We extract product listings, size-level availability, pricing signals, and brand catalogues from Shoes.com. 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 shoes.com. All fields typed and schema-versioned.
"product_id": "SH-994821", "title": "Men's Classic Leather Sneaker", "brand": "Reebok", "price": 74.99, "currency": "USD", "discount_pct": 15, "available_sizes": "['8', '8.5', '9', '10', '11']", "colours": "['White/Gum', 'Black/Grey']"
| # | product_id | sku | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Size & Inventory objects from shoes.com. All fields typed and schema-versioned.
"sku": "RBK-CL-WHT-090-W", "colourway": "White/Gum", "size_us": "9.0", "width": "Wide", "in_stock": true, "price_override": "None", "scraped_at": "2026-05-12T10:15:22Z"
| # | sku | colourway | size_us | size_uk | size_eu | width |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promotions objects from shoes.com. All fields typed and schema-versioned.
"sku": "RBK-CL-WHT-090-W", "current_price": 74.99, "msrp": 89.99, "discount_pct": 16.6, "promo_eligible": true, "clearance_flag": false, "price_timestamp": "2026-05-12T10:15:22Z"
| # | sku | current_price | msrp | discount_abs | discount_pct | promo_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from shoes.com. All fields typed and schema-versioned.
"review_id": "REV-8472910", "sku": "RBK-CL-WHT-090-W", "rating": 4.5, "fit_rating": "True to size", "comfort_rating": 5.0, "review_title": "Classic and comfortable", "verified_buyer": true, "review_date": "2026-04-22"
| # | review_id | sku | reviewer_name | rating | fit_rating | comfort_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand Taxonomy objects from shoes.com. All fields typed and schema-versioned.
"brand_id": "BRD-042", "brand_name": "Reebok", "total_products": 1245, "active_categories": "['Running', 'Walking', 'Classics', 'Training']", "average_price": 85.5, "brand_url": "https://www.shoes.com/reebok", "top_sellers": "['Classic Leather', 'Club C 85']"
| # | brand_id | brand_name | brand_url | total_products | active_categories | top_sellers |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Shoes.com scraper handles every layer of the platform: product listings, dynamic size matrices, pricing, and brand catalogues, with JavaScript rendering and session management built in.
Title, brand, materials, descriptions, and high-res imagery scraped at the SKU level.
Track stock availability across complex matrices of US/UK/EU sizes and footwear widths (Narrow, Medium, Wide).
Capture current price, MSRP, clearance tags, and promotional eligibility timestamped per crawl.
Extract all available colour variations and link them to parent product IDs.
Aggregate customer feedback including specific metrics for fit (runs small/large), comfort, and quality.
Map the complete footwear taxonomy from athletic shoes to formal boots.
Track total SKU counts, new arrivals, and discontinued lines per brand on Shoes.com.
Identify site-wide and SKU-specific discount codes applied at checkout.
Run one-off bulk exports or configure continuous pipelines at daily cadences.
Brief in. Clean data out.
Provide brand lists, category URLs, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for shoes.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.
Footwear retail sites use aggressive caching, dynamic inventory loading, and bot protection. Here is how we maintain reliable extraction.
E-commerce platforms block data center IPs. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Size and width availability often loads asynchronously via JavaScript. We run full Playwright browser sessions to trigger API calls and hydrate inventory state.
E-commerce layouts shift during sales events. Our selector strategy uses multiple fallback chains per field so a banner injection does not break your data pipeline.
For large brand catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs.
Every run emits structured logs to our observability stack. We alert on null-rate spikes and stock-out anomalies.
Footwear retailers monitor competitor pricing and clearance events to adjust their own pricing strategies.
Merchandisers track size-level stock availability across brands to identify supply gaps and popular colourways.
Footwear brands audit retail partners to ensure adherence to Minimum Advertised Price (MAP) policies.
Analysts correlate review velocity and stock depletion rates to predict upcoming seasonal footwear trends.
New entrants analyze brand density, average price points, and category saturation to identify whitespace.
ML teams use structured product descriptions, materials data, and imagery to train visual search and recommendation models.
"Shoes.com holds a critical dataset for footwear pricing and size-level inventory trends, but extracting the complex size, width, and colour matrix requires specialized infrastructure."
Most teams underestimate the investment required: reliable e-commerce scraping requires residential proxies, full JavaScript rendering for inventory states, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our shoes.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. Playwright handles JavaScript rendering for inventory matrices.
Pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About shoes.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Shoes.com is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and inventory data.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour.
Yes. We map the full matrix of sizes and widths for each colourway, capturing exact stock status and price overrides.
Full catalogue refreshes run daily. For specific high-priority SKUs, we can configure sub-daily tracking.
Yes. We paginate through all reviews and extract specific ratings for fit, comfort, and quality.
Our smallest packages start at a defined brand list or category with weekly delivery.
Yes. We provide a sample run of up to 500 SKUs to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off brand catalogue dump or a continuous price-monitoring feed across 100K SKUs, we scope, build, and operate the pipeline. Tell us what you need.
