We extract mattress specifications, pricing signals, dimension variants, and customer reviews from purple.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 purple.com. All fields typed and schema-versioned.
"product_id": "mattress-restore-premier", "title": "Purple RestorePremier Hybrid Mattress", "category": "Mattresses", "gelflex_type": "GelFlex Grid Plus", "firmness": "Firm", "base_price": 3495.0
| # | product_id | title | category | gelflex_type | firmness | dimensions |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Variants objects from purple.com. All fields typed and schema-versioned.
"variant_id": "var_89231", "product_id": "mattress-restore-premier", "size": "Queen", "price": 3495.0, "compare_at_price": 3895.0, "in_stock": true
| # | variant_id | product_id | size | price | compare_at_price | discount_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Customer Reviews objects from purple.com. All fields typed and schema-versioned.
"review_id": "rev_994821", "product_id": "mattress-restore-premier", "rating": 5, "title": "Best sleep in years", "verified_buyer": true, "helpful_votes": 14
| # | review_id | product_id | rating | title | body | author |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Store Locations objects from purple.com. All fields typed and schema-versioned.
"store_id": "loc_042", "name": "Purple Store Austin", "city": "Austin", "state": "TX", "zip_code": "78758", "latitude": 30.3951, "longitude": -97.7211
| # | store_id | name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Bedding Accessories objects from purple.com. All fields typed and schema-versioned.
"product_id": "softstretch-sheets", "title": "Purple SoftStretch Sheets", "type": "Sheets", "material": "Bamboo Blend", "price_min": 149.0, "price_max": 229.0
| # | product_id | title | type | material | thread_count | sizes_available |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our purple.com scraper extracts every product specification, dynamic price point, and customer review across the entire catalogue. Built with JavaScript rendering to handle dynamic variant selection and location based pricing.
Extract GelFlex Grid types, coil counts, foam density, and firmness ratings for every mattress model.
Map Twin, Queen, King, and California King sizes to specific SKUs, weights, and exact dimensions.
Capture base prices, promotional discounts, and bundle offers across all product categories.
Paginate through thousands of product reviews, capturing ratings, text, and verified buyer status.
Extract full geographic coordinates, store hours, and contact details for all physical retail locations.
Monitor inventory status and estimated shipping times for specific product variants.
Scrape specifications for adjustable bases, bed frames, and seating products.
Extract specific warranty periods and sleep trial conditions tied to individual products.
Receive automated updates when prices change, new variants are added, or stock depletes.
Brief in. Clean data out.
Provide target categories, product types, or specific URLs. We map the required data fields.
We configure Playwright crawlers to handle variant rendering, promotional popups, and anti-bot systems.
Schema validation, price-outlier detection, and variant mapping checks before full launch.
JSON, CSV, or Parquet pushed to your designated storage endpoint on the agreed cadence.
Direct to consumer brands use dynamic frontend frameworks and aggressive caching. Here is how we extract reliable data.
Purple's product pages dynamically load pricing and stock data based on user interactions with size and color selectors. We use Playwright to simulate these clicks and capture the resulting state changes.
To prevent IP blocking during full catalogue crawls, we route requests through US based residential proxies, ensuring consistent access to location specific pricing and inventory.
Customer reviews are often loaded via third party APIs. We intercept these network requests directly to extract clean, structured review data without parsing complex HTML.
DTC brands frequently update their frontend code for promotions. Our selector strategy relies on structured data layers and stable product attributes to prevent pipeline breakage.
We hash product records and only deliver data when pricing, stock, or specifications change, reducing your downstream processing overhead.
Mattress brands monitor Purple's pricing, discount cadences, and bundle offers to adjust their own promotional strategies.
Manufacturers analyze GelFlex Grid specifications and dimensional data to benchmark their own product development.
Data teams process thousands of customer reviews to identify common complaints, feature requests, and sleep quality trends.
Real estate analysts track Purple's physical store locations to map DTC retail footprint growth.
Analysts track stock availability and shipping delay estimates to infer inventory levels and supply chain health.
Investors correlate review velocity and variant availability with estimated sales volume for the DTC mattress sector.
"Purple's proprietary GelFlex Grid technology and direct to consumer pricing models present a highly structured dataset for sleep industry analysis."
Extracting structured data from modern DTC storefronts requires rendering dynamic variant selectors, handling geographic pricing, and parsing nested review structures. DataFlirt manages this pipeline completely so your team can focus on market analysis rather than maintaining scrapers.
Everything supported by our purple.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 manages crawl logic and deduplication, while Playwright handles dynamic frontend rendering and variant selection.
US based residential IPs ensure consistent access to Purple's catalogue without triggering rate limits or bot protection.
Airflow schedules extraction runs on AWS infrastructure, maintaining high availability and strict delivery SLAs.
Data delivered to where your team already works — no new tooling required.
About purple.com scraping, legality, and pipeline operations.
Ask us directly →Yes. Our pipeline iterates through all available variants, including Twin, Full, Queen, King, and California King, capturing the specific price and SKU for each.
We support daily, hourly, or custom scheduled runs depending on your monitoring requirements.
This specific pipeline targets purple.com directly. We can build separate pipelines for third party retailers if required.
We utilize residential proxies and realistic browser fingerprints via Playwright to ensure reliable data extraction without interruptions.
Yes, we extract the complete review corpus for each product, including ratings, text, date, and verified buyer badges.
We deliver structured data in JSON, CSV, or Parquet formats directly to your AWS S3 bucket, data warehouse, or via API.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one off product catalogue export or continuous price monitoring across all variants, we scope, build, and operate the pipeline. Tell us what you need.
