We extract designer catalogues, pricing signals, sizing availability, and material compositions from Yoox. 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 yoox.com. All fields typed and schema-versioned.
"product_id": "12345678AB", "title": "Silk Midi Dress", "designer": "GUCCI", "price": 850.0, "currency": "USD", "colour": "Black", "materials": "100% Silk", "made_in": "Italy"
| # | product_id | title | designer | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Discounts objects from yoox.com. All fields typed and schema-versioned.
"product_id": "12345678AB", "price": 850.0, "list_price": 1200.0, "discount_pct": 29, "sale_badge": "Clearance", "promotional_code_eligible": false, "currency": "USD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | product_id | price | list_price | discount_pct | discount_abs | sale_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sizing & Availability objects from yoox.com. All fields typed and schema-versioned.
"product_id": "12345678AB", "size_system": "IT", "sizes_available": "['38', '40', '42']", "sizes_out_of_stock": "['36', '44']", "low_stock_warning": true, "one_size_only": false, "colour_variant": "Black"
| # | product_id | size_system | sizes_available | sizes_out_of_stock | low_stock_warning | one_size_only |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Designer & Brand Data objects from yoox.com. All fields typed and schema-versioned.
"designer_id": "D_GUCCI", "designer_name": "GUCCI", "designer_url": "/us/women/shoponline/gucci_d", "total_products": 1432, "categories_covered": "['Clothing', 'Shoes', 'Bags']", "is_yooxygen": false, "gender_focus": "Unisex"
| # | designer_id | designer_name | designer_url | total_products | categories_covered | is_yooxygen |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from yoox.com. All fields typed and schema-versioned.
"keyword": "silk dress", "gender": "women", "position": 1, "product_id": "12345678AB", "title": "Silk Midi Dress", "designer": "GUCCI", "price": 850.0, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | gender | position | product_id | title | designer |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Yoox scraper handles every layer of the platform: designer catalogues, dynamic sizing availability, regional pricing, and material compositions. We manage the JavaScript rendering, session management, and anti-bot circumvention.
Extract designer, category, materials, dimensions, and high-resolution image URLs for every item.
Monitor exact size availability across IT, FR, UK, and US sizing systems with low-stock indicators.
Capture base prices, markdown percentages, and clearance tags timestamped per crawl.
Parse structured data for fabric composition, washing instructions, and manufacturing origin.
Isolate products tagged under Yoox sustainable fashion initiatives with specific certification data.
Link parent product IDs to all available colour variations and their specific inventory states.
Extract localized pricing and availability across yoox.com US, UK, EU, and JP storefronts.
Map the complete hierarchy of designers, capsule collections, and collaboration lines.
Run continuous data feeds at hourly or daily cadences with strict change-detection diffing.
Brief in. Clean data out.
Provide designer URLs, category paths, or search terms. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for yoox.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Yoox invests heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
Yoox uses Akamai bot protection. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management trained on real user behaviour patterns.
Yoox product grids and size selectors rely on client-side rendering. We run full Playwright browser sessions to trigger lazy-loads and hydrate inventory state.
Yoox displays vastly different prices and inventory based on the shipping country. We force strict regional proxy routing and session cookies to guarantee accurate local data.
Yoox frequently updates its frontend framework. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline.
For large designer catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Luxury retailers monitor Yoox markdown schedules and clearance events to adjust their own promotional calendars.
Merchandising teams analyse brand availability, size curves, and category depth to identify stocking opportunities.
Brands audit Yoox listings to detect unauthorised off-price distribution and parallel imports.
Fashion analysts track material prevalence, colour variant density, and designer volume over time.
Machine learning teams use structured material, cut, and designer data to train visual recommendation models.
Off-price buyers monitor Yoox stock depth and time-on-site to model luxury liquidation velocity.
"Yoox holds the largest off-price luxury catalogue on the web, but extracting accurate sizing and regional pricing requires a highly resilient infrastructure."
Most teams underestimate the investment required: reliable Yoox scraping requires residential proxies to bypass Akamai, full JavaScript rendering for size grids, and strict session management for regional pricing. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our yoox.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.
We maintain pools of residential ISP proxies across global regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. 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 yoox.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Yoox is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and inventory data.
Yoox uses strict CDN-level bot mitigation. We deploy residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and strict request pacing.
Yes. Yoox alters pricing and inventory based on geolocation. We use region-specific proxies and session headers to extract accurate local data for the US, UK, EU, and APAC markets.
We render the dynamic size selection components via Playwright to extract the exact stock status for every size variant, including low-stock indicators.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals. Full category refreshes at daily cadence complete within a 6-12 hour window.
Our smallest packages start at a defined designer list or category set with weekly delivery. We price based on volume and delivery frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off designer catalogue dump or a continuous price-monitoring feed across 300K products - we scope, build, and operate the pipeline. Tell us what you need.
