Platform · Next

Next Data Scraping

Its own range and a large hosted third-party estate sit on one site. Two price setters, one catalogue.

Next data scraping collects product listings, prices, size availability and range from a site that operates as both a retailer and a platform. Its own range sits alongside a large estate of hosted third-party brands — and those are priced by the brand rather than by the retailer. So price setter is a field, and pooling the two measures a mix rather than a strategy.

Our Target page makes this argument about a marketplace inside a retailer. This is the fashion version, and the hosted estate is large enough to dominate a naive panel.

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next.jsonl LIVE FEED

{"retailer":"next","department":"fashion", "price_setter":"retailer","is_hosted_brand":false, "price":38.00,"currency":"GBP", "on_sale":false} {"price_setter":"brand","is_hosted_brand":true, "brand_name":"hosted-brand-a","price":125.00, "also_direct":true,"direct_price_gap_pct":0.0} {"catalogue_mix":{"own_range":0.38,"hosted":0.62}, "caution":"62% hosted. a naive panel here is mostly OTHER brands, labelled as this retailer"}

3 of 3,204,880 sku rows · UKtwo PRICE SETTERS on one site · schema v1.0

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

How we handle Next specifically

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

Retailer: Next — UK, retailer and platform
Own range: Priced by the retailer
Hosted brands: Priced by the brand
So: price_setter is a field
Size of the hosted estate: Large enough to dominate a naive panel
Consequence: A pooled series moves with catalogue mix
Home and beauty: Also present. Category scope matters
Refresh: Daily; sale events are concentrated

Platform specifics

Two price setters on one site

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

Own range and hosted brands are different businesses

The site carries the retailer's own ranges and a large hosted estate of third-party fashion brands. They look alike in a catalogue and behave differently.

The retailer sets its own range's prices.
Hosted brands set theirs, frequently matching their own direct channel.
Discounting behaviour differs — the retailer's sale calendar is not the brands'.
Range turnover differs too.

So price_setter and is_hosted_brand are on every record, and a site-level figure is a computed rollup with catalogue_mix stated.

The practical risk is specific: the hosted estate is large, so a panel built by scraping category pages without separating them produces a series dominated by third-party brands and labelled as the retailer's pricing.

Which makes the comparison interesting

Where a hosted brand also sells direct, also_direct and the price gap from paired records shows whether the hosted price matches the brand's own channel. Computed from both sides, never asserted.

Own-brand range, categories and sale events

Own-range analysis

With price_setter filtering to the retailer's own range, everything a normal apparel panel supports applies: SKU lifespan, introduction and delisting rates, size availability, price movement.

That is the series most clients actually want, and it is only available once the hosted estate is separated.

Category scope

The site carries home and beauty alongside fashion. department is on every record and we name the departments in the scoping document rather than leaving it implied — the position our Target page takes for the same reason.

Sale events

Sale periods are concentrated and well known in this market. on_sale with sale_event_id where a window is identifiable, and collection aligned to the event rather than to an arbitrary schedule.

What we do not collect

Stock quantities, brand commercial terms, customer or account data. Size availability means buyable, not a count.

Scope

What we collect on Next, 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

price_setter and is_hosted_brand on every record
Site-level figures as rollups with catalogue_mix stated
also_direct with the gap computed from paired records
department on every record, with departments named at scoping
on_sale with sale_event_id, and collection aligned to events
SKU lifespan with introduction and delisting rates
size_availability per size, with counts
Own-range series available once the hosted estate is separated
Images and descriptions as published

❌ What we do not, and why

Own range and hosted brands pooled into one price series
A hosted-brand-dominated panel presented as the retailer's pricing
A hosted-versus-direct gap asserted from one side
A stock quantity inferred from size availability
Brand commercial terms, customer or account data

Core Next fields

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

Field	What it is on this platform
retailer / department / price_setter	Who set this price
is_hosted_brand / brand_name	Whose product it is
sku / product_name / colourway	Identity as published
price / currency	As displayed
catalogue_mix	Stated on any site rollup
also_direct / direct_price_gap_pct	From paired records only
on_sale / sale_event_id / discount_pct_displayed	As displayed
sku_first_seen / sku_lifespan_days	The range cycle
size_availability / sizes_available_count	Buyable, not a quantity
category_path	As the site presents it
observed_at	Timestamp

Use cases

What teams do with Next data

Own-range price tracking

The retailer's own ranges separated from the hosted estate, which is the series most clients want and is only available once the two are distinguished.

Hosted brand landscape

Which brands are hosted, at what prices and in what categories, which is a distribution question rather than a pricing one.

Hosted versus direct pricing

Where a hosted brand also sells direct, the gap computed from paired records on a shared schedule.

Sale event analysis

Concentrated sale windows with collection aligned to them, so promotional depth measures the event rather than the schedule.

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

Send us a Next 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.

Next is usually collected alongside its competitors

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

Next data scraping: frequently asked questions

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

Because they have different price setters. The retailer prices its own range; hosted brands price theirs, frequently matching their own direct channel.

The hosted estate is large enough that a panel built without separating them produces a series dominated by third-party brands and labelled as the retailer's pricing.

Yes, and it is what most clients actually want. With the price setter filtering to the retailer's own range, everything a normal apparel panel supports applies.

It is only available once the hosted estate is separated, which is why the field exists.

Frequently but not always, and it is measurable. Where a brand also sells direct we compute the gap from paired records on a shared schedule rather than asserting it from one side.

Yes — home and beauty alongside it. Department is on every record and we name the departments in the scoping document rather than leaving it implied.

Department scope changes the size of the engagement substantially.

Collection aligned to the event rather than to an arbitrary schedule. Sale periods here are concentrated and well known, so aligning costs nothing and makes promotional depth measure the event.

We quote individually on departments, catalogue scope and refresh. Whether the hosted estate is in scope matters a great deal — it is effectively a second catalogue.

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

See real Next 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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