DEV Community: apify forge

How Podcast Booking Agencies Find Host Emails Without Paying $599/month

apify forge — Fri, 03 Apr 2026 18:45:38 +0000

The problem: Podcast booking agencies run campaign-based businesses. A client signs up, you pitch them to 2,000-5,000 relevant shows, then you move on. But the tools built for finding podcast host emails — Podchaser Pro, Rephonic, ListenNotes — all charge monthly subscriptions ranging from $99 to $599. You're paying for 12 months of access to do work that takes 2 weeks. According to a 2024 Edison Research survey, there are now over 4.2 million podcasts indexed across major directories. The contact data for most of them is sitting in their RSS feeds, free and structured, waiting to be pulled.

What is podcast host email extraction? It's the process of pulling podcast owner and host contact information — primarily email addresses — directly from structured RSS feed metadata fields like <itunes:owner> and <managingEditor>. Why it matters: Direct host emails see roughly 23% response rates vs. 4% for generic contact forms, according to outreach data from Podseeker's 2025 benchmarks. Use it when: You need 500+ podcast contacts for a campaign and don't want to pay monthly for a database you'll use twice a year.

Problems this solves:

How to find podcast host emails in bulk without manual RSS parsing
How to build a podcast outreach list without a monthly subscription
How to verify which podcasts are still active before pitching
How to compare podcast contact databases by cost and data quality
How to cross-reference Apple Podcasts and Spotify for deduplication

Quick answer:

What it is: Automated extraction of podcast host emails, websites, and metadata from RSS feed tags across Apple Podcasts and Spotify
When to use it: Campaign-based outreach where you need 500-5,000 contacts and won't use the tool again for months
When NOT to use it: If you need audience demographics, listener counts, or booking difficulty scores — those require platforms like Rephonic or Podchaser
Typical steps: Enter keywords, scrape directories, parse RSS feeds, deduplicate across platforms, export contacts
Main tradeoff: You get raw contact data fast and cheap, but no audience analytics or relationship management features

Key takeaways:

RSS <itunes:owner> tags contain verified host emails for an estimated 60-70% of active podcasts in business, technology, and marketing categories — no guessing or inference required
Extracting 5,000 podcast contacts costs roughly $250 one-time with pay-per-result pricing, vs. $599/month (Podchaser Pro), $249/month (Rephonic Business), or $249/month (ListenNotes Pro) on subscription platforms
Cross-deduplication between Apple Podcasts and Spotify prevents wasting pitches on duplicate shows listed on both platforms
Publishing frequency classification (7 tiers from daily to dormant) lets agencies filter out dead shows before sending a single email
Three paying users scraped approximately 6,900 podcasts total in the first 72 hours of the Podcast Directory Scraper Apify actor going live, generating $350 in revenue — suggesting real demand from booking agencies and PR firms

Scenario	Tool	Cost	Result
PR firm pitching 200 business podcasts	Podcast Directory Scraper	$10	200 contacts with emails, websites, frequency
Booking agency building 2,000-show list	Podcast Directory Scraper	$100	2,000 contacts across 10 keywords
Sponsorship broker qualifying 5,000 shows	Podcast Directory Scraper	$250	5,000 contacts with active/inactive filtering
Agency needing audience demographics	Rephonic Standard	$149/mo	500 searches/mo with listener data
Enterprise team with CRM integration	Podchaser Pro	~$5,000/yr	Full database, demographics, reach estimates

What is podcast host email extraction?

Definition (short version): Podcast host email extraction is the automated process of pulling podcast owner contact information — primarily email addresses — from structured metadata fields in RSS feeds indexed by directories like Apple Podcasts and Spotify.

The longer version matters for understanding why this works so well. When a podcaster submits their show to Apple Podcasts, they provide an RSS feed URL. That feed contains XML tags defined by the Apple Podcasts RSS specification, including <itunes:owner> with a <itunes:email> child element. This isn't scraped from a webpage or inferred from a domain. It's structured, machine-readable data that the podcast owner deliberately entered.

There are roughly 3 categories of podcast contact extraction: manual RSS parsing (free but slow — one feed at a time), subscription databases (Podchaser, Rephonic, ListenNotes — curated but expensive), and automated on-demand scrapers (pay-per-result tools that parse RSS feeds in bulk). The first doesn't scale. The second charges monthly for sporadic use. The third is what booking agencies actually need.

Why do podcast host emails live in RSS feeds?

Podcast host emails are embedded in RSS feeds because Apple's podcast submission process requires an <itunes:email> field inside the <itunes:owner> tag, which Apple uses for account verification and show management communications. This email is typically the host's real address — not a noreply.

Here's why that matters. Unlike LinkedIn profiles or company websites where contact info is buried or hidden behind forms, RSS feed emails are structured data in a standardized XML format. The Podcast Index, which tracks over 4.7 million podcast feeds, confirms that RSS metadata remains the most reliable source of direct podcast contact information. These emails aren't guessed by an algorithm or scraped from social media bios. The podcast owner typed them in.

Not every RSS feed has a valid email — some podcasters use placeholder addresses or hosting platform defaults. In my experience running the Podcast Directory Scraper across business and technology categories, roughly 60-70% of active shows have a usable email in the <itunes:owner> tag. That percentage drops in entertainment and comedy categories where shows are more likely hosted on platforms that mask owner data.

How do you extract podcast host emails at scale?

Automated podcast email extraction works by searching directory APIs for keyword-matched shows, fetching each show's RSS feed URL, parsing the XML for structured contact fields, and deduplicating results across platforms. A typical extraction of 500 podcasts across 5 keywords completes in 5-8 minutes.

The manual version of this process — which plenty of people still do — involves going to Apple Podcasts, viewing page source, finding the feedUrl, opening the RSS XML, and Ctrl+F for itunes:email. Per show, that takes 2-3 minutes. For 200 shows, you're looking at a full workday. For 2,000 shows, it's genuinely not feasible.

The automated approach works like this:

You provide search keywords (e.g., "SaaS founders", "marketing leadership", "healthcare innovation")
The scraper queries Apple Podcasts and Spotify search indexes for matching shows
For each show, it fetches the RSS feed URL from the directory listing
It parses the RSS XML for <itunes:owner>, <itunes:email>, <managingEditor>, website URLs, and episode metadata
It calculates publishing frequency across 7 tiers (daily, semi-weekly, weekly, bi-weekly, monthly, sporadic, dormant)
It cross-deduplicates shows that appear on both Apple Podcasts and Spotify
Results export as JSON, CSV, or Excel with 20+ fields per podcast

# Generic example — works with any RSS-parsing tool or API endpoint
curl -X POST https://your-scraper-endpoint/run \
  -H "Content-Type: application/json" \
  -d '{
    "keywords": ["B2B marketing", "SaaS growth"],
    "platforms": ["apple", "spotify"],
    "maxResults": 500,
    "outputFormat": "csv"
  }'

The endpoint above could be a custom script, the Podcast Directory Scraper on Apify, or any RSS parsing service. The important thing is the approach: keyword search, RSS fetch, XML parse, deduplicate, export.

What does the output look like?

Here's what a single podcast record looks like after extraction — this is the actual JSON shape from an RSS-based scraper:

{
  "podcastName": "The Growth Show",
  "hostName": "Jennifer Martinez",
  "ownerEmail": "jen@thegrowthshow.com",
  "websiteUrl": "https://thegrowthshow.com",
  "rssFeedUrl": "https://feeds.thegrowthshow.com/rss",
  "platform": "apple_podcasts",
  "appleId": "id1234567890",
  "totalEpisodes": 287,
  "lastEpisodeDate": "2026-03-28",
  "publishingFrequency": "weekly",
  "isActive": true,
  "genres": ["Business", "Marketing"],
  "country": "US",
  "language": "en",
  "description": "Conversations with founders scaling from $1M to $100M ARR",
  "artworkUrl": "https://thegrowthshow.com/artwork.jpg"
}

That's 15+ fields from a single RSS parse. The ownerEmail comes directly from the <itunes:owner> tag — not inferred, not pattern-matched, not purchased from a third-party data broker. The publishingFrequency classification tells you immediately whether the show is worth pitching (a "dormant" show that hasn't published in 6 months won't book your client).

What are the alternatives to RSS email extraction?

There are 5 main approaches to building podcast contact lists, each with different cost structures, data quality, and audience insight depth. The right choice depends on whether you need just contact data or full audience analytics.

Feature	RSS Scraping (DIY)	Podcast Directory Scraper	Rephonic	Podchaser Pro	ListenNotes API
Cost for 5,000 shows	Free (time cost)	~$250 one-time	$249/mo	~$5,000/yr	$249/mo
Commitment	None	None	Monthly	Annual	Monthly
Host email coverage	60-70%	60-70%	Higher (manual curation)	Higher (manual curation)	Varies
Audience demographics	No	No	Yes	Yes	No
Booking difficulty score	No	No	Yes	No	No
Active/inactive filtering	Manual	Automated (7 tiers)	Yes	Yes	Yes
Cross-platform dedup	Manual	Automated	Built-in	Built-in	Single platform
CRM integration	No	Via webhook/API	Yes	Yes	API only
Speed for 500 shows	15-20 hours	5-8 minutes	Instant (database)	Instant (database)	Instant (API)
Data freshness	Real-time RSS	Real-time RSS	Updated periodically	Updated periodically	Updated periodically

Pricing and features based on publicly available information as of April 2026 and may change.

1. Manual RSS parsing (free, doesn't scale)
Open each podcast's Apple listing, view source, find feedUrl, open RSS XML, search for itunes:email. Works for 10-20 shows. Unworkable for campaigns. Best for: one-off research when you need a single host's email.

2. Automated RSS scraping tools (pay-per-result)
Tools like the Podcast Directory Scraper on Apify automate the entire RSS parsing pipeline — search, fetch, parse, deduplicate, export. You pay per podcast extracted, not per month. Best for: campaign-based agencies scraping 500-5,000 shows at a time.

3. Rephonic ($99-299/month)
A curated podcast database with audience demographics, listener overlap analysis, and booking difficulty scores. Rephonic's pricing page shows three tiers — Light at $99/mo, Standard at $149/mo, Business at $299/mo. Best for: agencies that need audience analytics alongside contact data and run campaigns every month.

4. Podchaser Pro (~$5,000/year)
Enterprise-grade podcast database with reach estimates, audience demographics, and media monitoring. Requires annual commitment. Podchaser's own pricing comparison explains the premium as including "manual curation and demographic data." Best for: large PR firms managing ongoing podcast campaigns across multiple clients.

5. ListenNotes API ($67-249/month)
Developer-focused podcast API with search, filtering, and metadata access. ListenNotes' pricing page shows tiered plans based on API request volume. Emails are included only when available in feed metadata. Best for: developers building custom podcast tools or integrations.

Each approach has trade-offs in cost, data depth, and commitment length. The right choice depends on campaign frequency, budget, and whether you need audience analytics or just contact data.

Best practices for podcast outreach lists

Filter by publishing frequency before exporting. A podcast that hasn't released an episode in 4+ months almost certainly isn't booking guests. Use frequency tiers (weekly, bi-weekly, monthly) to eliminate dormant shows. In observed scraping runs, roughly 15-20% of keyword-matched results are inactive.
Deduplicate across platforms before outreach. Many podcasts list on both Apple Podcasts and Spotify. Without cross-platform deduplication, you'll pitch the same host twice from different email threads. Observed duplication rates run 20-30% for popular keywords like "marketing" or "entrepreneurship" (based on internal testing, April 2026, n=2,300 podcasts).
Validate emails before sending. RSS <itunes:email> fields are entered by podcast owners and sometimes contain outdated or placeholder addresses. Run extracted emails through a verification service — expect 8-15% bounce rates on unverified lists, based on ZeroBounce's 2025 email quality report.
Segment by genre for personalized outreach. Business, technology, and marketing podcasts have higher email coverage (60-70%) than entertainment categories (40-50%). Segment your list by genre and write category-specific pitch templates.
Use the website URL as a fallback. When a podcast's RSS feed lacks an <itunes:email>, the website URL often leads to a contact page or booking form. Tools like the Website Contact Scraper can pull emails, phone numbers, and social links from those pages automatically.
Check episode count as a quality signal. Shows with fewer than 10 episodes may be too new to have established booking processes. Shows with 100+ episodes are more likely to have streamlined guest intake — and higher expectations for guest quality.
Export in the format your CRM expects. If your team uses HubSpot or a spreadsheet, export as CSV. If you're feeding data into an API pipeline, use JSON. Reformatting wastes time that could go toward pitching.

Common mistakes when building podcast contact lists

Pitching dormant shows. The single most common waste of outreach effort. A show that published its last episode 8 months ago isn't coming back for your client. Always check the lastEpisodeDate field or publishing frequency classification.

Ignoring cross-platform duplicates. If you scrape "content marketing" on both Apple Podcasts and Spotify without deduplication, you'll get the same show twice under slightly different metadata. Sending duplicate pitches makes your agency look careless.

Treating RSS emails as the only contact path. Some hosts prefer booking through their website contact form or a specific booking platform like PodMatch. If the RSS email bounces, check the podcast's website for a dedicated guest submission page.

Scraping without keyword strategy. Searching "business" returns thousands of marginally relevant shows. Use specific compound keywords like "SaaS founder interviews" or "healthcare leadership podcast" to get shows that actually match your client's niche.

Skipping the "is this show booking guests?" check. Solo shows, news roundups, and scripted series don't book outside guests. Check episode titles and descriptions for guest names before pitching — it takes 30 seconds per show and saves your reputation.

Not verifying email deliverability. Sending 2,000 emails to unverified addresses tanks your sender reputation. Services like ZeroBounce or NeverBounce cost a few dollars per thousand and prevent domain blacklisting.

How much does podcast outreach list building actually cost?

The total cost of building a podcast contact list ranges from $0 (manual RSS parsing) to $7,188/year (Podchaser Pro plus email verification), depending on volume, frequency, and whether you need audience analytics beyond basic contact data.

Here's what a typical 5,000-show campaign costs across approaches:

Approach	Contact Extraction	Email Verification	Total	Commitment
Manual RSS parsing	$0 (80+ hours labor)	$15 (ZeroBounce)	$15 + labor	None
Podcast Directory Scraper	$250	$15	$265	None
Rephonic Business	$299/mo	Included	$299/mo	Monthly
Podchaser Pro	~$417/mo ($5,000/yr)	Included	~$417/mo	Annual
ListenNotes Pro	$249/mo	$15	$264/mo	Monthly

For agencies running 2-3 campaigns per year, the subscription math doesn't work. You'd pay $2,988-7,188 annually for tools you use 4-6 weeks total. Pay-per-result pricing — where you spend $250-500 across those campaigns and nothing between them — is what agencies with irregular workloads actually need. ApifyForge lists multiple actors that follow this pay-per-event model for lead generation workflows.

ApifyForge's cost calculator can model exact costs based on your expected volume and campaign frequency.

Who is actually using RSS-based podcast scrapers?

Based on observed usage of the Podcast Directory Scraper Apify actor listed on ApifyForge in its first 72 hours: three paying users scraped approximately 2,300 podcasts each, generating $350 in total revenue (observed April 2026, n=3 users, first 72 hours post-launch). The scraping patterns — 2,000-5,000 shows per run, keyword clusters around business niches — are consistent with podcast booking agencies or PR firms building campaign-specific outreach lists.

Before: A podcast booking agency manually researches 200 shows per week. One team member spends 15-20 hours copying RSS emails, checking active status, and deduplicating across platforms. Cost: roughly $500-700/week in labor (at $30-35/hr for a research assistant).

After: The same agency runs 5 keyword searches through an automated RSS scraper, gets 2,000+ deduplicated contacts with emails, frequency data, and active status in under 10 minutes. Cost: $100 for the scrape plus $6 for email verification. Total: $106 vs. $2,000-2,800/month in labor.

That's not a small difference. It's the difference between podcast outreach being a profitable service and a money-losing one. These numbers reflect one agency's use case — results will vary depending on keyword specificity, niche email coverage rates, and outreach volume.

Implementation checklist

Define your keyword list. Start with 3-5 specific compound phrases that match your client's niche. "B2B SaaS interviews" is better than "business."
Choose your scraping method. For under 50 shows, manual RSS parsing works. For 200+, use an automated tool like the Podcast Directory Scraper or build your own RSS parser.
Run the extraction across both platforms. Apple Podcasts and Spotify have different indexes. Scraping both gives broader coverage — expect 20-30% overlap.
Deduplicate results. Match on podcast name + host name to remove cross-platform duplicates. Automated tools handle this; manual lists need a VLOOKUP pass.
Filter by publishing frequency. Remove anything classified as "dormant" or "sporadic" — these shows aren't actively booking.
Verify emails. Run extracted emails through ZeroBounce, NeverBounce, or a similar service. Budget $3-5 per 1,000 addresses.
Enrich missing contacts. For shows without RSS emails, use the website URL to find contact pages. The Email Pattern Finder can discover email formats for podcast production companies. For higher coverage, Waterfall Contact Enrichment chains multiple data sources to fill gaps.
Export and segment. Split your list by genre, episode count, and frequency. Write segment-specific pitch templates.
Track responses. Tag each contact with the source keyword and platform so you can measure which segments convert best.

Limitations of RSS-based email extraction

Not every RSS feed contains a valid email. Roughly 30-40% of podcasts in entertainment and lifestyle categories use placeholder addresses, hosting platform defaults (like noreply@anchor.fm), or omit the <itunes:email> field entirely. Business and technology categories have better coverage (60-70%), but it's never 100%.

No audience demographics. RSS feeds contain metadata about the show — not the audience. If you need listener counts, demographics, or geographic distribution, you need a platform like Rephonic or Podchaser that models audience data from multiple signals.

Email freshness isn't guaranteed. A podcast owner might have changed their email since submitting to Apple Podcasts. RSS feeds update when the owner pushes a new episode with updated metadata, but many never touch their <itunes:owner> tag after initial submission.

Rate limiting on directory APIs. Apple Podcasts and Spotify search APIs have undocumented rate limits. Aggressive scraping can trigger temporary blocks. Observed safe rates are roughly 50-100 requests per minute (based on internal testing, April 2026).

Cross-platform deduplication isn't perfect. Podcast names vary slightly between platforms ("The SaaS Show" vs "SaaS Show"), and host names may be formatted differently. Automated deduplication catches 85-90% of duplicates; manual review catches the rest.

Key facts about podcast host email extraction

Apple Podcasts indexes over 4.2 million podcasts, each with an RSS feed containing structured metadata including the <itunes:owner> email tag (Listen Notes, 2025)
Direct podcast host emails produce roughly 23% response rates vs. 4% for generic contact forms (Podseeker, 2025)
The <itunes:email> field is a required part of Apple's RSS podcast specification, making it the most standardized source of host contact data (Apple Podcasts Requirements)
Observed email coverage from RSS extraction is 60-70% for business and technology podcasts, dropping to 40-50% for entertainment categories (observed April 2026, n=6,900 podcasts)
Cross-platform duplication between Apple Podcasts and Spotify runs 20-30% for common business keywords (observed April 2026, internal testing)
Email bounce rates on unverified RSS-extracted addresses are typically 8-15% (ZeroBounce, 2025)
The Podcast Directory Scraper Apify actor processes 50 podcasts in 30-60 seconds and 500 podcasts across 5 keywords in 5-8 minutes

Glossary of key terms

<itunes:owner> — An XML tag in podcast RSS feeds that contains the podcast owner's name and email address, used by Apple Podcasts for account verification and communication.

Publishing frequency tier — A classification system (daily, semi-weekly, weekly, bi-weekly, monthly, sporadic, dormant) that categorizes how often a podcast releases new episodes, used to filter active shows from inactive ones.

Cross-platform deduplication — The process of matching podcast records across multiple directories (Apple Podcasts, Spotify) to remove duplicate listings of the same show.

Pay-per-event (PPE) pricing — A billing model where you pay only for successful results (e.g., $0.05 per podcast extracted) instead of a monthly subscription. ApifyForge covers how PPE pricing works in detail.

RSS feed — Really Simple Syndication, an XML-based format that podcasters use to distribute episode metadata to directories. Contains structured fields for show title, description, owner contact info, and episode data.

Podcast booking agency — A service business that pitches clients as guests on relevant podcasts, handling research, outreach, scheduling, and prep. Typically charges $2,000-5,000/month per client.

Where these patterns apply beyond podcast outreach

The underlying approach — extracting structured contact data from public metadata feeds instead of paying for curated databases — applies well beyond podcasting:

YouTube channel outreach: Creator emails are in channel "About" pages, extractable in bulk with scrapers like the Website Contact Scraper
Newsletter sponsorship sales: Publication contact data lives in RSS feeds and about pages, following the same XML parsing approach
Conference speaker outreach: Speaker bios on event websites contain structured contact data that can be scraped and enriched
App developer outreach: App store listings include developer contact emails, accessible through store APIs
Blog outreach for link building: Author pages and contact pages on blogs follow predictable URL patterns, making them targets for automated contact scraping

The principle is the same: public metadata feeds contain structured contact data that subscription databases repackage and mark up. If you can parse the source, you don't need the middleman. ApifyForge's lead generation actors follow this pattern across multiple verticals — podcasts, Google Maps, company websites, and more.

When you need podcast host email extraction

You probably need this if:

You run a podcast booking agency and build new outreach lists for each client
You're a PR firm adding podcast pitching as a service line
You broker podcast sponsorships and need to contact show owners directly
You scrape 500-5,000 podcasts per campaign, not 50
You run campaigns sporadically (quarterly or less) and can't justify monthly subscriptions
Your budget is per-campaign, not per-month

You probably don't need this if:

You need audience demographics and listener overlap analysis — use Rephonic or Podchaser
You pitch fewer than 50 shows per campaign — manual research is fine
You run weekly campaigns and need always-on database access — a subscription makes more sense
You need a CRM with built-in podcast relationship management — Podseeker or Rephonic offer this
You need booking difficulty scores or response rate predictions — these require curated platforms, not raw RSS data

Frequently asked questions

How do you find a podcast host's email address?

The most direct method is extracting the email from the podcast's RSS feed <itunes:owner> tag. Apple requires this field for podcast submission. You can find the RSS URL by viewing the page source of an Apple Podcasts listing and searching for "feedUrl", then opening the RSS XML and searching for "itunes:email". Automated tools like the Podcast Directory Scraper on Apify do this in bulk across thousands of shows.

Is it legal to scrape podcast emails from RSS feeds?

RSS feeds are public, machine-readable data that podcasters voluntarily publish for distribution. The <itunes:email> field is part of a public specification. However, sending bulk unsolicited email is governed by CAN-SPAM (US), GDPR (EU), and CASL (Canada). Scraping the data is generally permissible; what you do with it must comply with anti-spam laws. Consult your legal team for your specific jurisdiction.

What percentage of podcasts have host emails in their RSS feed?

Based on observed extraction runs across 6,900 podcasts (April 2026), roughly 60-70% of active shows in business, technology, marketing, health, and education categories have a valid <itunes:email> in their RSS feed. Entertainment and comedy categories drop to 40-50%. Podcasts hosted on platforms like Anchor (now Spotify for Podcasters) sometimes use platform-generated placeholder emails.

How is RSS email extraction different from Podchaser or Rephonic?

RSS extraction pulls raw contact data directly from podcast feeds in real time — you get the email the owner actually entered. Podchaser and Rephonic maintain curated databases with additional data layers like audience demographics, reach estimates, and booking difficulty. RSS extraction is cheaper and has no subscription, but doesn't include analytics. The comparison table above breaks down the differences across 10 dimensions.

Can you scrape both Apple Podcasts and Spotify at once?

Yes. Both platforms have searchable indexes, and both reference RSS feeds that contain <itunes:owner> data. The key is cross-deduplication — many shows are listed on both platforms, and without matching you'll contact the same host twice. Automated tools handle deduplication by matching on podcast name and host name.

How long does it take to scrape 1,000 podcast contacts?

With automated tools, roughly 2-4 minutes for 1,000 podcasts including RSS parsing and deduplication. Manual extraction takes 2-3 minutes per show, so 1,000 shows would require approximately 33-50 hours of manual work. The Podcast Directory Scraper Apify actor processes 50 podcasts in 30-60 seconds.

What should I do when a podcast's RSS feed doesn't have an email?

Use the podcast's website URL (also extracted from RSS metadata) to find a contact page. The Website Contact Scraper automates this across hundreds of sites. If the website also lacks contact info, try the Email Pattern Finder with the host's name and the podcast's domain to discover likely email addresses. You can also check for social media DMs or booking platforms like PodMatch.

Why do booking agencies prefer pay-per-result over subscriptions?

Podcast booking agencies run campaign-based businesses — they build a list for a client, pitch for 2-4 weeks, then move to the next client. Paying $299/month for Rephonic during the 10 months you're not actively scraping is dead cost. Pay-per-event pricing means you spend $100-250 per campaign and $0 between campaigns. For agencies running 3-4 campaigns per year, that's $400-1,000 vs. $3,588-5,000 on annual subscriptions.

Ryan Clinton operates 300+ Apify actors and builds developer tools at ApifyForge.

Last updated: April 2026

This guide focuses on podcast directory scraping and RSS-based email extraction, but the same patterns — parsing structured metadata from public feeds instead of paying for curated databases — apply broadly to any outreach workflow where contact data is embedded in public, machine-readable formats.

MCP Servers Are the Next Big Thing on Apify — Here's Why

apify forge — Thu, 26 Mar 2026 22:42:01 +0000

Something shifted on Apify in early 2026. They added a new category to the Store: MCP_TOOLS. If you're building actors and you haven't noticed this yet, you're about to miss the biggest distribution shift since pay-per-event pricing launched.

I'm Ryan, the developer behind ApifyForge. We've shipped over 80 MCP intelligence servers on the Apify Store under the username ryanclinton. That's not a typo. Eighty-plus servers, live right now, covering financial crime screening, counterparty due diligence, cybersecurity threat assessment, supply chain risk, regulatory intelligence, and about a dozen other domains. We started building these before Apify even had an official MCP category. And now that the category exists, the timing couldn't be better.

Here's what's actually going on, why it matters, and why most Apify developers are going to be late to this.

What is an MCP server on Apify?

An MCP server is a tool server that speaks the Model Context Protocol, an open standard created by Anthropic in late 2024 that lets AI assistants like Claude, ChatGPT, and Cursor call external tools directly. Instead of copying data into a chat window, the AI connects to an MCP server and runs tools on its own — querying databases, pulling records, scoring risk, returning structured results.

On Apify, an MCP server runs in standby mode. It stays warm, responds to tool calls in seconds, and charges per event. No idle compute costs. No monthly subscriptions. You pay when the AI actually uses the tool. Apify's infrastructure handles the scaling, the hosting, the uptime — the developer just builds the intelligence layer.

According to Anthropic's MCP specification docs, the protocol has seen adoption across Claude Desktop, Cursor, Windsurf, Continue, and dozens of other AI clients since its November 2024 launch. The MCP client directory now lists dozens of AI tools with native support, from Claude Desktop to VS Code to Cursor. The growth curve hasn't slowed down.

Why are MCP servers a bigger deal than regular actors?

Regular Apify actors are great. I've built over 170 of them. But they have a friction problem: somebody has to go to the Apify console, configure the input, hit run, wait for results, then do something with the output. That workflow made sense when humans were the primary interface.

MCP servers remove that friction entirely. An AI assistant discovers the server, reads its tool descriptions, and calls the right tool with the right parameters. No console. No manual configuration. The AI figures it out from the schema.

That matters because the buyer is changing. In 2024, the typical Apify customer was a developer or a growth marketer running scrapers. In 2026, the typical customer is an AI agent that needs real-time data. Those agents need tools. MCP is how they find and use them.

Here's the blunt version: if your actor can't be called by an AI agent, you're building for a shrinking audience.

How many MCP servers are on the Apify Store right now?

As of March 2026, the Apify Store's MCP_TOOLS category is still new. Most developers haven't built any MCP servers yet. ApifyForge has over 80 live — which, tbh, is a wild number. We're the most prolific MCP publisher on the platform by a significant margin.

That head start wasn't an accident. We started building MCP intelligence servers in late 2025, months before Apify launched the official category. The bet was simple: AI assistants would need domain-specific tools, not generic scrapers. A compliance officer using Claude doesn't want a "web scraper." They want a tool that screens an entity against sanctions lists, PEP databases, and adverse media — and returns a risk score they can act on.

So that's what we built. Eighty-plus of them.

What domains do ApifyForge MCP servers cover?

ApifyForge MCP servers span nine major intelligence domains: financial crime and AML screening, counterparty and corporate due diligence, cybersecurity and attack surface analysis, ESG and supply chain risk, regulatory and compliance monitoring, geopolitical risk assessment, M&A target intelligence, critical infrastructure analysis, and competitive intelligence.

Each server isn't a thin wrapper around a single API. That would be pointless — you can call a single API yourself. The value is in orchestration. A single tool call to the Counterparty Due Diligence MCP hits multiple public data sources in parallel, cross-references the results, and returns a scored assessment. The AI assistant gets a structured answer, not a pile of raw JSON it has to figure out.

The Financial Crime Screening MCP does the same thing for AML workflows. The ESG Supply Chain Risk MCP does it for sustainability and supply chain due diligence. The Entity Attack Surface MCP does it for cybersecurity. Different domains, same pattern: multiple sources, parallel execution, domain-specific scoring.

We didn't build 80+ toy demos. We built production intelligence tools that happen to speak MCP.

What does pay-per-event pricing mean for MCP servers?

Pay-per-event (PPE) pricing means the user pays a fixed price per tool call — not per minute of compute time, not per monthly subscription. ApifyForge MCP servers typically charge between $0.05 and $0.50 per tool call, depending on how many data sources the server orchestrates and how much processing each call requires.

This pricing model is a natural fit for AI agents. An agent doesn't know in advance how long a task will take or how much compute it needs. With PPE pricing, the cost is predictable: call the tool, get the result, pay the fixed rate. Apify's paid actors documentation explains how this works at the platform level — the developer sets the event price, Apify handles billing, and the user sees a clear per-call cost.

Compare that to building in-house. A single compliance screening tool costs six figures to build internally when you factor in development, API licensing, maintenance, and compliance validation. An ApifyForge MCP server that does the same screening costs $0.15 per call. Run it a thousand times and you've spent $150.

The economics are lopsided. That's the point.

Why hasn't everyone built MCP servers on Apify yet?

Honestly? Most Apify developers are still thinking in the old model. Build a scraper, put it in the Store, hope someone finds it. MCP servers require a different approach. You need to understand how AI assistants discover and invoke tools. You need to design tool schemas that an LLM can reason about. You need to handle standby mode, which behaves differently from standard actor runs. And you need domain expertise to build something that returns intelligence, not just data.

That last part is the real barrier. Anyone can scrape a website. Not everyone can build a financial crime screening workflow that knows which sanctions lists matter, how to handle fuzzy name matching across multiple alphabets, and what a risk score should actually look like for a given entity type.

The MCP servers that will win aren't the ones built fastest. They're the ones built by people who actually understand the domain. We've spent months studying compliance screening workflows, corporate due diligence processes, and cybersecurity assessment frameworks. That domain knowledge is baked into every scoring algorithm, every data source selection, every output schema.

How do AI assistants find MCP servers on Apify?

AI clients like Claude Desktop, Cursor, and Windsurf use MCP server URLs to connect. The user adds the server's standby URL to their client configuration, and the AI assistant automatically discovers the available tools through the MCP protocol's built-in tool listing mechanism. No plugins to install, no OAuth flows, no marketplace approvals.

Apify's Store acts as a discovery layer. When someone searches for "sanctions screening" or "due diligence" on the Apify Store, they find the MCP server, copy the connection URL, and add it to their AI client. The entire setup takes about 30 seconds, which I wrote about in our getting started guide.

This is a very different distribution model than traditional SaaS. There's no sales cycle. No demo call. No onboarding flow. An AI assistant just... connects and starts calling tools. The M&A Target Intelligence MCP gets used by investment analysts running due diligence through Claude. They didn't fill out a form or talk to sales. They added a URL.

The window is open right now

I'll be direct about this. The MCP category on Apify is new. Most developers haven't started building servers yet. The ones who move now will own the search real estate, build the usage history, and accumulate the reviews that make it hard for latecomers to compete.

We saw this exact pattern play out with regular actors. The early publishers — the ones who shipped quality actors in 2022 and 2023 — still dominate the Store rankings in 2026. Search position compounds. Usage history compounds. Reviews compound.

MCP servers are following the same curve, but earlier. They're the connective tissue between AI agents and the real world.

ApifyForge is already there. Eighty-plus servers. Nine intelligence domains. Production-grade scoring algorithms. PPE pricing that makes sense for agent workflows. You can browse the full catalog at apifyforge.com/actors/mcp-servers or find us in the Apify Store under ryanclinton.

MCP is where Apify is going. We're already there.

Last updated: March 2026

How to Find Podcast Host Emails for Guest Outreach (2026)

apify forge — Wed, 25 Mar 2026 03:42:45 +0000

Podcast guesting is still one of the highest-ROI B2B marketing channels in 2026. A 2025 survey by Demand Sage found that 82% of B2B marketers who appeared on podcasts said it directly influenced pipeline. The problem isn't whether it works. The problem is finding the right email address for the host so your pitch actually lands.

I run ApifyForge, where we've built and maintained 300+ web scraping actors on Apify. One of them -- the Podcast Directory Scraper -- exists specifically because I watched PR agencies burn hours manually hunting for podcast host contact info. There are exactly three reliable data sources for podcast host emails, and most people only know about one of them. Here's all three, plus the workflow that gets you from keyword to verified email in under 10 minutes.

What are the 3 data sources for podcast host emails?

The three primary sources for finding podcast host email addresses are: (1) the itunes:owner tag inside RSS feeds, which contains the email the host registered with their podcast hosting platform; (2) Apple Podcasts and Spotify metadata accessible through their public APIs; and (3) the podcast's own website, which often has a contact page, booking form, or team page with direct email addresses.

Most guides only mention the first one. That's a mistake, because RSS feeds only contain the owner email about 50-80% of the time, according to data from Podcast Index, the open-source podcast directory. If you stop at RSS, you're missing a huge chunk of your target list.

Let me break down each source and what you actually get from it.

Source 1: RSS feed `itunes:owner` tag

Every podcast submitted to Apple Podcasts has an RSS feed. Inside that feed, there's an XML block that looks like this:

<itunes:owner>
  <itunes:name>Sarah Chen</itunes:name>
  <itunes:email>booking@verdantmedia.com</itunes:email>
</itunes:owner>

This is the email the host gave to their podcast hosting platform (Buzzsprout, Libsyn, Podbean, Anchor, etc.) when they set up the show. It's the single best source for host emails because it's self-declared -- the host put it there intentionally. For professionally produced shows, this email is almost always monitored. It's where Apple sends communication about the show, so hosts don't let it go to a dead inbox.

The catch: you can't see this on the Apple Podcasts website or app. The itunes:owner block is only in the raw RSS XML. You have to fetch the feed URL, parse the XML, and extract it. Doing that manually for 200 podcasts is... not how you want to spend your Tuesday.

Source 2: Apple Podcasts and Spotify API metadata

The iTunes Search API returns structured data for every podcast in the Apple catalog -- title, author name, genre categories, episode count, artwork, and the RSS feed URL itself. Spotify's Web API gives you similar metadata plus the Spotify-specific show URL. Neither API gives you the host email directly. But they give you the RSS feed URL (which leads to source 1) and the author/artist name, which you need for source 3.

The iTunes API indexes over 4.2 million podcasts across 175+ country storefronts, per Listen Notes' 2025 statistics. That's the universe you're working with.

Source 3: The podcast website

This is the source most people skip entirely, and it's often the best one. About 65% of podcasts in the Apple catalog include a website URL in their RSS feed's <channel><link> tag. That website usually has:

A "Contact" or "Book a Guest" page with a direct email or form
A team/about page with host names and titles
Footer links with social profiles (LinkedIn is gold for warm outreach)
Sometimes a media kit with explicit booking instructions

For podcast guest outreach specifically, the website is where hosts tell you exactly how to pitch them. Some have a dedicated /guest or /apply page. Some have a Calendly link. Some say "email me at..." in plain text. You just have to actually visit the site and look.

How do you find podcast host emails at scale?

To find podcast host emails at scale, use a three-step process: search Apple Podcasts by keyword to discover shows in your niche, parse each show's RSS feed to extract the owner email, then scrape the podcast website for additional contact data. This workflow replaces 6-8 hours of manual research with a 5-10 minute automated run.

Here's the exact setup I recommend, with real input and output examples.

Step 1: Search and extract with Podcast Directory Scraper

ApifyForge's Podcast Directory Scraper searches Apple Podcasts (and optionally Spotify) by keyword, fetches every matching show's RSS feed, and pulls out the ownerEmail, ownerName, websiteUrl, episode frequency, active status, categories, and more. All in one run.

Here's what a typical input looks like for a PR agency doing guest outreach for a SaaS client:

{
  "searchTerms": ["B2B SaaS marketing", "revenue operations", "demand generation"],
  "maxResults": 100,
  "country": "us",
  "activeOnly": true,
  "includeEpisodes": false
}

Setting activeOnly: true is important. According to Podnews, only about 440,000 of the 4.2 million total podcasts published an episode in the last week. Dead shows waste your outreach time and hurt your sender reputation. Filter them out.

Setting includeEpisodes: false speeds up the run and shrinks the output when you only need host contact data. You don't need episode listings for a pitch email.

Here's a trimmed example of what comes back per podcast:

{
  "podcastId": 1482738706,
  "title": "The Revenue Engine",
  "author": "Pinnacle Growth Media",
  "categories": ["Business", "Entrepreneurship", "Marketing"],
  "episodeCount": 183,
  "lastEpisodeDate": "2026-03-18",
  "episodeFrequency": "weekly",
  "isActive": true,
  "websiteUrl": "https://www.revenueenginepodcast.com",
  "ownerName": "Pinnacle Growth Media LLC",
  "ownerEmail": "booking@pinnaclegrowth.com",
  "feedUrl": "https://feeds.pinnaclegrowth.com/the-revenue-engine.xml",
  "applePodcastsUrl": "https://podcasts.apple.com/us/podcast/the-revenue-engine/id1482738706",
  "scrapedAt": "2026-03-25T09:14:37.000Z"
}

That ownerEmail field is the one you want. You also get the websiteUrl for step 3, the episodeFrequency so you know the show is actively publishing, and the author name so you can personalize your pitch.

Three keywords at 100 results each, with deduplication, typically returns 150-250 unique active podcasts. Cost is $0.05 per podcast on pay-per-event pricing -- so around $7.50-$12.50 per campaign. Compare that to Podchaser Pro at $599/month.

Step 2: Verify the emails you found

Not every ownerEmail is still valid. People change hosting platforms, let domains expire, or set up new booking addresses. Before you send a single pitch, verify.

ApifyForge's Email Pattern Finder does more than find patterns -- it includes built-in email verification with MX record checks and catch-all domain detection. Feed it the domains from your podcast list, and it tells you whether each email is valid, invalid, or risky. The catch-all flag is especially useful: some podcast hosting platforms accept mail to any address at their domain, which means an email can "verify" even if nobody reads it.

Alternatively, for bulk verification without pattern detection, use Bulk Email Verifier directly on the ownerEmail values.

This step takes about 30-60 seconds per domain. On a list of 200 podcasts, expect to cut 15-25% of emails as undeliverable. Better to learn that now than after you've sent 200 pitches and 50 bounced.

Step 3: Fill gaps with Website Contact Scraper

Here's where most podcast outreach workflows fall short. After the RSS extraction in step 1, you'll have maybe 50-80% email coverage. The remaining 20-50% of shows had blank itunes:owner tags or feeds that couldn't be fetched. Those aren't lost -- they just need a different approach.

Take the websiteUrl values from shows where ownerEmail came back null and feed them into ApifyForge's Website Contact Scraper. It crawls each domain, discovers contact pages and team pages automatically, and extracts:

Personal and generic email addresses (separated and classified)
Phone numbers
Contact names with job titles
LinkedIn, Twitter/X, Facebook, Instagram, and YouTube profiles

Enable deep scan mode and it'll probe 14 hidden page paths including /imprint, /contact, /about, /team, and /booking. European podcast websites are legally required to show contact info on their imprint page, so deep scan catches those reliably.

The Website Contact Scraper also has built-in email verification, same as the Email Pattern Finder. So the emails you get back are already checked. No extra step needed.

For the 200-podcast example, if 40 shows had no owner email, running 40 website URLs through the contact scraper costs about $6 (at $0.15 per domain) and typically recovers contact data for 30-35 of those 40 sites. That pushes your total coverage from 80% to 95%+.

What's the best format for a podcast guest pitch email?

The best podcast guest pitch email is 4-6 sentences: a specific reference to a recent episode, one sentence on what you'd discuss, your credibility in one line, and a direct ask. Podcast hosts receive 50-200 pitches per week according to PodMatch's 2024 booking data, so brevity wins.

I'm not a PR specialist, but I've talked to enough podcast booking agencies using ApifyForge to know what works and what gets deleted. Here are the patterns that get replies:

Reference a specific episode. Not "I love your show." Literally: "Your episode with Marcus Webb about ditching outbound at Series B made me rethink our PLG motion." The Podcast Directory Scraper returns episode titles and descriptions if you set includeEpisodes: true -- use them.
Pitch a topic, not yourself. Hosts don't care about your bio. They care about what their audience wants to hear. Lead with "I'd love to talk about [topic that fits their show format]" not "I'm the CEO of..."
One credential, one sentence. "I've built 170+ public actors on Apify's marketplace that process 50,000+ runs per month" is specific and verifiable. "I'm a thought leader in data automation" is not.
Make it easy to say yes. Include your availability, your headshot link, and a 2-sentence bio they can use for show notes. The less work for the host, the more likely you get booked.

How is finding podcast host emails different from LinkedIn outreach?

Finding podcast host emails targets people who have published their contact information inside RSS feeds and on public websites specifically for listener and media communication. LinkedIn outreach operates against platform Terms of Service and reaches people who may not want cold messages. Podcast hosts expect to be contacted.

That's the real difference, and it's worth repeating: podcast hosts want to hear from potential guests. They published that itunes:owner email precisely so people could reach them. That's a fundamentally different dynamic than cold-messaging someone on LinkedIn who hasn't opted into anything.

Some numbers to illustrate. Lavender's 2024 Cold Email Benchmark Report found that average LinkedIn InMail response rates dropped to 1.6% in 2024. Podcast booking agencies I've spoken with consistently report 8-15% reply rates on well-crafted guest pitches sent to verified host emails. That's a 5-9x difference.

The tradeoff is volume. LinkedIn has a billion profiles. Apple Podcasts has 4.2 million shows. But if your goal is getting on 10-20 podcasts that your target audience listens to, you don't need a billion contacts. You need 200 good ones with verified emails.

For teams running account-based marketing alongside podcast guest outreach, ApifyForge's B2B Lead Qualifier can score podcast hosts against your ICP criteria -- company size, industry, geography, tech stack -- so you're pitching shows where the audience actually matches your buyer.

Can you extract podcast host emails without coding?

Yes. ApifyForge's Podcast Directory Scraper runs entirely in the browser through Apify's Console. Type your keywords, click Start, and download the results as CSV or Excel. No code, no API, no RSS parsing. The actor handles the iTunes API calls, RSS feed fetching, XML parsing, and email extraction behind the scenes.

For teams that do want API access, here's a quick Python example that searches, extracts, and prints host emails:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/podcast-directory-scraper").call(run_input={
    "searchTerms": ["fintech founders", "banking innovation"],
    "maxResults": 100,
    "activeOnly": True,
    "includeEpisodes": False,
})

for podcast in client.dataset(run["defaultDatasetId"]).iterate_items():
    if podcast.get("ownerEmail"):
        print(f'{podcast["title"]} | {podcast["ownerEmail"]} | {podcast.get("websiteUrl", "")}')

That's it. 12 lines. The API approach is what most PR agencies integrate into their CRM -- they schedule weekly runs on new keywords and pipe the results into HubSpot or their outreach sequencer via Zapier or Make.

Why do some podcasts not have an owner email in their RSS feed?

Approximately 20-50% of podcasts omit the itunes:owner > itunes:email tag from their RSS feed. This happens because some hosting platforms don't require it, some hosts intentionally remove it for privacy, and older feeds created before Apple enforced the field may never have included it. The percentage varies by niche -- business and technology podcasts average 70-80% coverage, while hobbyist and fiction podcasts average closer to 50%.

When ownerEmail comes back null, you have three fallback options:

Scrape the podcast website. Use Website Contact Scraper on the websiteUrl from the scraper output. This is your highest-yield fallback -- most podcast websites have some form of contact information.
Find the email pattern for the host's company. If you can identify the company behind the podcast from the author or ownerName field, run that domain through Email Pattern Finder. It detects the company's naming convention (first.last@, flast@, etc.) and generates a verified email for any name you provide. At $0.10 per domain, it's cheap insurance.
Check the show notes on recent episodes. Some hosts include booking info in their episode descriptions. If you ran the Podcast Directory Scraper with includeEpisodes: true, the episode descriptions are already in your dataset. Search them for "email", "book", "pitch", or "guest" to find hosts who publish their booking process there.

How much does it cost to find podcast host emails?

The full pipeline for finding and verifying podcast host emails for a typical outreach campaign of 200 targeted shows costs between $15-25 on ApifyForge, using pay-per-event pricing where you only pay for results.

Here's the breakdown:

Step	Actor	Volume	Cost per unit	Total
Search + extract	Podcast Directory Scraper	200 podcasts	$0.05	$10.00
Verify emails	Bulk Email Verifier	~160 emails	$0.02	$3.20
Fill gaps (websites)	Website Contact Scraper	~40 domains	$0.15	$6.00
Total				$19.20

That gets you 190+ verified host contacts for under $20. No monthly subscription. No per-lookup fees. Use ApifyForge's cost calculator to estimate your specific campaign before running.

Compare that to the alternatives. Podchaser Pro charges $599/month. Rephonic starts at $99/month. A virtual assistant doing this manually bills 8-10 hours at $25-50/hour for $200-500. And the manual approach doesn't include email verification.

What about using podcast booking platforms like PodMatch?

Podcast booking platforms like PodMatch, Podmatch, and Matchmaker.fm charge $49-199/month and limit you to their curated database, which is typically 10,000-50,000 shows. Direct outreach via email to hosts found through directory scraping covers the full 4.2 million show Apple Podcasts catalog with no monthly fee.

These platforms aren't bad. They're just limited. If you're doing 2-3 guest spots per year, a booking platform is fine. If you're a PR agency booking clients onto 10-20 shows per month, you need to work outside the platform's walled garden. The hosts who are easiest to book are already getting pitched by every other PodMatch user. The best opportunities are shows that aren't on any booking platform -- and those shows still have emails in their RSS feeds.

Direct outreach also lets you control the pitch entirely. No platform template, no character limits, no hoping the host logs into their PodMatch inbox. You send a real email to their real address and reference their actual content. That's how you stand out.

The complete workflow, start to finish

For PR agencies and B2B marketing teams doing podcast guest outreach at scale, here's the full pipeline I've built on ApifyForge:

Identify target keywords -- 3-5 terms that match the topics your guest expert can speak about. Be specific: "healthcare SaaS growth" beats "business."
Run Podcast Directory Scraper with those keywords, activeOnly: true, and maxResults: 100. Takes 2-4 minutes.
Filter the results -- sort by episodeFrequency (weekly and biweekly shows are most active), check episodeCount (shows with 50+ episodes are established), and verify isActive: true.
Verify the ownerEmail addresses -- run them through Bulk Email Verifier to catch dead addresses before you send.
Fill gaps on shows where ownerEmail is null -- feed their websiteUrl into Website Contact Scraper with deep scan enabled.
For remaining gaps, run the host's company domain through Email Pattern Finder to detect their naming convention and generate a verified email.
Personalize and send -- reference a specific recent episode, pitch a topic (not yourself), and keep it under 6 sentences.

Schedule the Podcast Directory Scraper weekly on the same keywords and you'll catch new shows as they launch. Some of the best guest spots come from shows in their first 20 episodes -- the host is hungry for guests and the audience is growing fast.

One thing I'll say honestly: this pipeline works best for targeted outreach, not spray-and-pray volume. If you're trying to blast 5,000 podcast hosts with the same template, don't. You'll get blacklisted and burn the channel for everyone else. 200-500 highly targeted hosts per quarter, with personalized pitches, is the sweet spot.

ApifyForge catalogs 300+ web scraping actors and 93 MCP intelligence servers. The podcast outreach pipeline above uses four of them. You can compare all the contact scraping options on our comparison page to see which combination fits your specific workflow.

Last updated: March 2026

Stop Manual Prospecting: How a 3-Actor Pipeline Finds and Scores B2B Leads

apify forge — Tue, 24 Mar 2026 13:13:00 +0000

Most SDRs I've talked to spend 2-3 hours a day on manual prospecting. Open LinkedIn. Find a company. Google their website. Hunt for emails on the contact page. Copy-paste into a spreadsheet. Repeat 50 times. Maybe score them by gut feel. A 2024 Gartner report on sales development found that SDR teams spend 21% of their working time on manual data entry and research alone. That's a full day per week, gone.

I built the B2B Lead Gen Suite because I got tired of watching people run three separate tools in sequence when the whole thing could be one pipeline. You give it company URLs. It gives you back scored, graded, enriched leads with emails, phone numbers, contact names, email patterns, tech stack signals, and a 0-100 quality score. One input, one output, three actors chained together under the hood.

Here's how it works, when to use it, and what it actually costs compared to doing this manually or paying for Clay.

What is the B2B Lead Gen Suite?

The B2B Lead Gen Suite is an Apify actor that chains three specialized sub-actors into a single automated pipeline: Website Contact Scraper extracts emails, phones, and named contacts; Email Pattern Finder detects the company's email format and generates addresses; B2B Lead Qualifier scores each lead 0-100 on reachability, legitimacy, and web presence.

That's the snippet version. Here's the longer story.

I already had the three pieces built as standalone actors on ApifyForge. The Website Contact Scraper was pulling 11,000+ runs with a 99.8% success rate. The Email Pattern Finder was detecting patterns like first.last@company.com across domains. The B2B Lead Qualifier was grading leads A through F based on five scoring dimensions. People were already using all three together — just manually, one after the other, copying datasets between runs.

The suite eliminates the copying. It runs all three in sequence, passes data between them automatically, merges everything into a single enriched lead record, and sorts by score so your best prospects are at the top.

How does the 3-actor pipeline work?

The pipeline runs in three sequential steps. Step 1 crawls each company website to extract raw contact data. Step 2 analyzes discovered emails to detect the company's naming pattern and generate additional addresses. Step 3 re-crawls each site to score leads on five quality dimensions and assign a letter grade.

Each step feeds into the next. The contact scraper's output becomes the pattern finder's input. Both outputs feed into the qualifier. The orchestrator merges all three datasets by domain, deduplicates emails and phone numbers, and produces one combined record per company.

Step 1: Website Contact Scraper crawls up to 20 pages per domain (default is 5) looking for emails, phone numbers, named contacts with job titles, and social media links. It uses Apify's CheerioCrawler — fast HTTP parsing, no browser overhead. According to Apify's 2025 platform benchmarks, CheerioCrawler processes pages 5-10x faster than browser-based approaches while using a fraction of the compute.

Step 2: Email Pattern Finder takes the scraped emails and contact names, detects the dominant pattern (first.last@, firstinitiallast@, first@, etc.), and generates email addresses for any named contacts who didn't have a discoverable email. It also searches GitHub commits for additional email evidence. According to research by Return Path (now Validity), the first.last@ pattern alone accounts for roughly 32% of corporate email formats globally — but there are over a dozen common variations, and guessing wrong means bounced emails and damaged sender reputation.

Step 3: B2B Lead Qualifier makes a second pass over each domain to gather business signals. It scores leads across five dimensions:

Dimension	What it measures
Contact Reachability	How many emails, phones, named contacts are accessible
Business Legitimacy	Company address, registration signals, terms/privacy pages
Online Presence	Social media profiles, review site presence
Website Quality	SSL, load speed, CMS detection, mobile-friendliness
Team Transparency	Named team members, leadership pages, org structure signals

Each lead gets a 0-100 score and an A-F letter grade. The output is sorted highest score first.

What data comes back in each lead record?

Every enriched lead record includes the domain, URL, all discovered emails and phone numbers, named contacts with titles, social links, detected email pattern with confidence score, generated email addresses, lead score, grade, score breakdown across five dimensions, qualification signals, tech stack signals, business address, CMS platform, and pipeline metadata.

That's a lot of fields. Let me show what one actually looks like in practice. Say you run it against apify.com:

{
  "domain": "apify.com",
  "url": "https://apify.com",
  "emails": ["info@apify.com", "support@apify.com"],
  "phones": [],
  "contacts": [
    { "name": "Jan Curn", "title": "CEO" }
  ],
  "socialLinks": {
    "linkedin": "https://linkedin.com/company/apify"
  },
  "emailPattern": "first@apify.com",
  "emailPatternConfidence": 0.85,
  "generatedEmails": [
    { "name": "Jan Curn", "email": "jan@apify.com" }
  ],
  "score": 82,
  "grade": "A",
  "scoreBreakdown": {
    "contactReachability": 18,
    "businessLegitimacy": 20,
    "onlinePresence": 16,
    "websiteQuality": 18,
    "teamTransparency": 10
  },
  "signals": [
    { "signal": "ssl-valid", "category": "websiteQuality", "points": 5, "detail": "Valid SSL certificate" },
    { "signal": "has-linkedin", "category": "onlinePresence", "points": 8, "detail": "LinkedIn company page found" }
  ],
  "techSignals": ["react", "next.js", "vercel"],
  "cmsDetected": "next.js",
  "pipelineSteps": ["contact-scraper", "email-pattern-finder", "lead-qualifier"],
  "processedAt": "2026-03-24T14:30:00.000Z"
}

You get contacts, patterns, scores, and signals in one record. Import it into your CRM, filter by grade, and start outreach. No spreadsheet gymnastics.

How much does automated lead prospecting cost?

The B2B Lead Gen Suite uses Apify's pay-per-event pricing at $0.50 per enriched lead. A batch of 100 companies costs $50. There are no monthly subscriptions, seat licenses, or minimum commitments. Apify's free tier includes $5 of monthly platform credits for testing.

Here's the real comparison that matters — what you're actually paying across different approaches:

Approach	Cost for 100 leads/month	Annual cost	Data freshness
B2B Lead Gen Suite	$50	$600	Live (scraped at runtime)
Running 3 actors manually	~$40	$480	Live
Clay	$149-$720/mo	$1,788-$8,640	Database (days-weeks old)
Apollo.io	$49-$119/mo	$588-$1,428	Database (days-weeks old)
ZoomInfo	$15,000+/yr	$15,000+	Database (quarterly refresh)
Manual research (SDR @ $60K/yr)	$250+ in labor	$3,000+	Live but slow

The manual research line comes from real numbers. If an SDR makes $60K/year and spends 20% of their time on prospecting research (that Gartner 21% figure again), that's $12,000/year in labor just to find contacts. For 100 leads a month, you're paying roughly $250 in labor time. The suite does it for $50 and takes minutes instead of hours.

The $0.50 per lead covers all three pipeline steps. You can also skip steps you don't need — set skipEmailPatternFinder or skipLeadQualifier to true and you'll only pay for the steps that run. ApifyForge has a cost calculator that estimates your monthly spend based on volume.

Why not just use Hunter.io or Apollo?

Because they're searching a database, not the actual website.

Hunter.io, Apollo, and ZoomInfo maintain pre-scraped databases of email addresses and company information. When you query them, you're searching an index that might be days, weeks, or months old. A 2024 study by Validity found that B2B contact databases decay at roughly 2-3% per month — meaning 25-30% of records in an annual database are stale or wrong.

The B2B Lead Gen Suite scrapes the live website every time. If a company updated their team page yesterday, you get yesterday's data. If they changed their contact email this morning, you get this morning's data. There's no index to go stale.

The other difference is the scoring. Hunter.io and Apollo give you email confidence scores, but they don't tell you whether the company itself is worth pursuing. The Lead Qualifier step in this pipeline analyzes the company's web presence, tech stack, team transparency, and business legitimacy. An "A" grade company with a real address, named leadership team, active social profiles, and modern tech stack is a fundamentally different prospect than an "F" with a parked domain and no contact info. That signal matters more than knowing whether the email is deliverable.

For teams already paying for Apollo or ZoomInfo, this isn't necessarily a replacement. It's a supplement. Use your database tools for broad searches, then run the suite on your shortlist to get fresh data and quality scores before outreach.

How do you set up the B2B Lead Gen Suite?

Setting it up takes about two minutes. There's no code to write unless you want to call it programmatically.

Go to the B2B Lead Gen Suite on Apify. Paste your company URLs into the input — one per line, bare domains like stripe.com or full URLs like https://stripe.com both work. The actor normalizes everything.

The key settings:

maxPagesPerDomain (default 5) — how many pages to crawl during contact scraping. 5 covers homepage, contact, about, and team pages for most sites. Bump to 10-15 for large corporate sites.
maxQualifierPagesPerDomain (default 5) — pages to crawl during the qualification step. Same logic.
minScore (default 0) — filter out leads below this score. Set to 50 to only get C-grade or better. Set to 70 for B-grade and above.
skipEmailPatternFinder — skip pattern detection if you only need raw contacts and scores.
skipLeadQualifier — skip scoring if you just want contacts and email patterns.

For programmatic access:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/b2b-lead-gen-suite").call(run_input={
    "urls": [
        "stripe.com",
        "notion.so",
        "linear.app",
        "vercel.com",
        "planetscale.com"
    ],
    "maxPagesPerDomain": 5,
    "minScore": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("type") == "summary":
        print(f"Pipeline complete: {item['totalLeads']} leads")
        continue
    print(f"{item['domain']}: Grade {item['grade']} (Score: {item['score']})")
    print(f"  Emails: {', '.join(item.get('emails', []))}")
    print(f"  Pattern: {item.get('emailPattern', 'none detected')}")

Five URLs, one API call, three pipeline steps, scored leads sorted in your terminal. The whole run typically finishes in 5-10 minutes depending on how many pages each site has.

What can you do with scored leads?

The score and grade aren't just vanity metrics. They change how you prioritize outreach.

I've seen users split their output into three tiers. A/B leads (score 70+) go directly to personalized email sequences. C leads (50-69) get added to nurture campaigns. D/F leads (below 50) get dropped or flagged for manual review. HubSpot's 2025 Sales Trends Report found that companies using lead scoring see 77% higher lead gen ROI compared to those without any scoring system.

The score breakdown tells you why a lead scored the way it did. If a company scores 85 overall but has a 5/20 on team transparency, you know they don't list their team publicly — which might mean a smaller startup, or a company that's intentionally opaque. That context shapes your approach.

Some specific workflows I've seen ApifyForge users run:

Agency client prospecting. A marketing agency scrapes 500 local businesses from Google Maps Lead Enricher, feeds those URLs into the suite, filters by grade B+, and pitches the top 50. The score breakdown tells them which companies have weak online presence (their service) versus which already have it together (harder sell).

Competitor customer mining. You have a competitor's customer list (from case studies, testimonials, G2 reviews). Run those URLs through the suite. The tech signals tell you what stack they're on. The contact data gives you decision-maker emails. The score tells you which ones are worth pursuing. ApifyForge's lead generation comparison covers how this fits into broader competitive intelligence.

Inbound lead qualification. Someone fills out your demo form. Before the SDR picks up the phone, run the company domain through the suite. In 2 minutes you know their team size signals, tech stack, online presence, and whether they even have a real business behind the website. A McKinsey 2024 analysis found that companies qualifying inbound leads with automated data enrichment close 23% faster than those relying on manual research.

What are the limitations?

I'd rather you know the boundaries before you hit them in production.

JavaScript-rendered sites. The contact scraper uses CheerioCrawler (static HTML parsing). React/Angular/Vue apps that load contact info via client-side JavaScript won't have that content captured. According to W3Techs' 2025 survey, about 20% of business sites are fully client-rendered. For those, you'd need to run the individual sub-actors separately with a browser-based crawler, or use the Pro version of the contact scraper. ApifyForge has a contact scraper comparison that breaks down the differences.

Pipeline step failures are graceful, not fatal. If the Email Pattern Finder fails on a domain, the suite continues without pattern data. If the Lead Qualifier fails, you still get contacts and patterns but no scores. The pipelineSteps field in each record tells you exactly which steps completed. This is by design — I'd rather return partial data than nothing.

1,000 domains per run is the practical ceiling. The orchestrator calls sub-actors sequentially and caps dataset reads at 1,000 items. For bigger batches, split into multiple runs.

No CRM push built in. The output is a dataset you download as JSON, CSV, or Excel. If you want automatic Salesforce/HubSpot sync, use Apify's webhook integrations or their Zapier connector.

PPE spending limits are respected. If you set a spending cap on your Apify account, the suite stops mid-output when the cap is hit and tells you how many leads were processed versus skipped. No surprise bills.

How does this compare to building your own pipeline?

You could absolutely wire up the three sub-actors yourself. Call Website Contact Scraper, parse its dataset, transform the output into Email Pattern Finder input, call that, parse again, transform into Lead Qualifier input, call that, then merge all three datasets by domain. I know because people were doing exactly this before I built the suite.

The suite saves you from writing and maintaining that glue code. It handles URL normalization, error recovery (if a sub-actor fails, the pipeline continues), result merging with email/phone deduplication across all three data sources, score-based sorting, and the minScore filter. The transforms are non-trivial — the contact-scraper-to-pattern-finder transform matches emails to contact names, passes discovered names for email generation, and skips the website search since the scraper already covered it.

If you need custom logic between steps — say, filtering domains by industry before qualifying them — then running the actors individually gives you more control. ApifyForge's learn guide on PPE pricing explains how pay-per-event works across chained actors.

But if you just want "URLs in, scored leads out," the suite is the path of least resistance.

Is web scraping contacts legal?

This comes up in every conversation about lead gen tools, so let me address it directly.

Scraping publicly accessible business contact information from company websites is broadly considered legal in the US and EU, provided you're collecting data the company intentionally published for public consumption. The 2022 US Ninth Circuit ruling in hiQ v. LinkedIn confirmed that scraping publicly available data doesn't violate the Computer Fraud and Abuse Act. In the EU, GDPR allows processing of business contact data under the "legitimate interest" basis (Article 6(1)(f)), though you need to be able to justify your purpose.

That said, I'm not a lawyer, and you should consult one if you're scraping at scale for commercial outreach. Some ground rules: only scrape data the company published intentionally (contact pages, team pages, footer info). Don't scrape behind logins. Respect robots.txt. And if you're sending cold email with scraped addresses, comply with CAN-SPAM (US), GDPR (EU), or CASL (Canada) depending on your recipients' location.

ApifyForge's personal data exposure report can help you understand what data is publicly visible about individuals — useful for both prospecting and privacy compliance.

Who should use the B2B Lead Gen Suite?

SDRs who prospect more than 20 companies a week. Growth marketers who need enriched lead lists for campaigns. Agency owners who build prospect databases for clients. RevOps teams enriching CRM records. Recruiters mapping out teams at target companies.

Basically, anyone who's currently doing the manual version of "find company website, hunt for emails, look up who works there, decide if they're worth contacting." If that takes you more than an hour a week, the suite pays for itself on the first run.

The actor is live at apify.com/ryanclinton/b2b-lead-gen-suite. Run it on 5 domains and see if the output matches what you'd spend 30 minutes finding manually. That's the pitch.

Last updated: March 2026

Built and maintained by ApifyForge — 300+ Apify actors and 93 MCP intelligence servers for web scraping, lead generation, and compliance screening.

How to Scrape Podcast Directories for B2B Leads

apify forge — Mon, 23 Mar 2026 07:43:44 +0000

Every week, thousands of founders, VPs, and department heads go on podcasts and voluntarily share their company name, job title, and website URL. It's public, it's structured, and almost nobody in B2B sales is using it.

Podcast directories like Apple Podcasts hold structured metadata for over 4.2 million shows globally, according to Listen Notes' 2025 podcast statistics. That metadata includes host names, owner email addresses, website links, RSS feed URLs, episode frequency, and — critically — guest information embedded in show notes. If you're doing B2B lead generation, this is an overlooked goldmine sitting right next to the overfarmed fields of LinkedIn and Apollo.

I built ApifyForge's Podcast Directory Scraper specifically because I kept seeing this gap. People search Apple Podcasts for keywords in their industry, find podcast hosts and guests who match their ICP, and then... manually copy-paste everything into a spreadsheet. That's insane for any list over 20 shows.

Here's how the automated version works, and why it feeds into a broader enrichment pipeline that turns raw podcast data into qualified B2B contacts.

What data can you extract from podcast directories?

Podcast directories store structured metadata about every listed show, including the host's name, owner email address, company website URL, RSS feed link, episode count, publishing frequency, genre categories, and active/inactive status. Apple Podcasts alone indexes over 4.2 million shows with this data publicly accessible.

That's the short version. The longer version is more interesting.

When someone submits a podcast to Apple Podcasts, they fill in a bunch of fields that most people don't think of as "lead data." But it is. The owner email is usually a real person's address — not a noreply@. The website URL points to the company or personal brand behind the show. The show description often names the host's title and company explicitly. And if the podcast has guests (most B2B podcasts do), the episode descriptions frequently include the guest's full name, company, title, and sometimes even a direct link to their LinkedIn profile.

What ApifyForge's Podcast Directory Scraper pulls from Apple Podcasts for each show:

Host/owner name and email address
Company website URL
RSS feed URL (which often contains even more metadata)
Total episode count and publishing frequency
Genre and category tags
Active vs. inactive status
Last episode publish date

The RSS feed alone is worth the scrape. According to Podcast Index, RSS feeds for business podcasts contain structured <itunes:owner> and <itunes:author> tags with contact details that don't appear in the Apple Podcasts UI. That's data most people browsing the directory never even see.

Why are podcast guests good B2B leads?

Podcast guests are strong B2B leads because they've publicly demonstrated expertise, willingness to engage with new audiences, and active involvement in their industry. A 2024 study by Edison Research found that 47% of monthly podcast listeners in the US are in the 18-44 age bracket with above-median household income — the exact demographic making B2B purchasing decisions.

But the real reason podcast guests convert well isn't demographic. It's behavioral.

Someone who goes on a podcast is already doing three things that matter for outbound sales: they're building their personal brand, they're open to conversations with strangers, and they've implicitly said "yes, I want more visibility." That's a fundamentally different prospect than someone you cold-scraped off LinkedIn who hasn't posted in 8 months.

I've talked to SDR teams who run podcast-based outbound and they consistently report 2-3x higher reply rates compared to traditional cold email lists. The opener writes itself: "Hey, I caught your episode on [Podcast Name] about [Topic]. You mentioned [specific thing]..." That's not a template. It's a real conversation starter with a specific hook.

There's a second angle here too. Podcast hosts are leads themselves. If someone runs a podcast about, say, supply chain management, they're either (a) a consultant selling supply chain services, (b) an executive at a supply chain company, or (c) a media person who can introduce you to both. Either way, they're worth talking to.

How to extract company websites from podcast episodes

The process for extracting company website URLs from podcast directories involves three steps: searching for shows by industry keyword, scraping the show metadata including website fields, and following RSS feeds to extract additional contact details from episode-level data.

Here's the practical workflow I use and what I recommend to anyone building this pipeline.

Step 1: Keyword search across Apple Podcasts. Use industry-specific search terms, not generic ones. "Supply chain logistics" beats "business." "Fintech founders" beats "startups." The more specific your keyword, the higher the ICP match rate in the results. ApifyForge's Podcast Directory Scraper accepts multiple search terms and deduplicates results across them, so you can run 5-10 keyword variations in a single scrape.

Step 2: Filter for active shows. A podcast that hasn't published in 6+ months is probably dead. The people behind it may have moved on, changed companies, or shut down the business. The scraper returns last-publish dates and episode frequency, so you can filter for shows that published within the last 90 days. According to Podnews, only about 440,000 podcasts published an episode in the last 7 days out of the 4.2 million total — so active status is a real signal.

Step 3: Follow the website URL. This is where it gets good. Once you have the company website from the podcast metadata, you feed those URLs into a contact scraper to get emails, phone numbers, and team pages. I built Website Contact Scraper for exactly this — give it a list of domains and it returns structured contact data. We go deeper into how that works in the contact scraper comparison page.

What's the difference between podcast scraping and LinkedIn scraping for leads?

Podcast directory scraping extracts data from publicly listed show metadata on platforms like Apple Podcasts, which is published voluntarily by show owners. LinkedIn scraping operates against LinkedIn's Terms of Service and risks account bans. Podcast data carries lower legal risk and higher response rates for outbound.

That's the featured-snippet answer. The more honest answer has some texture.

LinkedIn is where everyone goes for B2B leads. Which is exactly the problem. Every VP of Sales at a mid-market SaaS company gets 40+ cold LinkedIn messages per week, according to Lavender's 2024 Cold Email Benchmark Report. The channel is saturated. Response rates on LinkedIn InMail dropped to 1.6% average in 2024, down from 2.3% in 2022.

Podcast directories are the opposite. Almost nobody is mining them for leads at scale. The data quality is higher because it's self-reported by show owners (not scraped from a stale database), and the outreach angle is warmer because you can reference specific episodes.

There are tradeoffs though. LinkedIn gives you a bigger universe — 1 billion profiles vs. 4.2 million podcasts. And LinkedIn data includes job title and company directly in structured fields, while podcast data sometimes requires a second enrichment step to get from "website URL" to "VP of Engineering at Acme Corp." That's where tools like Email Pattern Finder and Waterfall Contact Enrichment come in — they bridge the gap between "I have a domain" and "I have a named contact with a verified email."

Factor	Podcast Directories	LinkedIn
Data freshness	Real-time (scraped live)	Days to months stale
Legal risk	Low (public RSS/API data)	High (ToS violations)
Contact availability	Website + owner email	Direct profile data
Outreach angle	Episode-specific hooks	Generic connection request
Response rate	5-8% (reported by SDR teams)	1.6% average InMail
Universe size	~4.2M shows	~1B profiles
Enrichment needed	Yes (domain to contact)	Minimal

How to build a podcast-to-lead enrichment pipeline

The real power isn't in the podcast scrape alone. It's in what you do with that data next. Here's the pipeline I've built and that ApifyForge users run every week.

Podcast Directory Scraper pulls show metadata by keyword from Apple Podcasts. You get owner emails, website URLs, and show details. Cost is about $0.10-0.15 per podcast scraped on Apify's pay-per-event pricing — so scraping 500 shows runs you $50-75.

Feed the website URLs into Website Contact Scraper. It crawls each domain and extracts emails, phone numbers, team member names, and job titles. I covered how that extraction engine works in my 11,000 runs deep dive. It uses CheerioCrawler with 19 contact-page path patterns, three extraction strategies for emails/phones/names, and 13 junk-email filters. Success rate sits at 99.8%.

For contacts where Website Contact Scraper doesn't find a direct email (smaller companies without a team page, for example), run the domains through Email Pattern Finder. It determines the email pattern for a company (first.last@, firstlast@, first@, etc.) and generates likely email addresses for known contacts.

For maximum coverage, Waterfall Contact Enrichment runs multiple enrichment strategies in sequence — it tries each data source in order and stops when it gets a hit. Think of it as a fallback chain: if source A doesn't have the email, try source B, then source C.

The end result: you started with a keyword like "ecommerce logistics" and ended up with a list of podcast hosts and guests, their company names, verified email addresses, phone numbers, and job titles. All from public data, no LinkedIn required.

Does podcast scraping actually work at scale?

I'll be direct: it works well for targeted B2B outreach, not for building massive cold email lists. If you need 100,000 generic leads, this isn't the right channel. If you need 200-500 highly qualified prospects in a specific niche, podcast directories are one of the best sources I've found.

The math works like this. A keyword search for "B2B SaaS" on Apple Podcasts returns roughly 300-500 active shows. Each show has at least one host, most have a website, and many have identifiable guests across their episode archive. Run that through the enrichment pipeline above and you're looking at 400-1,500 named contacts with company websites and emails. Per keyword.

Stack 5-10 keywords and you've got a pipeline of 2,000-15,000 contacts that are genuinely relevant to your market. That's not spray-and-pray volume. That's a targeted list where every contact has publicly demonstrated interest in your industry by either hosting or appearing on a podcast about it.

ApifyForge tracks over 300 web scraping actors and 93 MCP intelligence servers — I've seen a lot of lead gen approaches. The podcast angle is underused relative to its conversion potential. The data is fresh, it's public, and the outreach hooks are built into the content itself.

For teams doing account-based marketing (ABM), there's another angle worth mentioning. If you're targeting specific companies, search for podcasts where their executives have appeared as guests. Our B2B Lead Qualifier can score these leads against your ICP criteria automatically — company size, industry, geography, tech stack.

What about legal and compliance considerations?

Podcast metadata on Apple Podcasts and in RSS feeds is published publicly by show owners for the explicit purpose of being discovered. Scraping this data for business use falls under publicly available information, which is generally permissible under US law following the hiQ Labs v. LinkedIn ruling where the Ninth Circuit held that scraping public data does not violate the Computer Fraud and Abuse Act.

That said, I'm not a lawyer, and you should think about a few things.

GDPR applies if you're collecting data on EU residents — even from public sources. Article 6(1)(f) allows processing based on legitimate interest, but you still need to be able to justify the processing and honor data subject access requests. The UK ICO published guidance on legitimate interest assessments that's worth reading if you're doing this at scale.

CAN-SPAM in the US is more straightforward: you can email anyone for commercial purposes as long as you include an opt-out mechanism, honor unsubscribe requests within 10 business days, and don't use deceptive headers or subject lines. The podcast data itself isn't the compliance concern — it's what you do with it afterward.

For compliance-heavy industries, ApifyForge offers compliance screening tools and MCP servers like the Counterparty Due Diligence MCP that can screen your lead lists against sanctions databases and adverse media before outreach.

Getting started

The fastest path from "I want podcast leads" to "I have a qualified list":

Pick 3-5 industry keywords that match your ICP
Run Podcast Directory Scraper with those keywords
Filter results for active shows (published in last 90 days)
Feed the website URLs into Website Contact Scraper for emails and team data
Run gaps through Email Pattern Finder or Waterfall Contact Enrichment
Personalize outreach with episode-specific references

You need an Apify account to run the actors. ApifyForge's cost calculator can estimate what a full pipeline run costs based on your keyword count and expected result volume. All actors use pay-per-event pricing, so you only pay when you get results — no monthly subscriptions or unused credits.

Podcast directories won't replace LinkedIn for raw volume. But for targeted, high-reply-rate outbound to people who are actively building their public presence? It's one of the better channels I've seen. And almost nobody's running it at scale yet.

How I Built a Website Contact Scraper That Hit 11,000+ Runs

apify forge — Sun, 22 Mar 2026 08:19:39 +0000

I've been building web scraping actors on Apify for a while now. Over 170 public actors, 93 MCP intelligence servers, the whole thing. But one actor keeps outperforming everything else in my portfolio: Website Contact Scraper.

It's hit 11,516 runs, 236 users, and a 99.8% success rate. Not the most exciting numbers you'll ever hear, but for a single-purpose scraping tool on Apify's marketplace? That's solid traction. And the reason it works is dead simple: it does one thing well.

You give it a list of business website URLs. It gives you back emails, phone numbers, team member names with job titles, and social media links. One structured JSON record per domain. No login required, no API keys to configure, no code to write.

This post is about how it works under the hood, what I learned building it, and why it keeps getting picked over alternatives that cost 10x more.

What does Website Contact Scraper actually extract?

Website Contact Scraper crawls business websites and returns structured contact data including email addresses, phone numbers, team member names with job titles, LinkedIn profiles, and social links for Twitter/X, Facebook, Instagram, and YouTube. It outputs one deduplicated record per domain.

Here's the full breakdown of what comes back in each result:

Email addresses from mailto: links, body text (with script/style nodes stripped), and anchor hrefs
Phone numbers from tel: links and formatted numbers in contact areas (header, footer, nav, address blocks)
Team member names via Schema.org Person markup, 11 CSS team-card selectors, and heading-paragraph pair analysis
Job titles matched against 35+ title keywords (CEO through Finance) from adjacent elements
Social links for LinkedIn, Twitter/X, Facebook, Instagram, YouTube
Metadata: domain, pages scraped count, ISO timestamp

The important bit: everything is deduplicated across all pages crawled. Emails by exact lowercase string. Phones by digit-only key, so +1 (415) 555-0192 and 4155550192 collapse to one entry. Contacts by case-insensitive name. Social links first-match-per-platform.

How does the contact extraction work technically?

The actor uses Apify's CheerioCrawler to parse static HTML without a browser. It runs up to 10 concurrent connections with a 120 requests/minute rate limit and automatic retries. For each domain, it crawls the homepage first, then discovers and follows same-domain links matching 19 contact-page path keywords like /contact, /about, /team, /leadership, and /people.

Three extraction strategies run in parallel on every page:

Email extraction runs a regex (/\b[a-zA-Z0-9._%+\-]+@[a-zA-Z0-9.\-]+\.[a-zA-Z]{2,12}\b/g) against three sources: mailto link hrefs, cleaned body text (script and style nodes stripped to avoid tracking pixel emails), and all anchor href attributes. Thirteen junk patterns filter out noreply@, test@, admin@, webmaster@, and addresses from placeholder domains like example.com, sentry.io, and wixpress.io.

Phone extraction prioritizes tel: links as the most reliable source. For text-based numbers, it only searches contact-specific page areas — header, footer, nav, address elements, and anything with contact/phone/info CSS classes. Three regex patterns cover international (+1 (555) 123-4567), parentheses ((555) 123-4567), and separator formats (555-123-4567). Numbers must be 7-15 digits, can't be all-same-digit sequences, and can't contain 1234567.

Name extraction uses three strategies: Schema.org Person structured data, 11 team-card CSS selectors (.team-member, .team-card, .staff-member, etc.), and heading-paragraph pairs where an h3/h4 matches a strict proper-name regex and the next sibling contains job title keywords. A 40-word junk-name blocklist catches false positives like "Free Plan" and "Our Services."

This strict approach is why the success rate sits at 99.8%. I'd rather miss a phone number buried in body copy than return a random 10-digit sequence from a ZIP code or product SKU.

Why build another contact scraper?

Honestly, I built it because the existing options annoyed me.

Hunter.io charges $49-$149/month for access to a pre-scraped database. Clay runs $149-$720/month. Both are querying stale indexes — you're paying to search what they scraped weeks or months ago, not what's actually on the website right now.

Website Contact Scraper crawls the live site every time you run it. The data is as fresh as the website itself. And the pricing model is fundamentally different: $0.15 per website scanned. No subscription, no monthly minimum, no seat licenses.

According to Apify's 2025 web scraping report, pay-per-result pricing has grown 340% year-over-year on their platform. Users don't want subscriptions for tools they use irregularly. They want to pay when they actually get value. That's exactly what PPE (pay-per-event) pricing does — you're charged per website successfully scanned, not per API call or monthly seat.

Quick math: scanning 200 websites costs $30. At Hunter.io's Professional plan ($149/month), you'd blow through that budget in one month whether you use it or not. Most ApifyForge users I've talked to spend $5-$30/month on contact scraping and cancel nothing because there's nothing to cancel.

Who's actually using this thing?

The 236 users break down into a few clear patterns based on what I see in the run logs:

SDRs building prospect lists. They paste 50-200 company URLs from LinkedIn Sales Navigator exports or CRM lists. The actor returns emails, direct phone numbers, and LinkedIn profiles they feed into outreach sequences. A 2024 Gartner report found that SDR teams spend 21% of their time on manual data entry and research. This cuts that to near zero for the contact-finding portion.

Marketing agencies doing lead gen for clients. They scrape industry directories and trade association member pages to build prospect databases. The CSV export maps directly to email marketing tools and CRM imports. ApifyForge has a full lead generation comparison page if you want to see how the contact scraper stacks up against other tools in the category.

Recruiters pulling team pages. They want to know who works at target companies, what their titles are, and how to reach them — before making first contact. The contacts array with names and titles is built for this.

RevOps teams enriching CRM data. They run batches of existing company records through the scraper to fill in missing emails, phones, and social profiles. Then they verify addresses with the Bulk Email Verifier before importing.

How much does website contact scraping cost?

Website Contact Scraper costs $0.15 per website scanned using Apify's pay-per-event pricing model. A batch of 100 websites costs $15, and 500 websites costs $75. There is no monthly subscription or minimum commitment. Apify's free tier includes $5 of monthly platform credits, which covers about 33 sites at no cost.

Here's how that compares to alternatives:

Tool	Pricing model	Cost for 200 sites/month	Annual cost
Website Contact Scraper	$0.15/site	$30	$360
Hunter.io	$49-$149/mo subscription	$49-$149	$588-$1,788
Clay	$149-$720/mo subscription	$149-$720	$1,788-$8,640
Apollo.io	$49-$119/mo subscription	$49-$119	$588-$1,428
Manual research (15 sites/hr at $25/hr)	Labor	$333	$4,000

The manual research line is real. Forrester's 2024 B2B data quality study found that companies spend an average of $15,000/year on manual prospect research across their sales teams. Even small teams burn hours on this.

You can set a spending cap per run, and the actor stops gracefully when you hit it. It logs exactly how many domains were processed versus how many were skipped. No surprise charges.

What makes the extraction actually reliable?

Two things I obsessed over: minimizing false positives and maximizing useful signal.

Most contact scrapers just run a regex across the entire page body. That gets you emails from tracking pixels, phone numbers from postal codes, and "names" that are actually navigation headings. The noise-to-signal ratio is terrible.

Here's what I did differently:

Phone numbers only come from structured sources. Tel: links first (most reliable), then formatted numbers in contact-specific page areas only. I intentionally skip the main body copy. A Stanford NLP study on web data extraction showed that restricting extraction to structurally relevant page regions reduces false positives by 60-80% compared to full-page regex.

Email filtering is aggressive. Thirteen junk patterns remove noreply@, admin@, webmaster@, and addresses from known placeholder domains. The body text extraction strips script, style, and noscript nodes before running the regex — this catches tracking pixel emails that other scrapers happily return.

Name validation uses a strict regex plus a blocklist. The name must be 2-4 capitalized words, under 40 characters, and not contain any of 40+ common false-positive words. It catches "Free Trial", "Our Services", "Read More" — all the headings that look like names if you're not careful.

The result: 99.8% success rate across 11,516 runs. That number isn't just "the actor didn't crash." It means the output was structured, clean, and usable.

How to use Website Contact Scraper step by step

Go to Website Contact Scraper on Apify
Paste your website URLs into the input field (root domains like https://acmecorp.com, not deep URLs)
Keep maxPagesPerDomain at 5 — this covers homepage + contact + about + team pages for most sites
Click Start and wait. 50 websites typically finish in 3-5 minutes, 500 in under an hour.
Download results as JSON, CSV, or Excel from the Dataset tab

Or call it programmatically:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("ryanclinton/website-contact-scraper").call(run_input={
    "urls": [
        "https://pinnacleventures.com",
        "https://meridiantech.io",
    ],
    "maxPagesPerDomain": 5,
    "includeNames": True,
    "includeSocials": True,
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['domain']}: {len(item['emails'])} emails")
    for contact in item.get("contacts", []):
        print(f"  {contact['name']} - {contact.get('title', 'no title')}")

The JavaScript and cURL equivalents work the same way — check the full API docs on ApifyForge for examples in every language.

What are the limitations of HTML-based contact scraping?

The biggest one: no JavaScript rendering. Website Contact Scraper uses CheerioCrawler, which parses static HTML. React, Angular, and Vue apps that load contacts via client-side JS won't have that dynamic content captured.

I built Website Contact Scraper Pro for exactly this case — it uses a real browser to render SPAs. But for the roughly 70-80% of business websites that serve contact info in static HTML (according to W3Techs' 2025 web technology survey, only about 20% of business sites are fully client-rendered), the Cheerio version is faster, cheaper, and more reliable.

Other limitations worth knowing:

Same-domain links only — external team directories or hosted about pages won't be discovered
Name extraction depends on HTML patterns — custom layouts may not trigger any of the three strategies
First social link per platform — if a page has multiple LinkedIn profiles, only the first is captured
No authentication — login-gated employee directories aren't supported
Static data — reflects what's on the page at run time, not historical

I list these in the README because I'd rather you know the boundaries upfront than find out mid-project.

How do you combine contact scraping with email verification?

The best workflow chains Website Contact Scraper with Email Pattern Finder and email verification. First, scrape contacts to get names and whatever emails are publicly listed. Then use Email Pattern Finder to predict missing personal emails from the company's naming convention. Finally, verify everything before import.

ApifyForge has a contact scraper comparison page that breaks down how different tools in this pipeline work together. The short version:

Step	Tool	Cost
Scrape contacts	Website Contact Scraper	$0.15/site
Predict missing emails	Email Pattern Finder	$0.10/domain
Verify addresses	Bulk Email Verifier	$0.005/email
Score leads	B2B Lead Qualifier	$0.15/lead

For a batch of 100 companies, that entire pipeline costs about $25-$30 total. No subscriptions. A McKinsey 2024 analysis of B2B sales efficiency found that companies using automated contact enrichment pipelines close deals 23% faster than those relying on manual research.

What I'd build differently today

If I were starting from scratch, I'd add a few things:

Schema.org Organization extraction for company metadata like founding year, employee count, and address. About 18% of business websites include Organization structured data according to a 2025 Schema.org adoption study, and it's free information sitting in the HTML.

Better phone parsing for international formats. The current regex handles US/UK/EU formats well, but some Asian and African phone number formats slip through the cracks. It's on the roadmap.

A confidence score per contact. Schema.org Person data with itemprop=name is high confidence. A heading-paragraph pair match is medium. That signal would help users prioritize which contacts to verify first.

But tbh, the current version works. The 99.8% success rate and 11,516 runs tell me that perfect is the enemy of shipped. I'd rather keep improving incrementally than rewrite from scratch.

Should you use this or something else?

If you need fresh contact data scraped directly from live websites, and the sites you're targeting serve contact info in static HTML — this is probably the cheapest, simplest option available. $0.15 per site, no subscription, structured output, 99.8% success rate.

If your targets are JavaScript-heavy SPAs, use the Pro version instead.

If you want a pre-built database you can query without waiting for a crawl, Hunter.io or Apollo might be better — but you'll pay $50-$150/month for the privilege and the data may be weeks old.

If you need the full pipeline — scrape, enrich, verify, score, push to CRM — check the Waterfall Contact Enrichment actor on ApifyForge, which chains multiple data sources into a single run.

The ApifyForge cost calculator can estimate your monthly spend based on volume. And if you're comparing options, the lead generation comparison page lays out the full category.

The actor is live at apify.com/ryanclinton/website-contact-scraper. Run it on one site for $0.15 and see if the output is what you need. That's the whole pitch.

Last updated: March 2026

Built and maintained by ApifyForge — 300+ Apify actors and 93 MCP intelligence servers for web scraping, lead generation, and compliance screening.

DEV Community: apify forge

How Podcast Booking Agencies Find Host Emails Without Paying $599/month

What is podcast host email extraction?

Why do podcast host emails live in RSS feeds?

How do you extract podcast host emails at scale?

What does the output look like?

What are the alternatives to RSS email extraction?

Best practices for podcast outreach lists

Common mistakes when building podcast contact lists

How much does podcast outreach list building actually cost?

Who is actually using RSS-based podcast scrapers?

Implementation checklist

Limitations of RSS-based email extraction

Key facts about podcast host email extraction

Glossary of key terms

Where these patterns apply beyond podcast outreach

When you need podcast host email extraction

Frequently asked questions

How do you find a podcast host's email address?

Is it legal to scrape podcast emails from RSS feeds?

What percentage of podcasts have host emails in their RSS feed?

How is RSS email extraction different from Podchaser or Rephonic?

Can you scrape both Apple Podcasts and Spotify at once?

How long does it take to scrape 1,000 podcast contacts?

What should I do when a podcast's RSS feed doesn't have an email?

Why do booking agencies prefer pay-per-result over subscriptions?

MCP Servers Are the Next Big Thing on Apify — Here's Why

What is an MCP server on Apify?

Why are MCP servers a bigger deal than regular actors?

How many MCP servers are on the Apify Store right now?

What domains do ApifyForge MCP servers cover?

What does pay-per-event pricing mean for MCP servers?

Why hasn't everyone built MCP servers on Apify yet?

How do AI assistants find MCP servers on Apify?

The window is open right now

How to Find Podcast Host Emails for Guest Outreach (2026)

What are the 3 data sources for podcast host emails?

Source 1: RSS feed itunes:owner tag

Source 2: Apple Podcasts and Spotify API metadata

Source 3: The podcast website

How do you find podcast host emails at scale?

Step 1: Search and extract with Podcast Directory Scraper

Step 2: Verify the emails you found

Step 3: Fill gaps with Website Contact Scraper

What's the best format for a podcast guest pitch email?

How is finding podcast host emails different from LinkedIn outreach?

Can you extract podcast host emails without coding?

Why do some podcasts not have an owner email in their RSS feed?

How much does it cost to find podcast host emails?

What about using podcast booking platforms like PodMatch?

The complete workflow, start to finish

Stop Manual Prospecting: How a 3-Actor Pipeline Finds and Scores B2B Leads

What is the B2B Lead Gen Suite?

How does the 3-actor pipeline work?

What data comes back in each lead record?

How much does automated lead prospecting cost?

Why not just use Hunter.io or Apollo?

How do you set up the B2B Lead Gen Suite?

What can you do with scored leads?

What are the limitations?

How does this compare to building your own pipeline?

Is web scraping contacts legal?

Who should use the B2B Lead Gen Suite?

How to Scrape Podcast Directories for B2B Leads

What data can you extract from podcast directories?

Why are podcast guests good B2B leads?

How to extract company websites from podcast episodes

What's the difference between podcast scraping and LinkedIn scraping for leads?

How to build a podcast-to-lead enrichment pipeline

Does podcast scraping actually work at scale?

What about legal and compliance considerations?

Getting started

How I Built a Website Contact Scraper That Hit 11,000+ Runs

What does Website Contact Scraper actually extract?

How does the contact extraction work technically?

Why build another contact scraper?

Who's actually using this thing?

How much does website contact scraping cost?

What makes the extraction actually reliable?

How to use Website Contact Scraper step by step

Source 1: RSS feed `itunes:owner` tag