Podcast Guest Prospect Miner

Mine podcast episode pages and show notes for guest prospects, expertise themes, companies, and booking outreach hooks.

Podcast Guest Prospect Miner is built for one focused job: turning public source pages or pasted research snippets into usable podcast guest prospect records. It does not try to be a generic webpage scraper. The parser, fields, examples, and dataset schema are shaped around this actor's specific business question.

Use it when you already have public pages, listings, announcements, reviews, directories, or copied snippets and want a repeatable way to extract the information that matters without manually filling a spreadsheet.

Who Uses It

Podcast booking agencies, founder-led sales teams, PR teams, content marketers, and expert-roundup researchers.

What It Finds

The actor looks for signals connected to mine podcast episode pages and show notes for guest prospects, expertise themes, companies, and booking outreach hooks. It keeps the output focused on fields a buyer can review, filter, enrich, or send into a CRM, spreadsheet, product backlog, procurement workflow, or research database.

The extraction method is specific to this actor:

Podcast/show-name detection, interview-pattern parsing, guest/company extraction, expertise classification, and booking-hook summarization.

How It Works

1. The actor accepts public URLs or pasted snippets in episodeSources.
2. Public URLs are fetched when possible and converted into visible text.
3. Pasted snippets are processed directly, which is useful for pages that block automated fetching or require manual browsing.
4. Actor-specific parsing logic extracts only the fields listed below.
5. Empty or low-information inputs are skipped safely, with clean logs instead of noisy failed rows.

Input

Use episodeSources for the sources you want to process. Each item can be a public URL or a focused text snippet.

{
    "episodeSources": [
        "The Revenue Lab podcast episode interviews Dana Wright, Founder of ScaleDesk, on outbound operations and AI prospecting.",
        "SaaS Security Weekly features Priya Nair from VaultGrid discussing SOC 2 readiness, vendor risk, and audit workflows.",
        "Commerce Builders podcast with Leo Martins, CMO at CartPilot, about retention marketing and checkout optimization."
    ],
    "maxResults": 3
}

Output Fields

• podcastName - Podcast or show where the guest appeared.
• episodeTitle - Episode headline or interview topic.
• episodeDate - Episode date when visible.
• guestName - Guest name found in the episode text.
• guestRole - Guest title, founder role, or executive function.
• guestCompany - Company connected to the guest.
• expertiseTheme - Main expertise area the guest discusses.
• outreachHook - Specific angle to reference in outreach.
• bookingReason - Why this person looks like a strong guest prospect.
• episodeUrl - Podcast episode or show-notes URL.

Example Output

{
    "podcastName": "The Revenue Lab podcast",
    "episodeTitle": "Dana Wright on outbound operations",
    "episodeDate": "July 22, 2026",
    "guestName": "Dana Wright",
    "guestRole": "Founder",
    "guestCompany": "ScaleDesk",
    "expertiseTheme": "sales and outbound operations",
    "outreachHook": "outbound operations and AI prospecting",
    "bookingReason": "Already speaks publicly about sales and outbound operations",
    "episodeUrl": "https://example.com/episode"
}

Best Use Cases

• Build a focused spreadsheet from repeated source pages.
• Review public signals before sales, partnership, product, procurement, or market research work.
• Monitor a category over time using the same output fields.
• Turn copied notes from newsletters, communities, directories, review pages, or search results into a structured dataset.

Tips For Better Results

• Use source pages that visibly contain the signal you want this actor to extract.
• For hard-to-fetch sites, paste the relevant public text snippet directly into the input.
• Keep each input item focused on one page, listing, announcement, review, or profile.
• Review extracted rows before making high-stakes business, legal, procurement, or security decisions.

Limitations

• The actor does not bypass logins, paywalls, private systems, CAPTCHA, or anti-bot walls.
• The actor extracts from visible text and may miss information hidden behind scripts or user interactions.
• Some fields may be empty when the source does not contain that information.
• Output quality depends on source quality, so focused source text produces the strongest rows.