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Fashion data scraping — style, size, sale-cycle
Fashion isn't generic e-commerce. Size availability matters more than overall stock. EORS and Black Friday cycles drive everything. Style-level matching is the only correct unit. We capture all of it across global fashion marketplaces and D2C storefronts.
Fashion data, structured the way fashion teams need it.
Style-level granularity, size-level depth, brand-catalogue context.
Pricing & MRP
Selling price, MRP, discount %, sale-event price, member-tier price (Insider, Plus).
Size availability
Per-size in-stock/OOS, low-stock flags, restock detection. The signal fashion brands actually need.
Brand & sub-brand
Brand attribution, sub-brand, private labels, designer collaborations and capsules.
Style attributes
Colour, fabric, pattern, fit, length, occasion, sleeve, neckline — full structured attribute set.
Variant tree
Parent style to colour variants to size variants — preserved, not flattened into duplicate rows.
Sale-event tracking
EORS, BBD, Black Friday, Boxing Day — pre/during/post pricing with timestamps.
Ratings & reviews
Star rating, review count, recent reviews, photo reviews, "fit" review signals where exposed.
Category & rank
Position on category browse, "New", "Bestseller", "Trending" badges, ranking trends.
New arrivals tracking
New styles detected with first-seen timestamps — useful for trend scouting and competitor launches.
The things that make fashion data different.
Generic e-commerce scraping flattens these into nothing — we model them.
Style-level matching, not URL-level
One style with 8 colours and 12 sizes is one parent style, not 96 rows. Variant tree preserved.
Size-by-size stock signals
Which sizes go out first matters more than overall stock. We capture it for every style on every refresh.
Sale-cycle aware
EORS, BBD, Black Friday, Boxing Day — capture cadence ramps automatically around sale-event windows.
Trend & new-arrival detection
New styles flagged with first-seen timestamps — competitor drop tracking and trend scouting in the same feed.
What fashion brands & retailers do with this data.
Competitor style pricing
Daily pricing on competitor styles, matched by attribute and category to yours.
Size demand patterns
Which sizes go out fastest on hero styles — plan production and inventory accordingly.
Sale-cycle preparation
Historical EORS / BFCM / Boxing Day data to plan your discount stance for next event.
Brand catalogue audit
Your brand's full presence on Myntra/ASOS/Macy's — total styles, new arrivals, OOS rates.
Private label tracking
Marketplace private labels (Roadster, HRX, Anouk, ASOS Design) competitive positioning vs your brand.
Trend & new-arrival scouting
What's new in your category this week across competitor brands and marketplaces.
Cross-marketplace parity
Your styles across Myntra vs Ajio vs Nykaa Fashion — spot pricing inconsistencies.
Markdown timing
When do competitor brands start marking down a season — track to time your own markdowns.
Review & sentiment tracking
Rating velocity, photo-review presence and sentiment patterns on your styles over time.
Whatever format your team consumes.
No forced dashboard, no proprietary lock-in. You own the data.
CSV / Excel
Scheduled file drops to email, SFTP, S3 or Azure.
JSON / API
REST API with auth tokens, paginated by SKU or ItemID.
Database / warehouse
Direct push to Snowflake, BigQuery, Redshift or your data lake.
Hosted dashboard
Optional dashboard if you want a no-tech-needed view.
From style list to clean data in 24 hours.
Send the scope
Style IDs, brand list, or category brief. Plus refresh frequency and sale-event coverage need.
Free sample dataset
Within 24 hours, a real sample with size-level stock and brand context.
Production pipeline
QA'd pipeline with delivery in your preferred format. First full run shipped.
Run & iterate
Scheduled refreshes with sale-event cadence ramps and a named delivery lead.
Fashion scraping FAQs
Global marketplaces (Amazon Fashion, eBay), fashion-specific (Myntra, Ajio, Nykaa Fashion, ASOS, SHEIN, Zalando, Farfetch), department stores (Nordstrom, Macy's, John Lewis, Selfridges), fast-fashion (Zara, H&M, Uniqlo, Boohoo, Shein), and luxury (Net-a-Porter, MyTheresa, Mr Porter, plus brand storefronts on Shopify).
Yes — per-size in-stock/OOS state is captured for every style. This is the most important fashion-specific signal: which sizes go out first tells you demand patterns and stockout risk.
We key on the retailer's Style ID (Myntra), parent SKU (Amazon Fashion), or product handle (Shopify storefronts) so colour and size variants roll up correctly. No duplicate rows for the same style.
EORS (Myntra), Big Billion Days (Flipkart Fashion), Black Friday / Cyber Monday (US/UK/EU), Boxing Day (UK/AU), White Sale Season (US), Eid Al Fitr / DSS (UAE) and major brand sale events — with start/end timestamps and pre/post pricing logged.
Daily refresh standard. Hourly available during major sale events, new collection drops, and on hero-style monitoring.