LinkedIn Comments
A prospect's comments on someone else's LinkedIn post.
Overview
LinkedIn Comments capture when a prospect engages with someone else's post — often revealing more about their thinking than their own posts do. On average, LinkedIn users produce comments 1.2 - 2x more frequently than they post.
We track comment activity across millions of contacts, prioritizing substantive comments where prospects ask questions, share opinions, or reveal pain points. Each comment is analyzed for intent, pain points, initiatives, and technologies mentioned, with full context from the parent post.
The result: high-intent engagement signals that show what topics a prospect cares about, what they're evaluating, and how they think—perfect for starting a conversation that feels relevant, not random.
Schema
{
"signal_id": "b99a32af-43fb-554f-8ec3-43ce5a9228fa",
"signal_type": "linkedin-comments-contact",
"signal_subtype": "linkedinPostComment",
"detected_at": "2026-09-20T23:48:09.116Z",
"association": "contact",
"contact": { // The contact who commented
"email": "[email protected]",
"first_name": "David",
"last_name": "Barajas",
"full_name": "David Barajas",
"job_title": "Senior Software Engineer, Billing and Monetization",
"linkedin_url": "https://www.linkedin.com/in/david-barajas-swe" // Recommended match key
},
"company": { // We also enrich with the contact's employer
"name": "Calendly",
"domain": "calendly.com",
"linkedin_url": "linkedin.com/company/calendly",
"industries": ["Software Development"],
"employee_count_low": 1001,
"employee_count_high": 5000,
"revenue": "250 Million to 500 Million"
},
"data": { // The signal data itself
"comment_text": "I think the challenge is framing around what customer success looks like. Most of a SaaS DAU or MAU counts come from app usage. Logins to the site, the mobile app, interactions with the dashboard. If you let users consume you as an MCP/API and let someone else own the UX, you give up a lot of upsell opportunities. Being invisible has a cost that people start thinking \"Do I really need that service?\". I think it's a tricky balance to play but I agree. As a consumer I want everything to plug and play into a central hub. Business growth is at odds with that in the traditional sense.", // The prospect's own words - quote them in outreach
"comment_summary": "Engineer agrees agent-first consumption is coming but warns invisible products lose upsell leverage.",
"comment_url": "https://www.linkedin.com/feed/update/urn:li:activity:7506838202792615936?commentUrn=urn%3Ali%3Acomment%3A%28activity%3A7506838202792615936%2C7507527351925108737%29&dashCommentUrn=urn%3Ali%3Afsd_comment%3A%287507527351925108737%2Curn%3Ali%3Aactivity%3A7506838202792615936%29",
"comment_posted_at": "2026-09-20T19:53:23.813Z",
"comment_num_likes": 2,
"comment_num_comments": 3,
"comment_intent": "insight", // question, disagreement, celebration, praise, tagging, insight, recommendation
"signal_quality": 0.85, // 0-1 scale, higher = more actionable
"relationship_context": { // Inferred relationship between the commenter and the poster
"inferred_relationship": "industry_peer",
"confidence": 0.7
},
"pain_points": [
{
"topic": "losing upsell opportunities when others own the UX",
"intensity": 0.7
},
{
"topic": "invisible products get questioned at renewal",
"intensity": 0.65
}
],
"initiatives": [
{
"topic": "balancing MCP and API exposure with product engagement",
"urgency": 0.6 // 0-1 scale
}
],
"technologies_mentioned": [
{
"name": "MCP",
"status": "considering"
}
],
"competitors_mentioned": [],
"parent_post": { // The post they commented on - same AI enrichment as LinkedIn Post signals
"signal_id": "2f3e2c35-8b9a-5578-a09c-cda70baf2d46", // Cross-reference to the parent post signal
"post_url": "https://www.linkedin.com/feed/update/urn:li:activity:7506838202792615936/",
"post_text": "If you're still focused on building AI into your product, you're missing the point.\n\nI just opened an app I use at work almost everyday.\n\nThey had a pop-up announcing new AI functionality in their product.\n\nI literally do not care.\n\nAll I care about is how I can get your API's/MCP's exposed so I can plug them into my universal agents that run everything for me.\n\nCan't stop thinking about how many eng hours were wasted on getting that feature live...\n\nThe big players have already won the race for the AI homepage.\n\nIf you're not Anthropic, SpaceX, Meta, etc., you shouldn't be spending time developing AI tools in your platform.\n\nInstead, you should be focusing on how to make your platform play well with agents. \nie. Instinct, GrokBot, Muse, Claude Agents, etc.\n\nAt this point, if I can't use your product/solution via my own internal tooling, I'm not buying it. \n\nSo think less about how to make AI work INSIDE your product, and instead think about how to make AI work OUTSIDE of your product.",
"post_summary": "RevOps leader urges vendors to expose APIs and MCPs for agents over in-product AI.",
"posted_date": "2026-09-18T22:14:57.855Z",
"num_likes": 163,
"num_comments": 71,
"poster_name": "Mike Christensen", // The person whose post drew the comment
"poster_job_title": "Head of Revenue Operations",
"poster_company_name": "Redo",
"poster_company_description": "Redo helps online retailers optimize their post-purchase operations by consolidating tech stacks and enhancing returns management, order tracking, and customer service with AI.",
"pain_points": [
{
"topic": "engineering hours wasted on unused in-product AI features",
"intensity": 0.7
},
{
"topic": "products unusable through agent-based internal tooling",
"intensity": 0.65
}
],
"initiatives": [
{
"topic": "plugging vendor APIs and MCPs into universal agents",
"urgency": 0.85
},
{
"topic": "evaluating products on agent interoperability before buying",
"urgency": 0.7
}
],
"technologies_mentioned": [
{
"name": "MCP",
"status": "using"
},
{
"name": "Claude Agents",
"status": "using"
},
{
"name": "Instinct",
"status": "considering"
},
{
"name": "GrokBot",
"status": "considering"
},
{
"name": "Muse",
"status": "considering"
}
],
"competitors_mentioned": []
}
}
}Field Reference
Core Fields
| Field | Type | Description |
|---|---|---|
signal_id | string (UUID v4) | Unique identifier for this signal |
signal_type | string | Always "linkedin-comments-contact" |
signal_subtype | string | Always "linkedinPostComment" |
detected_at | string (ISO 8601) | Timestamp when signal was detected |
association | string | Always "contact" |
Contact Object
| Field | Type | Description |
|---|---|---|
contact.first_name | string | Contact's first name |
contact.last_name | string | Contact's last name |
contact.full_name | string | Contact's full name |
contact.email | string | Contact's email address |
contact.job_title | string | Contact's job title |
contact.seniority | string | Seniority level, when resolved (e.g., "Cxo", "Director") |
contact.department | string | Department, when resolved (e.g., "Marketing", "Operations") |
contact.linkedin_url | string | Contact's LinkedIn URL |
Company Object
| Field | Type | Description |
|---|---|---|
company.name | string | Company name |
company.domain | string | Company website domain |
company.linkedin_url | string | LinkedIn company URL |
company.industries | array[string] | Industry classifications |
company.employee_count_low | integer | Lower bound of employee count |
company.employee_count_high | integer | Upper bound of employee count |
company.description | string | Company description, when resolved |
company.revenue | string | Annual revenue bucket, when resolved (e.g., "250 Million to 500 Million") |
Comment Data
| Field | Type | Description |
|---|---|---|
data.comment_summary | string | AI-generated summary of the comment |
data.comment_text | string | Full text of the prospect's comment |
data.comment_url | string | Direct URL to the comment |
data.comment_num_likes | integer | Number of likes the comment received |
data.comment_num_comments | integer | Number of replies the comment received |
data.comment_posted_at | string (ISO 8601) | When the comment was posted on LinkedIn |
data.comment_intent | string | Intent classification (question, disagreement, celebration, praise, tagging, insight, recommendation) |
data.signal_quality | float | Quality score (0.0-1.0) - higher = more actionable |
data.relationship_context | object | Inferred relationship to poster |
data.relationship_context.inferred_relationship | string | Relationship type (competitor, colleague, unknown, prospect, vendor) |
data.relationship_context.confidence | float | Confidence score (0.0-1.0) |
data.pain_points | array[object] | Identified pain points |
data.pain_points[].topic | string | Pain point topic |
data.pain_points[].intensity | float | Intensity score (0.0-1.0) |
data.initiatives | array[object] | Identified initiatives |
data.initiatives[].topic | string | Initiative topic |
data.initiatives[].urgency | float | Urgency score (0.0-1.0) |
data.technologies_mentioned | array[object] | Technologies mentioned |
data.technologies_mentioned[].name | string | Technology name |
data.technologies_mentioned[].status | string | Status (evaluating, using, implemented, migrating_from, migrating_to, churned, considering, integrated, building_on, hiring_for) |
data.competitors_mentioned | array[object] | Competitors mentioned in comment |
data.competitors_mentioned[].name | string | Name of the company mentioned |
Parent Post Object
| Field | Type | Description |
|---|---|---|
data.parent_post.signal_id | string | Reference to parent post signal (if exists) |
data.parent_post.post_summary | string | AI-generated summary of the post |
data.parent_post.post_text | string | Full text of the parent post |
data.parent_post.post_url | string (URL) | Link to the parent post |
data.parent_post.posted_date | string (ISO 8601) | When the parent post was published |
data.parent_post.num_likes | integer | Likes on the post |
data.parent_post.num_comments | integer | Total comments on the post |
data.parent_post.pain_points | array[object] | Pain points from parent post |
data.parent_post.initiatives | array[object] | Initiatives from parent post |
data.parent_post.technologies_mentioned | array[object] | Technologies from parent post |
data.parent_post.competitors_mentioned | array[object] | Competitors from parent post (name per entry) |
data.parent_post.poster_name | string | Name of post author |
data.parent_post.poster_job_title | string | Job title of post author |
data.parent_post.poster_company_name | string | Company of post author |
data.parent_post.poster_company_description | string | Description of poster's company |
Example Output
"Your recent comment on the Adobe–Marketo acquisition post really stood out. Calling out the challenge of scaling enablement after a merger shows deep awareness of the real pain points sales teams face..."
Identity Resolution
Every LinkedIn comment signal is pre-resolved to a business contact record with a work email. Here's how:
- LinkedIn profile URL captured from the comment activity — this is a deterministic, unique identifier
- Profile URL matched against our contact database (250M+ contacts, 75M+ companies), ingested monthly with continuous updates from our pipeline exhaust
- Business email, job title, and company resolved from the matched record
- Company firmographic data (domain, LinkedIn URL, industries, headcount) attached
Key Points
- Business emails only. The
contact.emailfield is a professional/work email. Our domain validation excludes generic providers (gmail.com, yahoo.com, etc.). We do not deliver personal emails. - Match accuracy: 99.8%. We prefer no match over a false match — if we can't resolve a profile to a business contact with high confidence, the signal is not delivered.
- Coverage:
contact.linkedin_urlis populated on 100% of signals. - No false positives from common names. LinkedIn profile URLs are deterministic unique identifiers — there is no ambiguity in the match.
- Quality scoring included. Every comment carries a
signal_qualityscore (0.0-1.0) and acomment_intentclassification, so you can prioritize the most actionable engagement.
Full matching guide with SQL examples: Resolution
Updated 2 days ago

