YouTube

YouTube signals capture when contacts appear in video content — conference talks, webinars, product demos, and interviews where they discuss their priorities, challenges, and tech stack decisions on camera.

We monitor YouTube channels and conference playlists to identify videos featuring tracked contacts. Each video is analyzed to extract pain points, initiatives, technologies mentioned, and competitors referenced from the spoken content. Video appearances are high-signal because a person speaking at a conference or on a webinar is investing significant time and reputation into the topic — it's a strong indicator of what they truly care about.

The result: you can reference a prospect's conference talk in outreach, understand what they publicly champion before the first call, and identify the exact technology themes they're passionate about.

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See real delivered data → Sample Files

Subtype

Signal	Subtype Enum	Description
YouTube Video	`youtubeVideo`	A contact appeared in or published a video

Example Signal

What a single entry looks like in a delivered signal file:

{
  "signal_id": "b2d47e91-8c3a-4f56-9d7b-15e8a6c02f43",
  "batch_id": "2026-03-15-00-00-00",
  "signal_type": "youtube-contact",
  "signal_subtype": "youtubeVideo",
  "detected_at": "2026-03-14T12:08:44.301Z",
  "association": "contact",
  "company": {
    "name": "Snowflake",
    "domain": "snowflake.com",                     // match on domain
    "linkedin_url": "linkedin.com/company/snowflake-computing",
    "industries": ["Software Development"],
    "employee_count_low": 5001,
    "employee_count_high": 10000,
    "description": "Cloud data platform for analytics and AI..."
  },
  "contact": {
    "name": "Priya Sharma",
    "first_name": "Priya",
    "last_name": "Sharma",
    "email": "[email protected]",         // match on email
    "job_title": "VP of Data Engineering",
    "linkedin_url": "linkedin.com/in/priyasharma-data"  // or match on LinkedIn URL
  },
  "data": {
    "summary": "Priya Sharma discusses Snowflake's migration from batch to real-time streaming pipelines and challenges with observability at scale...",
    "video_title": "Building Real-Time Data Pipelines at Snowflake Scale",
    "video_description": "In this talk from DataEngConf 2026, Priya Sharma shares how Snowflake's data engineering team transitioned from batch to real-time streaming...",
    "videoLink": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
    "channelTitle": "DataEngConf",
    "contact_youtube_channel_url": "https://www.youtube.com/channel/UC1234567890abcdef",
    "publishedAt": "2026-03-08T14:00:00Z",
    "viewCount": "12847",
    "commentCount": "94",
    "tags": ["data engineering", "real-time streaming", "observability", "pipeline architecture"],
    "pain_points": [
      { "topic": "observability gaps in streaming pipelines", "intensity": 0.82 },
      { "topic": "batch-to-streaming migration complexity", "intensity": 0.76 }
    ],
    "initiatives": [
      { "topic": "real-time streaming pipeline architecture", "urgency": 0.88 },
      { "topic": "unified observability across batch and streaming", "urgency": 0.79 }
    ],
    "technologies_mentioned": [
      { "name": "Apache Kafka", "status": "in_use" },
      { "name": "Apache Flink", "status": "evaluating" },
      { "name": "Datadog", "status": "in_use" }
    ],
    "competitors_mentioned": [
      { "name": "Databricks" }
    ]
  }
}

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 YouTube video.

Field	Type	Description
`summary`	string	One-line headline describing the video's relevance (e.g., "VP Data Engineering discusses streaming migration challenges at DataEngConf"). Designed for notifications. Typically 10–20 words, always includes the contact and core topic
`video_title`	string	Title of the YouTube video. Useful for display and quick assessment of relevance
`video_description`	string	The video's YouTube description. Often contains additional context, speaker bios, and timestamps. May be truncated for very long descriptions
`videoLink`	string (URL)	Direct link to the YouTube video. Useful for display and verification
`channelTitle`	string	Name of the YouTube channel that published the video (e.g., conference name, company channel, personal channel). Useful for understanding context — a conference talk carries different weight than a personal vlog
`contact_youtube_channel_url`	string (URL)	Link to the contact's own YouTube channel, if identified. Useful for finding more content from this person
`publishedAt`	string (datetime)	When the video was published on YouTube. Useful for recency filtering
`viewCount`	string	Number of views at time of processing. Higher view counts indicate wider reach and more influential content. Stored as string per YouTube API convention
`commentCount`	string	Number of comments on the video. High comment counts indicate active discussion. Stored as string per YouTube API convention
`tags`	array[string]	Topic tags extracted from the video content and metadata. Useful for filtering signals by theme
`pain_points`	array[object]	Challenges discussed in the video. Each entry has `topic` (the pain point) and `intensity` (0.0–1.0, how central it was to the talk). Conference talks often reveal deep technical pain points that don't surface on social media
`pain_points[].topic`	string	Description of the pain point discussed
`pain_points[].intensity`	float (0.0–1.0)	How central the pain point was to the presentation. Higher = more focus devoted to it
`initiatives`	array[object]	Projects or priorities the contact discusses working on. Each entry has `topic` and `urgency` (0.0–1.0). Conference presentations almost always center on active initiatives
`initiatives[].topic`	string	Description of the initiative
`initiatives[].urgency`	float (0.0–1.0)	How actively the initiative is being pursued
`technologies_mentioned`	array[object]	Technologies referenced in the video. Each entry has `name` and `status` (`in_use`, `evaluating`, `replacing`, `mentioned`). Especially valuable from conference talks where speakers detail their actual tech stack
`technologies_mentioned[].name`	string	Name of the technology
`technologies_mentioned[].status`	string	Relationship to the technology
`competitors_mentioned`	array[object]	Competitors referenced in the video. Each entry has `name`. Conference speakers often compare approaches across vendors
`competitors_mentioned[].name`	string	Competitor name

Timing & Delivery

detected_at is when we processed the video. Use publishedAt for the original upload date.
One signal per contact per video. If a contact appears in multiple videos in a delivery window, each generates a separate signal.
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: Monthly
Coverage: Conference channels, company channels, and personal channels of tracked contacts
Best for: Referencing conference talks in outreach, identifying deep technical priorities, understanding what a prospect publicly champions

Contact Sales →

See real delivered data → Sample Files

Subtype

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage