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Social Intelligence

LinkedIn Post Signals

Every LinkedIn post from 4M+ monitored contacts, analyzed into pain points with intensity scores, initiatives with urgency scores, technology mentions with adoption status, and topic tags from a 300+ value taxonomy. Resolved to work emails and refreshed every 2 weeks.

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4M+

Contacts Covered

300+

Tag Types

Bi-weekly

Refresh Cadence

5-12

Avg Tags per Post

Social Intelligence300+ subtypes · Bi-weekly refresh

What Are LinkedIn Contact Signals?

LinkedIn contact signals capture what individual prospects publicly post on LinkedIn, delivered as structured data. Autobound monitors 4M+ contacts and analyzes every post with AI to extract pain points scored 0-1 for intensity, initiatives scored for urgency, technologies mentioned with one of 10 adoption statuses (evaluating, using, migrating_from, and more), competitors referenced, topic tags from a 300+ value taxonomy, and a 10-15 word factual summary.

Every signal arrives resolved to a business contact: LinkedIn URL on 90-98% of records, work email on 85-95%, matched at 99.8% accuracy against a 250M+ contact database. Posts refresh every 2 weeks, so you can reference a prospect's own words in outreach while the post is still earning engagement.

Example Signal Subtypes

linkedinPost

Data Schema

LinkedIn Post Signal Schema

LinkedIn contact signals include the full post content, 300+ AI-generated tags with intensity scoring, engagement metrics, and contact resolution data.

linkedin_contact_signals.schema.json
{
  "signal_id": "5349e887-baee-4974-aa3e-02294badfa94",
  "signal_type": "linkedin-post-contact",
  "signal_subtype": "linkedinPost",
  "signal_name": "Contact posted on LinkedIn",
  "association": "contact",
  "detected_at": "2026-06-18T15: 36: 11.235Z",
  "contact": {
    "email": "sarah.chen@datadoghq.com",  // match on email
    "name": "Sarah Chen",
    "first_name": "Sarah",
    "last_name": "Chen",
    "job_title": "VP of Engineering",
    "linkedin_url": "linkedin.com/in/sarahchen-eng"  // or match on LinkedIn URL
  },
  "company": {
    "name": "Datadog",
    "domain": "datadoghq.com",  // match on domain
    "linkedin_url": "linkedin.com/company/datadog",
    "description": "Cloud monitoring and observability platform",
    "industries": ["Software Development"],
    "employee_count_low": 5001,
    "employee_count_high": 10000
  },
  "data": {
    "post_url": "https://www.linkedin.com/feed/update/urn:li:activity: 7416003205425303552/",
    "post_text": "We've been running five different data pipeline tools for three years. The cognitive overhead is crushing our on-call team, so this quarter we're consolidating onto a single platform...",
    "posted_date": "2026-06-11T06: 29: 06.850Z",
    "num_likes": 234,
    "num_comments": 47,
    "tags": ["Infrastructure", "Consolidation", "DevOps"],
    "summary": "VP of Engineering plans data tooling consolidation to reduce on-call overhead.",
    "pain_points": [
      { "topic": "tool sprawl overwhelming on-call team", "intensity": 0.8 }
    ],
    "initiatives": [
      { "topic": "consolidating pipeline tooling this quarter", "urgency": 0.9 }
    ],
    "technologies_mentioned": [
      { "name": "Airflow", "status": "migrating_from" }
    ],
    "competitors_mentioned": []
  }
}
GCS Bucket
gs://autobound-linkedin-post-contact-v3/
Formats
JSONL · Parquet
Refresh
Bi-weekly
Full schema docs →Measured coverage →

Applications

What teams build with LinkedIn Post Signals (Contact-Level)

05 documented applications

  1. 01

    Pain-Point-Based Outreach

    Detect when prospects publicly discuss challenges your product solves. A VP of Sales complaining about pipeline visibility is a perfect opening for a CRM analytics vendor.

  2. 02

    Technology Evaluation Signals

    Identify contacts actively evaluating or discussing technologies in your category. Posts mentioning tool comparisons, vendor evaluations, or migration plans indicate active buying intent.

  3. 03

    Thought Leadership Engagement

    Find prospects sharing strong opinions about topics related to your product category. Engaging with their content first builds rapport before any sales outreach.

  4. 04

    Milestone-Based Triggers

    Professional milestones like promotions, new roles, or project completions create natural conversation openings. LinkedIn signals capture these events with timing precision.

  5. 05

    Champion Identification

    Find internal champions who are already advocating for solutions like yours. Posts discussing specific technology benefits or vendor recommendations reveal potential advocates within target accounts.

Measured, Not Estimated

What's actually inside LinkedIn Post Signals (Contact-Level)

Volumes, subtypes, geography, and join-key fill rates audited directly from our production database, so you can verify coverage before you buy.

Measured coverageaudited from production · minimums, not ceilings
269,241
contacts with fresh data / month
basis: 6-mo avg
772,916
records created / month
basis: published baseline

Top subtypes

1 total
Linkedin Post5.4M · 100%

Seniority

full breakdown →
C-Suite154K · 15%
Director140K · 14%
VP140K · 14%
Staff75K · 7.5%
Manager43K · 4.4%
Consultant2.3K · 0.2%

Departments

full breakdown →
Business Management232K · 23%
Sales39K · 3.9%
Marketing33K · 3.3%
Human Resources26K · 2.6%
Technology, Software, Information Technology24K · 2.4%
Executive23K · 2.3%
Operations21K · 2.1%

Join keys

fill rate
Contact LinkedIn100%
Contact name100%
Email76%
Records measured 5,410,415+Contacts reached 995,315+Full schema & examples →

See the real records for yourself.

Real records from production, delivered to your inbox.

See full coverage & segmentation across all 35 signal types →

Worked Example

One signal, traced to outcome

One LinkedIn Post Signals (Contact-Level) record followed from detection, through the action a rep took on it, to the measured result. Illustrative composite of observed customer workflows.

01 signal → 02 action → 03 outcome

01

Signal detected

A VP of Engineering at Datadog posts on LinkedIn about struggling with observability tool sprawl: 'We've been running 5 different monitoring tools for 3 years. The cognitive overhead is killing our on-call team.'

02

Action taken

Your SDR sends a reply referencing the exact post:

“Saw your post about monitoring tool consolidation. We helped a similar team go from 5 tools to 1 and cut on-call fatigue by 60%.”
03

Outcome

4x higher reply rate

Because you're responding to a publicly stated pain point, not a cold pitch.

Run this against live data →

FAQ

Frequently Asked Questions

How are LinkedIn contact signals different from company-level LinkedIn signals?
Contact-level signals track individual posts from decision-makers, including their pain points, technology evaluations, and career moves. Company-level signals track the company page's announcements and social presence. Contact signals are more actionable for personalized outreach.
How many contacts are covered by LinkedIn post signals?
Our LinkedIn contact signal database covers 4M+ contacts, with bi-weekly refresh cycles. Coverage is concentrated on B2B decision-makers including VPs, directors, C-suite executives, and technical leads.
What does the 300+ tags classification include?
Our AI models classify LinkedIn posts into 300+ semantic tags covering pain points (with intensity scoring), technology evaluations, initiatives (with urgency levels), hiring announcements, thought leadership, vendor comparisons, and more. Each tag includes a confidence score.
Can I use LinkedIn signals for ABM campaigns?
Yes. LinkedIn contact signals are ideal for account-based marketing. You can target specific accounts and monitor when decision-makers post about relevant pain points, technology evaluations, or initiatives that align with your solution.

How It Works

From Raw Data to Your Stack

Autobound ingests from LinkedIn API, Glassdoor, GitHub, Reddit, G2, extracts structured signals with AI, and delivers them however your infrastructure needs.

01

Autobound Ingests

Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized across millions of sources.

02

AI Extracts & Scores

ML models extract 300+ signal subtypes with relevance scoring, confidence levels, and entity resolution.

03

You Receive

Structured JSONL delivered via your preferred method, updated on a bi-weekly cadence.

REST API

Real-time access with subtype filtering

300 req/min

GCS Push

Automated delivery to your bucket

JSONL + Parquet

Enrich API

On-demand LLM-ranked insights

AI relevance scoring

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet
3 vendors consolidated
“By consolidating three data vendors into Autobound's Enrich API, we added 100+ new signal types and saved 4 months of engineering time.”

AiSDR Team

Engineering, AiSDR

Ready to License LinkedIn Post Signals (Contact-Level)?

Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.

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