We extract mattress specifications, bundle pricing, Snow Technology details, and verified reviews from casper.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 Mattresses & Furniture objects from casper.com. All fields typed and schema-versioned.
"sku": "CSP-WAVE-HYB-Q", "product_name": "Wave Hybrid Snow Mattress", "category": "Mattresses", "mattress_type": "Hybrid", "snow_technology": true, "price": 3395.0, "list_price": 3395.0, "currency": "USD", "dimensions": "60 x 80 x 13 inches", "in_stock": true
| # | sku | product_name | category | mattress_type | snow_technology | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sleep Bundles objects from casper.com. All fields typed and schema-versioned.
"bundle_id": "BNDL-SLP-01", "bundle_name": "The Casper Sleep Bundle", "included_items": "['Original Mattress', 'Foundation', 'Mattress Protector']", "total_price": 1495.0, "list_price": 1785.0, "discount_pct": 16, "size_configuration": "Queen", "in_stock": true
| # | bundle_id | bundle_name | included_items | total_price | list_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from casper.com. All fields typed and schema-versioned.
"review_id": "REV-982341", "product_sku": "CSP-WAVE-HYB-Q", "star_rating": 5, "review_title": "Best sleep I have had in years", "review_date": "2023-11-14", "verified_buyer": true, "sleep_position": "Side Sleeper", "mattress_size": "Queen"
| # | review_id | product_sku | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bedding & Accessories objects from casper.com. All fields typed and schema-versioned.
"sku": "ACC-SHT-PER-Q-WHT", "product_name": "Percale Sheet Set", "category": "Bedding", "material": "100% Organic Cotton", "size": "Queen", "colour": "White", "price": 139.0, "in_stock": true
| # | sku | product_name | category | material | thread_count | size |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Locations objects from casper.com. All fields typed and schema-versioned.
"store_id": "LOC-NY-SOHO", "store_name": "Casper Sleep Shop SoHo", "city": "New York", "state": "NY", "zip_code": "10012", "latitude": 40.7245, "longitude": -73.9973, "services_offered": "['Mattress Testing', 'Same Day Delivery']"
| # | store_id | store_name | address_line_1 | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Casper scraper handles every layer of the platform: mattress specifications, dynamic bundle pricing, accessory catalogues, and the review corpus. JavaScript rendering and anti-bot circumvention are built in.
Extract foam layers, coil counts, and Snow Technology cooling specs across the Wave Hybrid, Nova, and Original lines.
Monitor dynamic bundle discounts when users combine mattresses with foundations and protectors.
Extract verified buyer reviews, star ratings, and specific sleep positions reported by customers.
Capture data on pillows, sheets, glow lights, and dog beds, including material and thread count details.
Map all sizes from Twin to California King, alongside colour options for bedding and frames.
Track out-of-stock SKUs and restock indicators across all product categories.
Extract physical Casper Sleep Shop locations, opening hours, and available in-store services.
Map recommended add-ons and related products displayed on primary mattress product pages.
Run daily diffs to track pricing changes or full catalogue exports on a weekly basis.
Bypass retail bot protection and rate limits using residential proxy rotation.
Brief in. Clean data out.
Provide target categories: mattresses, bedding, furniture, or sleep shops. We design the schema together.
We configure Scrapy and Playwright crawlers to handle Casper's React frontend and dynamic pricing widgets.
Schema validation, null-rate checks for dimensions, and pricing anomaly detection before launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern direct-to-consumer sites use complex frontend frameworks. Here is how we extract clean data from Casper.
Casper uses heavy JavaScript for mattress configuration and bundle builders. We run full Playwright browser sessions with JS execution to hydrate dynamic price widgets and capture data that headless clients miss.
Bundle discounts update dynamically based on cart additions and size selections. We simulate these interactions to extract the exact list price, discount percentage, and final bundle price.
Customer reviews are often loaded via third-party widgets. Our pipeline intercepts these backend API calls to paginate through thousands of reviews without relying on fragile DOM scraping.
Mattress sizes and bedding colour options create complex SKU matrices. We iterate through all available variants on a single product page to generate flat, queryable records for every combination.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs, reducing compute cost and downstream processing load when tracking daily price fluctuations.
Direct-to-consumer mattress brands monitor Casper's premium pricing, promotional periods, and bundle discounts to adjust their own positioning.
Product teams analyse Snow Technology feature adoption and pricing tiers to inform their own cooling mattress development.
Analysts mine verified reviews to correlate sleep positions with specific mattress models and identify common customer pain points.
Monitor stock depth and out-of-stock frequency across niche sizes like California King to gauge demand and supply constraints.
Real estate and retail analysts track new Casper Sleep Shop locations to map the brand's physical retail footprint expansion.
Compare direct-to-consumer prices on casper.com against retail partners to track Minimum Advertised Price compliance.
"Casper's direct-to-consumer catalogue defines modern sleep retail, but extracting structured variant and bundle pricing requires dedicated infrastructure."
Extracting data from Casper involves navigating complex JavaScript bundle builders, dynamic pricing matrices, and third-party review widgets. DataFlirt manages this pipeline end-to-end, delivering clean, normalised retail data so your analysts can focus on market strategy rather than maintaining web scrapers.
Everything supported by our casper.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 handles JavaScript rendering, cookie sessions, and frontend interaction flows required for Casper's site.
We maintain pools of residential ISP proxies. Rotation happens per-request to bypass retail bot protection and prevent IP bans.
Pipelines run on AWS infrastructure. 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 casper.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and review data is generally permissible. DataFlirt does not extract personal customer data or bypass authentication walls.
We use Playwright to simulate user interactions, selecting mattress sizes and adding foundations to trigger and extract the dynamic bundle discount logic.
Yes. Our pipeline iterates through all variant selectors on the product page to capture pricing and stock data for every size, from Twin to California King.
We intercept the backend API calls made by the third-party review widgets, allowing us to extract the full corpus of reviews without DOM scraping limitations.
Pipelines can be scheduled for daily runs to track price and stock changes, or weekly for full catalogue refreshes.
Yes. Every variant record includes a boolean in_stock flag based on current availability.
Yes. We provide a sample run of up to 50 SKUs during the scoping process so you can validate the schema and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off mattress catalogue export or continuous bundle price tracking, we scope, build, and operate the pipeline. Tell us what you need.
