The Links & Email Addresses Scraper extracts contact and profile signals from webpages.

Give it a list of URLs and it will look for:

  • Email addresses
  • LinkedIn profile links
  • LinkedIn company page links
  • Instagram profile links

This is a strong fit when the website is your starting point and you want to turn it into lead data.

What makes this scraper useful?

Most teams do this work by hand:

  1. Open the site
  2. Check the footer or contact page
  3. Hunt for LinkedIn and Instagram
  4. Copy the results into a sheet

That does not scale.

This enrichment automates the pass and also gives you controls for:

  • Proxy usage on protected pages
  • HTML rendering for JavaScript-heavy pages
  • Cache control
  • Ignore lists for noisy LinkedIn or Instagram matches
  • Optional email scraping inside scripts and comments

Step-by-step guide

Step 1: Import your URL list

Upload your CSV or Excel file into Datablist.

Step 2: Select the enrichment

Open Enrich and choose Links & Email Addresses Scraper.

Step 3: Map URLs and save the outputs

Map the webpage URL column, add the outputs you want, and run the enrichment.

If some websites block requests, enable proxy mode. You can either use the proxy only on errors or force it from the start.

For JavaScript-heavy pages, you can also enable HTML rendering in a headless browser.

What the enrichment returns

The scraper returns comma-separated values for:

  • Email addresses
  • LinkedIn profiles or company pages
  • Instagram profiles
  • A scraping status field

If no result is found, the row still gets a clear status so you can filter failures from clean misses.

Best use cases

  • Turn company websites into contact research starting points
  • Extract LinkedIn and Instagram links at scale
  • Pull visible email addresses from public pages
  • Qualify local business lists before manual outreach
  • Build social and contact fields before a deeper enrichment run

Pricing and example

  • 0.10 credits for basic scraping
  • 0.50 credits when proxy mode is used
  • 0.05 credits for cached pages when the content was already stored in the last 7 days
  • 2 credits when proxy rendering is used to load JavaScript pages

Example:

  • 1,000 standard pages cost 100 credits
  • 1,000 proxy-assisted pages cost up to 500 credits

That makes it practical for list cleanup and research workflows.