Podcasts

Podcast Appearance signals extract structured business intelligence from B2B podcast episodes — identifying company mentions, vendor switches, funding rounds, executive moves, competitive dynamics, and more. Unlike simple mention detection, each episode can yield multiple distinct signals across 68 subtypes.

We discover episodes daily from 41K+ scored podcast feeds (sourced from PodcastIndex's 4.6M feed database), transcribe audio, and run structured extraction via LLM with a strict JSON schema. Each signal is enriched with company domain resolution and contact matching against our B2B dataset.

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See real delivered data → Sample Files

Each signal is classified into one of 68 subtypes — the specific business event extracted from the episode (funding rounds, vendor evaluations, executive hires, competitive dynamics, and more).

Available Subtypes (68)

Technology & Investment techInvestment, aiInvestment, dataInvestment, softwareImplementation, cloudInvestment, cybersecurityInvestment, platformStrategy, budgetAndSpending, vendorEvaluation

Growth & Revenue revenueAcceleration, customerGrowth, customerWin, marketExpansion, internationalGrowth, channelShift, recurringRevenueShift, growthSignal

Strategy & Operations strategicInitiative, strategicPartnership, jointVenture, productLaunch, acquisitionAnnounced, acquisitionCompleted, divestiture, restructuring, digitalTransformation, pivotOrModelChange

Leadership & People ceoChange, ctoChange, cfoChange, cmoChange, croChange, executiveHire, executiveDeparture, executiveCompensation, founderStory, leadershipOpinion, careerTransition, boardChange, successionAnnouncement

Challenges & Pain Points costPressure, competitivePressure, scalingChallenge, talentShortage, customerChurn, marginPressure, supplyChainPain, regulatoryBurden, legacyModernization

Market & Competitive industryTrend, competitorNamed, competitorReference, marketShareGain, marketShareLoss, pricingPressure

Financial & Funding fundingRound, ipoSignal, earnings, ratingChange, debtRefinancing

References & Mentions productMention, customerReference, vendorReference, stockMovement, regulatoryAction, productCapability, marketCommentary

Example Signal

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

{
  "signal_id": "podcast-53744610182-funding",
  "batch_id": "2026-04-22-00-00-00",
  "signal_type": "podcast-appearance",
  "signal_subtype": "fundingRound",
  "association": "contact",
  "detected_at": "2026-04-22T12:48:00.000Z",
  "company": {
    "name": "Flip",
    "domain": "flipcx.com",                   // match on domain
    "linkedin_url": "linkedin.com/company/flipcx",  // or match on LinkedIn URL
    "industries": ["Artificial Intelligence", "Customer Service", "SaaS"],
    "employee_count_low": 51,
    "employee_count_high": 200,
    "description": "Voice AI platform automating customer support..."
  },
  "contact": {
    "name": "Brian Schiff",
    "first_name": "Brian",
    "last_name": "Schiff",
    "email": "[email protected]",              // match on email
    "job_title": "Co-founder and CEO",
    "linkedin_url": "linkedin.com/in/bschiff"  // or match on LinkedIn URL
  },
  "data": {
    "headline": "Flip raises $20M Series A at $100M valuation for AI voice support",
    "detail": "Brian Schiff, CEO of Flip, discusses raising a $20M Series A at a $100M valuation. The company automates up to 90% of routine support calls...",
    "signal_category": "financial",
    "relevance": 0.95,                        // 0.0-1.0; higher = more actionable for outreach
    "confidence": "high",                     // how certain this signal is accurate
    "sentiment": "positive",
    "entity_role": "guest",
    "evidence": [
      {
        "speaker_name": "Brian Schiff",
        "speaker_title": "Co-founder and CEO",
        "speaker_company": "Flip",
        "role": "guest",
        "quotes": [
          "We just raised a $20 million Series A at a $100 million valuation.",
          "We're at $12 million ARR now, serving over 250 enterprise brands.",
          "We automate up to 90 percent of routine customer support calls with voice AI."
        ]
      }
    ],
    "entities_referenced": [],
    "metric_dollar_millions": 20.0,
    "metric_pct": 90.0,
    "podcast_name": "The Top Entrepreneurs",
    "episode_title": "Flip Reaches $12M ARR with AI Voice Support for 250 Brands",
    "episode_url": "https://nathanlatkathetop.libsyn.com/flip-reaches-12m-arr",
    "source": {
      "episode_date": "2026-04-22",
      "podcast_popularity": 78
    }
  }
}

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 podcast episode.

Field	Type	Description
`headline`	string	One-line signal summary (max 80 chars). Designed to be shown directly to end users as a notification or list item. Always includes the company name and core event
`detail`	string	Multi-sentence explanation of the signal written for a salesperson. Explains what was disclosed, why it matters commercially, and what kind of vendor or solution they might need. Typically 2–4 sentences
`signal_category`	string	Category grouping: `technology`, `growth`, `strategy`, `leadership`, `pain`, `competitive`, `financial`, `reference`. Useful for routing signals to the right sales motion
`relevance`	float (0–1)	How actionable this signal is for outreach. Higher = stronger commercial signal. Useful for prioritization and filtering
`confidence`	string	Confidence that this event actually occurred and was categorized accurately. `high`, `medium`, or `low`. Useful for filtering in production
`sentiment`	string	Whether the disclosed event is favorable (`positive`), unfavorable (`negative`), or informational (`neutral`) for the company
`entity_role`	string	Relationship of the matched entity to the podcast: `guest`, `host`, or `mentioned`. Useful for filtering — guests are highest-intent
`evidence`	array[object]	Direct quotes from the transcript supporting this signal. Each entry includes speaker name, title, company, role, and verbatim quotes
`evidence[].speaker_name`	string	Who said it
`evidence[].speaker_title`	string	Speaker's title at time of recording
`evidence[].speaker_company`	string	Speaker's company
`evidence[].role`	string	`guest`, `host`, or `mentioned`
`evidence[].quotes`	array[string]	Verbatim quotes from the transcript. Useful for displaying as proof or powering personalized outreach
`entities_referenced`	array[string]	Other companies or products mentioned in context. Useful for competitive intelligence and relationship mapping
`metric_dollar_millions`	number \| null	Dollar figure mentioned in millions (e.g., funding amount, revenue). Null when no dollar amount is mentioned
`metric_pct`	number \| null	Percentage figure mentioned (e.g., growth rate, automation rate). Null when no percentage is mentioned
`podcast_name`	string	Podcast show name. Useful for display and deduplication
`episode_title`	string	Episode title
`episode_url`	string	Link to the episode. Useful for fact-checking or sharing with reps
`source.episode_date`	string (date)	Episode publication date
`source.podcast_popularity`	integer	Feed relevance score (0–100). Higher = more established B2B podcast. Useful for prioritizing signals from top-tier shows

Timing & Delivery

detected_at is the episode publication date. Use source.episode_date and episode_url for the original content context.
One signal per subtype per entity per episode. A single episode can produce multiple signals across different subtypes, but won't fire the same subtype twice for the same entity in the same episode.
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: Daily
Coverage: 41,000+ B2B podcast feeds monitored (from 4.6M scored)
Best for: Account-based selling, competitive intelligence, executive outreach, technology buying signals

Contact Sales →

See real delivered data → Sample Files

Example Signal

Field Reference

Signal-Specific Fields

Timing & Delivery

Coverage