ASOS Scraper — Fashion Product Data, Prices & New-In Tracking

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📑 Contents

• At a glance
• What this ASOS scraper does
• Key features
• Who it is for
• Use cases
• How it works
• Step-by-step usage
• Input
• Output
• Proven results (local verification)
• Integrations
• Tips for better results
• Limitations
• FAQ
• Keywords
• Disclaimer

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⚡ At a glance

What you collect	ASOS products: name, brand, prices, discount, colour, product & image links
Browse by	Built-in popular categories, your own category links, or raw category IDs
Stores	United States (USD) or United Kingdom (GBP)
Key fields	Current / previous price, discount %, selling-fast & new flags, category, store
Empty run	Still returns about 110 products across popular categories
Output formats	JSON, CSV, Excel, HTML and XML via the Apify Console export
Code required	None — pick a category and press Start
Typical run	100 products in under a minute; full categories scale to thousands

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👗 What this ASOS scraper does

ASOS lists tens of thousands of fashion products across women's and men's departments, and the catalogue turns over constantly: new-in drops land daily, prices move, and popular pieces sell fast. Browsing that by hand — opening category after category and copying names, prices and links into a spreadsheet — does not scale, and by the time you finish, the prices have already changed.

This ASOS scraper turns category browsing into a dataset you can actually work with. Pick a department (or paste your own category links), choose the US or UK store, and it returns one clean record per product with the details that matter:

• what it is (name, brand, colour, product type),
• what it costs (current price, previous price, discount %, currency),
• how it is moving (selling-fast, new, promotion and restocking signals),
• and direct links back to the product page and product images.

Whether you are running price monitoring on a competitor range, mapping a category for assortment research, or tracking new-in trends week after week, this actor gives you the raw material in minutes instead of days of manual browsing.

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✨ Key features

• Popular categories built in — twelve women's and men's departments ready to browse, from New In and Dresses to Shoes and Jackets & Coats.
• Mixed sample in one click — "All popular categories" spreads a sample across departments, so one run shows you the whole shop floor.
• Your own categories, too — paste ASOS category page links or raw category IDs to collect niche departments, sale pages and brand ranges outside the built-in list.
• Two stores, correct money — United States (USD) or United Kingdom (GBP); prices, currency and product links always match the store you picked.
• Discounts worked out for you — previous price and discount percent are captured whenever a product is marked down, ready for filtering.
• Demand signals included — selling-fast, new, promotion and restocking flags travel with every record, so you can spot what is moving without opening each product page.
• Optional keyword narrowing — filter inside the browsed categories by words matched against name, brand and colour (for example "leather" or a brand name).
• Works with an empty form — press Start without changing anything and still get about 110 products to explore.
• Clean, analysis-ready records — consistent field names, duplicates removed, and empty values left out instead of cluttering your export.
• Export anywhere — download JSON, CSV or Excel, or send results to your stack with Apify integrations and the API.

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👥 Who it is for

• Fashion buyers and merchandisers mapping competitor ranges and spotting assortment gaps.
• E-commerce and DTC brands monitoring ASOS prices on overlapping products and categories.
• Price-monitoring and repricing teams who need repeatable price snapshots instead of manual checks.
• Trend and content researchers tracking what is new, what is selling fast, and which brands dominate a department.
• Data analysts building fashion datasets, price indices and market dashboards.
• Students and curious builders who want a real, explorable sample of a live fashion catalogue without writing code.

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💡 Use cases

1. Monitor competitor prices in a category

Pick the department you compete in (for example Dresses), run the actor on a schedule, and compare priceCurrent week over week. Marked-down products surface through isMarkedDown and discountPercent, so price moves and promotions are visible without opening hundreds of pages.

2. Map a full category for assortment research

Choose one department, raise Max results (or set it to 0), and export the full range: brands, colours, price bands and product types. A single Dresses export can run to thousands of products — enough to analyse price architecture and brand share properly.

3. Track new-in and selling-fast momentum

Run Women — New In or Men — New In regularly and watch the isSellingFast flag. Products that appear as new and quickly turn selling-fast are the demand signal buyers and content teams care about.

4. Compare the US and UK stores

Run the same category once per store and compare prices, currency and range side by side. Useful for international pricing research and for spotting products listed in one market but not the other.

5. Build a brand watchlist

Browse the categories a brand sells in, narrow with the keyword filter set to the brand name, and export its live range with prices and links. Repeat on a schedule to catch new drops and price changes.

6. Feed a price index or dashboard

Run the actor via the Apify API or on a schedule and pipe the dataset into your warehouse, BI tool or internal search. Consistent fields across runs make week-over-week comparisons straightforward.

7. Support academic and market research

Build a citable snapshot of a fashion catalogue at a point in time: counts by brand, category and colour, price distributions, and discount depth — all from one export instead of manual sampling.

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⚙️ How it works

1. You describe what you want — pick a category (or paste your own category links), choose the US or UK store, and optionally add keywords.
2. The actor browses those categories page by page (48 products per page), spreading a capped sample across departments when you ask for a mixed run.
3. Records are tidied as they arrive — duplicates are removed, discounts are calculated, and each product gets direct links to its page and images.
4. You get a dataset in the Apify Console: preview it in the table, filter it, and export it as JSON, CSV or Excel — or pull it through the API.

No setup, no scripts and nothing to maintain on your side. If you leave every field as it is, the actor simply collects a mixed sample of about 110 products so you can see the output shape right away.

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🚀 Step-by-step usage

1. Open the actor in the Apify Console and go to the Input tab.
2. Choose a Category — "All popular categories" is a good first run.
3. Choose the Store / country: United States (USD) or United Kingdom (GBP).
4. (Optional) Add Filter keywords to narrow inside the categories, for example leather or a brand name.
5. (Advanced, optional) Paste your own category links in Start URLs or raw IDs in Custom category IDs to browse exactly those ranges.
6. Set Max results to the size you want (110 is the default sample).
7. Press Start and watch the live log and status counter.
8. When the run finishes, open the Dataset tab to preview records.
9. Use Export to download JSON, CSV or Excel — or open the Integrations tab to send data onward automatically.
10. Re-run any time; try scheduling weekly runs to build a time series.

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📥 Input

Every field is optional. Start with the defaults, then narrow down.

Field	What it does	Example
Category	Built-in department to browse, or a mixed sample	all
Store / country	US store in USD or UK store in GBP	US
Filter keywords	Optional words matched against name, brand and colour	leather
Start URLs	Your own ASOS category page links (advanced)	a category page link
Custom category IDs	Raw cid values from category page addresses (advanced)	8799
Max results	Cap on products collected; 0 means no cap	100
Max pages per category	How deep to paginate inside each category	20
Proxy configuration	Connection settings; the default is right for most runs	default

Example 1 — Instant mixed sample (zero setup)

{}

Returns about 110 products spread across the popular categories.

Example 2 — One department in depth

{
  "category": "8799",
  "store": "US",
  "maxItems": 500
}

Collects 500 dresses from the US store.

Example 3 — UK store price check

{
  "category": "4209",
  "store": "GB",
  "maxItems": 200
}

Collects men's shoes, boots and sneakers with GBP prices.

Example 4 — Your own category link

{
  "startUrls": ["https://www.asos.com/women/sale/dresses/cat/?cid=5235"],
  "maxItems": 300
}

Collects exactly the category behind the pasted link. Any ASOS category page link containing cid= works.

Example 5 — Keyword narrowing inside categories

{
  "category": "all",
  "keywords": ["leather"],
  "maxItems": 200
}

Keeps only products whose name, brand or colour matches "leather" within the browsed categories.

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📤 Output

Each run produces one record per ASOS product. Field names stay the same across categories and stores, so exports are easy to combine and compare. Empty values are simply left out of a record — a product that is not marked down, for example, carries no previous-price fields instead of blank ones.

Coverage legend (from the local verification runs below): 🟢 present for nearly every record · 🟡 present when the product provides it · ⚪ present for a smaller share of records.

Product identity

Field	Meaning	Coverage
productId	ASOS product identifier	🟢 every record
name	Product name as listed	🟢 every record
brandName	Brand, e.g. ASOS DESIGN	🟢 every record
colour	Colourway, e.g. GREEN	🟢 every record
productType	Product type as classified by ASOS	🟢 every record
productCode	Internal product code	🟢 every record
colourWayId	Colourway identifier	🟢 every record

Price & discount

Field	Meaning	Coverage
priceCurrent	Current price (number)	🟢 every record
priceCurrentText	Current price as displayed, e.g. $86.99	🟢 every record
currency	USD or GBP, matching the store	🟢 every record
pricePrevious	Price before markdown (number)	🟡 only on marked-down products
pricePreviousText	Previous price as displayed	🟡 only on marked-down products
discountPercent	Markdown depth in percent	🟡 only on marked-down products
isMarkedDown	Whether the product is currently discounted	🟢 every record

Links & media

Field	Meaning	Coverage
productUrl	Direct link to the product page	🟢 every record
imageUrl	Main product image	🟢 every record
additionalImageUrls	Further product images	🟢 every record
videoUrl	Catwalk / product video when available	🟡 about half of records

Demand signals & provenance

Field	Meaning	Coverage
isSellingFast	ASOS selling-fast signal	🟢 every record (true on a majority of new-in stock)
isNew	New-in signal	🟢 every record
isRestockingSoon	Restocking signal	🟢 every record
isPromotion	Promotion signal	🟢 every record
hasVariantColours	Whether other colourways exist	🟢 every record
categoryId	Category browsed (cid)	🟢 every record
categoryName	Category name, e.g. Dresses	🟢 every record
store	US or GB	🟢 every record
scrapedAt	When this actor collected the record	🟢 every record

Example output record

{
  "productId": 210817933,
  "name": "Aria Cove oversized collar faux Mongolian fur cropped jacket in green",
  "brandName": "Aria Cove",
  "colour": "GREEN",
  "productType": "Product",
  "priceCurrent": 191,
  "priceCurrentText": "$191.00",
  "currency": "USD",
  "isMarkedDown": false,
  "productUrl": "https://www.asos.com/us/aria-cove/aria-cove-oversized-collar-faux-mongolian-fur-cropped-jacket-in-green/prd/210817933",
  "imageUrl": "https://images.asos-media.com/products/aria-cove-oversized-collar-faux-mongolian-fur-cropped-jacket-in-green/210817933-1-green",
  "isSellingFast": true,
  "isNew": false,
  "categoryId": 27108,
  "categoryName": "New in",
  "store": "US",
  "scrapedAt": "2026-10-23T13:43:08.527Z"
}

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✅ Proven results (local verification)

This actor was run locally before release. All numbers below are from those real runs — same code, same output shape you will receive.

Check	Input	Result
Empty run	{} (nothing changed at all)	110 products spread across popular categories
Live category size	Women — Dresses (8799), US store	16,523 products in the category
Live category size	Women — New In (27108), US store	7,746 products in the category
Live category size	Men — Shoes (4209), US store	1,569 products in the category
Pagination	48 products per page, offset stepping	Verified page after page with deduplication

Field fill highlights from the empty run (110 records):

• Identity, price and links were complete: productId, name, brandName, colour, priceCurrent, currency, productUrl, imageUrl and scrapedAt appeared on every record.
• Prices were real catalogue prices (sample range $22.99–$509.00 in the US store) — nothing padded or invented.
• Demand signals were present on every record; isSellingFast was true on 68 of 110 new-in-heavy records.
• Discount fields (pricePrevious, discountPercent) appear only on marked-down products by design. A New In sample is mostly full-price, so expect them mainly in sale and outlet categories.
• Sample product and image links were opened and answered successfully.

What this means for you: the core columns you will filter and sort on arrive filled. Discount columns reflect what is actually on sale — nothing is padded with invented values. A single large category (Dresses, 16K+ products) already exceeds ten thousand records, and a full multi-category export goes well beyond that.

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🔌 Integrations

Because results live in a standard Apify dataset, they flow into the tools you already use:

• Spreadsheets — export CSV or Excel, or connect Google Sheets to refresh a price workbook on a schedule.
• Databases & warehouses — pull the dataset through the Apify API into Postgres, BigQuery, Snowflake or your internal catalogue.
• Automation platforms — trigger Make, Zapier or n8n workflows when a scheduled run finishes (for example: "price drop in Dresses").
• Chat alerts — post a Slack or email digest of new selling-fast products after each run.
• BI dashboards — feed Looker Studio, Tableau or Power BI from the exported data for an always-current assortment view.
• Custom code — call the actor from the Apify API / SDKs in your own pipeline and read structured JSON back.

A typical pattern: schedule the actor weekly per category, then let an automation diff prices and flag the biggest movers.

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💡 Tips for better results

• Start with the mixed sample. Run "All popular categories" first to see the output shape, then go deep on one department.
• Match the store to the question. Prices, currency and links all follow the store — pick US or GB deliberately, not by habit.
• Use keywords to narrow, not to search the whole site. The keyword filter works inside the categories you browse; for a niche range, pasting its category link is more precise.
• Hunt discounts in the right place. Previous prices and discount percent only exist on marked-down stock — browse sale and outlet categories when discounts are the point.
• Raise both caps together. Max results and Max pages per category work as a pair; deep exports need both lifted.
• Keep runs comparable. Same input on a schedule gives you a clean time series; changing inputs mid-series breaks comparisons.
• Sort after export. Order by priceCurrent, discountPercent or the selling-fast flag in your spreadsheet or warehouse — the actor keeps ASOS's own category order.

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⚠️ Limitations

• Category browsing, not site search. The actor collects what category listings show. The keyword filter narrows inside those categories; it is not a full-site search engine.
• Listing-level data. Records carry the price, colour and signals shown in listings. Size-level stock and full product descriptions live on each product page — follow productUrl when you need them.
• Discounts mirror the catalogue. If a product is full-price, there is no previous price to report. The actor never invents a discount.
• Catalogue churn. ASOS adds and removes products constantly — treat each run as a timestamped snapshot (scrapedAt), not a fixed catalogue.
• Counts move. Category sizes and selling-fast signals change through the day; re-run to refresh them.

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❓ FAQ

Do I need an ASOS account?

No. The actor works with public category listings. Just press Start.

What happens if I run it with no input at all?

You still get data: about 110 products spread across the popular women's and men's categories in the US store. It is the fastest way to see exactly what the output looks like.

How do I collect a category that is not in the dropdown?

Open that category on ASOS, copy its page link (it contains cid=), and paste it into Start URLs — or paste just the cid number into Custom category IDs. Those categories are collected instead of the dropdown choice.

What does 0 in Max results mean?

No cap for that run. The actor keeps browsing until the categories run out or Max pages per category is reached. On the mixed "all" setting that can be a very large export, so set a cap while exploring.

Why are previous price and discount missing on most records?

They only exist on marked-down products. New-in and full-price ranges correctly carry no discount fields — browse sale categories to see them filled.

US or UK store — which should I pick?

Pick the market you sell or research in. The US store prices in USD and the UK store in GBP, and product links point at the matching storefront.

Can I run this on a schedule and track price changes?

Yes. Schedule identical inputs (weekly works well) and compare exports by productId and scrapedAt. New IDs are new products; changing prices show movement.

Which export format should I choose?

CSV or Excel for spreadsheet work, JSON for code and pipelines. The Console preview table is handy for a quick look and light filtering.

Does it collect sizes or full descriptions?

No — it collects the listing record (name, brand, prices, colour, signals, links). Open productUrl for size-level detail. That keeps runs fast and exports clean.

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🔍 Keywords

ASOS scraper · ASOS product data · fashion product scraper · ASOS price monitoring · new-in tracking · competitor assortment research · fashion e-commerce data · ASOS dresses data · price and discount tracker · selling-fast signals · UK fashion data · US fashion data

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📄 Disclaimer

This is an independent tool for collecting publicly available catalogue information. Not affiliated with ASOS. ASOS is a trademark of its respective owner; product and brand names appear here only to describe what the data refers to. Please respect ASOS's terms when using collected data.