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Scrape Knix and extract data on women's clothing from knix.com. Our Knix API lets you crawl product information and pricing. The saved data can be downloaded as HTML, JSON, CSV, Excel, and XML.
What does Knix Scraper do?
This scraper creates an API for Knix to enable you to extract product information and pricing from the knix.com online store.
The Knix API is free to use and will let you scrape and extract e-commerce data as often as you need to. The data you extract can be downloaded in structured format so that you can use it in your own applications, spreadsheets, reports, or tools.
Scraping Knix could help you identify competition, opportunities, potential leads, and useful contacts. The right data can help you with product tracking, market research, and price monitoring in the women's clothing category.
If you need more inspiration, check out how scraping can help you get an edge over the competition and stay ahead in e-commerce.
How much will it cost to scrape Knix?
Running Knix Scraper will consume platform credits. Platform credits are counted in USD and each plan includes a set amount. Check our platform pricing page for details.
The exact number of platform credits consumed will depend on the actor input settings, the type of job it performs, how often it runs, and for how long.
The best way to find out how many platform credits an actor will consume is to perform a test run. You can then review platform usage in Billing and figure out the best Apify subscription plan for your needs.
How to scrape Knix
knix.com is built using Shopify and Knix Scraper is one of our huge range of online e-commerce scrapers designed to create a universal Shopify API.
Check out our quick start guide to using any Apify scraper or feel free to send an email to support@apify.com if you have any questions.
You can also watch our video about scraping Amazon to understand more about scraping e-commerce data and how it can help your business succeed.
For more ideas, read about our top 5 web scraping tools to help you gather retail analytics.
Mark Carter
