We extract footwear catalogues, size and width availability matrices, pricing signals, and store inventory from shoeshow.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 shoeshow.com. All fields typed and schema-versioned.
"sku": "SS-94821", "brand": "Skechers", "title": "Men's Afterburn Memory Foam", "price": 54.99, "list_price": 65.0, "style_number": "50081", "category": "Mens > Athletics"
| # | sku | brand | title | category | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory Matrix objects from shoeshow.com. All fields typed and schema-versioned.
"sku": "SS-94821", "store_id": "WEB", "size": "10.5", "width": "Wide", "stock_status": "In Stock", "quantity": 14
| # | sku | store_id | size | width | stock_status | quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Clearance objects from shoeshow.com. All fields typed and schema-versioned.
"sku": "SS-94821", "current_price": 54.99, "original_price": 65.0, "discount_pct": 15, "on_clearance": false, "currency": "USD"
| # | sku | current_price | original_price | discount_pct | on_clearance | promo_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Data objects from shoeshow.com. All fields typed and schema-versioned.
"category_id": "C-104", "parent_category": "Mens", "name": "Athletics", "url": "/mens/athletics", "product_count": 1240, "gender": "Male"
| # | category_id | parent_category | name | url | product_count | breadcrumb |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Store Locations objects from shoeshow.com. All fields typed and schema-versioned.
"store_id": "STR-402", "name": "Shoe Show Mega", "city": "Charlotte", "state": "NC", "zip": "28262", "phone": "704-555-0198"
| # | store_id | name | address | city | state | zip |
|---|---|---|---|---|---|---|
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Our Shoeshow scraper handles dynamic inventory matrices, local store stock checks via zip code sessions, and pagination layers to extract complete footwear catalogues.
Extract brands, styles, descriptions, and material specs across all men's, women's, and kids' categories.
Capture availability for every size and width combination per SKU, handling complex variant structures.
Track markdowns, original prices, and promotional banners to monitor discount velocity.
Simulate local zip code sessions to extract store-level stock availability for specific footwear models.
Retrieve high-resolution product image URLs and colourway variants for visual catalogues.
Map full breadcrumb hierarchies and category trees to understand site taxonomy.
Link distinct colourways back to parent styles for normalised product records.
Capture site-wide promotions, BOGO offers, and cart-level discounts applied to specific SKUs.
Run continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, brand names, or zip codes. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management for shoeshow.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket or Snowflake stage on agreed cadence.
Retail sites use aggressive caching and anti-bot layers. Here is how we ensure reliable footwear data extraction.
Footwear requires multi-dimensional matrices for sizes and widths. We flatten these nested JSON structures into normalised relational rows for easy downstream querying.
Retail firewalls block datacentre IPs. We route requests through US-based residential proxies to maintain high success rates during large catalogue crawls.
Store-level stock requires persistent location cookies. We manage concurrent sessions across multiple zip codes to map regional inventory availability.
We maintain a state index of last-seen prices and stock levels. Subsequent runs only push diffs, reducing your ingestion compute costs.
We use multiple fallback chains per field so a minor frontend update on shoeshow.com does not break your data feed.
Retailers track Shoeshow pricing and promotional cadence to optimise their own pricing strategies.
Analysts monitor stock depth across size and width matrices to identify supply chain gaps.
Footwear brands audit listings to ensure their products are not priced below minimum advertised price policies.
Merchandisers track markdown velocity to understand which styles are underperforming.
Category managers analyse brand representation and style counts to identify whitespace opportunities.
Machine learning teams use structured product descriptions and attributes to train classification models.
"Shoeshow holds critical mid-market footwear inventory and pricing signals, but extracting the multi-dimensional size and width matrix requires dedicated infrastructure."
Most teams underestimate the investment required: reliable retail scraping requires residential proxies, local zip code session handling for store inventory, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers focus on analysis, not infrastructure.
Everything supported by our shoeshow.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 zip code session cookies.
We maintain pools of residential proxies to bypass retail bot protection, rotating IPs to ensure high success rates.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and alerting.
Data delivered to where your team already works — no new tooling required.
About shoeshow.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and inventory information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated retail data. We do not extract personal data or circumvent authentication walls.
We use residential proxies, browser fingerprint spoofing, and request timing modelled on human behaviour to ensure reliable extraction without triggering rate limits.
Yes. We can simulate sessions using provided zip codes to extract local store availability for specific footwear models.
Pipelines can be configured to run daily or at custom intra-day intervals to capture flash sales and clearance updates.
We iterate through the underlying JSON payloads or DOM elements to capture every available size and width combination, outputting a normalised matrix.
Yes. We provide a sample run of up to 500 SKUs as part of the scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extract or daily inventory monitoring, we scope, build, and operate the pipeline. Tell us what you need.
