How to build a podcast lead list for B2B sales and partnerships

Podcasters are customers for a surprising number of businesses. Hosting platforms, microphone and software makers, editing services, ad networks, music libraries, transcription tools, booking agencies, merchandise printers and accountants all sell to them. Brands, on the other side, want podcasters as partners: as sponsorship inventory, as affiliates, as distribution.

Either way, the job starts with a list. A good podcast lead list is targeted (the right kind of show), qualified (active and of the right size), reachable (with a legitimate contact route) and fresh. This playbook shows how to build one from catalog data, how to score and enrich it, and how to contact people in a way that is effective and lawful.

Step 1: define your ideal customer profile

Write down what a perfect prospect looks like before you touch any data. For podcast leads, the useful dimensions are:

• Topic or category. A legal-software company sells best to law and business shows; a kids' toy brand to Kids & Family.
• Language and market. Your sales team's languages and the countries you serve.
• Stage. Brand-new shows need tools and hosting; established shows need monetization, editing at scale and audience growth.
• Cadence. Daily and weekly shows have recurring production needs; limited series have bursts.
• Format. Video podcasts, long interviews, short daily briefs and narrative series all buy differently.
• Platform. If your product integrates with specific hosting platforms, or competes with them, the host matters.

Translate each dimension into a filter you can actually apply. "Serious, growing shows" becomes "first episode at least a year ago, latest episode within 30 days, at least 50 episodes".

Step 2: pull the raw list

On the podsearch.org datasets page you can apply these filters directly to the Podcast Index catalog of about 4.7 million feeds:

The page shows the number of matching shows and a sample before you buy. The export is a CSV with, among other fields, title, author, description, website, email, categories, language, episode_count, latest_pub_date, earliest_pub_date, update_frequency_hours, avg_audio_length_sec, is_video_podcast, itunes_id and rss_url.

Use search for topics categories cannot express

Categories are broad. If your ideal customer is "podcasts about veterinary medicine" or "shows that review board games", category filters will either miss them or bury them. Use full-text search to find matching shows first:

curl -s -G "https://podsearch.org/api/v2/search" \
  --data-urlencode 'q=title:veterinar* OR desc:"veterinary medicine" OR desc:"vet tech"' \
  --data-urlencode "syntax=dql" --data-urlencode "type=podcast" --data-urlencode "language=en" \
  -H "X-ListenAPI-Key: $PODSEARCH_API_KEY"

Collect the id values (these are Podcast Index feed IDs, the same as feed_id in exports) and either fetch details through the API or use them to check your exported list.

Step 3: qualify and score

A raw list is a starting point, not a pipeline. Score each show so your team calls the best ones first. A simple, transparent score beats a black box:

import pandas as pd

df = pd.read_csv("leads.csv", parse_dates=["latest_pub_date", "earliest_pub_date"])
now = pd.Timestamp.utcnow().tz_localize(None)

days_idle = (now - df["latest_pub_date"]).dt.days
tenure_years = (now - df["earliest_pub_date"]).dt.days / 365

score = 0
score += (days_idle <= 14) * 3 + ((days_idle > 14) & (days_idle <= 45)) * 2 + ((days_idle > 45) & (days_idle <= 90)) * 1
score += (df["episode_count"] >= 100) * 3 + ((df["episode_count"] >= 30) & (df["episode_count"] < 100)) * 2
score += (tenure_years >= 2) * 2
score += (df["update_frequency_hours"] <= 24 * 8) * 2          # weekly or more often
score += df["email"].notna() * 2
score += df["website"].notna() * 1
df["score"] = score

df.sort_values("score", ascending=False).to_csv("leads_scored.csv", index=False)

Adjust the weights to your product. A tool for new podcasters should reward short tenure; an ad network should reward cadence and longevity.

Disqualify ruthlessly

Remove rows that waste a salesperson's time:

• Dormant shows. No episode in 90 days (or whatever fits your cycle).
• Duplicates. Several feeds for the same show (ad-free, video, old host). Group by normalized title and author.
• Network catch-alls. Some big publishers list one generic contact for hundreds of shows. Group by email domain and decide whether to approach the network centrally instead.
• Machine-generated shows, if they are not your market. Where podsearch.org's classifier has judged a show, the export includes ai_generated; the label is applied only at 0.9 confidence or higher, and blank means not yet judged.

Step 4: enrich

The feed gives you the basics. Enrichment makes outreach personal and helps you prioritize.

Recent episodes. Pull the latest few episode titles for each top lead via GET /podcasts/{id}. Referencing a specific recent episode is the single most effective personalization in podcast outreach.

def recent_titles(api, feed_id, n=3):
    pod = api.get(f"/podcasts/{feed_id}")
    return [e["title_original"] for e in pod.get("episodes", [])[:n]]

Guests and topics. Episode titles and descriptions reveal who the show interviews and what it covers. A simple keyword pass flags shows that already discuss your product category.

Chart presence. podsearch.org tracks Apple Podcasts charts per country and category on its browse pages. A show that appears in charts is likely to have a larger audience, and a different sales motion.

Company data. The website field lets you match shows to companies with your CRM's enrichment tools. Many business podcasts are run by companies that are prospects in their own right.

Audience estimates. If you need download or listener estimates, they come from modeling vendors, not from feeds. Rephonic, for example, lists downloads-per-episode and weekly download estimates on its developers page (as of October 2026). podsearch.org does not estimate audience size.

Step 5: contact, lawfully and well

Know where the email came from

The email field is the owner email that the publisher put in their feed, typically for directory verification. It is published, but it was published for a purpose. Treat it as a business contact for the show, use it sparingly, and stop immediately if someone asks you to.

Know the rules that apply

This is not legal advice, but every team sending outreach should know the basics of the regimes that commonly apply:

• United States (CAN-SPAM Act). Commercial email must not use misleading headers or subject lines, must identify itself as an advertisement where required, must include a valid physical postal address, and must offer a working way to opt out, honored promptly.
• European Union and United Kingdom (GDPR and PECR or national equivalents). Contact details of identifiable people are personal data. You need a lawful basis (often legitimate interests for relevant B2B contact, assessed and documented), you must tell people how you got their data, and electronic marketing rules may require prior consent depending on the country and whether the recipient is an individual or a company.
• Canada (CASL). Generally requires consent for commercial electronic messages, with limited exceptions such as conspicuously published business addresses where the message is relevant to the person's role.

When in doubt, ask counsel, and keep a record of where each contact came from.

Write messages people answer

Podcasters receive a lot of pitches. The ones that work share a pattern:

1. Reference a specific episode by name, and say something real about it.
2. Explain the fit in one sentence. Why their show, why now.
3. Make a small ask. A reply, a 15-minute call, a free trial, not a contract.
4. Be human. One person, one signature, no fake urgency.

Sequence: one first email, one polite follow-up a week later, then stop. Volume is not a strategy; relevance is.

Use more than email

Many podcasts list a website contact form, social profiles or a booking page. For partnership and sponsorship conversations, a short message through the channel the show advertises often performs better than cold email to a verification address.

Segmenting the list for different sales motions

One list rarely suits one approach. Split it into tiers and treat each differently.

Tier 1: named accounts. The top few dozen shows by score, often established, weekly, with a website and a real contact. These deserve manual research: listen to part of a recent episode, find the producer or business contact, and write a fully personal message. Track them as accounts, not leads.

Tier 2: personalized sequences. The next few hundred. Personalize the first line with a recent episode title pulled from the API and the show's category, and keep the rest of the message templated. This is where most pipeline comes from.

Tier 3: low-touch. The long tail of small or new shows. Rather than cold email, reach them through content, communities, partnerships with hosting platforms, and offers they can self-serve. A blog post, a free tier or a webinar often converts this segment better than outreach.

Networks. When dozens of shows share one email domain, they belong to a network or production company. Approach the network once, with a proposal that covers its portfolio, rather than emailing each show.

Tiering also protects your sender reputation. Sending fewer, better messages keeps complaint rates low, which keeps your email out of spam folders for the prospects who matter most.

Step 6: keep the list fresh

Podcasts launch, pause and move constantly. A lead list starts to decay the day you export it.

• Re-pull quarterly with the same filters and diff by feed_id to find new entrants and shows that dropped out.
• Refresh active deals through the API before each touch, so you never pitch a show that stopped publishing last month.
• Feed your CRM the key. Store feed_id and rss_url on every account so refreshes are a join, not a manual search.

What it costs

Lead lists are a classic one-time purchase. podsearch.org datasets cost $0.10 per podcast under 1,000, $0.08 for 1,000 or more, $0.05 for 5,000 or more and $0.03 for 10,000 or more, with a $60 minimum, paid once through Stripe. Some examples:

Because the rate is set by the total order, a list just above a tier boundary can cost less than one just below it: 1,000 shows ($80) cost less than 999 ($99.90).

Subscription research tools are an alternative when you need ongoing prospecting features such as contact finding, audience estimates and campaign management. Rephonic's web app, for instance, lists plans at $99, $149 and $299 a month (pricing, as of October 2026). Many teams use a one-time export for the broad list and a subscription tool or manual research for the top tier.

A worked example

A company selling an AI editing tool to podcasters wants 1,000 high-fit leads in English.

1. Profile: English, active within 30 days, at least 20 episodes, weekly or faster cadence, has email, hosted on a platform the tool integrates with. Average length over 30 minutes (long-form shows spend the most time editing) is applied after export, using avg_audio_length_sec.
2. Pull: apply those filters on the datasets page. Suppose the live count shows about 3,000 matches; exporting all of them costs 3,000 × $0.08 = $240.
3. Score: with the script above, weighting cadence and length.
4. Disqualify: remove duplicates and network catch-all emails, leaving about 2,400.
5. Enrich the top 1,000: recent episode titles via the API.
6. Contact: a two-touch sequence referencing a recent episode, with a free trial offer and a clear opt-out, sent from a named person.
7. Measure: reply rate and trial starts by score band, to tune the weights next quarter.

Measuring list quality over time

Track three numbers for every list you build: the share of contacts that bounce, the reply rate by score band, and the share of replies that turn into opportunities. Bounces tell you whether contact data is fresh enough; replies by score band tell you whether your scoring weights are right; and opportunities tell you whether your ideal customer profile is. Revisit the filters and weights each quarter using these numbers rather than intuition.

Frequently asked questions

Where do podcast contact emails come from?

From the feed itself: many publishers include an owner email for directory verification. Not every feed has one, and some are shared agency or network inboxes.

Is it legal to email podcasters?

It can be, depending on where you and they are and how you do it. Follow the applicable rules (for example CAN-SPAM in the US, GDPR and national e-marketing rules in Europe, CASL in Canada), keep records of where contacts came from, and always honor opt-outs. Seek legal advice for your situation.

How do I find shows in a niche that has no category?

Use full-text search over titles and descriptions with boolean syntax, then collect the matching feed IDs and join them to your export.

How do I avoid pitching dead podcasts?

Filter on the latest publish date when you export, and refresh each lead through the API before you contact it.

Can I get listener numbers for my leads?

Not from podsearch.org, which does not estimate audience size. Some vendors offer modeled estimates; treat them as estimates and name the source.

Summary

A podcast lead list is a precise filter, a transparent score, a little enrichment and respectful outreach. Define your ideal customer in filterable terms, pull it from the catalog, disqualify ruthlessly, personalize with recent episodes, follow the law, and refresh by feed_id. The result is a pipeline that salespeople trust and podcasters actually answer.