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High street & fast fashion

Mango Product Data

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

source specification

Source
shop.mango.com
Segment
High street & fast fashion
Origin
Spain — founded 1984 in Barcelona
Categories
Womenswear, Menswear, Shoes, Bags, Accessories
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily

about the source

What Mango sells, and how its catalogue behaves

Mango sits a step above the pure fast-fashion tier, with a Mediterranean tailoring and occasionwear bias that separates it from its Spanish neighbour Inditex. Its assortment turns over quickly but holds price better than the true value end of the market, which makes it a useful mid-point when you are trying to place a brand between the high street and contemporary labels.

why teams track it

Why Mango data is worth having

For anyone benchmarking European mid-market apparel, Mango is the comparison that stops a price index being purely Inditex-shaped. Its promotional calendar is also distinctly Spanish, so tracking it surfaces markdown windows that do not line up with US or UK retail rhythms.

Visit shop.mango.com

collection method

How we collect Mango

Mango is collected through Mango's structured catalogue responses, read per category so the brand's own taxonomy is preserved on the way out. 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 shop.mango.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 Mango 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 Mango

  • Colourway variants with per-variant imagery
  • Category and subcategory as classified by Mango
  • Composition text where the product detail exposes it
  • Original and discounted price during promotional periods

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.

The US storefront by default, which returns USD prices. Mango runs materially different pricing and assortment across its European markets, so if you are benchmarking Spain or the wider EU we collect those locales separately and hand back one feed per market, joinable on product reference.
Womenswear is the default scope because it is what most customers benchmark, but the collector is not limited to it. Menswear and kidswear are collected on request and arrive on the same schema with a department field, so you can widen coverage without changing anything downstream.
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 shop.mango.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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