We extract fashion catalogues, beauty stock, pricing signals, size variants, and promotional data from House of Fraser. 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 houseoffraser.co.uk. All fields typed and schema-versioned.
"product_id": "318029", "title": "Polo Ralph Lauren Custom Fit Oxford Shirt", "brand": "Polo Ralph Lauren", "department": "Men", "price": 109.0, "original_price": 109.0, "currency": "GBP", "discount_pct": 0, "category_path": "Men > Clothing > Shirts"
| # | product_id | title | brand | category_path | department | price |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Promotions objects from houseoffraser.co.uk. All fields typed and schema-versioned.
"product_id": "318029", "current_price": 85.0, "was_price": 109.0, "discount_absolute": 24.0, "discount_percentage": 22, "promo_badge": "20% OFF AT CHECKOUT", "sale_category": "Outlet", "currency": "GBP"
| # | product_id | current_price | was_price | rrp_price | discount_absolute | discount_percentage |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Stock & Variants objects from houseoffraser.co.uk. All fields typed and schema-versioned.
"variant_id": "318029-BLU-M", "product_id": "318029", "colour_name": "Light Blue", "size": "Medium", "in_stock": true, "low_stock_warning": false, "sku": "55819203", "delivery_options": "['Standard', 'Next Day', 'Click & Collect']"
| # | product_id | variant_id | colour_name | colour_swatch_url | size | in_stock |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Hierarchies objects from houseoffraser.co.uk. All fields typed and schema-versioned.
"category_id": "c-men-shirts", "category_name": "Men's Shirts", "parent_category": "Men's Clothing", "url_slug": "/men/clothing/shirts", "product_count": 1452, "featured_brands": "['Boss', 'Polo Ralph Lauren', 'Tommy Hilfiger']", "scraped_at": "2023-10-24T08:12:00Z"
| # | category_id | category_name | parent_category | url_slug | product_count | featured_brands |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Reviews & Ratings objects from houseoffraser.co.uk. All fields typed and schema-versioned.
"review_id": "rev-849201", "product_id": "318029", "rating": 5, "review_title": "Great quality and fit", "review_date": "2023-09-15", "verified_buyer": true, "recommended": true, "helpful_votes": 12
| # | review_id | product_id | author_name | rating | review_title | review_text |
|---|---|---|---|---|---|---|
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Our House of Fraser scraper handles the entire Frasers Group infrastructure: variant mapping, dynamic stock availability, promotional pricing, and category hierarchies — with anti-bot circumvention built in.
Title, description, material composition, care instructions, and high-resolution image URLs — scraped at product level with parent-child variant mapping.
Capture current price, was price, RRP, sale badges, and multibuy offers — timestamped per crawl to track exact promotional windows.
Extract every SKU combination. Track which specific sizes and colours are currently in stock or marked with low-stock warnings.
Track specific brands across all departments. Monitor new arrivals, stock depletion, and brand-wide promotional events.
Monitor the Outlet section for deep discounts. Track how long products sit in clearance and at what markdown percentage.
Extract available delivery options per item, including standard, next day, and store collection availability flags.
Traverse the entire site taxonomy. Track product counts per category to understand inventory depth and merchandising focus.
Extract customer feedback, star ratings, and verified buyer status to monitor product sentiment and quality issues.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide category URLs, brand names, or specific product IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for houseoffraser.co.uk.
Schema validation, null-rate checks, price-outlier detection, and sample variants before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Frasers Group invests heavily in scraping detection. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Frasers Group properties utilise aggressive bot mitigation. Our crawlers use UK residential ISP proxies with realistic browser fingerprints, randomised request timing, and full TLS spoofing to bypass WAF challenges.
Stock availability and size selectors are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution to trigger variant hydration — capturing data that headless HTTP clients miss entirely.
Retail sites change DOM structures frequently for promotions. Our selector strategy uses multiple fallback chains per field — CSS selectors, XPath, and JSON state extraction — so a layout change doesn't break your data pipeline overnight.
For large brand 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. You get a clean changelog rather than full re-dumps.
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. SLA uptime is contractual, not aspirational.
Retailers track House of Fraser pricing, sale events, and discount depths to adjust their own promotional strategies.
Fashion and beauty brands monitor their product listings to ensure minimum advertised price (MAP) compliance and track unauthorised discounting.
Analysts track size-level stock depletion rates to estimate sales velocity and inform procurement models.
Brands analyse category share of voice, measuring how many SKUs they hold in a department versus competitors.
Merchandisers track new arrivals, colour variants, and category expansion to identify emerging fashion and home trends.
ML teams use structured product descriptions, attributes, and images to train retail recommendation engines and computer vision models.
"House of Fraser represents a critical node in UK retail pricing — but extracting accurate, variant-level stock data requires navigating aggressive bot mitigation."
Most teams underestimate the investment required: reliable retail scraping requires UK residential proxies, full JavaScript rendering for variant selection, CAPTCHA handling, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our houseoffraser.co.uk 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. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across UK regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 houseoffraser.co.uk scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from houseoffraser.co.uk is generally permissible under UK law, provided it does not breach terms of service in a legally binding manner or extract personal data. DataFlirt targets only public, non-authenticated product, pricing, and stock data. We do not extract personal data, circumvent authentication walls, or violate GDPR. Clients should consult legal counsel for specific use cases.
We use UK residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour to bypass WAF challenges. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
Yes. We iterate through the JavaScript-rendered variant selectors to capture exact stock status (in stock, low stock, out of stock) for every size and colour combination on a product page.
For targeted competitor monitoring, pipelines can run hourly to capture flash sales and promotional shifts. Full catalogue refreshes typically complete within a 6-12 hour window.
Our smallest packages start at a defined category or brand list (typically 1,000-50,000 SKUs) with weekly delivery. For full-site extraction or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 products as part of the pre-engagement scoping process — so you can validate schema fit, field completeness, and data quality before signing any contract.
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 200K products — we scope, build, and operate the pipeline. Tell us what you need.
