H&M Product Scraper

Extract structured H&M product search data at scale for catalog analysis, pricing monitoring, merchandising research, and market intelligence. Collect product listings with pricing, images, availability, swatches, and pagination metadata in clean dataset output.

Features

• Keyword or URL input — Start from a keyword or an H&M search URL.
• Structured product output — Collect product ids, titles, prices, links, images, and stock signals.
• Pagination support — Crawl multiple result pages until your limit is reached.
• Null-free dataset records — Empty and null values are removed before saving.
• Simple input contract — Use only core fields while advanced API options stay managed internally.

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Use Cases

Product Catalog Research

Build a searchable dataset of H&M products for category discovery, assortment reviews, and collection planning.

Pricing Intelligence

Track visible pricing and product availability across multiple pages and compare changes over time.

Merchandising Monitoring

Analyze product titles, image coverage, and category distribution to identify merchandising opportunities.

Competitive Benchmarking

Export product data into BI tools and compare assortment depth, pricing structure, and stock signals.

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Input Parameters

Parameter	Type	Required	Default	Description
startUrl	String	No	—	Optional H&M search URL to parse query and locale from.
keyword	String	Yes	shirt	Search keyword used for result retrieval.
locale	String	No	en_us	Locale in ll_cc format.
results_wanted	Integer	No	20	Maximum number of products to save.
max_pages	Integer	No	10	Safety cap on number of pages to process.
proxyConfiguration	Object	No	Apify Proxy	Proxy settings for reliability and anti-blocking.

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Output Data

Each dataset item may contain:

Field	Type	Description
id	String	Product identifier
trackingId	String	Tracking identifier
productName	String	Product title
brandName	String	Brand name
productUrl	String	Product detail URL
formattedPrice	String	Displayed formatted price
price	Number	Numeric price value
priceMin	Number	Minimum listed price
priceMax	Number	Maximum listed price
isNewProduct	Boolean	New product flag
stockState	String	Availability state
isOnline	Boolean	Online sale availability
isComingSoon	Boolean	Coming-soon flag
colorName	String	Primary color name
colorCode	String	Primary color code
swatchColorNames	Array	Flattened swatch color names
swatchColorCodes	Array	Flattened swatch color codes
allSizes	Array	Flattened list of all size labels
availableSizes	Array	Flattened list of available size labels
outOfStockSizes	Array	Flattened list of out-of-stock size labels
sizesCount	Number	Count of total size labels
imageProductSrc	String	Product image URL
imageModelSrc	String	Model image URL
galleryImages	Array	Gallery image URLs
mainCatCode	String	Main category code
searchQuery	String	Query used for this run
locale	String	Locale used for this run
pageNumber	Number	Result page number

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Usage Examples

Basic Keyword Search

{
	"keyword": "shirt",
	"results_wanted": 20,
	"max_pages": 3
}

Start From H&M Search URL

{
	"startUrl": "https://www2.hm.com/en_us/search-results.html?q=shirt&page_key=2026-03-30T08:08:11.763Z--shirt",
	"results_wanted": 100,
	"max_pages": 8
}

Keyword Run With Proxy

{
	"keyword": "linen shirt",
	"locale": "en_us",
	"results_wanted": 150,
	"max_pages": 10,
	"proxyConfiguration": {
		"useApifyProxy": true,
		"apifyProxyGroups": ["RESIDENTIAL"]
	}
}

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Sample Output

{
	"id": "1293921003",
	"trackingId": "OzU7IzsxMjkzOTIxMDAzOyM7Izo=",
	"productName": "Oxford Shirt",
	"brandName": "H&M",
	"productUrl": "https://www2.hm.com/en_us/productpage.1293921003.html",
	"formattedPrice": "$24.99",
	"price": 24.99,
	"priceMin": 24.99,
	"priceMax": 24.99,
	"stockState": "Available",
	"isOnline": true,
	"colorName": "Blue/striped",
	"colorCode": "333859",
	"swatchColorNames": ["Blue/striped", "White"],
	"allSizes": ["XS", "S", "M", "L", "XL"],
	"availableSizes": ["S", "M", "L", "XL"],
	"outOfStockSizes": ["XS"],
	"sizesCount": 5,
	"imageProductSrc": "https://image.hm.com/assets/hm/example-product.jpg",
	"imageModelSrc": "https://image.hm.com/assets/hm/example-model.jpg",
	"mainCatCode": "men_shirts",
	"searchQuery": "shirt",
	"locale": "en_us",
	"pageNumber": 1
}

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Tips For Best Results

Start Small First

• Begin with results_wanted: 20 to validate run behavior.
• Increase limits after checking output quality.

Use Stable Query Terms

• Prefer specific product terms like linen shirt or cotton shirt.
• Keep keyword spelling consistent for repeatable monitoring runs.

Use Proxies For Reliability

• Residential proxies improve stability for larger runs.
• Keep a reasonable page cap when running very frequent schedules.

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Integrations

• Google Sheets — Create live product tracking sheets.
• Airtable — Build searchable product databases.
• Looker Studio / BI tools — Visualize pricing and category trends.
• Webhooks — Forward fresh product data to external systems.
• Make / Zapier — Automate downstream workflows.

Export Formats

• JSON — API and engineering workflows
• CSV — Spreadsheet analysis
• Excel — Reporting and operations
• XML — Legacy integrations

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Frequently Asked Questions

How many products can I collect?

You can collect across multiple pages based on results_wanted and max_pages.

Does the dataset include empty values?

No. Null and empty values are removed before records are saved.

Can I run this on a schedule?

Yes. You can schedule recurring runs in Apify and export new data automatically.

Why use proxy configuration?

Proxy configuration helps improve stability and reduces blocking during repeated runs.

Can I use a full search URL instead of keyword?

Yes. Provide startUrl and the actor will parse query and locale automatically.

Where are advanced controls like touch point and sorting?

The actor uses stable internal defaults for advanced API parameters so the input remains simple and reliable.

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Support

For issues or feature requests, use Apify Console support and actor issue channels.

Resources

• Apify Documentation
• Apify API Reference
• Scheduling Runs

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Legal Notice

This actor is designed for legitimate data collection and analytics workflows. Users are responsible for complying with applicable terms, policies, and laws in their jurisdiction.