We extract footwear listings, pricing signals, sizing matrices, colour variants, and reviews from stevemadden.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 stevemadden.com. All fields typed and schema-versioned.
"product_id": "712498520", "sku": "SM-MADDEN-12", "title": "Madden Girl Platform Heels", "category": "Women", "sub_category": "Heels", "price": 89.95, "list_price": 99.95, "currency": "USD"
| # | product_id | sku | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Sizing objects from stevemadden.com. All fields typed and schema-versioned.
"variant_id": "V-984213", "parent_id": "712498520", "colour_name": "Black Patent", "size": "8", "size_system": "US", "stock_status": "in_stock", "low_stock_warning": true, "price": 89.95
| # | variant_id | parent_id | colour_name | colour_hex | size | size_system |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Sales objects from stevemadden.com. All fields typed and schema-versioned.
"sku": "SM-MADDEN-12", "current_price": 89.95, "original_price": 99.95, "discount_pct": 10, "is_clearance": false, "promo_eligible": true, "promo_code": "SPRING20", "price_timestamp": "2024-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | is_clearance | promo_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Customer Reviews objects from stevemadden.com. All fields typed and schema-versioned.
"review_id": "REV-482910", "sku": "SM-MADDEN-12", "rating": 5, "reviewer_name": "Sarah J.", "review_title": "Perfect fit and comfortable", "verified_buyer": true, "fit_rating": "True to size", "review_date": "2024-04-18"
| # | review_id | sku | rating | reviewer_name | review_date | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category Aggregation objects from stevemadden.com. All fields typed and schema-versioned.
"category_name": "Women's Boots", "total_products": 342, "sort_order": "Newest", "page_number": 1, "extracted_skus": "['BOOT-1', 'BOOT-2', 'BOOT-3']", "scraped_at": "2024-05-12T09:14:33Z"
| # | category_name | url | total_products | applied_filters | sort_order | page_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our pipeline handles the complexities of fashion eCommerce: complex variant matrices, size-level stock indicators, dynamic pricing, and paginated review feeds.
Title, description, materials, heel height, and closure types extracted at the SKU level with parent-child variant mapping.
Capture availability down to the exact size and colour combination. Track low-stock warnings and out-of-stock statuses.
Monitor current price, original price, clearance flags, and promo code eligibility across the entire site.
Extract colour names, hex codes, and variant-specific image URLs to build a complete visual database.
Full review text, star ratings, verified buyer flags, and specific fit feedback (runs small, true to size, runs large).
Track product positions within specific categories and monitor how filters affect product visibility.
Extract data from different regional versions of the site to compare international pricing and availability.
Monitor limited-time sales events and capture discounted pricing before the promotional window closes.
Run continuous pipelines that only output changed records. Reduce compute cost and downstream processing load.
Brief in. Clean data out.
Provide target categories, specific product URLs, or search terms. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, and session management to navigate the storefront.
Schema validation, null-rate checks, and variant mapping verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Fashion sites use complex front-end frameworks to manage variants and stock. Here is how our infrastructure maintains stable extraction.
Fashion retail relies on nested variants. A single product might have 5 colours and 10 sizes, each with unique stock states. Our parsers reconstruct the complete JSON payload embedded in the page source to build a flat, queryable matrix.
Stock levels update dynamically via XHR requests when a user selects a size. We intercept these API calls directly or use Playwright to simulate selections, ensuring accurate stock data rather than stale HTML snapshots.
Retail sites employ basic bot protection to block datacenter IPs. We route requests through residential proxies with realistic TLS fingerprints to ensure uninterrupted access to the catalogue.
eCommerce sites frequently update layouts for seasonal campaigns. We use multiple fallback chains per field and monitor null-rates to detect and fix selector drift before it impacts your data delivery.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing downstream processing load and storage bloat. You get a clean changelog of price and stock movements.
Fashion retailers monitor pricing, discount depth, and promotional cadence to adjust their own merchandising strategies.
Analysts track new product introductions, colour popularity, and category expansions to predict upcoming fashion trends.
Merchandisers analyse size-level stockouts and restock frequencies to optimise their own inventory purchasing decisions.
Consultancies aggregate review sentiment and fit feedback to evaluate brand perception and product quality.
Brands monitor authorised retailers to ensure compliance with Minimum Advertised Price policies and promotional guidelines.
Machine learning teams use structured product descriptions, materials, and images to train visual search and recommendation models.
"Steve Madden represents a critical node in trend-driven footwear pricing, but tracking size-level stock dynamics requires a dedicated extraction pipeline."
Extracting fashion retail data requires handling complex variant matrices where price and availability change per size and colour. DataFlirt manages the JavaScript rendering, proxy rotation, and schema normalisation so your engineers receive clean warehouse-ready tables.
Everything supported by our stevemadden.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 retry logic. Playwright manages JavaScript execution and dynamic variant loading.
Requests are routed through residential ISP proxies to avoid IP bans and ensure consistent access to the catalogue.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependencies. Postgres stores pipeline state.
Data delivered to where your team already works — no new tooling required.
About stevemadden.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data is generally permissible. DataFlirt extracts only public, non-authenticated information. We do not extract personal data or bypass authentication walls.
We extract the underlying JSON payload or simulate selections using Playwright to build a complete matrix of all possible size and colour combinations, capturing the specific price and stock status for each.
Yes. Our change detection system monitors stock indicators at the variant level. You receive updates when a specific size or colour transitions from in-stock to out-of-stock.
Pipelines can be configured for daily or sub-daily runs depending on your requirements. Change-detection diffs ensure you receive updates immediately after a crawl completes.
Yes. We paginate through the review sections on product pages to extract ratings, text, verified buyer status, and specific fit feedback.
Yes. We can target regional storefronts and normalise the output schema so you can compare pricing and availability across different markets.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily catalogue sync or continuous stock monitoring across thousands of SKUs. Tell us what you need.
