Pull product data from asos.com as structured rows - names, images, prices, availability, sizes and colours, plus the reviews shoppers left. Built for the way fashion catalogues actually behave: one product, many variants, and an assortment that turns over fast.
ASOS publishes product detail in the page rather than through a public feed, so the alternative to a tool like this is a scraper you build and keep fixing every time the markup shifts.
The tool shows the input formats it accepts and the parameters available when you open it, and the estimator prices the run before it starts.
A fashion range is not a stable catalogue. Lines arrive, sell through and disappear within a season, so a snapshot taken by hand is out of date before anyone reads it.
One product is many SKUs once you count sizes and colours - and availability differs across them. Variant-level data is where stock-outs and demand signals actually live.
CSV, JSON or Excel, in the same shape as every other Livescraper tool - so a sheet or pipeline you already built keeps working when you add a new source.
Coverage spans the product detail ASOS puts on the page: product names, images, prices, availability status, the sizes and colours a line is offered in, and the reviews shoppers have left against it.
We don't print a fixed column list on this page. Retailer markup differs by category and changes over time, and a list that drifts out of date is worse than no list - the header row of a run is always the authoritative answer. The free tier exists partly for this: pull a handful of products, read the columns, then decide how you want to model it.
A short, clean schema - every column arrived on all 505 rows. Note what is not in it, though: there is no size and no stock column.
$34.95.Product and MixMatchProduct.No sizes and no stock. Across 505 products, all nine columns were populated on every row - but the export contains no size column, no availability or stock column, and no list of alternative colours: colour is one value for the row. If your work depends on which sizes are in stock, this output will not answer it. What it does give you cleanly is a per-product row with brand, price and colour, which is enough for range and price comparison across a search.
The thing that makes fashion catalogues awkward to analyse isn't volume - it's shape.
A single ASOS line might be offered in eight sizes and three colours. That's one product name, one description, one price point - and up to twenty-four combinations that can each be in or out of stock independently. Treat the product as one row and you lose the signal; treat every combination as a row and your file multiplies.
Which shape you want depends on the question. Assortment and price comparison work at product level. Stock-out analysis, size-curve work and demand inference only work at variant level. Decide that before you model the export, not after - it's the difference between a useful dataset and a reshaping exercise.
A few examples of how teams use ASOS product data.
Re-run the same product set on a cadence and watch what enters and leaves the range. In fast fashion, what a rival stops stocking is often a clearer signal than what they add - it tells you what didn't sell.
Price plus availability, captured repeatedly, shows the discount curve on a line and how fast it clears. That's the input for your own markdown timing rather than a guess at when to cut.
If you wholesale to ASOS, export your own lines and check them: names, imagery, the sizes and colours actually shown, and what reviewers say. Listing errors and missing variants surface in a sheet instead of a quarterly complaint.
No subscription, no minimum, no per-source licence. Your first 500 products are on us - after that, pay-as-you-go.
Every new account, one-time. No credit card required. All scrapers unlocked, full feature set - enough to read the columns and decide how to model them.
Roughly $2 per 1,000 products. The pre-flight estimator shows the count and credit cost before a run starts - no surprises, no compute units to translate.
Volume pricing, SLAs, dedicated workers and bespoke onboarding for continuous catalogue monitoring. Tell us your numbers and we'll quote.
LIVESCRAPER10 at checkout.Teams tracking a category rarely watch a single storefront.
The same job on the largest marketplace, across 17 regional storefronts - useful when a brand sells through both and you want the price gap between them.
Returns a shopping block per query - title, link, price and merchant - so you can see who else lists a garment and at what price beyond the retailers you track directly.
Short answer: yes for the public listing data - and that is all we ever collect.
A product page is public. ASOS shows the name, price, imagery, available sizes and colours to anyone browsing, signed in or not. Collecting publicly visible facts about products for price comparison and market research is long-established practice, and as long as the data is publicly available and the process doesn't disrupt the site there are no federal laws prohibiting it.
Worth being straight about the commercial side, as we are on every retailer page: ASOS's terms of service restrict automated access, so this is a terms question as much as a legal one. We handle no logins and touch nothing behind an account. If you have a wholesale or partner relationship with ASOS, check your agreement - that call is yours to make, not ours to make for you.
We collect no personal data beyond what reviewers chose to publish, run no third-party trackers on the data layer, and your exports auto-delete after 30 days.
The questions we hear most. Anything else? Talk to us - humans, not bots, write the answers.
500 one-time free products on every new account - no expiry. After that it's $0.002 per product, pay-as-you-go - no card on file until you say so.
Livescraper's Asos Products Scraper turns asos.com listings into structured rows. You add the products you want covered, pick an output format, and download the result as CSV, JSON or Excel - no parser to write, no proxy pool to run, no markup changes to chase when the retailer redesigns a template.
Coverage spans the product detail ASOS publishes on the page: product names, images, prices, availability status, the sizes and colours each line is offered in, and the reviews shoppers have left. Because retailer markup varies by category and shifts over time, the header row of an actual run is the authoritative column reference - which is part of why the free tier exists.
Merchandising teams re-run a competitor's product set on a cadence to see what enters and leaves the range, which in fast fashion says more than a launch announcement does. Pricing teams pair price with availability over time to read the markdown curve and how quickly a line clears. Brands that wholesale to ASOS export their own products to audit names, imagery, the variants actually shown, and what reviewers report.
Fashion data has a particular shape worth planning for: one product is many combinations once sizes and colours are counted, and availability moves independently across them. Product-level rows answer assortment and pricing questions; variant-level rows answer stock-out and size-curve ones. Start free - your first 500 products cost nothing and need no credit card - and use that run to decide which shape your analysis needs.