The Problem: Why scraping LinkedIn leads is so painful LinkedIn is the holy grail of B2B prospecting. But when it comes to extracting data at scale, reality kicks in: Copy-pasting profile info manually is time-consuming and error-prone. Traditional scraping methods depend on cookies, browser hacks, or proxy juggling. They break constantly. Your sales and marketing teams need structured, reliable lead data — yesterday. The result? Incomplete databases, poor segmentation, and lost opportunities. The Solution: Apify + LinkedIn Profile Batch Scraper (No Cookies) Apify provides a cookie-free, scalable, and reliable way to scrape LinkedIn leads at scale. With the LinkedIn Profile Details Batch Scraper + EMAIL (No Cookies) actor, you get clean datasets in JSON or CSV format, including: Basic info: full name, headline, current company, profile URL, location, follower count. Work experience: roles, companies, dates, seniority. Education: schools, degrees, timeframes. Influence signals: creator/influencer flags and number of followers. Additional enrichment: projects, certifications, languages (if publicly available). 👉 Example: Satya Nadella — Chairman & CEO at Microsoft, 11.5M followers, education at Booth School of Business + Manipal Institute. Neal Mohan — CEO at YouTube, 2.1K connections, Stanford grad. Imagine importing structured data like this directly into Salesforce, HubSpot, or Pipedrive — ready for segmentation and outreach. Step-by-Step: How to Scrape LinkedIn Leads 1. From Apify Console (Quick Test) Open the LinkedIn Profile Batch Scraper (No Cookies) actor. Input LinkedIn profile URLs or public identifiers. Run → download results in JSON or CSV. 2. With Python (Automation at Scale) from apify_client import ApifyClientimport csvfrom datetime import datetimeclient = ApifyClient("<YOUR_API_TOKEN>")run_input = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella", "https://www.linkedin.com/in/neal-mohan" ]}run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)dataset_id = run["defaultDatasetId"]items = list(client.dataset(dataset_id).iterate_items())def row_from_item(it): bi = it.get("basic_info", {}) or {} loc = (bi.get("location") or {}) return { "full_name": bi.get("fullname"), "headline": bi.get("headline"), "company_current": bi.get("current_company"), "city": loc.get("city"), "country": loc.get("country"), "followers": bi.get("follower_count"), "linkedin_url": bi.get("profile_url"), }rows = [row_from_item(it) for it in items]out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"with open(out_file, "w", newline="", encoding="utf-8") as f: w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) w.writeheader() for r in rows: w.writerow(r)print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")print("CSV ready:", out_file) With just a few lines of Python, you turn LinkedIn into a lead automation engine: Bulk scrape 100 or 100K profiles. Export leads directly to your CRM. Run on a schedule (daily, weekly, monthly). Business Benefits of Scraping LinkedIn Leads with Apify Faster prospecting: Spend less time searching, more time closing deals. Better segmentation: Filter by role, company, location, or influence. Consistent data: Structured JSON/CSV that plugs into any CRM. Scalability: From a few profiles to thousands — no extra complexity. Want to stop scraping profiles one by one and start working with datasets of high-quality LinkedIn leads? 👉 Try it now with Apify: LinkedIn Profile Batch Scraper (No Cookies) And if you want to go further — building a full lead automation machine that runs 24/7, feeds your CRM, and scores leads automatically —  📩 Contact me at kevinmenesesgonzalez@gmail.com Let’s turn LinkedIn into your best-performing lead engine. How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this storyThe Problem: Why scraping LinkedIn leads is so painful LinkedIn is the holy grail of B2B prospecting. But when it comes to extracting data at scale, reality kicks in: Copy-pasting profile info manually is time-consuming and error-prone. Traditional scraping methods depend on cookies, browser hacks, or proxy juggling. They break constantly. Your sales and marketing teams need structured, reliable lead data — yesterday. The result? Incomplete databases, poor segmentation, and lost opportunities. The Solution: Apify + LinkedIn Profile Batch Scraper (No Cookies) Apify provides a cookie-free, scalable, and reliable way to scrape LinkedIn leads at scale. With the LinkedIn Profile Details Batch Scraper + EMAIL (No Cookies) actor, you get clean datasets in JSON or CSV format, including: Basic info: full name, headline, current company, profile URL, location, follower count. Work experience: roles, companies, dates, seniority. Education: schools, degrees, timeframes. Influence signals: creator/influencer flags and number of followers. Additional enrichment: projects, certifications, languages (if publicly available). 👉 Example: Satya Nadella — Chairman & CEO at Microsoft, 11.5M followers, education at Booth School of Business + Manipal Institute. Neal Mohan — CEO at YouTube, 2.1K connections, Stanford grad. Imagine importing structured data like this directly into Salesforce, HubSpot, or Pipedrive — ready for segmentation and outreach. Step-by-Step: How to Scrape LinkedIn Leads 1. From Apify Console (Quick Test) Open the LinkedIn Profile Batch Scraper (No Cookies) actor. Input LinkedIn profile URLs or public identifiers. Run → download results in JSON or CSV. 2. With Python (Automation at Scale) from apify_client import ApifyClientimport csvfrom datetime import datetimeclient = ApifyClient("<YOUR_API_TOKEN>")run_input = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella", "https://www.linkedin.com/in/neal-mohan" ]}run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)dataset_id = run["defaultDatasetId"]items = list(client.dataset(dataset_id).iterate_items())def row_from_item(it): bi = it.get("basic_info", {}) or {} loc = (bi.get("location") or {}) return { "full_name": bi.get("fullname"), "headline": bi.get("headline"), "company_current": bi.get("current_company"), "city": loc.get("city"), "country": loc.get("country"), "followers": bi.get("follower_count"), "linkedin_url": bi.get("profile_url"), }rows = [row_from_item(it) for it in items]out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"with open(out_file, "w", newline="", encoding="utf-8") as f: w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) w.writeheader() for r in rows: w.writerow(r)print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")print("CSV ready:", out_file) With just a few lines of Python, you turn LinkedIn into a lead automation engine: Bulk scrape 100 or 100K profiles. Export leads directly to your CRM. Run on a schedule (daily, weekly, monthly). Business Benefits of Scraping LinkedIn Leads with Apify Faster prospecting: Spend less time searching, more time closing deals. Better segmentation: Filter by role, company, location, or influence. Consistent data: Structured JSON/CSV that plugs into any CRM. Scalability: From a few profiles to thousands — no extra complexity. Want to stop scraping profiles one by one and start working with datasets of high-quality LinkedIn leads? 👉 Try it now with Apify: LinkedIn Profile Batch Scraper (No Cookies) And if you want to go further — building a full lead automation machine that runs 24/7, feeds your CRM, and scores leads automatically —  📩 Contact me at kevinmenesesgonzalez@gmail.com Let’s turn LinkedIn into your best-performing lead engine. How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story

How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify

2025/09/26 21:29

The Problem: Why scraping LinkedIn leads is so painful

LinkedIn is the holy grail of B2B prospecting. But when it comes to extracting data at scale, reality kicks in:

  • Copy-pasting profile info manually is time-consuming and error-prone.
  • Traditional scraping methods depend on cookies, browser hacks, or proxy juggling. They break constantly.
  • Your sales and marketing teams need structured, reliable lead data — yesterday.

The result? Incomplete databases, poor segmentation, and lost opportunities.

The Solution: Apify + LinkedIn Profile Batch Scraper (No Cookies)

Apify provides a cookie-free, scalable, and reliable way to scrape LinkedIn leads at scale.
With the LinkedIn Profile Details Batch Scraper + EMAIL (No Cookies) actor, you get clean datasets in JSON or CSV format, including:

  • Basic info: full name, headline, current company, profile URL, location, follower count.
  • Work experience: roles, companies, dates, seniority.
  • Education: schools, degrees, timeframes.
  • Influence signals: creator/influencer flags and number of followers.
  • Additional enrichment: projects, certifications, languages (if publicly available).

👉 Example:

  • Satya Nadella — Chairman & CEO at Microsoft, 11.5M followers, education at Booth School of Business + Manipal Institute.
  • Neal Mohan — CEO at YouTube, 2.1K connections, Stanford grad.

Imagine importing structured data like this directly into Salesforce, HubSpot, or Pipedrive — ready for segmentation and outreach.

Step-by-Step: How to Scrape LinkedIn Leads

1. From Apify Console (Quick Test)

  • Open the LinkedIn Profile Batch Scraper (No Cookies) actor.
  • Input LinkedIn profile URLs or public identifiers.
  • Run → download results in JSON or CSV.

2. With Python (Automation at Scale)

from apify_client import ApifyClient
import csv
from datetime import datetime

client = ApifyClient("<YOUR_API_TOKEN>")

run_input = {
"profileUrls": [
"https://www.linkedin.com/in/satyanadella",
"https://www.linkedin.com/in/neal-mohan"
]
}

run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)

dataset_id = run["defaultDatasetId"]
items = list(client.dataset(dataset_id).iterate_items())

def row_from_item(it):
bi = it.get("basic_info", {}) or {}
loc = (bi.get("location") or {})
return {
"full_name": bi.get("fullname"),
"headline": bi.get("headline"),
"company_current": bi.get("current_company"),
"city": loc.get("city"),
"country": loc.get("country"),
"followers": bi.get("follower_count"),
"linkedin_url": bi.get("profile_url"),
}

rows = [row_from_item(it) for it in items]

out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"
with open(out_file, "w", newline="", encoding="utf-8") as f:
w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
w.writeheader()
for r in rows:
w.writerow(r)

print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")
print("CSV ready:", out_file)

With just a few lines of Python, you turn LinkedIn into a lead automation engine:

  • Bulk scrape 100 or 100K profiles.
  • Export leads directly to your CRM.
  • Run on a schedule (daily, weekly, monthly).

Business Benefits of Scraping LinkedIn Leads with Apify

  • Faster prospecting: Spend less time searching, more time closing deals.
  • Better segmentation: Filter by role, company, location, or influence.
  • Consistent data: Structured JSON/CSV that plugs into any CRM.
  • Scalability: From a few profiles to thousands — no extra complexity.

Want to stop scraping profiles one by one and start working with datasets of high-quality LinkedIn leads?

👉 Try it now with Apify: LinkedIn Profile Batch Scraper (No Cookies)

And if you want to go further — building a full lead automation machine that runs 24/7, feeds your CRM, and scores leads automatically — 
📩 Contact me at kevinmenesesgonzalez@gmail.com

Let’s turn LinkedIn into your best-performing lead engine.


How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story.

Sorumluluk Reddi: Bu sitede yeniden yayınlanan makaleler, halka açık platformlardan alınmıştır ve yalnızca bilgilendirme amaçlıdır. MEXC'nin görüşlerini yansıtmayabilir. Tüm hakları telif sahiplerine aittir. Herhangi bir içeriğin üçüncü taraf haklarını ihlal ettiğini düşünüyorsanız, kaldırılması için lütfen service@support.mexc.com ile iletişime geçin. MEXC, içeriğin doğruluğu, eksiksizliği veya güncelliği konusunda hiçbir garanti vermez ve sağlanan bilgilere dayalı olarak alınan herhangi bir eylemden sorumlu değildir. İçerik, finansal, yasal veya diğer profesyonel tavsiye niteliğinde değildir ve MEXC tarafından bir tavsiye veya onay olarak değerlendirilmemelidir.

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