YouTube Videos (Company)

Company YouTube signals surface what organizations are publicly communicating through their YouTube channels — product launches, strategic roadmaps, partnership announcements, conference keynotes, and hiring pushes.

We track YouTube channels across 4M+ companies, pulling recent video activity with engagement metrics. Each video is analyzed by LLMs to extract structured pain points, initiatives, technologies mentioned, and competitors referenced. Company YouTube content is polished, long-form marketing — initiatives (product launches, partnerships, expansions) are the primary signal. Pain points are rare but valuable when they surface in post-mortems or "lessons learned" content. Our extraction layer distinguishes between problems a company solves (their product) and problems a company has (real signals).

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See real delivered data → Sample Files

Filter by topic — This signal uses freeform tags (not the fixed taxonomy used by LinkedIn/Twitter) and summary for topic identification. Tags here are AI-generated from video content and vary by post. Use them for semantic matching rather than exact-value filtering. Combine with pain_points, initiatives, technologies_mentioned, and competitors_mentioned for targeting and routing.

Example Signal

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

{
  "signal_id": "e9a1b2c3-4d5e-6f78-9012-3456789abcde",
  "batch_id": "2026-04-25-00-00-00",
  "signal_type": "youtube-company",
  "signal_subtype": "youtubeVideo",
  "detected_at": "2026-04-25T09:14:22Z",
  "association": "company",
  "company": {
    "name": "Datadog",
    "domain": "datadoghq.com",                  // match on domain
    "linkedin_url": "linkedin.com/company/datadog",  // or match on LinkedIn URL
    "industries": ["Software Development", "IT Services"],
    "employee_count_low": 5001,
    "employee_count_high": 10000,
    "description": "Cloud monitoring and security platform..."
  },
  "contact": [],
  "data": {
    "video_title": "Introducing Datadog AI Integrations: Unified Observability for LLM Applications",
    "video_description": "Learn how Datadog's new AI Integrations provide end-to-end observability for LLM-powered applications, from prompt tracing to cost attribution...",
    "summary": "Datadog launched AI Integrations for LLM observability, positioning against fragmented point solutions and signaling a major platform expansion into AI ops...",
    "channelTitle": "Datadog",
    "publishedAt": "2026-04-23T16:00:00Z",
    "videoLink": "https://www.youtube.com/watch?v=xK9pL2mQ3nR",
    "company_youtube_channel_url": "https://www.youtube.com/@DatadogHQ",
    "viewCount": "12400",
    "commentCount": "87",
    "tags": ["observability", "AI", "LLM", "monitoring", "MLOps"],
    "initiatives": [
      { "topic": "AI/ML observability platform expansion", "urgency": 0.92 },
      { "topic": "LLM cost attribution and optimization tooling", "urgency": 0.78 },
      { "topic": "Prompt engineering workflow integration", "urgency": 0.65 }
    ],
    "pain_points": [
      { "topic": "Fragmented AI monitoring across multiple vendor dashboards", "intensity": 0.84 },
      { "topic": "Inability to trace LLM failures back to specific prompts in production", "intensity": 0.71 }
    ],
    "technologies_mentioned": [
      { "name": "OpenAI GPT-4", "status": "integrated" },
      { "name": "LangChain", "status": "integrated" },
      { "name": "AWS Bedrock", "status": "integrated" },
      { "name": "Weights & Biases", "status": "competitor" }
    ],
    "competitors_mentioned": [
      { "name": "Weights & Biases" },
      { "name": "Arize AI" },
      { "name": "Helicone" }
    ]
  }
}

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 company's YouTube video.

Field	Type	Description
`video_title`	string	Title of the YouTube video as published. Useful for personalization ("I saw your video on AI Integrations...")
`video_description`	string	YouTube video description, truncated. Contains the company's own framing of what the video covers. Useful for understanding intent and messaging
`summary`	string	AI-generated one-line headline of what the video signals commercially. Focuses on the business implication, not just the video content. Typically 15–25 words. Useful for notifications and list displays
`channelTitle`	string	Name of the YouTube channel. Usually matches the company name but may differ for sub-brands or product-specific channels
`publishedAt`	string (ISO 8601)	When the video was published on YouTube. Useful for recency filtering and timing outreach to the announcement window
`videoLink`	string (URL)	Direct link to the YouTube video. Useful for research, validation, and referencing in outreach
`company_youtube_channel_url`	string (URL)	Link to the company's YouTube channel. Useful for broader content research
`viewCount`	string	Number of views at time of signal detection. String type (YouTube API format). Higher views on B2B content indicate strong market interest in the topic
`commentCount`	string	Number of comments at detection time. High comments relative to views suggest controversial or highly engaging content
`tags`	array[string]	Video tags/keywords set by the publisher. Useful for topic-based filtering and understanding the company's SEO strategy
`initiatives`	array[object]	Strategic initiatives extracted from the video content. Each has `topic` (what they're doing) and `urgency` (0.0–1.0, how actively they're pursuing it). Useful for identifying companies in active buy/build mode for specific capabilities
`pain_points`	array[object]	Problems or challenges the company acknowledged (not ones they solve for customers). Each has `topic` and `intensity` (0.0–1.0). Rare in polished YouTube content but extremely valuable when present — signals genuine need
`technologies_mentioned`	array[object]	Technologies referenced in the video. Each has `name` and `status` (integrated, evaluating, competitor, deprecated). Useful for tech-stack mapping and competitive positioning
`competitors_mentioned`	array[object]	Competitors named in the video. Each has `name`. Useful for competitive intelligence and positioning

Timing & Delivery

detected_at is when we processed the video. Use publishedAt for the actual upload date — there's typically a 24–72 hour detection lag depending on channel monitoring frequency.
One signal per video. Each published video produces exactly one signal. Companies posting multiple videos in a week will generate multiple signals.
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: 4,000,000+ companies with tracked YouTube channels
Best for: Identifying product launch windows, competitive intelligence, understanding company messaging priorities, timing outreach to announcement momentum

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See real delivered data → Sample Files

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage