We extract product listings, sizing matrices, markdown signals, fabric intelligence, and fit reviews from Lorna Jane. 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 lornajane.com. All fields typed and schema-versioned.
"sku": "LB0321_BLK", "title": "Compress & Compact Sports Bra", "category": "Sports Bras", "price": 75.0, "currency": "AUD", "colour_name": "Black", "support_level": "High Support", "rating": 4.8, "review_count": 142
| # | sku | title | category | sub_category | price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from lornajane.com. All fields typed and schema-versioned.
"sku": "LB0321_BLK", "current_price": 50.0, "original_price": 75.0, "discount_pct": 33.3, "promo_badge": "Sale", "sale_category": "End of Season", "currency": "AUD", "price_timestamp": "2026-05-12T09:14:00Z"
| # | sku | current_price | original_price | discount_pct | discount_abs | promo_badge |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from lornajane.com. All fields typed and schema-versioned.
"sku": "LB0321_BLK_S", "size": "Small", "colour": "Black", "in_stock": true, "low_stock_warning": true, "stock_level": "Low", "scraped_at": "2026-05-12T09:14:00Z"
| # | sku | parent_id | size | colour | in_stock | low_stock_warning |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Fit objects from lornajane.com. All fields typed and schema-versioned.
"review_id": "REV-98234", "sku": "LB0321_BLK", "star_rating": 5, "fit_rating": "True to Size", "review_title": "Best running bra", "review_date": "2026-04-18", "verified_buyer": true
| # | review_id | sku | reviewer_name | star_rating | fit_rating | comfort_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Categories & Navigation objects from lornajane.com. All fields typed and schema-versioned.
"category_name": "High Support Sports Bras", "parent_category": "Sports Bras", "url": "/collections/high-support-sports-bras", "product_count": 45, "breadcrumb_path": "Home > Sports Bras > High Support", "scraped_at": "2026-05-12T09:14:00Z"
| # | category_id | category_name | parent_category | url | product_count | breadcrumb_path |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Lorna Jane scraper handles the complete retail matrix: product listings, complex size-and-colour variants, dynamic pricing, and fit reviews - with full JavaScript rendering built in.
Title, fabric composition, support levels, care instructions, and high-resolution image URLs - scraped across all activewear categories.
Capture the complete matrix of available sizes and colours per parent SKU, including low stock indicators and out-of-stock flags.
Extract current price, original RRP, and discount percentages. Track promotional badges and seasonal sale inclusions.
Full review text, star ratings, and specific fit metrics (e.g. True to Size) to analyse garment performance and customer sentiment.
Scrape location-specific catalogues to compare pricing and assortment across Australian, US, and UK storefronts.
Run continuous pipelines at daily or weekly cadences. We maintain state and only push records that have changed.
Collect CDN links for all product imagery, including front, back, detail, and model lifestyle shots.
Map the entire category tree and breadcrumb structure to understand merchandising hierarchy and product density.
Extract 'Wear it with' and 'You may also like' product associations directly from the product detail pages.
Brief in. Clean data out.
Provide category URLs, specific SKUs, or geographic regions. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle regional routing, and map the complex variant DOM structures.
Schema validation, null-rate checks, and variant completeness testing before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern eCommerce sites rely heavily on dynamic hydration and complex variant matrices. Here is how we extract clean data without breaking.
Product pages and variant selectors are heavily JavaScript-rendered. We run full Playwright browser sessions to trigger size and colour selections, capturing dynamic price and stock changes that headless HTTP clients miss entirely.
Retailers nest SKUs deeply. Our extractors flatten parent-child relationships into clean, queryable rows, ensuring every size and colour combination is represented with its specific stock status and price.
High-frequency scraping triggers rate limits. Our crawlers use residential ISP proxies with realistic browser fingerprints and request timing to blend in with legitimate shopper traffic.
eCommerce layouts change during major sales events. Our selector strategy uses fallback chains - CSS selectors, XPath, and JSON-LD extraction - so a layout change does not break your data pipeline overnight.
We maintain a hash index of last-seen values per SKU. Subsequent runs only push diffs - reducing compute cost and downstream processing load. You get a clean changelog of markdowns and stockouts.
Retailers monitor competitor promotional cadences, discount depths, and end-of-season sale timing to optimise their own pricing strategies.
Merchandising teams analyse category density, colour trends, and new product introductions to inform their buying and design cycles.
Apparel analysts track technical fabric adoption, support level distributions, and silhouette changes in the activewear market.
Product teams mine customer reviews for fit complaints and sizing inconsistencies to improve their own garment grading.
Supply chain analysts track inventory depletion rates across specific sizes to estimate sales velocity and demand curves.
Investors and analysts track catalogue size, review velocity, and markdown frequency to evaluate brand health and market positioning.
"Lorna Jane's activewear catalogue contains critical signals on technical fabric trends, sizing distributions, and markdown cadences - data that requires persistent extraction."
Fashion scrapers fail on variant explosion and dynamic inventory states. We handle the complex matrix of sizes, colours, and regional pricing grids. DataFlirt manages the proxy rotation and DOM parsing so your team can focus on merchandising analytics rather than pipeline maintenance.
Everything supported by our lornajane.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 deduplication. Playwright handles JavaScript rendering, cookie sessions, and the complex variant selection flows required for modern apparel sites.
We maintain pools of residential ISP proxies across target regions. Rotation happens per-request with sticky sessions to ensure consistent regional pricing and currency data.
Pipelines run on AWS Lambda and ECS. 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 lornajane.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from retail websites is generally permissible under applicable law. DataFlirt targets only public product, pricing, and review data. We do not extract personal data or circumvent authentication walls. Clients should consult legal counsel for specific use cases.
Our pipelines use Playwright to interact with the DOM, systematically selecting every available size and colour combination to extract the specific price, SKU, and stock status for each variant. The data is then normalised into a flat, queryable structure.
Yes. We route requests through region-specific residential proxies to capture accurate localised pricing, currency, and availability for Australian, US, UK, and other international storefronts.
We configure pipeline frequency based on your requirements. Daily sweeps capture broad markdown trends, while high-frequency intra-day runs can monitor stockouts on high-velocity SKUs.
Yes. We paginate through all product reviews, capturing star ratings, review text, and specific fit metrics such as 'True to Size' or 'Runs Small' to provide a complete view of customer sentiment.
Absolutely. We provide a sample run of up to 100 SKUs as part of the pre-engagement scoping process so you can validate schema fit, variant 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 catalogue dump or a continuous price-monitoring feed across thousands of SKUs - we scope, build, and operate the pipeline. Tell us what you need.
