LinkedIn Posts
A person's posts on LinkedIn.
Contact LinkedIn Posts capture what individual prospects are publicly saying about their priorities, challenges, and initiatives on LinkedIn.
We maintain a monitoring pool of 4M+ LinkedIn profiles, with heavy concentration on hot buyers — senior decision-makers (Director+, VP, C-suite) at high-value accounts — who make up roughly 5% of the total pool. Beyond that core, we do substantial sampling across industries, geographies, and revenue ranges to capture contacts globally. Multiple billion-dollar enterprise CRM and sales tech companies have built their entire social monitoring products on our refresh pools, validating the pool size and quality. Coverage is refreshed every two weeks in line with delivery cadence.
Each post is analyzed to extract structured pain points, initiatives, technologies mentioned, and competitors referenced — turning unstructured social content into actionable signal.
The result: you can reference a prospect’s own words in outreach, understand what they care about before the first call, and time your message to what’s top of mind right now.
See real delivered data → Sample Files
Filter by topic — This signal uses Tags Taxonomy (300+ values) rather than subtypes for topic-based filtering. Use tags alongside pain_points, initiatives, and technologies_mentioned for targeting and routing.
Example Signal
What a single entry looks like in a delivered signal file:
{
"signal_id": "b7f41a23-9c8e-4d5b-a1f3-62e8d9047b15",
"batch_id": "2026-03-15-00-00-00",
"signal_type": "linkedin-posts",
"signal_subtype": "linkedinPost",
"detected_at": "2026-03-14T18:22:41.003Z",
"association": "contact",
"company": {
"name": "Gong",
"domain": "gong.io", // match on domain
"linkedin_url": "linkedin.com/company/gong-io",
"industries": ["Software Development"],
"employee_count_low": 1001,
"employee_count_high": 2000,
"description": "Revenue intelligence platform for sales..."
},
"contact": {
"name": "Rachel Kim",
"first_name": "Rachel",
"last_name": "Kim",
"email": "[email protected]", // match on email
"job_title": "VP of Revenue Operations",
"linkedin_url": "linkedin.com/in/rachelkim-revops", // or match on LinkedIn URL
"city": "San Francisco",
"state": "California",
"country": "United States",
"department": "Operations",
"seniority_level": "VP"
},
"data": {
"summary": "Rachel Kim shares frustration with pipeline forecasting accuracy and announces a new scoring model initiative at Gong...",
"post_text": "Hot take: if your forecast accuracy is below 70%, you don't have a pipeline problem - you have a scoring problem. We just rebuilt our entire...",
"post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7170234567890123456",
"posted_date": "2026-03-13",
"num_likes": 847,
"num_comments": 63,
"tags": ["revenue operations", "pipeline forecasting", "scoring models"],
"pain_points": [
{ "topic": "pipeline forecast accuracy", "intensity": 0.88 },
{ "topic": "lead scoring reliability", "intensity": 0.72 }
],
"initiatives": [
{ "topic": "rebuilding pipeline scoring model", "urgency": 0.91 },
{ "topic": "integrating intent data into forecasting", "urgency": 0.74 }
],
"technologies_mentioned": [
{ "name": "Salesforce", "status": "in_use" },
{ "name": "6sense", "status": "evaluating" }
],
"competitors_mentioned": [
{ "name": "Clari" }
]
}
}Field Reference
Standard envelope and entity fields are shared across all signals - see Schema and Resolution. The fields below are specific to this signal:
Signal-Specific Fields
The data object contains everything unique to this signal type - the intelligence extracted from the LinkedIn post.
| Field | Type | Description |
|---|---|---|
summary | string | One-line headline describing the post's relevance (e.g., "VP RevOps shares frustration with forecast accuracy"). Designed to be shown directly to end users as a notification or list item. Typically 10-20 words, always includes the contact name and core theme. Generated by our model analyzing the full post text |
post_text | string | The original text of the LinkedIn post. May be truncated for very long posts. Useful for displaying to users as context or for building personalized outreach referencing the prospect's own words |
post_url | string (URL) | Direct link to the LinkedIn post. Useful for displaying to users who want to read the full post or verify context |
posted_date | string (date) | Date the post was published on LinkedIn. Useful for recency filtering and timing outreach |
num_likes | integer | Number of likes on the post at time of processing. Useful as a proxy for engagement and reach - high-engagement posts indicate topics the prospect cares deeply about |
num_comments | integer | Number of comments on the post. High comment counts indicate active discussion and potentially controversial or thought-provoking content |
tags | array[string] | Topic tags extracted from the post content. Useful for filtering signals by theme or building topic-based segments |
pain_points | array[object] | Challenges or frustrations expressed in the post. Each entry has topic (what the pain point is) and intensity (0.0-1.0, how strongly expressed). Useful for identifying prospects actively experiencing problems your product solves |
pain_points[].topic | string | Description of the pain point expressed |
pain_points[].intensity | float (0.0-1.0) | How strongly the pain point was expressed. Higher = more urgent/frustrated |
initiatives | array[object] | Projects or priorities the contact mentions working on. Each entry has topic (the initiative) and urgency (0.0-1.0, how time-sensitive). Useful for identifying active buying cycles or greenfield opportunities |
initiatives[].topic | string | Description of the initiative or project |
initiatives[].urgency | float (0.0-1.0) | How time-sensitive the initiative appears. Higher = more immediate |
technologies_mentioned | array[object] | Technologies referenced in the post. Each entry has name (the technology) and status (in_use, evaluating, replacing, mentioned). Useful for tech-stack-based targeting and identifying active vendor evaluations |
technologies_mentioned[].name | string | Name of the technology or platform |
technologies_mentioned[].status | string | Relationship to the technology: in_use, evaluating, replacing, or mentioned |
competitors_mentioned | array[object] | Competitors referenced in the post. Each entry has name. Useful for competitive displacement plays |
competitors_mentioned[].name | string | Name of the competitor mentioned |
Timing & Delivery
detected_atis when we processed the post. Useposted_datefor the original publication date.- One signal per contact per post. A contact who posts multiple times in a delivery window will generate one signal per post.
- Each delivery arrives in a timestamped folder. Treat all signals in a new folder as recent - no need to diff against prior deliveries.
Coverage
- Refresh: Biweekly-weekly
- Coverage: 4M+ LinkedIn profiles (senior decision-makers at high-value accounts)
- Best for: Personalizing outreach with prospect's own words, timing messages to active priorities, identifying pain points before calls
Updated 9 days ago