We extract footwear listings, size availability matrices, colour variants, and pricing signals from Metroshoes. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your schedule.
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 metroshoes.com. All fields typed and schema-versioned.
"sku": "32-9871", "title": "Metro Mens Black Formal Shoes", "brand": "Metro", "price": 2490.0, "mrp": 2990.0, "colour": "Black", "material": "Leather", "discount_pct": 16
| # | sku | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Size & Inventory objects from metroshoes.com. All fields typed and schema-versioned.
"sku": "32-9871", "size_uk": "8", "size_eu": "42", "in_stock": true, "stock_level": "low", "check_timestamp": "2026-05-12T09:14:00Z"
| # | sku | size_uk | size_eu | in_stock | stock_level | delivery_days |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Variants & Colours objects from metroshoes.com. All fields typed and schema-versioned.
"parent_sku": "32-9870", "child_sku": "32-9871", "colour_name": "Black", "is_primary": true, "price_diff": 0, "image_gallery": "['url1.jpg', 'url2.jpg']"
| # | parent_sku | child_sku | colour_name | colour_hex | image_gallery | is_primary |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from metroshoes.com. All fields typed and schema-versioned.
"review_id": "REV-9921", "sku": "32-9871", "rating": 4.5, "title": "Comfortable for daily wear", "date": "2023-10-12", "verified_buyer": true
| # | review_id | sku | reviewer_name | rating | title | body |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Search objects from metroshoes.com. All fields typed and schema-versioned.
"keyword": "mens formal shoes", "category_path": "Men > Shoes > Formal", "position": 4, "sku": "32-9871", "price": 2490.0, "is_bestseller": true
| # | keyword | category_path | position | sku | title | price |
|---|---|---|---|---|---|---|
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Our Metroshoes scraper captures every product attribute, size matrix, and pricing signal across all internal brands.
Extract SKUs, titles, and brands including Metro, Mochi, Crocs, Walkway, and FitFlop directly from the storefront.
Capture stock status across all UK and EU size variants for every product model.
Link child SKUs to parent models based on colour selections and image galleries.
Capture MRP, selling price, and active promotional discounts timestamped per crawl.
Extract sole material, upper material, heel height, and occasion tags from product specifications.
Map products to their exact breadcrumb trail across Men, Women, and Kids categories.
Gather customer feedback, star ratings, and review text for sentiment analysis.
Monitor specific categories for newly added SKUs and seasonal collection drops.
Run daily or weekly syncs to keep your database updated with the latest inventory changes.
Brief in. Clean data out.
Provide category URLs, brand names, or specific SKU lists. We design the extraction schema.
We configure Scrapy and Playwright crawlers to handle dynamic loading and size selectors.
Schema validation, out-of-stock detection, and attribute normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or warehouse on an agreed schedule.
Extracting accurate stock levels requires navigating dynamic category pages and JavaScript-rendered size selectors.
Metroshoes category pages load products dynamically. We use Playwright to simulate scrolling and intercept API responses to capture the full list without missing items.
Size and colour availability often require JavaScript execution to populate. Our crawlers trigger these elements to record accurate stock status for every variant.
We distribute requests across residential proxies to avoid IP bans and maintain consistent pipeline throughput during large catalogue crawls.
We normalise attributes like colour names and size formats (UK vs EU) to ensure your downstream databases receive clean, structured records.
We differentiate between temporarily out-of-stock sizes and discontinued variants, providing clear inventory signals.
Retailers monitor pricing and discount strategies across Metroshoes brands to adjust their own promotional calendars.
Merchandisers analyse category depth, material trends, and heel heights to inform seasonal buying decisions.
Fashion analysts track the introduction of new colours and styles to identify emerging market trends.
Machine learning teams use structured footwear descriptions and image URLs to train computer vision models.
Supply chain analysts monitor stock depletion rates across specific sizes to estimate sales velocity.
Partner brands verify that their products are represented correctly with accurate descriptions and pricing.
"Footwear eCommerce relies on granular variant data. Tracking a shoe is useless unless you know exactly which sizes and colours are actually in stock."
Extracting data from Metroshoes requires navigating dynamic category pages, JavaScript-rendered size selectors, and complex variant groupings. DataFlirt manages the proxy rotation and stateful browser sessions required to capture accurate stock and pricing signals at the SKU level.
Everything supported by our metroshoes.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 orchestrates the crawl while Playwright handles JavaScript execution for size variants and infinite scrolling.
We rotate residential proxies to maintain connection stability and bypass rate limits during extensive catalogue crawls.
Pipelines run on AWS infrastructure, managed by Apache Airflow, ensuring reliable execution and data delivery.
Data delivered to where your team already works — no new tooling required.
About metroshoes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and stock information is generally permissible. We target only public data and do not extract personal user information or bypass authentication walls.
We use Playwright to execute the JavaScript on the product pages, simulating user interactions to reveal the stock status for each specific size and colour combination.
Pipelines can be configured to run daily or multiple times a day depending on your requirements, ensuring you have the latest pricing and discount information.
Yes. We can filter extraction by brand, category, or specific SKU lists to target exactly the data you need.
Our minimum engagements typically start with a defined category or brand list with weekly deliveries. Contact us for a specific quote based on your volume requirements.
Yes. We provide sample exports of up to 500 SKUs during the scoping phase so you can validate the schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. From single-brand monitoring to full catalogue extraction, we build and manage the infrastructure. Tell us what you need.
