We extract product listings, size and width variations, material specifications, and real-time stock levels from clarks.com. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
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 clarks.com. All fields typed and schema-versioned.
"product_id": "26155519", "title": "Wallabee Boot", "category": "Mens Originals", "price": 130.0, "currency": "GBP", "colour": "Maple Suede"
| # | product_id | title | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Sizing & Stock objects from clarks.com. All fields typed and schema-versioned.
"sku": "26155519_8_G", "product_id": "26155519", "size_uk": "8", "width": "G (Standard)", "stock_status": "IN_STOCK", "price": 130.0
| # | sku | product_id | size_uk | size_us | width | stock_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from clarks.com. All fields typed and schema-versioned.
"product_id": "26155519", "current_price": 104.0, "original_price": 130.0, "discount_pct": 20, "currency": "GBP", "promo_eligible": true
| # | product_id | current_price | original_price | discount_pct | currency | promo_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews objects from clarks.com. All fields typed and schema-versioned.
"review_id": "REV-99281", "product_id": "26155519", "rating": 5, "title": "Classic comfort", "date": "2023-10-14", "recommended": true
| # | review_id | product_id | rating | title | body | date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories objects from clarks.com. All fields typed and schema-versioned.
"category_id": "mens-boots", "name": "Men's Boots", "parent_category": "Mens", "url": "https://www.clarks.com/en-gb/mens/boots/c/m130", "product_count": 142, "meta_title": "Men's Boots | Leather & Suede Boots | Clarks"
| # | category_id | name | parent_category | url | product_count | description |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Clarks relies on complex size, width, and colour permutations. Our pipeline iterates through every JavaScript-rendered variant to capture accurate stock and pricing.
Shoes, boots, sandals, and accessories extracted with full metadata including descriptions, materials, and care instructions.
Capture the full grid of sizes (UK/US/EU) and widths (Standard, Wide, Extra Wide) for every style.
Track in-stock, low stock, and out of stock flags per specific size and width combination.
Monitor sale prices, discount percentages, and promotional code eligibility at the SKU level.
Extract specific material compositions including leather types, suede finishes, and synthetic specifications.
Map all colourways back to the parent product, capturing unique image sets and pricing per colour.
Extract data from UK, US, and EU regional sites to monitor international pricing parity.
Isolate and track high-demand lines like the Wallabee and Desert Boot across all variations.
Run daily catalogue syncs or hourly stock diffs to monitor fast-moving inventory.
Brief in. Clean data out.
Provide category URLs, regional targets, or specific product lines. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and size-matrix iteration logic.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Extracting footwear data requires more than simple HTTP requests. Here is how we handle the multi-dimensional nature of Clarks' catalogue.
Stock availability for specific sizes and widths on clarks.com is loaded dynamically via JavaScript. We use Playwright to simulate user interactions, selecting each size/width combination to reveal the true stock status.
A single shoe style can have dozens of permutations. Our pipeline structures this data relationally, mapping every SKU back to a parent product ID so your database stays clean and queryable.
To prevent IP bans during high-volume variant iteration, we route requests through residential proxies located in the target region, maintaining realistic request patterns.
Retail sites update their front-end frequently. We deploy multiple fallback selectors for critical fields like price and stock status to ensure uninterrupted data flow.
For frequent monitoring, we hash the state of each SKU. Subsequent runs only output records where price or stock status has changed, reducing your processing overhead.
Retailers track Clarks' pricing and discount strategies to inform their own markdown cadence.
Merchandisers analyse category depth, colour availability, and size ranges to benchmark their own offerings.
Analysts monitor stock depletion rates across specific sizes to estimate sales velocity.
Track when specific lines move to sale and measure the depth of the discounts applied.
Brands monitor official pricing to identify unauthorised sellers undercutting MAP.
Extract review data to run sentiment analysis on comfort, fit, and durability.
"Clarks maintains a highly complex matrix of sizes, widths, and regional stock levels. Extracting this requires a pipeline built for multi-dimensional variants."
Footwear extraction is notoriously difficult due to the multi-dimensional nature of the data. A single shoe might have 40 permutations of size, width, and colour. DataFlirt handles the complex DOM traversal and JavaScript rendering required to accurately map every variant to its real-time stock status.
Everything supported by our clarks.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 and deduplication. Playwright handles JavaScript rendering and the interaction flows required to select sizes and widths.
We maintain pools of residential proxies across target regions. Rotation happens per request to prevent blocking during intensive variant iteration.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About clarks.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and stock information is generally permissible. We do not extract personal data or bypass authentication walls.
Our Playwright integration programmatically selects each size and width combination on the product page to trigger the JavaScript that reveals the specific stock status for that SKU.
Yes. We can scope the pipeline to specific categories, sub-categories, or search terms to isolate high-value lines like Clarks Originals.
We can configure pipelines to run daily for full catalogue syncs, or hourly for specific high-priority SKUs to monitor fast-moving stock.
Yes. We can target clarks.co.uk, clarksusa.com, and European variants, standardising the output schema across all regions.
Our minimum engagement typically starts with a defined category list or a specific regional site, delivered on a weekly schedule.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily catalogue sync or hourly stock monitoring across key lines. Tell us your requirements.
