--- title: "Podcast Discovery Platform: Every Episode Findable" description: Podcast discovery runs on friends and YouTube. The engine that moved multi-page sessions +46% on publisher web makes every episode findable, cross-show. url: https://www.artificialpoets.com/use-cases/podcast-discovery/ site: Artificial Poets type: page date: 2026-08-09T19:46:31+00:00 modified: 2026-08-25T23:07:47+00:00 --- # Every Episode Findable "We make great shows nobody finds — how does a listener discover the next episode, or the next show?" [See it on my numbers](/request-a-demo/) Horse & Rider · Ride TV · Equine Network · Equus Magazine · The Score · Equine Network Lockup ## Great shows that nobody finds The problem - **Discovery is the top problem** — 72% of podcast creators name discoverability and audience growth as their biggest challenge. Making the show was the easy part. - **Listeners find shows off-platform** — Most new shows are found through friends and family or on YouTube. Almost none are found inside the app that hosts them. - **The next episode is not suggested** — When an episode ends, the app shows the feed it was already in. A listener who would stay for a related show is never offered one. ## How listeners find the next show - **Next episode, offered** When an episode ends, the engine offers the related show or the next episode, ranked on what this listener has finished. - **Found in your app** Recommendations happen on the surface you own, not on YouTube or in a group chat. The listener never has to leave to find more. - **Measured like web** Completion, return and series depth against a frozen baseline. The same method that produced the published web results. ## When an episode ends, the next show is already offered. Same engine, new surface Podcast discovery happens almost entirely off your platform. Listeners find shows through friends or on YouTube, and when an episode ends the app shows them the feed they were already in. The listener who would have stayed for a related show is never offered one. The Artificial Poets Platform builds an anonymous interest profile from what each listener has finished and ranks the next episode or the next show against the meaning of your whole catalogue. The recommendation lands on the surface you own, inside your app, so the listener never has to leave to find more. It is the same mechanism that produced our web results, pointed at audio. Those results are from publisher web surfaces and we say so. An audio deployment starts with four weeks of baseline before anything is served, so what you see afterward is measured against your own listeners, not a projection. **Introducing** ## Artificial Poets Platform One engine behind every solution on this site. It learns your archive and your readers, then acts inside your CMS, your templates and your ad stack. - It learns your archive: **Every story you have published, current again** — The engine understands each piece by what it is about, not when it ran or where it was filed. A feature from 2019 competes for the next slot on merit with one from this morning. - It reads the visit: **What a reader wants, without asking** — Interest builds from what someone actually does in the session. No login, no third-party cookies, nothing leaving your domain. Useful on the second pageview, not the tenth visit. - It chooses: **The right next read, not the popular one** — Someone comparing products and someone following a running story want different things. A most-read list gives both the same five links and serves neither. - It serves: **There before the reader leaves** — The feed, the recommendations, the search answer and the signup ask all run on the same engine, in your templates and your ad stack. Any slot that arrives with them is yours to sell. - It proves: **A lift you can defend, or we say so** — Every deployment runs beside titles that did not get it, plus a serving pause. That is how a result becomes a number you can take to a board instead of a vendor claim. ## FAQ ### Almost none of our listening happens on our site. What is a module on our own pages supposed to change? It works where you own the surface, and that is a smaller share than the pitch usually admits: website mentions account for a single-digit share of how listeners find a favourite show, against YouTube in the high thirties (Sounds Profitable, 2026). What your own pages can do is connect readers who are already deep in a subject to the show about it. ### We have a few hundred episodes, not a Netflix-scale catalogue — what will this surface that our producers can't hand-pick? Producers pick per show; the engine picks per listener. A few hundred episodes across a handful of shows is tens of thousands of listener-to-episode pairs nobody has time to curate — and the pairs that matter cross show boundaries, where no producer is looking. Matching runs on meaning, not popularity, so a small catalogue is matchable from day one, and the back catalogue is where it earns: an episode recorded three years ago resurfaces when a fingerprint says it is relevant now. ### If a listener acts on a recommendation inside Spotify, how would either of us know? Neither of us would, and anyone claiming otherwise is mismeasuring. Platform analytics are aggregate, anonymised and platform-scoped, with no join key back to a site session. We can measure what happens on surfaces you own. We will not convert that into a claim about listening we cannot see. ### There is no cookie in an RSS feed. How do you personalise for a listener you cannot identify? On your own surfaces, where a first-party anonymous profile exists. In the feed itself there is no identity to personalise against, and podcast telemetry is device-shaped and delayed by design. This is why the honest scope here is discovery on properties you control, not personalisation inside the apps. ### We run on a hosted stack — a Megaphone- or Art19-class platform — with no dev team. What does integration take? Hosting stays where it is. Serving runs on the web surfaces you already control — show pages, episode pages, the editorial site — as a JavaScript integration carried out by our team. Feeds are untouched; where the graph suggests a promo swap or a feed drop, your producers run it in the tools they already use. ### We have four thousand episodes and no transcripts. What is the lift on our side? Transcripts are what makes an episode matchable by subject, so producing them for the back catalogue is the real preparatory work. Adoption is low industry-wide, so this is a common starting point rather than a disqualifier, and it is worth scoping before anything else is discussed. ### Show me incremental lift, not engagement vanity metrics. How would you prove this worked? The way the web numbers were produced: a four-week measurement-only baseline, metrics frozen before enablement — listen-through rate, second-episode conversion, cross-show starts — a comparison group of shows held back, and the failure condition agreed before the switch is flipped. No podcast deployment has been measured yet; the first one is designed so its numbers survive your analyst. ## Related - [One Audience. Every Property.](https://www.artificialpoets.com/solutions/media-networks/) — "We own a dozen properties — why does our audience behave like it belongs to strangers?" - [Get Them to the Second Episode](https://www.artificialpoets.com/use-cases/video-recommendations/) — "Subscribers churn before a second episode — how do I get them watching in the first session?" - [You Can’t Get the Traffic Back. You Can Get the Session Back.](https://www.artificialpoets.com/resources/traffic-back/) — The economics of session depth, and the causal evidence behind it: +53% pageviews per user, 95% CI [+48%, +58%], with the modelling assumptions written down. ## Unlock new revenue with our AI Artificial Poets Platform can help you with [Book a demo](/request-a-demo/) ## Questions this page answers ### Why is podcast discovery so hard? Because discovery happens where you are not. Demand is at a record, with 58% of Americans listening monthly (Edison Research), but the top named sources of new shows are people they know at 56% and YouTube at 52% (Sounds Profitable). The surfaces you own are barely part of the discovery path. ### How would the Platform surface the right episode? Every episode is vectorized through its transcript, so a show becomes searchable and matchable by what it is actually about, and one anonymous interest profile per listener carries across shows and formats. Stated plainly: our measured results are on publisher web pages, and this surface carries no measured claim yet. ### How much does it cost? There is no public price list; pricing follows network size and formats. Because podcast discovery is unproven for us, a first deployment is scoped as a measured pilot: four-week baseline, frozen metrics, and a comparison group of shows held back so the result can be checked. ### Who is it for? Publishers with a podcast network and an editorial site, where the articles and the shows cover the same subjects for the same audience but live in separate systems that never refer to each other. ### What are the alternatives? Cross-promotion in episode reads reaches only existing listeners. Platform charts reward what is already large. Paid discovery buys attention without keeping it. The alternative here builds discovery on surfaces you own, which is the only place the referral is yours, and it is not yet measured. ### How do I get started with podcast discovery? Transcripts and feeds are what the engine indexes, so the first work is making the catalogue readable to it. A first podcast deployment then gets the same measurement design as everything else: four-week baseline, frozen metrics, and a comparison group of shows held back.