LinkedIn Email + Profile Scraper ⚡ Active Leads (No Login)

Skip dormant LinkedIn profiles. Target real decision-makers. This scraper finds emails and full profile data — and tells you which profiles are currently active, who's a decision-maker, and how confident the email prediction is. No LinkedIn account needed. Zero risk of getting banned.

💸 Pay only for results — failed scrapes are free

$0.012 per successfully scraped profile. No subscription, no actor-start fee. You're charged only when we successfully deliver enriched profile data — failed scrapes are always free.

🚀 What you get per profile

• Identity — name, current title, current company, location, profile photo
• Activity score (0–100) — skip dormant profiles, focus on active leads
• Decision-maker score (0–100) — prioritize C-Suite / VP / Director-level contacts
• Seniority level — IC / Senior / Manager / Director / VP / C-Suite / Founder
• Industry — auto-classified from company name (e.g., "FinTech", "Software & Technology")
• Real company domain — the company's confirmed domain whenever we can resolve one (not just a guess from its name), with domain_source so you always know how reliable it is
• Top-3 email patterns — first.last@, flast@, first@ (most likely first)
• Email confidence — high / medium / low / unknown, based on domain source + DNS + provider signals (we never claim "verified" because we cannot SMTP-verify)
• Catch-all risk — heuristic warning for domains where bounces are likely
• Provider info — Google Workspace / Microsoft 365 / etc.
• Profile details — about, work history, education, follower count

Failed profile = $0 charge. You only pay when we successfully extract data.

👥 Who this is for

• B2B sales / SDRs prioritizing leads by activity and seniority
• Recruiters filtering for actively engaged candidates
• Investors / M&A researchers mapping decision-makers at target companies
• Journalists / analysts verifying current employment context

⚖️ How it compares

Feature	This Actor	Most LinkedIn email scrapers
Activity score (skip dormant)	✅	❌ no signal
Decision-maker score	✅	❌ no signal
Real, confirmed company domain	✅ verified domain, not a guess	❌ string-mangles the company name
Industry inference	✅	❌
Email confidence (honest)	✅ high/medium/low	❌ Often claims "verified" (impossible without SMTP)
Catch-all risk warning	✅	❌
Email patterns delivered	✅ Top 3 (most likely first)	❌ Often 15+ random guesses
Pay only for successful scrapes	✅ Failed = free	❌ Often charges for empty results
No LinkedIn login needed	✅ Zero ban risk	❌ Often requires cookies

💰 Pricing

$0.012 per successfully scraped profile. Single price. No actor-start fee. No tiered events.

Cost example

10 LinkedIn URLs, 7 successful:

• 7 × $0.012 = $0.084 total
• 3 failed → free

Compare to Hunter.io ($0.07/credit) and Apollo ($0.25/credit) — this Actor costs a fraction per contact, adds activity + decision-maker signals they don't have, and skips the dormant profiles those tools include.

📥 Input

{
  "profileUrls": [
    { "url": "https://www.linkedin.com/in/satyanadella" },
    { "url": "williamhgates" },
    { "url": "ACoAAA8BYqEBCGLg_vT_aUBkSLMiSFG3DFIE0s" }
  ]
}

Three input formats supported, mix and match:

• Full URL: https://www.linkedin.com/in/williamhgates
• Public ID: williamhgates
• Profile ID: ACoAAA8BYqEBCGLg_vT_aUBkSLMiSFG3DFIE0s

📤 Output schema

Each row is a CRM-ready profile.

Identity

Field	Description
first_name	First name
last_name	Last name
full_name	Display name
profile_image_url	Profile photo URL (LinkedIn CDN — may be time-limited)

Position + decision-maker signals

Field	Description
current_title	Most recent job title (when exposed by the public page)
headline	Public LinkedIn headline (e.g. "Founder @ Acme | B2B SaaS")
current_company	Current employer
seniority_level	IC / Senior / Manager / Director / VP / C-Suite / Founder / Unknown — detected from the job title, falling back to the headline
decision_maker_score	0–100 (>=60 = decision-maker)
is_decision_maker	Boolean (Director-level and above)
industry	Inferred from company (e.g., "Software & Technology")

Activity (the differentiator)

Field	Description
activity_score	0–100 from visible LinkedIn activity
is_active	Boolean (activity_score >= 50)
activity_status	Active / Recent / Quiet / Dormant / Unknown
last_post_date	ISO timestamp of most recent public post
days_since_activity	Days since last visible post (-1 if unknown)

What this means: activity_score is based on visible public activity (posts, articles, engagement references). Profiles with private posting will look quieter than they are. High score = confidently active. Low score = either inactive or private (we cannot tell which).

Email predictions (honest, never claimed as "verified")

Field	Description
company_domain	The company's email domain — its real, confirmed domain whenever we can resolve one
domain_source	company_page (real domain confirmed — reliable) / name_guess (best-effort guess from the company name — confidence capped at low) / none
mail_server_status	Active (MX exists) / Inactive / Unknown
mail_provider	Detected provider (Google Workspace, Microsoft 365, etc.)
email_pattern_primary	Most likely format (first.last@domain is the modal pattern industry-wide)
email_pattern_alt1	Second pattern (flast@domain)
email_pattern_alt2	Third pattern (first@domain)
email_confidence	high / medium / low / unknown (DNS-based signals only)
catch_all_risk	low / medium / high / unknown (provider heuristic)
is_free_email_provider	True if domain is gmail.com, yahoo.com, etc.
is_disposable_domain	True if domain is on disposable list

Why we don't say "verified": SMTP RCPT TO requires port 25 outbound, which is blocked by Apify infrastructure. Predictions stay predictions. The email_confidence label reflects how solid the underlying domain and mail-server signals are — never a real send-test. Don't trust any LinkedIn scraper claiming "verified" without disclosing how.

company_page vs name_guess: when we can confirm the company's real domain, domain_source is company_page and the email can reach high confidence. When we can't, we fall back to a best-effort guess from the company name (name_guess) and cap confidence at low — a guessed domain may belong to a different company entirely. Use domain_source to decide which rows to trust for cold outreach.

Profile details

Field	Description
about	Profile summary
experiences	Comma-separated work history
educations	Comma-separated education history
follower_count	LinkedIn followers

Metadata

Field	Description
profile_url	Canonical LinkedIn URL
public_identifier	LinkedIn slug
scrape_status	Always success — failed profiles are never added to the dataset (and never charged); they are listed in the run log instead
scraped_at	ISO 8601 timestamp

🧮 How activity scoring works

The activity_score (0–100) blends several publicly visible signals — how recently the person has posted, how broadly they engage, and how established/complete the profile is. Recency of the latest public post is the dominant factor; reach and profile depth refine it.

activity_status summarizes the score:

• Active → posted very recently (within ~30 days)
• Recent → posted in the last few months
• Quiet → only older posts visible
• Dormant → no visible posts, but the profile shows other signs of life
• Unknown → not enough public signal to tell

activity_score is a lower bound: it reflects publicly visible activity only. People who post privately or to limited audiences will look quieter than they are. A high score means confidently active; a low score means either inactive or simply private — we don't pretend to know which.

🧮 How decision-maker scoring works

Seniority	Base score
Founder / Co-founder	95
C-Suite (CEO, CTO, etc.)	90
VP / SVP / EVP	75
Director / Head of / Principal	60
Manager / Lead	40
Senior IC	25
IC	10

Plus a follower-count bonus (max +10 for 100k+ followers).

is_decision_maker = score >= 60 (Director-level and above).

Seniority is inferred from the role signals on the public profile, ignoring past/former roles. When the public profile carries no clear seniority signal, seniority_level stays Unknown and the score is near zero — we don't guess.

💡 Tips for best results

1. Filter by is_active in your CRM workflow to skip dormant profiles before outreach.
2. Sort by decision_maker_score to prioritize who to contact first.
3. Use email_confidence: high as your first-tier outreach list. Use medium after warm-up. Skip domain_source: name_guess rows for cold email — the domain is unconfirmed.
4. Skip is_free_email_provider: true for B2B sales — those are personal addresses, not corporate.
5. Treat catch_all_risk: medium / unknown with caution — these domains may accept-then-bounce; low (e.g., Google Workspace) is the safest.

⚠️ What we intentionally don't do

• No SMTP verification — Apify infrastructure blocks port 25 outbound. Predictions only. We never claim "verified" and disclose this honestly.
• No phone numbers — LinkedIn doesn't expose them publicly.
• No private-profile content — only publicly visible data.
• No login / cookie scraping — your LinkedIn account is never used. Zero ban risk.

🔗 Related Actors by the same publisher

• slothtechlabs/google-maps-lead-extractor — Email-verified leads from Google Maps.

📬 Feedback & support

If you have any questions, feature requests, or encounter any issues, please open an issue on the Actor's Issues tab. We'd love to hear from you!