We extract shoe variants, size availability, clearance pricing, store level inventory, and brand metrics from DSW. 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 dsw.com. All fields typed and schema-versioned.
"style_id": "512394", "brand": "Nike", "product_name": "Court Vision Low Sneaker", "price": 74.99, "colour": "White/Black", "material": "Leather", "sizes": "['7', '7.5', '8', '8.5', '9']", "widths": "['Medium', 'Wide']"
| # | url | style_id | brand | product_name | price | colour |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Pricing & Offers objects from dsw.com. All fields typed and schema-versioned.
"style_id": "512394", "base_price": 74.99, "clearance_price": 59.98, "discount_pct": 20, "vip_points_multiplier": 2, "promo_eligible": true, "promo_code": "DEAL20", "price_timestamp": "2026-05-12T09:14:00Z"
| # | style_id | base_price | clearance_price | discount_pct | vip_points_multiplier | promo_eligible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Inventory & Availability objects from dsw.com. All fields typed and schema-versioned.
"style_id": "512394", "colour_id": "102", "size": "8.5", "width": "Medium", "online_stock_status": "In Stock", "bopis_eligible": true, "store_id": "0812", "store_stock_status": "Limited Stock"
| # | style_id | colour_id | size | width | online_stock_status | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from dsw.com. All fields typed and schema-versioned.
"review_id": "REV9823471", "style_id": "512394", "rating": 4.5, "fit_rating": "True to size", "comfort_rating": 5, "quality_rating": 4, "review_text": "Very comfortable for daily wear.", "date": "2026-04-18"
| # | review_id | style_id | reviewer_name | rating | fit_rating | comfort_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Search objects from dsw.com. All fields typed and schema-versioned.
"keyword": "mens running shoes", "category_path": "Mens > Shoes > Sneakers", "position": 3, "style_id": "512394", "brand": "Nike", "price": 74.99, "rating": 4.5, "total_reviews": 1248
| # | keyword | category_path | position | style_id | product_name | brand |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our DSW scraper handles every layer of the platform: footwear listings, dynamic VIP pricing, store inventory tracking, and the review corpus. JavaScript rendering and anti-bot circumvention come built in.
Title, description, material, heel height, and every metadata field DSW surfaces, scraped at the style level.
Extract complete availability matrices across all sizes and widths for every colour variant.
Capture base price, clearance markdowns, promotional eligibility, and VIP point multipliers.
Track Buy Online Pick Up In Store (BOPIS) availability across specific zip codes and store IDs.
Extract overall ratings alongside specific fit, comfort, and quality scores submitted by customers.
Map products to their exact category hierarchy and brand taxonomy for accurate catalogue matching.
Identify which SKUs qualify for sitewide promo codes and which are excluded by brand restrictions.
Run continuous pipelines at hourly or daily cadences to track fast moving clearance inventory.
Maintain a hash index of last seen values to only push diffs, reducing downstream processing load.
Brief in. Clean data out.
Provide style IDs, category URLs, keyword sets, or store zip codes. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for dsw.com.
Schema validation, null rate checks, price outlier detection, and sample data review before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
DSW uses dynamic rendering and geo-fencing for store inventory. Here is how we stay resilient.
DSW product pages and inventory checkers are heavily JavaScript rendered. We run full Playwright browser sessions with lazy load triggering and dynamic widget hydration.
We route requests through US based residential proxies to bypass data centre IP blocks and maintain high success rates during intensive crawls.
Footwear requires multi dimensional variant tracking. Our schema flattens the complex relationship between colour, size, width, and availability into queryable rows.
To extract store level inventory, we inject specific zip codes and store IDs into the session state, allowing us to map local availability across the country.
For large catalogues, we maintain a hash index of last seen values per field. Subsequent runs only push diffs, providing a clean changelog rather than full re-dumps.
Retailers monitor DSW clearance markdowns and promotional pricing to adjust their own competitive positioning.
Merchandising teams analyse DSW brand mix and category depth to identify gaps in their own footwear offerings.
Supply chain analysts track size specific stockouts across DSW to predict broader market demand for specific styles.
Footwear brands audit DSW pricing to ensure compliance with Minimum Advertised Price policies.
Fashion researchers correlate review volume and rating velocity with specific colours and materials to forecast trends.
Shopping aggregators ingest DSW catalogue data to provide unified search and price comparison for consumers.
"DSW holds one of the largest structured footwear catalogues online, but capturing accurate size level inventory requires a dedicated extraction pipeline."
Footwear scraping presents unique challenges: complex size and width matrices, dynamic VIP pricing, and store specific inventory levels. DataFlirt manages the JavaScript rendering and proxy rotation required to extract this data reliably, letting your team focus on merchandising analysis.
Everything supported by our dsw.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 dsw.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and inventory information from DSW is generally permissible under applicable law. DataFlirt targets only public, non authenticated product data. We do not extract personal data or circumvent authentication walls.
We use US residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time.
Yes. We can configure the pipeline to query specific zip codes or store IDs, allowing you to track local inventory levels for Buy Online Pick Up In Store availability.
Pipelines can be configured to run at hourly or daily cadences. High frequency tracking ensures you capture clearance markdowns and promotional changes as they happen.
We extract the publicly advertised VIP tier pricing and points multipliers shown on product pages. We do not log into specific VIP accounts to extract personalised offers.
Absolutely. We provide a sample run of up to 500 styles 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 footwear catalogue dump or a continuous inventory feed across thousands of SKUs, we scope, build, and operate the pipeline. Tell us what you need.
