We extract apparel listings, pricing signals, size availability, brand intelligence, and clearance markdowns from Dillard's. 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 dillards.com. All fields typed and schema-versioned.
"sku": "05893421", "name": "Classic Fit Cotton Polo Shirt", "brand": "Ralph Lauren", "price": 98.5, "original_price": 98.5, "category": "Men", "sub_category": "Shirts", "materials": "100% Cotton"
| # | sku | name | brand | category | sub_category | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from dillards.com. All fields typed and schema-versioned.
"sku": "05893421", "current_price": 68.95, "original_price": 98.5, "discount_pct": 30, "clearance_flag": true, "promotional_text": "Extra 30% off clearance", "currency": "USD", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | clearance_flag | sale_end_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Sizing objects from dillards.com. All fields typed and schema-versioned.
"sku": "05893421", "size": "Large", "colour": "Navy", "online_stock_status": "In Stock", "low_stock_warning": false, "stock_depth": 14, "store_id": "ONLINE", "scraped_at": "2026-05-12T09:15:22Z"
| # | sku | store_id | online_stock_status | size | colour | stock_depth |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from dillards.com. All fields typed and schema-versioned.
"review_id": "REV-99384", "sku": "05893421", "rating": 4.5, "reviewer_name": "John D.", "review_title": "Great fit and quality", "date_posted": "2026-04-10", "helpful_votes": 12, "verified_purchase": true
| # | review_id | sku | rating | reviewer_name | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from dillards.com. All fields typed and schema-versioned.
"store_id": "STR-042", "store_name": "Dillard's NorthPark Center", "city": "Dallas", "state": "TX", "zip_code": "75225", "latitude": 32.8687, "longitude": -96.7744, "services_offered": "['Alterations', 'Personal Shopper']"
| # | store_id | store_name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Dillard's scraper handles the entire digital storefront: variant mapping, dynamic pricing, clearance tracking, and size availability matrices with session management and anti-bot circumvention built in.
Title, descriptions, materials, care instructions, and brand metadata extracted precisely from the DOM.
SKU-level tracking for every size and colour combination, preserving the exact retail catalogue structure.
Monitor price drops, clearance flags, and discount percentages to capture pricing strategies.
Online stock status and low-stock warnings per size and colour variant.
BOPIS (Buy Online Pick Up In Store) availability extracted by store location or zip code.
Extract entire brand collections or department categories with automated pagination handling.
Extract star ratings, review text, and helpful votes to measure customer sentiment.
Capture all product gallery images and colour swatches for visual analysis.
Run one-off bulk exports or continuous pipelines with change-detection diffing.
Brief in. Clean data out.
Provide brand names, category URLs, or SKU lists. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for dillards.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Retail sites use aggressive rate limiting and dynamic DOM rendering. Here is how we stay resilient.
Retail sites block datacentre IPs immediately. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management.
Product variants and inventory statuses load asynchronously. We run full Playwright browser sessions with JavaScript execution to capture dynamic content.
Retail DOM structures change during sales events. Our selector strategy uses multiple fallback chains per field to prevent pipeline breakage.
Apparel requires matrix extraction. We iterate through every colour and size combination to capture discrete SKU pricing and inventory.
For large catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs to reduce downstream processing load.
Retailers monitor markdowns and clearance pricing to adjust their own promotional calendars.
Brands audit retail pricing to ensure Minimum Advertised Price adherence across online channels.
Merchandisers analyse category depth and brand mix to optimise their own inventory purchasing.
Track stockouts and low-stock warnings across sizes to model demand velocity for specific styles.
Correlate review velocity and stock depletion with seasonal trends to identify breakout products.
Feed structured apparel descriptions, materials, and attributes into fashion machine learning models.
"Dillard's holds critical pricing and inventory signals for premium apparel brands, but extracting variant-level stock data requires specialised infrastructure."
Retail scraping fails when crawlers cannot handle complex size and colour matrices or dynamic inventory requests. DataFlirt absorbs that complexity, managing the proxies, JavaScript execution, and schema maintenance so your engineers can focus on analysis.
Everything supported by our dillards.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About dillards.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Dillard's is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and inventory data. We do not extract personal data or circumvent authentication walls.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour.
Yes. Our crawlers iterate through the available DOM elements to capture discrete pricing, stock status, and identifiers for every variant combination.
Yes. We can inject specific zip codes or store IDs during the crawl to extract local BOPIS availability and store-specific pricing.
Full catalogue refreshes typically run daily. For targeted SKU lists, we can configure hourly pipelines to monitor fast-moving clearance items.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can 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 categories, we scope, build, and operate the pipeline. Tell us what you need.
