YouTube Videos (Contact)

Overview

Contact YouTube Videos surface when prospects post, appear in, or are mentioned in YouTube content — conference talks, product reviews, technical walkthroughs, "building in public" vlogs, and more.

We search YouTube for prospect names, company references, and product mentions across 4M+ contacts, then match results against our contact database. Each video is analyzed by LLMs to extract structured pain points, initiatives, technologies mentioned, and competitors referenced — the same intelligence layer used across LinkedIn and Twitter/X signals.

YouTube content is longer-form and more deliberate than social posts. Conference talks and webinar recordings reveal deep initiative and pain point signals. Product review and comparison videos surface evaluation and migration signals. "Building in public" content indicates active projects.

Subtype

Schema

{
  "signal_id": "b2c3d4e5-f6a7-8901-bcde-f12345678901",
  "signal_type": "youtube-video-contact",
  "signal_subtype": "youtubeVideo",
  "signal_name": "Contact posted YouTube video",
  "association": "contact",
  "detected_at": "2026-01-22T15:36:11.235Z",
  "contact": {
    "email": "[email protected]",
    "name": "Sarah Martinez",
    "first_name": "Sarah",
    "last_name": "Martinez",
    "job_title": "VP of Revenue Operations"
  },
  "company": {
    "name": "GrowthCo",
    "domain": "growthco.io",
    "description": "B2B SaaS platform for sales engagement.",
    "industries": ["Software Development"],
    "employee_count_low": 51,
    "employee_count_high": 200
  },
  "data": {
    "videoLink": "https://www.youtube.com/watch?v=xR7qK3mPzL4",
    "channelTitle": "Sarah Martinez — RevOps Unplugged",
    "publishedAt": "2026-01-18T10:00:00.000Z",
    "viewCount": "4,892",
    "commentCount": "127",
    "video_title": "Why We're Ripping Out Looker (and What We're Replacing It With)",
    "video_description": "After 2 years on Looker, our BI stack hit a wall. In this video I walk through why Looker stopped working for our RevOps team at GrowthCo, what we evaluated (Sigma Computing, ThoughtSpot, Hex), and why we ultimately chose Sigma.",
    "contact_youtube_channel_url": "https://www.youtube.com/@smartinez_revops",
    "tags": [
      "Business Intelligence",
      "Data Analytics",
      "Tool Migration",
      "Revenue Operations"
    ],
    "summary": "VP RevOps explains why GrowthCo is migrating from Looker to Sigma Computing.",
    "pain_points": [
      {"topic": "Looker BI stack unable to scale with growth", "intensity": 0.80},
      {"topic": "RevOps team blocked by BI tool limitations", "intensity": 0.65}
    ],
    "initiatives": [
      {"topic": "migrating BI stack from Looker to Sigma Computing", "urgency": 0.90},
      {"topic": "evaluating BI tool alternatives", "urgency": 0.75}
    ],
    "technologies_mentioned": [
      {"name": "Looker", "status": "migrating_from"},
      {"name": "Sigma Computing", "status": "migrating_to"},
      {"name": "ThoughtSpot", "status": "evaluating"},
      {"name": "Hex", "status": "evaluating"}
    ],
    "competitors_mentioned": [
      {"name": "Outreach"},
      {"name": "Salesloft"}
    ]
  }
}

Field Reference

Core Fields

Contact Object

Company Object

Data Object — Video Metadata

Data Object — LLM-Extracted Intelligence

Pain Points Object

Initiatives Object

Technologies Mentioned Object

Competitors Mentioned Object

Key Signal Patterns

YouTube content is longer-form and more deliberate than social posts. Here are the most valuable patterns:

Role context matters: A CTO posting "Why We Chose Snowflake" is a strong buying signal. The same video from a Snowflake solutions engineer is marketing. We use job_title and company.description to make this judgment.

Example Output

"Sarah — saw your video on migrating off Looker. Scaling issues with BI tools in RevOps are more common than people realize. We're helping teams like yours surface the right signals during transitions like this. Would love to share what's worked."

Identity Resolution

Every contact YouTube signal is pre-resolved to a business contact record with a work email. Here's how:

1. YouTube videos, channels, and comments searched for prospect full name + job title + current company name
2. Matches validated against video descriptions, channel names, and transcript content
3. Strict matching criteria applied — we require strong alignment between the YouTube identity and the known contact record
4. Business email and company resolved from the matched contact record in our database (250M+ contacts, 75M+ companies)

Key Points

• Business emails only. The contact.email field is a professional/work email. Our domain validation excludes generic providers (gmail.com, yahoo.com, etc.). We do not resolve to personal emails.
• Match accuracy: 99.8%. We use strict search of full name + title + current company name against video descriptions and channel names. We prefer no match over a false match.
• Resolution fields exposed. Each signal includes contact.email, contact.name, contact.job_title, and the full company.* object for traceability.
• Lower coverage, higher value. YouTube coverage is 1-5% — naturally lower than LinkedIn or Twitter — but signals that do match tend to be high-value (keynotes, product reviews, industry commentary).

Full matching guide with SQL examples: Resolution

Coverage

• Refresh: Monthly
• Coverage: 1-5% of contacts
• Best for: Executive outreach, thought leadership engagement, industry influencer targeting

Related Signals

For company-level YouTube activity, see YouTube Videos (Company).