We extract product listings, pricing signals, sizing availability, fabric composition, and variant mapping from Mango. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Listings objects from mango.com. All fields typed and schema-versioned.
"sku": "47095861-MIA-LM", "title": "100% linen suit blazer", "category": "Women > Blazers", "price": 119.99, "currency": "EUR", "colour": "Sand", "sizes_available": "['XS', 'S', 'M', 'L', 'XL']"
| # | sku | title | category | sub_category | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from mango.com. All fields typed and schema-versioned.
"sku": "47095861-MIA-LM", "current_price": 89.99, "original_price": 119.99, "discount_pct": 25, "promo_label": "Mid Season Sale", "stock_status": "in_stock", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | promo_label | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variant Mapping objects from mango.com. All fields typed and schema-versioned.
"parent_id": "47095861", "sku": "47095861-MIA-LM", "colour_name": "Sand", "size": "M", "in_stock": true, "low_stock_warning": false, "variant_url": "https://shop.mango.com/es/women/blazers/100-linen-suit-blazer_47095861.html?c=08"
| # | parent_id | sku | colour_name | colour_hex | size | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Fabric & Care objects from mango.com. All fields typed and schema-versioned.
"sku": "47095861-MIA-LM", "main_fabric": "100% linen", "lining_fabric": "100% polyester", "sustainability_label": "Committed", "washing_instructions": "Machine wash up to 30°C / 85°F", "ironing_instructions": "Iron up to 110°C / 230°F", "origin_country": "Morocco"
| # | sku | main_fabric | lining_fabric | sustainability_label | washing_instructions | ironing_instructions |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Taxonomy objects from mango.com. All fields typed and schema-versioned.
"category_name": "Blazers", "gender": "Women", "breadcrumb_1": "Women", "breadcrumb_2": "Clothing", "breadcrumb_3": "Blazers", "product_count": 142, "url": "https://shop.mango.com/es/women/blazers_c16823908"
| # | category_id | category_name | gender | breadcrumb_1 | breadcrumb_2 | breadcrumb_3 |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Mango scraper handles every layer of the platform: product catalogues, dynamic pricing, sizing matrices, fabric metadata, and multi-region storefronts — with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, description, category taxonomy, and every metadata field Mango surfaces — scraped at SKU level with parent-child variant mapping.
Capture current price, original price, discount percentages, and promotional labels — timestamped per crawl.
Extract available sizes, out-of-stock indicators, and low-stock warnings across all colour variants.
Map parent products to all available child colour variants, including hex codes and specific variant URLs.
Extract material composition, care instructions, origin country, and 'Committed' collection sustainability tags.
Capture direct URLs to high-resolution product imagery for all angles and variants.
mango.com/us, mango.com/gb, mango.com/es, mango.com/in and all other regional storefronts — mapped to a unified schema.
Extract 'Wear it with' and styling recommendation links to map complete outfit combinations.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, regional market targets, or SKU sets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for mango.com.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fashion retail sites deploy dynamic frontends and aggressive geo-fencing. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Mango employs standard Web Application Firewalls. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to prevent IP bans.
Mango's product pages rely on JavaScript to render size availability and regional pricing. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss entirely.
Mango automatically redirects users based on IP location, complicating cross-market price tracking. We map specific regional proxies to specific storefront URLs to ensure accurate locale data capture.
Frontend structures change frequently. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and JSON state extraction — so a layout change doesn't break your data pipeline overnight.
For large SKU catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Fashion retailers monitor Mango's pricing, markdown timing, and promotional periods to optimise their own pricing strategies.
Merchandising teams analyse category depth, colour trends, and fabric choices to inform seasonal buying and design decisions.
Pricing teams track the velocity of Mango's mid-season and end-of-season sales to adjust their own markdown cadences.
ML teams use Mango's high-resolution product imagery and structured metadata to train garment classification and visual search models.
Analysts monitor size out-of-stock rates to identify supply chain bottlenecks or high-demand product categories.
Retailers track price disparities across Mango's international storefronts to identify margin opportunities in grey market distribution.
"Mango's digital catalogue represents critical intelligence on global fast-fashion pricing and assortment strategy — but extracting it requires navigating dynamic inventory states and geo-fenced markets."
Most teams underestimate the investment required: reliable Mango scraping requires residential proxies mapped to specific locales, full JavaScript rendering for sizing widgets, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our mango.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About mango.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Mango is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and catalogue data. We do not extract personal data, circumvent authentication walls, or violate GDPR.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes don't break the pipeline.
We support all Mango regional storefronts including mango.com/us, mango.com/gb, mango.com/es, mango.com/in, and others — mapping local pricing and currency to a unified schema.
Real-time streaming pipelines achieve sub-60-minute latency for price and stock signals on a defined SKU set. Full catalogue refreshes at daily cadence complete within a 4-8 hour window depending on size.
By default, we extract the direct URLs to the high-resolution images hosted on Mango's CDN. We can also configure pipelines to download images and mirror them to your S3 bucket upon request.
Our smallest packages start at a defined category or SKU list with weekly delivery. For full catalogue extraction across multiple regional markets, we price based on volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 500 SKUs or specific category pages as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across multiple regions — we scope, build, and operate the pipeline. Tell us what you need.
