LinkedIn is the world‘s largest professional network with over 850 million members worldwide. For sales and business development professionals, LinkedIn can be an invaluable source of leads and prospects. However, extracting and managing that data from LinkedIn requires tools beyond the basic LinkedIn account.
LinkedIn Sales Navigator is a premium subscription tool from LinkedIn designed specifically for sales professionals. It provides advanced searching, lead recommendations, and notifications to help identify and track promising leads. But to fully leverage Sales Navigator, most users need tools to extract and collect data for use outside of LinkedIn.
Enter web scraping. Web scraping is the automated extraction of data from websites. Because LinkedIn does not provide an API for robust data exports, scrapers are the best option for extracting Sales Navigator data.
In this guide, we will cover:
- The top 8 LinkedIn Sales Navigator scrapers for 2024
- An overview of scraping LinkedIn Sales Navigator
- How to build a custom scraper with Python
- Final thoughts and related resources
Let‘s start with the top tools and services for scraping LinkedIn Sales Navigator in 2024.
Contents
Here are the top LinkedIn scrapers that can extract data from LinkedIn Sales Navigator profiles and beyond:
1. Phantombuster
Our top choice is Phantombuster. It offers an easy-to-use Chrome extension to extract LinkedIn profiles, posts, connections, and more. You can scrape data without any coding required.
Key features:
- Scrape LinkedIn profiles, posts, articles, connections, and more
- Find email addresses associated with profiles
- Schedule and automate scrapes
- Chrome extension for easy setup
- Generous free plan available
Phantombuster is the easiest way to scrape LinkedIn without coding. Their scraper templates are ready-made for LinkedIn, including Sales Navigator. Plans start at $50/month.
2. Octoparse
Octoparse is another visual web scraping tool perfect for LinkedIn. It uses a click-and-select interface to extract data from web pages.
With Octoparse you can:
- Build scrapers without writing any code
- Scrape LinkedIn profiles, posts, connections, etc.
- Extract tables, text, images, PDFs, and more
- Export scraped data to CSV, Excel, etc.
- Automate extraction on a schedule
They offer a free trial to test it out. Paid plans start at $99/month after that.
3. BrightData
BrightData (formerly Luminati) offers robust datacenter proxies and scraping solutions. Their tools can access data behind logins and tough security.
Key features:
- Rotating IPs and residential proxies to access data
- Managed proxies starting at $500/month
- Web scraper starting at $500/month
- Custom scraping solutions
- Excellent reliability and speeds
For heavily guarded data like LinkedIn, BrightData has the residential IPs and tools to extract it.
4. ScraperAPI
ScraperAPI is a solid API-based web scraper. Use it to build browser automation and extract data from sites.
Key features:
- Generous free plan with 1000 requests/month
- APIs for browser automation in Python, Node.js, etc.
- Residential proxies to access protected sites like LinkedIn
- Affordable pricing starting at $39/month
While ScraperAPI isn‘t purpose-built for LinkedIn, its tools can extract most LinkedIn data without issue.
5. ParseHub
ParseHub is a visual web scraper perfect for beginners. Use the point-and-click interface to extract data.
Key features:
- Visually build web scrapers in a browser
- Extract data from LinkedIn and Sales Navigator
- Convert scraped data to JSON, CSV, etc.
- Schedule and automate data extraction
- Affordable pricing starting at $99/month
ParseHub makes it easy for non-coders to build scrapers. While not specialized for LinkedIn, it can handle most LinkedIn scraping needs.
6. Import.io
Import.io is another code-free web scraping tool. It uses "crawlers" to extract web page contents.
Key features:
- Build scrapers visually without coding skills
- Schedule and automate scraping
- Integrate scraped data with Zapier, Integromat, etc.
- Affordable pricing starting at $79/month
Import.io isn‘t purpose-built for LinkedIn, but its DIY scrapers can extract most LinkedIn data.
7. ScrapingBee
ScrapingBee offers an API-based web scraping service. Send requests to extract data from sites.
For LinkedIn, ScrapingBee offers:
- APIs for web scraping in Python, Node.js, etc.
- Support for scraping 500+ sites including LinkedIn
- Generous free plan with 5000 requests/month
- Residential proxies to access protected sites
While not built specifically for LinkedIn, ScrapingBee can handle most LinkedIn scraping tasks.
8. 80legs
80legs is an enterprise-level web scraping solution. They offer fully managed scraping services.
Key features:
- Full service web scraping & data extraction
- Large residential proxy network
- Highly scalable for large projects
- Integrate scraped data with internal systems
- Enterprise pricing (contact for quote)
For large-scale LinkedIn scraping, 80legs has the proxies, tools, and services to handle it.
Sales Navigator does not provide any official way to export its data. So to leverage Sales Navigator data outside of LinkedIn requires scraping. Here‘s an overview of how web scraping can extract data from Sales Navigator.
The LinkedIn website is generated dynamically via JavaScript running on the browser. So scraping Sales Navigator requires a browser automation tool like Selenium or Puppeteer. These tools control an actual browser like Chrome to load JavaScript and render pages.
Once the target page is loaded, we can use a parser like Beautiful Soup in Python or $ in JavaScript to parse the rendered HTML. This allows us to extract the profile data we want.
The main challenges in scraping LinkedIn are:
- JavaScript rendering – The pages rely heavily on JavaScript, so we need browser automation to properly extract data.
- Bot detection – LinkedIn tries to detect scrapers with captcha and blocking. Scrapers need to mimic human behavior to avoid this.
- Data access limits – Free LinkedIn accounts have strict limits, while Sales Navigator has higher (undisclosed) limits. Scrapers may need residential proxies to scale.
Overall, scraping Sales Navigator at scale requires tools like headless browser automation, proxies, scrapers tuned to avoid bot detection, and robust infrastructure. Hands-on coding is also required unless you use a ready-made scraping service.
Next let‘s walk through an example using Python and Selenium to scrape LinkedIn profiles.
To illustrate web scraping, here is some sample Python code to scrape simple profile info from LinkedIn using Selenium and Beautiful Soup.
This covers:
- Launching a Chrome browser with Selenium
- Logging in to a LinkedIn account
- Navigating to profile URLs
- Scraping profile data
- Saving results to a CSV file
Import Libraries
We‘ll need the following libraries installed:
from selenium import webdriver # browser automation
from selenium.webdriver.common.keys import Keys
import time # pause execution
from bs4 import BeautifulSoup # HTML parser
import csv # write to CSV Launch Browser
First we‘ll launch a Chrome browser using Selenium:
chrome_options = webdriver.ChromeOptions()
chrome_options.add_argument("user-data-dir=C:\\Users\\{username}\\AppData\\Local\\Google\\Chrome\\User Data\\Default") # login profile dir
browser = webdriver.Chrome(options=chrome_options)This launches Chrome and loads our desired login profile so we are already logged in to LinkedIn.
Next we will navigate to a profile URL:
profile_url = "https://www.linkedin.com/in/exampleprofile/"
browser.get(profile_url)
time.sleep(3)Selenium will now drive Chrome to load the target page. We add a pause to allow the page to fully render.
Parse Page Source
Once loaded, we can parse the page source with Beautiful Soup:
source = browser.page_source
soup = BeautifulSoup(source, ‘html.parser‘)Beautiful Soup gives us easy access to elements on the page.
Extract Data
We can now use CSS selectors to extract key profile data:
name = soup.select_one("h1.text-heading-xlarge").get_text().strip()
job_title = soup.select_one("div.text-body-medium.break-words").get_text().strip()
connections = soup.select_one("span.text-body-small.inline-block").get_text().strip()This will grab the name, job title, and number of connections.
Save to CSV
Finally, we can write the extracted data to a CSV file:
with open(‘linkedin_data.csv‘, mode=‘w‘, encoding=‘utf-8‘) as f:
writer = csv.writer(f, delimiter=‘,‘, quotechar=‘"‘, quoting=csv.QUOTE_MINIMAL)
writer.writerow([‘Name‘, ‘Job Title‘, ‘Connections‘])
writer.writerow([name, job_title, connections]) The full scraper would loop through an array of profile URLs, scrape each, and write the results. This covers the core concepts.
Challenges
Some challenges this basic scraper would face:
- Very limited data extraction
- No proxy rotation (likely get blocked)
- No random delays (easy to detect as bot)
- Basic exception handling
A production scraper would be far more advanced, robust, and evasive. But this demonstrates core techniques like Selenium browser automation and Beautiful Soup parsing.
Final Thoughts
Sales Navigator is a valuable sales tool, but its data is trapped within LinkedIn. To fully leverage it for outreach and sales, most businesses need to extract the data.
Web scraping is currently the best way to scrape LinkedIn and Sales Navigator. To scrape effectively at scale requires robust tools and infrastructure. Hands-on coding is also needed unless you use a ready-made scraping service.
For most sales teams, the easiest approach is to use a web scraping service. Tools like Phantombuster, Octoparse, and BrightData offer point-and-click scrapers to extract Sales Navigator data. For larger scale scraping, enterprise services like ScrapingHub offer fully managed solutions.
With the right web scraping tools in place, you can liberate all that valuable Sales Navigator data. This allows using it for other sales tools like CRMs, email outreach, and ad retargeting.
Scraping opens up the full possibilities for leveraging your LinkedIn data for sales.
