Scrape Nike Products — Prices, Sizes, Colors & Launch Dates

What does Nike Product Scraper do?

Nike Product Scraper is a powerful Apify actor that extracts detailed product data from Nike.com. Search for any product category — running shoes, basketball sneakers, lifestyle apparel, Jordan retros — and get structured data including prices, sale discounts, color variants, product images, and promotional badges.

Built with Puppeteer to handle Nike's JavaScript-heavy product grid and infinite scroll pagination, this scraper delivers reliable results for market research, price monitoring, and competitive analysis in the athletic footwear and apparel space.

Features

• Search any Nike product — shoes, apparel, accessories, equipment
• Full price extraction — current price, original price, and sale/discount detection
• Color variant counts — see how many colorways are available per model
• Badge detection — captures "New", "Best Seller", "Just In", "Sustainable Materials", and other promotional tags
• Infinite scroll handling — automatically scrolls and loads additional products beyond the initial page
• High-resolution images — extracts product card hero image URLs
• Deduplication — ensures no duplicate products in output
• Configurable limits — control how many products to scrape per run

Input Parameters

Parameter	Type	Required	Default	Description
searchQuery	String	Yes	"running shoes"	The search term to look up on Nike.com
maxResults	Integer	No	50	Maximum number of products to return (1-500)

Example Input

{
    "searchQuery": "Air Jordan 1",
    "maxResults": 100
}

Output Dataset

Each scraped product includes the following fields:

Field	Type	Description
name	String	Product name (e.g., "Nike Air Max 90")
subtitle	String	Product category/type (e.g., "Men's Shoes")
price	Number	Current listed price in USD
salePrice	Number or null	Discounted price if the product is on sale
priceText	String	Raw price text as displayed on Nike.com
colorCount	Number or null	Number of available color variants
imageUrl	String	URL of the product card hero image
productUrl	String	Direct link to the product detail page
badge	String or null	Promotional badge text (New, Best Seller, Just In, etc.)
searchQuery	String	The search query that produced this result
scrapedAt	String	ISO 8601 timestamp of when the product was scraped
source	String	Always "nike.com"

Example Output

{
    "name": "Nike Air Max 90",
    "subtitle": "Men's Shoes",
    "price": 130,
    "salePrice": null,
    "priceText": "$130",
    "colorCount": 12,
    "imageUrl": "https://static.nike.com/a/images/...",
    "productUrl": "https://www.nike.com/t/air-max-90-mens-shoes-...",
    "badge": "Best Seller",
    "searchQuery": "Air Max 90",
    "scrapedAt": "2025-01-15T10:30:00.000Z",
    "source": "nike.com"
}

Use Cases

• Price monitoring — track Nike product prices over time and detect sales or markdowns
• Competitive analysis — compare Nike's pricing and product lineup against other athletic brands
• Sneaker market research — identify trending models, new releases, and best sellers
• Inventory tracking — monitor color variant availability and new colorway launches
• Resale market intelligence — find retail prices for sneakers traded on secondary markets
• E-commerce data feeds — integrate Nike product data into comparison shopping engines

Tips for Best Results

1. Be specific with queries — "Nike Dunk Low" returns more targeted results than just "shoes"
2. Start with lower maxResults — test with 20-30 products before scaling up to 500
3. Use product line names — search "Air Force 1", "Pegasus", "Vaporfly" for focused results
4. Monitor for sales — compare price and salePrice fields to find active discounts

Technical Details

• Built with Puppeteer for full JavaScript rendering
• Runs on the apify/actor-node-puppeteer-chrome:20 Docker image
• Handles Nike's infinite scroll product grid
• Includes automatic popup/modal dismissal
• Randomized delays to respect Nike.com rate limits
• Failed requests are logged with error details in the dataset

Pricing

This actor runs on the Apify platform. Usage is billed based on compute units consumed. A typical run scraping 50 products takes approximately 1-3 minutes depending on network conditions and scroll depth.

Author

Developed by donnycodesdefi. For questions, feature requests, or bug reports, open an issue on the actor's GitHub repository.