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Autobound

Social Intelligence

Podcast Appearance Signals

Know when a prospect goes on a podcast, and what they said. Autobound indexes episodes from 20+ publishers, matches guests against 4M+ contacts, and extracts pain points, initiatives, technologies, and competitors across 50 signal subtypes.

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50,000+

Podcasts monitored

68

Signal subtypes

Daily

Update frequency

~70%

Contact resolution rate

Social Intelligence68 subtypes · Daily refresh

What Are Podcast Appearance Signals?

Podcast appearance signals fire when a tracked contact appears on or is discussed in a podcast episode. Autobound indexes episodes from 20+ podcast publishers, matches guest names against 4M+ contacts, and classifies each appearance into 50 subtypes: executiveOpinion, productLaunch, painPointDisclosed, techAdoption, marketExpansion, aiInvestment, and more. Each signal carries the episode title, show name, publication date, duration, an AI summary, and extracted pain points, initiatives, technologies, and competitors.

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 to reference. Reaching out while their own words are still fresh turns a cold email into a continuation of a conversation they started.

Example Signal Subtypes

executiveOpinionproductLaunchpainPointDisclosedgrowthSignaltechAdoptionmarketExpansionaiInvestmentpartnership

Data Schema

Podcast Signal Data Schema

Each podcast signal includes full contact resolution, episode metadata, topic classification, and relevance scoring. Here is a production example.

podcast_appearances.schema.json
{
  "signal_id": "c3d4e5f6-a7b8-9012-cdef-234567890123",
  "signal_type": "podcast-contact",
  "signal_subtype": "executiveOpinion",
  "signal_name": "Contact mentioned in podcast",
  "association": "contact",
  "detected_at": "2026-06-10T08: 45: 22.118Z",
  "contact": {
    "email": "brian@flipcx.com",  // match on email
    "name": "Brian Schiff",
    "first_name": "Brian",
    "last_name": "Schiff",
    "job_title": "Co-founder and CEO"
  },
  "company": {
    "name": "Flip",
    "domain": "flipcx.com",  // match on domain
    "description": "Voice AI platform automating customer support",
    "industries": ["Software Development"],
    "employee_count_low": 51,
    "employee_count_high": 200
  },
  "data": {
    "title": "Flip Reaches $12M ARR with AI Voice Support for 250 Brands",
    "snippet": "Brian Schiff joins to discuss raising a $20M Series A at a $100M valuation and automating up to 90% of routine support calls with voice AI.",
    "website": "https://podcasts.apple.com/us/podcast/flip-reaches-12m-arr/id1492305911",
    "show_name": "The Top Entrepreneurs",
    "published_at": "2026-06-08T10: 00: 00.000Z",
    "duration_minutes": 42,
    "tags": ["Funding", "Artificial Intelligence", "Customer Service"],
    "summary": "CEO discusses $20M Series A and scaling voice AI support automation to 250 enterprise brands.",
    "pain_points": [
      { "topic": "routine support calls consuming agent capacity", "intensity": 0.7 }
    ],
    "initiatives": [
      { "topic": "scaling voice AI support automation", "urgency": 0.9 }
    ],
    "technologies_mentioned": [
      { "name": "Salesforce", "status": "integrated" }
    ],
    "competitors_mentioned": [
      { "name": "Decagon" }
    ]
  }
}
GCS Bucket
gs://autobound-podcast-appearance/
Formats
JSONL · Parquet
Refresh
Daily
Full schema docs →Measured coverage →

Applications

What teams build with Podcast Appearance Signals

03 documented applications

  1. 01

    Warm Outreach to Thought Leaders

    Reference the specific episode and insight your prospect shared. Instead of a cold intro, your email starts with 'I just listened to your appearance on The Growth Hub, your point about PLG and enterprise expansion was spot on.' That opens doors that 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. Marketing technology vendors can fire sequences when CMOs discuss marketing ops, sales tech vendors when VPs of Sales talk about pipeline. The signal does the qualification for you.

  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

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%

Seniority

full breakdown →
C-Suite412 · 44%
Staff107 · 11%
VP48 · 5.1%
Director42 · 4.4%
Manager28 · 3.0%
Consultant2 · 0.2%

Departments

full breakdown →
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 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

Marcus Rodriguez, Head of Marketing at Figma, appears on The Growth Hub Podcast discussing PLG strategy and enterprise pipeline.

02

Action taken

Your AE receives the signal, listens to the 3-minute highlight, and sends a personalized email referencing Marcus's specific insight about community-driven enterprise adoption.

03

Outcome

Marcus replies the same day, he's impressed someone actually engaged with the content. The conversation leads to a discovery call the following week.

Run this against live data →

FAQ

Frequently Asked Questions

Which podcasts do you monitor?
We monitor 50,000+ B2B podcasts across technology, sales, marketing, finance, and industry verticals. Coverage includes shows from Spotify, Apple Podcasts, YouTube, and independent RSS feeds. If a podcast has a B2B audience and regular publishing cadence, it's likely in our database.
How quickly do signals appear after an episode publishes?
Most signals are processed within 24-48 hours of an episode publishing. For high-traffic shows, processing is often same-day.
How are contacts matched to episodes?
Guest names from episode titles, show notes, and transcripts are matched against our contact database using name + company co-reference. Match confidence scores are included with every signal. Unmatched guests are still delivered as company-level signals.

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 68 signal subtypes with relevance scoring, confidence levels, and entity resolution.

03

You Receive

Structured JSONL delivered via your preferred method, updated on a daily 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?

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

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