Platform · Revolve

Revolve Data Scraping

It looks like a multi-brand stockist. A large share of the brands are its own labels — which changes who is setting the price.

Revolve data scraping collects product listings, prices, availability and range across the retailer and its luxury banner. The point that decides how the data is read: a large share of the brands on the site are labels the retailer owns or develops, presented alongside third-party brands. So the site looks like a stockist and prices like a brand owner for much of its range, and those labels have no other stockist to compare against.

Our own-label page is about retailers whose own brand carries the retailer's name. This is the harder version: own labels that look like independent brands.

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revolve.jsonl LIVE FEED

{"retailer":"revolve","banner":"main", "brand_name":"label-a","brand_ownership":"retailer_owned", "ownership_source":"retailer published statement", "price":168.00,"cross_retailer_matched":false} {"brand_name":"brand-b","brand_ownership":"third_party", "match_basis":"brand_style_code","price":245.00} {"owned_label_share_category":0.46,"category":"dresses", "caution":"46% priced by the retailer as brand owner. a brand count would overstate breadth"}

3 of 1,204,110 product rows · USowned labels that look like brands · schema v1.0

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

How we handle Revolve specifically

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

Retailer: Revolve — US, with a luxury banner
The point: Many brands are owned labels
Which look like: Independent brands
Consequence: No other stockist to compare them against
So: brand_ownership recorded from published mappings, not guessed
Third-party brands: Match across stockists normally
Luxury banner: A separate surface with a different brand mix
Refresh: Daily; range turnover is fast

Platform specifics

Owned labels that look like brands

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

Why ownership changes the analysis

An owned label is priced by the retailer, sold only by the retailer, and positioned as a brand. It behaves like own label in every analytical respect except the name.

It has no other stockist, so a cross-retailer price comparison has nothing to pair with.
Its discounting is the retailer's decision, not a brand's markdown passed through.
Its share of the range is a strategy measure in itself.
A "brand count" figure overstates independent brand breadth if owned labels are counted as independents.

So we record brand_ownership as retailer_owned, third_party or unstated, from the retailer's own published statements rather than inferred from exclusivity. Being sold only here is consistent with ownership and does not establish it.

owned_label_share_category ships per batch. Owned labels are unmatched across retailers, the position our own label page sets out.

Third-party brands, the luxury banner and range

Third-party brands match normally

For independent brands carried here and elsewhere, style-code matching and markdown timing apply as on our John Lewis page. matched_share_category is reported before quoting.

The luxury banner

A separate storefront with a different brand mix and price level. banner is on every record, and the two are not pooled.

Range turnover

Fast, so SKU first-seen, last-seen and lifespan are recorded with delisted items retained.

What we do not collect

Influencer, creator or customer data, affiliate terms, stock quantities, or an ownership status we inferred from exclusivity.

Scope

What we collect on Revolve, 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_ownership from the retailer's published statements
owned_label_share_category per batch
Owned labels unmatched across retailers
Style-code matching for third-party brands
matched_share_category reported before quoting
banner separating the main site from the luxury banner
SKU lifespan with delisted items retained
Size availability per size
on_sale and discount as displayed

❌ What we do not, and why

An ownership status inferred from exclusivity
Owned labels counted as independent brands in a brand-count figure
An owned label matched to another retailer's product
The two banners pooled into one series
Influencer, creator, affiliate or customer data

Core Revolve fields

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

Field	What it is on this platform
retailer / banner	Main site or luxury banner
brand_name / brand_ownership	retailer_owned, third_party or unstated
product_id / product_name / colourway	As published
price / currency	As displayed
owned_label_share_category	Per batch
brand_style_code / match_basis	Third-party brands only
matched_share_category	Reported before quoting
sku_first_seen / sku_lifespan_days	Fast turnover
size_availability	Per size
on_sale / discount_pct_displayed	As displayed
observed_at	Timestamp

Use cases

What teams do with Revolve data

Owned-label strategy tracking

Owned-label share by category over time, which shows how much of the range the retailer prices as a brand owner rather than as a stockist.

Honest brand breadth

Brand counts that separate owned labels from independents, so breadth is not overstated.

Third-party stockist parity

Independent brands matched on style code against other stockists, with markdown timing.

Banner-level positioning

Main site and luxury banner kept separate, since brand mix and price level differ.

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

Send us a Revolve 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.

Revolve is usually collected alongside its competitors

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

Revolve data scraping: frequently asked questions

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

Because an owned label is priced by the retailer, sold only by the retailer, and has no other stockist to compare against. It behaves like own label in every analytical respect except the name.

Counting owned labels as independent brands also overstates brand breadth.

From the retailer's own published statements. Where none exists, the field is unstated.

Being sold only on this site is consistent with ownership and does not establish it, so we do not infer it.

Yes, on style code against other stockists, with matched share reported per category before quoting.

Only if in scope, and always as a separate banner. Its brand mix and price level differ.

No. Creator, affiliate and customer data are outside what we collect anywhere. We collect the products.

We quote individually on banners, categories and refresh. Daily is usually right given the range turnover.

One scoping call, a free pilot within 24 hours including owned-label share, then a fixed monthly quote. Request a quote.

See real Revolve 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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