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Contemporary & direct-to-consumer

J.Crew Product Data

Prices, variants, availability, and images from jcrew.com — normalised, validated, and delivered into your stack. No collector to build, and none to maintain.

source specification

Source
jcrew.com
Segment
Contemporary & direct-to-consumer
Origin
United States — New York
Categories
Womenswear, Menswear, Shoes, Accessories
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily

about the source

What J.Crew sells, and how its catalogue behaves

J.Crew is the definitive American preppy classic label, built on chinos, oxford shirts, and cashmere, with a promotional calendar that is aggressive even by US retail standards. Sitewide percentage-off events run frequently enough that list price and selling price diverge for much of the year.

why teams track it

Why J.Crew data is worth having

J.Crew is the best available case study in US promotional retail: the gap between list and realised price is large, persistent, and visible. Pricing teams use the feed to model promotional depth and cadence rather than just to read a current price, which is what makes it more valuable than a single-snapshot comparison.

Visit jcrew.com

collection method

How we collect J.Crew

J.Crew is collected through listing and product page collection with the long-form product description extracted into its own field. Every source gets a dedicated collector rather than a generic crawler, because the field detail that makes this data useful only survives if the extraction is built for the site it runs against.

Structured at the source

Records are read from jcrew.com's own structured responses wherever they exist, rather than reconstructed from page markup. That keeps the feed stable across visual redesigns.

Validated every run

Each field is checked against expected types and historical ranges. A collector producing anomalies is quarantined and repaired upstream instead of emitting bad prices into your pipeline.

Normalised to one schema

Every brand in the catalogue lands on the same schema, so adding a source is a configuration change on your side rather than another integration to write.

15min
Fastest refresh
99.9%
Uptime SLA
3
Delivery formats
49+
Fashion sources

what you get back

Fields in the J.Crew feed

A normalised core that is identical across every source, plus the attributes that are specific to this one.

Standard across every source

  • Product name, brand, and source URL
  • Current price, original price, and currency
  • Category and subcategory as the source classifies them
  • Colour, size, and variant availability
  • Product images, deduplicated across variants
  • Description, composition, and care text
  • Collection timestamp on every record

Specific to J.Crew

  • Long-form product descriptions carried through as structured text
  • List price alongside promotional price during sitewide events
  • Colourway variants with dedicated imagery
  • Category path across both departments

SOC 2 Type II

Audited controls across security, availability, and confidentiality. Report available under NDA.

GDPR & CCPA

Public catalogue data only. No personal data collected, and a DPA is available on request.

99.9% Uptime SLA

Contractual availability with monitored collectors and a public status page.

Data residency

Choose EU or US processing and storage regions to match your obligations.

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

It captures list and selling price separately on every run, so a scheduled collection reconstructs the promotional calendar as a by-product. For a retailer that discounts as often as J.Crew, that series is usually more useful than any single price reading.
Yes — Madewell has its own dedicated collector and delivers on the identical schema, so the two sister brands can be analysed as one feed with a brand field or kept separate. Denim price comparison across the pair is a common reason customers take both.
Into the systems you already run, rather than through an API you have to integrate against. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks can push price and availability changes as they are detected. A solutions engineer fixes the schema, cadence, and destination with you during onboarding, so the first delivery already matches your pipeline.
Maintaining the collector is our job, not yours. Every field is validated against expected types and historical ranges on each run, and a collector that starts producing anomalies is quarantined rather than allowed to emit bad records. We repair it upstream, and the schema we deliver to you does not move — which is the entire reason to buy this rather than run a scraper in-house.
We collect only publicly visible catalogue data — the prices, descriptions, images, and availability any shopper sees without logging in. We do not bypass authentication, we do not collect personal data, and we honour rate limits so the source is never disrupted. Our infrastructure is SOC 2 Type II certified, our processing is GDPR and CCPA compliant, and we sign DPAs as part of procurement.
Pricing is scoped per engagement, driven by catalogue size, refresh cadence, the number of locales you need, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample pulled from the live jcrew.com catalogue, so you can check the data against your own benchmarks before committing to anything.

Still have questions?

Talk to an engineer

Ready to Get Started?

Talk to us about your sources and volume. We'll return a sample dataset from your target sites before you commit to anything.

SOC 2 Type II
GDPR & CCPA compliant
99.9% uptime SLA

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