduction and delisting rates, with delisted SKUs retained.

Here the introduction rate is itself a headline measure, because it is high enough to be a competitive variable rather than a background fact.

Several brands

The group operates several consumer brands at different positions. brand on every record, with group figures as rollups stating brand_mix — as on our H&M page.

Markets

country as a dimension with FX stamped per observation.

What we do not collect or produce

A discount percentage presented as a measurement when it came from a displayed reference.
An observed-high discount over a window we did not observe.
Stock quantities. Size availability means buyable.
Returns, sell-through or customer data. Not published.

Scope

What we collect on Boohoo, 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

reference_price_displayed and discount_pct_displayed as labelled retailer figures
discount_vs_observed_high computed from our own series
price_high_observed, price_low_observed and observation_window_days
Every discount figure stating the window it was computed over
brand on every record, with group rollups stating brand_mix
SKU lifespan with introduction and delisting rates
Introduction rate as a headline measure, not a background fact
Delisted SKUs retained, never deleted
country as a dimension, with FX stamped per observation

❌ What we do not, and why

A displayed discount percentage presented as a measurement
An observed-high discount computed over a window we did not observe
A discount comparison against a concentrated-sale retailer as like for like
A stock quantity inferred from size availability
Returns, sell-through or customer data

Core Boohoo fields

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

Field	What it is on this platform
group / brand / country	Brand is a position; country a market
sku / product_name / colourway	Identity as published
price / currency / fx_observed_at	The current price
reference_price_displayed / discount_pct_displayed	Theirs, labelled as theirs
price_high_observed / price_low_observed	From our series
discount_vs_observed_high / observation_window_days	Ours, with its window
sku_first_seen / sku_last_seen / sku_lifespan_days	High velocity here
introduction_rate / delisting_rate	A headline measure on this group
size_availability / sizes_available_count	Buyable, not a quantity
brand_mix	Stated on any group rollup
observed_at	Timestamp

Use cases

What teams do with Boohoo data

Honest discount measurement

Discount computed against our own observed high with the window stated, which is the only version that measures a price change rather than the retailer's reference figure.

Introduction rate as a competitive variable

New SKU arrival rate per brand and category, which on this group is high enough to be a strategy indicator rather than a background fact.

Brand-level positioning

Brand on every record, since the group's consumer brands sit at different positions and a group figure moves with the mix.

Price floor tracking

Observed lows over a stated window, which is more informative than a headline price on a catalogue where the headline is almost always discounted.

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

Send us a Boohoo 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.

Boohoo is usually collected alongside its competitors

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

Boohoo data scraping: frequently asked questions

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

Because where a product is on offer almost all the time, the reference figure it is discounted against is not a price the product traded at for any meaningful period.

A percentage built from it describes the marketing rather than a price change — and a 'deepest discount' ranking built that way ranks by reference price, not by value.

A discount computed against our own observed high, with the observation window stated on every figure.

That only exists if we have been observing, and it cannot be backfilled — which is the strongest cadence argument in this whole category.

Only carefully. Comparing a near-continuous discount state against a concentrated-sale retailer compares a permanent condition with an event.

Both are legitimate measurements; they are just not the same measurement.

Fast enough that the introduction rate is a headline measure rather than a background fact. New SKUs arrive constantly and many have short lives.

Delisted SKUs stay in the panel with a last-seen date and a lifespan.

Yes, with brand on every record. They sit at different positions, so a group figure moves with brand mix rather than with pricing.

We quote individually on brands, markets and refresh. Daily is the minimum here because price movement is constant, and the observed-high figure depends on continuity of observation.

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

See real Boohoo 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.

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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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