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Social Intelligence

Podcast Appearance Signals

Know when a prospect goes on a podcast, and what they actually said. Autobound transcribes ~39K B2B episodes, resolves guests to real contacts, and extracts verbatim talking points, topics, and business claims across podcastAppearance plus 50 claim subtypes.

50,000+

Contact-level signals

~39,000

Transcribed episodes

podcastAppearance + 50 claims

Signal subtypes

Weekly

Update frequency

Social Intelligence50+ subtypes · Weekly refresh

What Are Podcast Appearance Signals?

Podcast appearance signals fire when a tracked contact appears on a podcast episode. Autobound transcribes ~39K B2B episodes, resolves guests against its contact database, and ships each appearance with the episode metadata, the topics discussed, and talking_points: {point, quote} pairs where every quote is verbatim from the transcript. On top of appearances, each on-air business claim lands as its own signal, classified into 50 subtypes shared with our SEC and earnings vocabulary: aiInvestment, techAdoption, painPointDisclosed, executiveOpinion, fundingEvent, and more.

Podcast appearances are unusually strong outreach signals: guests skew heavily toward the C-suite, and a 30-60 minute episode gives you the prospect's actual viewpoints, in their own words, to reference. Reaching out while their words are still fresh turns a cold email into a continuation of a conversation they started.

Example Signal Subtypes

podcastAppearanceexecutiveOpinionpainPointDisclosedtechAdoptionaiInvestmentfundingEventproductLaunchdigitalTransformation

Data Schema

Podcast Signal Data Schema

Real production signal (lightly trimmed). Every quote is verbatim from the transcript; episode_id joins all signals extracted from the same episode.

podcast_appearances.schema.json
{
  "signal_id": "dca86039-9c23-54d3-b9c2-00eacdb38baf",
  "signal_type": "podcast-contact",
  "signal_subtype": "podcastAppearance",
  "signal_name": "Arundhati Bhattacharya (President & CEO, Salesforce, South Asia @ Salesforce) on CII Podcasts",
  "association": "contact",
  "detected_at": "2026-08-24T10: 25: 28.000Z",
  "contact": {
    "name": "Arundhati Bhattacharya",
    "email": "a.bhattacharya@salesforce.com",
    "job_title": "Chairperson & CEO Salesforce India",
    "linkedin_url": "https://in.linkedin.com/in/arundhati-bhattacharya-salesforce",
    "seniority": "Cxo"
  },
  "company": {
    "name": "Salesforce",
    "domain": "salesforce.com",
    "industries": ["Software Development"],
    "revenue": "1 Billion and Over"
  },
  "data": {
    "podcast_name": "CII Podcasts",
    "episode_title": "India@100: Powering the Future through Digital Acceleration ft Arundhati Bhattacharya",
    "episode_url": "https://shows.acast.com/ciipodcasts/episodes/india100-powering-the-future-through-digital-acceleration-ft",
    "episode_id": "55821260383",  // joins all signals from this episode
    "host": "Rajan Navani",
    "published_at": "2026-06-05",
    "occasion": "Discussing the CII's 'India@100' initiative",
    "topics": ["Digital transformation", "AI adoption", "Cloud computing", "Technology policy"],
    "talking_points": [
      {
        "point": "Generative AI has democratized technology, accelerating corporate adoption.",
        "quote": "even the input and the output, both of them have been democratized. And because they have been democratized, people are now realizing that this is something that every other person is going to use..."
      },
      {
        "point": "Board-level 'fear of missing out' is driving AI investment.",
        "quote": "once you realize that it is something that's here to stay, then there is a huge fear of FOMO that builds up..."
      }
    ],
    "is_ad": false,
    "transcript_source": "transcript"
    // company_snapshot: present when the guest discusses their own company
  }
}
GCS Bucket
gs://autobound-podcast-contact/
Formats
JSONL · Parquet
Refresh
Weekly

Applications

What teams build with Podcast Appearance Signals (Contact)

03 documented applications

  1. 01

    Warm Outreach to Thought Leaders

    Reference the specific episode and the exact quote your prospect said on air. Instead of a cold intro, your email starts with 'Your point on CII Podcasts about boards feeling AI FOMO matched exactly what we're seeing.' That opens doors cold emails can't.

  2. 02

    Content-Triggered Sequences

    Build automated sequences that trigger when a target account's executive appears on a show in your category, or when a specific claim subtype fires: painPointDisclosed for the pains you solve, techAdoption for the platforms you complement, aiInvestment for AI budgets in motion.

  3. 03

    Account-Based Personalization at Scale

    When you have 200 target accounts and can't manually monitor each contact's media activity, podcast signals surface the ones who are actively engaged in your topic area. Prioritize the accounts whose executives are podcasting about the problems you solve.

Measured, Not Estimated

What's actually inside Podcast Appearance Signals (Contact)

Volumes, subtypes, geography, and join-key fill rates audited directly from our production database, so you can verify coverage before you buy.

Measured coverageaudited from production · minimums, not ceilings
150,000
contacts with fresh data / month
basis: published baseline
90,000
records created / month
basis: published baseline

Top subtypes

50 total
Executive Opinion741 · 28%
Product Launch298 · 11%
Pain Point Disclosed259 · 9.9%
Growth Signal254 · 9.7%
Tech Adoption118 · 4.5%
Market Expansion113 · 4.3%
Ai Investment96 · 3.7%
Partnership86 · 3.3%
C-Suite412 · 44%
Staff107 · 11%
VP48 · 5.1%
Director42 · 4.4%
Manager28 · 3.0%
Consultant2 · 0.2%
Business Management355 · 37%
Executive72 · 7.6%
Education25 · 2.6%
Sales17 · 1.8%
Operations16 · 1.7%
Marketing15 · 1.6%
Creative, Design10 · 1.1%

Join keys

fill rate
Contact LinkedIn96%
Contact name100%
Email44%
Records measured 2,613+Contacts reached 947+Full schema & examples →

See the real records for yourself.

Real records from production, delivered to your inbox.

See full coverage & segmentation across all 35 signal types →

Worked Example

One signal, traced to outcome

One Podcast Appearance Signals (Contact) record followed from detection, through the action a rep took on it, to the measured result. Illustrative composite of observed customer workflows.

01 signal → 02 action → 03 outcome

01

Signal detected

The Chairperson & CEO of Salesforce India appears on CII Podcasts discussing AI adoption, and the signal arrives with her four key talking points, each backed by a verbatim quote.

02

Action taken

Your AE reads the two-line quotes in the signal - no listening required - and sends an email referencing her specific point about board-level AI FOMO.

03

Outcome

The reply comes back the same day: she's impressed someone engaged with the substance. The thread turns into a discovery call the following week.

FAQ

Frequently Asked Questions

Which podcasts do you cover?
We transcribe ~39,000 B2B podcast episodes across technology, sales, marketing, finance, and industry verticals, with new episodes added in weekly batches that are reviewed before publish. Coverage skews toward shows with executive guests and business content.
How quickly do signals appear after an episode publishes?
Signals ship in weekly batches (Sunday), each reviewed before publish. Every signal carries published_at and a recorded_at_estimate so you can filter by the episode's actual recency.
How are contacts matched to episodes?
Guests are resolved from transcripts and episode metadata against our contact database using name and company co-reference. Claims that can't be resolved to a person still land as company-level signals (see Podcast Mentions), so nothing is dropped.
Can I filter to specific subtypes?
Yes - pass signal_subtypes on the search or enrich endpoints (OR semantics, max 25 values). Discover the full vocabulary via GET /v1/signals/types.

How It Works

From Raw Data to Your Stack

Autobound ingests from LinkedIn API, Glassdoor, GitHub, Reddit, G2, extracts structured signals with AI, and delivers them however your infrastructure needs.

01

Autobound Ingests

Raw data from LinkedIn API, Glassdoor, GitHub, Reddit, G2 is continuously collected and normalized across millions of sources.

02

AI Extracts & Scores

ML models extract 50+ signal subtypes with relevance scoring, confidence levels, and entity resolution.

03

You Receive

Structured JSONL delivered via your preferred method, updated on a weekly cadence.

REST API

Real-time access with subtype filtering

300 req/min

GCS Push

Automated delivery to your bucket

JSONL + Parquet

Enrich API

On-demand LLM-ranked insights

AI relevance scoring

Flat File

Bulk exports for data warehouses

CSV, JSON, Parquet
3 vendors consolidated
By consolidating three data vendors into Autobound's Enrich API, we added 100+ new signal types and saved 4 months of engineering time.

AiSDR Team

Engineering, AiSDR

Ready to License Podcast Appearance Signals (Contact)?

Custom pricing based on signal types, delivery frequency, and volume. Full schema documentation and integration guides included.