We extract product catalogues, Easy Pay tiers, on-air video metadata, and customer reviews from QVC. 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 qvc.com. All fields typed and schema-versioned.
"item_number": "A398214", "title": "Denim & Co. Essentials V-Neck Short Sleeve Top", "brand": "Denim & Co.", "qvc_price": 34.5, "easy_pay_installments": 3, "easy_pay_amount": 11.5, "in_stock": true, "rating": 4.2, "review_count": 412
| # | item_number | title | brand | category | description | retail_value |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Offers objects from qvc.com. All fields typed and schema-versioned.
"item_number": "A398214", "qvc_price": 34.5, "feature_price": 29.98, "shipping_handling": 3.5, "easy_pay_installments": 3, "easy_pay_amount": 9.99, "currency": "USD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | item_number | qvc_price | feature_price | clearance_price | retail_value | shipping_handling |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for On-Air & Video objects from qvc.com. All fields typed and schema-versioned.
"item_number": "A398214", "host_name": "Jane Treacy", "show_name": "Fashion Night In", "air_date": "2026-05-11", "air_time": "20:00:00", "video_id": "vid_98214_fni", "live_stream_status": "archived"
| # | item_number | host_name | show_name | air_date | air_time | video_id |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from qvc.com. All fields typed and schema-versioned.
"review_id": "REV-883921", "item_number": "A398214", "user_nickname": "ShopperMom44", "star_rating": 5, "review_title": "Perfect summer staple", "verified_buyer": true, "helpful_votes": 12, "fit_rating": "True to size"
| # | review_id | item_number | user_nickname | star_rating | review_title | review_text |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search & Category objects from qvc.com. All fields typed and schema-versioned.
"keyword": "v-neck tops", "position": 3, "item_number": "A398214", "brand": "Denim & Co.", "current_price": 29.98, "is_new": false, "on_air_badge": true, "rating": 4.2
| # | keyword | category_id | position | item_number | title | brand |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our QVC scraper handles every layer of the platform: storefront listings, Easy Pay tiers, on-air video metadata, and the review corpus - with JavaScript rendering and anti-bot circumvention built in.
Title, descriptions, specifications, dimensions, and materials. Scraped at the item level with full size and colour variant mapping.
Track QVC Price, Feature Price, Clearance Price, and detailed Easy Pay installment breakdowns per item.
Extract show names, host details, air dates, and associated video presentation URLs for broadcast items.
Monitor stock availability, size/colour variations, and waitlist eligibility across the catalogue.
Capture full review text, star ratings, fit metrics, and helpful vote counts across all paginated reviews.
Map entire brand storefronts like Denim & Co., Susan Graver, or IT Cosmetics to track assortment changes.
Extract precise S&H fees and delivery estimates attached to specific items and promotional periods.
Monitor discount velocity and markdown percentages on clearance and returned as-is inventory.
Run daily catalogue sweeps or hourly price checks with change-detection diffing to monitor flash sales.
Brief in. Clean data out.
Provide item number lists, category URLs, or brand names. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for qvc.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
QVC invests in bot detection to protect its pricing data and media assets. Here is how we stay resilient - and why teams choose managed infrastructure over DIY.
QVC uses Akamai bot management. Our crawlers use residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to blend in with normal shopper traffic.
QVC's video players, dynamic sizing charts, and Easy Pay calculators require full DOM hydration. We run full Playwright browser sessions with JavaScript execution to capture data that headless HTTP clients miss entirely.
QVC updates its frontend frequently to support new broadcast events. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline overnight.
For large apparel catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops - and respond before you notice.
Retailers monitor QVC's Feature Prices and Easy Pay structures to adjust their own financing offers and promotional calendars.
Brands audit QVC listings to ensure minimum advertised price compliance and accurate product representation during broadcast events.
Merchandisers analyse QVC's brand mix, size availability, and clearance velocity to inform their own purchasing decisions.
Analysts correlate on-air schedules and host appearances with product stock depletion rates to measure broadcast effectiveness.
Extract product reviews to identify sizing issues, material complaints, and overall brand perception across specific demographics.
ML teams use structured apparel descriptions, images, and pricing data to train retail recommendation engines and visual search models.
"QVC blends traditional retail with broadcast media. Extracting its data requires handling complex video metadata, dynamic pricing tiers, and real-time inventory shifts."
Most teams underestimate the investment required to scrape QVC reliably. Akamai bot protection, heavy JavaScript rendering for product variations, and constantly shifting on-air schedules demand dedicated infrastructure. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our qvc.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 to load Easy Pay data.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required to bypass Akamai bot management.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 qvc.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from QVC is generally permissible. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to navigate Akamai bot management systems.
Yes. We capture the number of installments, the amount per installment, and any promotional financing text associated with the product.
Yes. We extract host names, show schedules, air dates, and URLs for the product presentation videos directly from the item pages.
We can configure pipelines to run daily sweeps across the entire catalogue or high-frequency checks on specific high-velocity items during broadcast events.
Our smallest packages start at a defined brand list or category set with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Yes. We capture the full review text, star ratings, and specific fit feedback like 'runs small' or 'true to size' across all paginated reviews.
Absolutely. We provide a sample run of up to 500 items 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 full apparel catalogue dump or a continuous tracking feed for on-air items, we scope, build, and operate the pipeline. Tell us what you need.
