We extract jewellery listings, hair accessory variations, promotional pricing, store inventory, and reviews from claires.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 claires.com. All fields typed and schema-versioned.
"sku": "123456-1", "title": "Silver Tone Crystal Stud Earrings", "category": "Jewellery", "sub_category": "Earrings", "price": 12.99, "currency": "USD", "in_stock": true
| # | sku | title | category | sub_category | price | list_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Promotions & Pricing objects from claires.com. All fields typed and schema-versioned.
"sku": "123456-1", "base_price": 12.99, "promo_price": 0.0, "promo_type": "BOGO", "is_bogo": true, "bogo_details": "Buy 3 Get 3 Free", "clearance_flag": false, "timestamp": "2026-08-14T10:00:00Z"
| # | sku | base_price | promo_price | promo_type | is_bogo | bogo_details |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variations & SKUs objects from claires.com. All fields typed and schema-versioned.
"parent_sku": "123456", "child_sku": "123456-2", "variation_type": "metal", "colour": "Gold", "metal_type": "Gold Tone", "stock_status": "In Stock", "price_diff": 2.0
| # | parent_sku | child_sku | variation_type | colour | size | metal_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from claires.com. All fields typed and schema-versioned.
"review_id": "REV-98765", "sku": "123456-1", "rating": 5, "reviewer_name": "Sarah J.", "review_date": "2026-07-21", "title": "So cute and shiny", "helpful_votes": 12, "verified_buyer": true
| # | review_id | sku | rating | reviewer_name | review_date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Store Inventory objects from claires.com. All fields typed and schema-versioned.
"store_id": "STR-402", "store_name": "Mall of America", "city": "Bloomington", "state": "MN", "zip": "55425", "piercing_available": true, "piercing_price_min": 30.0, "open_hours": "10:00 AM - 9:00 PM"
| # | store_id | store_name | address | city | state | zip |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our claires.com scraper handles dynamic category pagination, complex BOGO promo logic, variation grids, and store-level inventory data with automated retry logic and proxy rotation.
Title, description, materials, images, and care instructions scraped at the SKU level.
Capture complex promotional logic like Buy 3 Get 3 Free and clearance markdowns.
Link parent products to child SKUs across metal types, colours, and sizes.
Extract physical store details, operating hours, and ear piercing service availability.
Track online inventory status and Online Exclusive tags across the catalogue.
Map the full breadcrumb structure from root categories down to specific accessory niches.
Extract customer ratings, review text, and helpful votes across all product pages.
Identify points multipliers and rewards-eligible items.
Extract data from US, UK, and European Claire's storefronts with localised pricing.
Brief in. Clean data out.
Provide category URLs, search terms, or store regions. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and parsing logic for claires.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.
Retail sites deploy aggressive caching and dynamic pricing widgets. Here is how we ensure reliable data extraction.
Claire's relies heavily on conditional promotions. We execute JavaScript to render the final cart price and extract the exact promotional rules applied to each SKU.
Products often have multiple colours and metal types. We extract the underlying JSON product data to map all child SKUs to their parent without missing out-of-stock variants.
Retail firewalls block datacenter IPs rapidly. We route all requests through US and UK residential proxies to maintain high success rates and avoid CAPTCHA triggers.
CDN caching can show outdated stock levels. We append cache-busting parameters and utilise session cookies to retrieve real-time inventory status.
We interact directly with the backend store locator endpoints to extract the complete directory of physical locations and their piercing service details.
Retailers monitor Claire's base prices and promotional frequency to adjust their own accessory pricing strategies.
Fashion analysts track new arrivals and category expansion to identify emerging trends in youth accessories.
Merchandisers analyse Claire's product mix across jewellery, hair, and beauty categories to benchmark their own catalogues.
Marketing teams track the cadence and depth of BOGO and clearance events to understand discount strategies.
Real estate analysts monitor store openings, closures, and piercing service availability across regions.
Product teams mine customer reviews to identify quality issues or popular materials in low-cost jewellery.
"Claire's catalogue represents a highly dynamic mix of promotional pricing and rapid inventory turnover. Extracting this accurately requires handling complex variation logic."
Retail scraping goes beyond simple HTML parsing. Extracting accurate pricing from claires.com requires executing JavaScript to resolve BOGO rules, mapping complex parent-child SKU relationships, and bypassing aggressive CDN caching. DataFlirt manages this entire infrastructure so you receive clean, normalised data without maintaining parsers.
Everything supported by our claires.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 executes JavaScript to resolve dynamic promotional pricing and inventory widgets.
Requests route through ISP-grade residential proxies to bypass retail WAFs and maintain high throughput without IP bans.
Pipelines run on AWS ECS with Airflow scheduling. Postgres stores crawl state and deduplication hashes for incremental delivery.
Data delivered to where your team already works — no new tooling required.
About claires.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available product, pricing, and store data is generally permissible. DataFlirt does not extract authenticated user data or bypass login walls.
Our parsers extract the raw promotional text and calculate the effective price per unit based on the specific Buy X Get Y rules active on the listing.
Yes. We support region-specific extraction across claires.com, claires.co.uk, and other international storefronts using localised proxy IPs.
We can configure pipelines to run daily or hourly depending on your requirements for stock availability tracking.
Yes. We map all child SKUs to their parent product, capturing specific prices, stock levels, and images for each metal type or colour.
Our monitoring stack detects schema drift immediately. We maintain the selectors and update the parsing logic as part of our managed service SLA.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a full catalogue extraction or daily promotional tracking across thousands of SKUs. We scope, build, and operate the pipeline. Tell us what you need.
