We extract apparel listings, sizing matrices, pricing signals, fabric compositions, and customer reviews from Madewell. 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 madewell.com. All fields typed and schema-versioned.
"sku": "NM294", "title": "The Perfect Vintage Straight Jean", "category": "Clothing", "sub_category": "Jeans", "price": 128.0, "colour": "Montclair Wash", "sizes_available": "['23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33']", "fabric_composition": "99% cotton/1% elastane", "is_new_arrival": false
| # | sku | title | category | sub_category | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Denim Fit Data objects from madewell.com. All fields typed and schema-versioned.
"sku": "NM294", "fit_type": "Straight", "rise_inches": "11.25", "stretch_level": "Comfort Stretch", "inseam_options": "['Petite', 'Standard', 'Tall']", "wash_name": "Montclair Wash", "leg_opening": "15.5", "review_rating": 4.6, "fit_feedback_score": "True to size"
| # | sku | fit_type | rise_inches | stretch_level | inseam_options | wash_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Inventory & Pricing objects from madewell.com. All fields typed and schema-versioned.
"sku": "NM294", "colour": "Montclair Wash", "size": "28", "stock_status": "In Stock", "current_price": 98.5, "original_price": 128.0, "discount_pct": 23, "promo_eligible": true, "final_sale": false, "scraped_at": "2026-05-12T10:15:00Z"
| # | sku | colour | size | stock_status | current_price | original_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from madewell.com. All fields typed and schema-versioned.
"review_id": "REV-992817", "sku": "NM294", "rating": 5, "fit_rating": "True to size", "quality_rating": "Excellent", "review_title": "My new favourite jeans", "review_text": "The rise is perfect and the denim has just enough stretch.", "reviewer_size_purchased": "28", "reviewer_height": "5'6"", "review_date": "2026-04-20"
| # | review_id | sku | rating | fit_rating | quality_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category & Taxonomy objects from madewell.com. All fields typed and schema-versioned.
"category_id": "womens-jeans", "breadcrumb": "Clothing > Jeans > Straight", "sku": "NM294", "rank_position": 4, "is_new": false, "is_bestseller": true, "badge_text": "Best Seller", "total_results": 142
| # | category_id | breadcrumb | page_url | sku | rank_position | is_new |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Madewell scraper handles every layer of the platform: apparel listings, dynamic pricing, multi-dimensional size matrices, and the review corpus, with JavaScript rendering and session management built in.
Title, description, fabric composition, care instructions, and every metadata field Madewell surfaces, scraped at the SKU level.
Extract rise measurements, stretch levels, wash names, and inseam lengths for comprehensive denim market analysis.
Capture sale prices, list prices, promotional eligibility, and final sale markers, timestamped per crawl.
Monitor stock availability for every size and colour combination to map inventory depth and sell-through rates.
Extract full review text, star ratings, fit feedback, quality scores, and reviewer dimensions like height and purchased size.
Link parent styles to all available child colourways, capturing specific image URLs and pricing for each variant.
Capture 'Wear It With' product recommendations to map styling algorithms and outfit combinations.
Track product position across category pages and monitor 'Best Seller' or 'New Arrival' badge assignments.
Run continuous pipelines that only emit records when price, stock status, or promotional eligibility changes.
Brief in. Clean data out.
Provide category URLs, search terms, or specific SKU lists. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for madewell.com.
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.
Apparel scraping requires handling complex variant matrices and dynamic inventory states. Here is how we stay resilient.
Madewell renders size availability and stock alerts dynamically via JavaScript. We run full Playwright browser sessions to trigger size selections and capture accurate inventory states across all dimensions.
Apparel SKUs exist in multi-dimensional matrices of colour, size, and inseam length. Our parsers flatten these relationships into queryable records, ensuring no variant combination is missed.
Retail sites aggressively rate-limit sequential page requests. We use residential ISP proxies with realistic browser fingerprints and randomised request timing to maintain high-throughput extraction.
E-commerce layouts change frequently. Our selector strategy uses multiple fallback chains per field, including CSS, XPath, and JSON-LD extraction, ensuring pipeline stability during site updates.
For inventory tracking, we maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Apparel brands track Madewell's markdown cadences, promotional events, and final sale transitions to optimise their own pricing strategies.
Merchandising teams analyse fabric compositions, colour trends, and sizing availability gaps to inform future product development.
Analysts track denim fit trends, comparing the prevalence of low-rise versus high-rise styles, and stretch levels across the catalogue.
Machine learning teams use Madewell's 'Wear It With' cross-sell recommendations to train automated styling and outfit generation algorithms.
Product teams analyse review text and fit feedback scores to understand consumer preferences regarding sizing accuracy and material quality.
Retail analysts monitor new arrivals and category rank movements to identify emerging trends and bestseller velocity.
"Madewell's catalogue represents a goldmine of denim fit data and consumer sizing preferences, but the multi-dimensional variant structures require a custom extraction approach."
Extracting apparel data requires handling complex parent-child relationships across colours, sizes, and inseams. DataFlirt manages the JavaScript rendering and residential proxy rotation required to capture accurate inventory levels and promotional pricing without triggering rate limits. We deliver clean, normalised variant matrices directly to your warehouse.
Everything supported by our madewell.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 dynamic inventory widget interaction. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required to prevent rate limiting during deep category sweeps.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state is stored in managed PostgreSQL.
Data delivered to where your team already works — no new tooling required.
About madewell.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available catalogue and pricing information is generally permissible. DataFlirt targets only public, non-authenticated product data. We do not extract personal data or circumvent authentication walls.
We extract parent-child relationships and flatten multi-dimensional matrices. Every combination of colour, size, and inseam becomes a discrete, queryable record in the final dataset.
Yes. We capture stock status at the variant level, allowing you to track exactly which sizes and colours are sold out or running low in inventory.
We extract publicly visible promotional pricing and sale markers. We do not extract account-specific discounts that require authenticated user sessions.
We can configure pipelines for daily category sweeps or sub-daily cadences for specific high-priority SKU lists, depending on your monitoring requirements.
Yes. We extract cross-sell and styling recommendation modules, mapping the target SKU to its suggested companion products.
Our minimum engagements typically start at a defined list of 5,000 SKUs or full sweeps of specific categories like Denim, with weekly or daily delivery cadences.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue export or a continuous inventory monitoring feed, we scope, build, and operate the pipeline. Tell us what you need.
