We extract fast fashion catalogues, variant stock levels, pricing signals, and sizing availability from New Look. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Product Catalogue objects from newlook.com. All fields typed and schema-versioned.
"product_id": "876543210", "name": "Black Ribbed Knit Midi Dress", "brand": "New Look", "category": "Womens", "sub_category": "Dresses", "fit_type": "Standard", "fabric_care": "95% Polyester, 5% Elastane. Machine washable."
| # | product_id | name | brand | category | sub_category | fit_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from newlook.com. All fields typed and schema-versioned.
"product_id": "876543210", "current_price": 15.0, "original_price": 25.99, "discount_pct": 42, "promo_badge": "Up to 50% off", "sale_status": true, "currency": "GBP"
| # | product_id | current_price | original_price | discount_pct | promo_badge | sale_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Variants & Stock objects from newlook.com. All fields typed and schema-versioned.
"product_id": "876543210", "sku": "876543210-BLK-10", "colour": "Black", "size": "UK 10", "stock_status": "In Stock", "low_stock_warning": true
| # | product_id | sku | colour | size | stock_status | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from newlook.com. All fields typed and schema-versioned.
"review_id": "REV-98765", "product_id": "876543210", "rating": 4.5, "title": "Great everyday dress", "text": "True to size and very comfortable.", "fit_rating": "True to size", "date": "2023-10-14"
| # | review_id | product_id | rating | title | text | date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Category & Navigation objects from newlook.com. All fields typed and schema-versioned.
"category_id": "CAT-1023", "name": "Midi Dresses", "breadcrumb": "Home > Womens > Dresses > Midi Dresses", "parent_category": "Dresses", "product_count": 412, "url": "https://www.newlook.com/uk/womens/clothing/dresses/midi-dresses/c/uk-womens-clothing-dresses-midi-dresses"
| # | category_id | name | breadcrumb | parent_category | product_count | url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our New Look scraper handles every layer of the platform: product details, dynamic pricing, SKU-level stock availability, and the review corpus — with JavaScript rendering and session management built in.
Name, description, fabric composition, care instructions, model dimensions, and fit types extracted at the product level.
Capture every size and colour combination. We map child SKUs to parent product IDs to maintain catalogue structure.
Monitor stock availability across all variants. Track 'low stock' warnings and out-of-stock statuses in real time.
Extract current price, original price, discount percentages, promotional badges, and multibuy offers.
Identify and categorise products across standard, Petite, Tall, Maternity, and Curve collections.
Full review text, star ratings, fit feedback, and quality scores paginated across all review pages.
Map the full taxonomy of the site. Extract breadcrumbs, parent categories, and total product counts per node.
Extract primary product images, variant-specific colour images, and model shots for visual analysis.
Run pipelines at hourly or daily cadences. We maintain state and only push records that have changed.
Brief in. Clean data out.
Provide category URLs, search terms, or specific product IDs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for newlook.com.
Schema validation, null-rate checks, and sample data reviews before full pipeline launch.
Clean structured data pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on an agreed cadence.
Fast fashion sites rely on heavy JavaScript hydration and rapid catalogue churn. Here is how we maintain data integrity.
New Look loads variant availability and sizing data asynchronously. We use Playwright to execute JavaScript and intercept the underlying API responses, ensuring 100% accuracy on stock statuses.
Fast fashion catalogues change daily. Our pipelines detect 404s and soft-deletes, flagging discontinued products in your dataset rather than failing the run.
We route requests through UK-based residential proxies to mimic legitimate shopper traffic, preventing IP bans and rate limits during high-frequency stock checks.
Large categories often cap pagination visibility. We use filter permutations (size, colour, brand) to force the site to reveal all products within broad categories like 'Womens Dresses'.
Retail sites frequently update their frontend frameworks. We use multiple fallback chains for CSS and XPath selectors to ensure layout tweaks do not break your data feed.
Retailers track New Look pricing and discount depth to optimise their own markdown strategies.
Fashion analysts monitor new arrivals, colour distribution, and category growth to identify emerging consumer trends.
Brands analyse stock depletion rates across different sizes (Petite, Curve) to optimise their own manufacturing ratios.
Pricing teams correlate stock depth with promotional badges to understand clearance velocity.
Machine learning teams ingest product descriptions, fabric compositions, and images to train outfit recommendation engines.
Manufacturers monitor out-of-stock patterns to identify supply chain bottlenecks in specific fabric types or product categories.
"Fast fashion moves at exceptional speed. If you cannot track daily stock depletion and promotional shifts, your market intelligence is already obsolete."
Tracking a high-velocity catalogue like New Look requires infrastructure that handles rapid SKU churn, dynamic JavaScript hydration, and aggressive rate limiting. DataFlirt manages the proxies, the parsers, and the state tracking, delivering clean data directly to your warehouse.
Everything supported by our newlook.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 orchestration and retry logic. Playwright executes JavaScript to intercept API responses for sizing and stock data.
We maintain UK-based residential IP pools to mimic legitimate shopper traffic and bypass rate limits.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling, dependency tracking, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About newlook.com scraping, legality, and pipeline operations.
Ask us directly →Yes. We iterate through the variant matrix on each product page, intercepting the backend API responses to capture precise stock statuses and low-stock warnings for every SKU.
We support daily, hourly, or custom cadences. For high-frequency price monitoring, we recommend targeted runs on specific categories rather than full-site crawls to optimise latency.
Yes. These are captured as distinct fit types and mapped within the category hierarchy, allowing you to segment your analysis by demographic fit.
Our pipelines track state. If a previously scraped URL returns a 404 or redirects to a generic category page, we flag the product as discontinued in your dataset rather than deleting it.
Yes. If provided with a list of target postal codes or store IDs, we can query the 'check store stock' endpoint to map local availability for specific SKUs.
We typically deliver a flat relational structure where each row represents a unique SKU (size/colour combination), linked back to a parent product ID. We can also provide nested JSON documents if preferred.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue extract or continuous stock monitoring across 40,000 products — we build and operate the pipeline. Tell us what you need.
