Apple's new stats could revolutionize the way we find podcasts
The news moves so quickly these days that a podcast published on Thursday might not be relevant on Monday. But it’s hard to tell which news or political podcasts have the latest information just by scrolling through the iPhone app.
The iTunes store categorizes podcasts by “top episodes” and “top podcasts” in certain genres, but it’s difficult to figure out what day they’re released, or whether certain episodes are evergreen (meaning they never go out of date).
RadioPublic, a podcasting app from PRX, shares hand-curated playlists. NPR One creates serendipitous playlists. Overcast, an app from developer Marco Arment, shares recommendations from Twitter. Stitcher has playlists for different situations.
But it’s harder to say “I want to listen to the most relevant political podcast for a situation that happened yesterday,” or “I want to sort through all of the political podcasts that dropped a new episode today” or “Show me a podcast that’s produced by a local news organization that can help bring me up to speed on a certain event” or “Don’t show me any podcasts that are no longer relevant because the news cycle has changed.”
Related Training: Podcasting for Everyone
The closest thing we have may be recommendations from other people. There are hundreds of questions on Ask Metafilter, Quora, Ask Reddit and The New York Times Podcast Club asking for suggestions or recommendations for podcasts about certain topics, or podcasts for certain times of day, or podcasts that will make the listener smarter — regardless of topic.
But even then, if you look through the answers, you’ll see that many answers mimic the top suggestions in iTunes, which remains the biggest discovery mechanism for podcasts — in other words, it’s hard to discover podcasts not tied to a major publisher or distributor because most people discover podcasts through the same mechanisms, which favor the same major publishers and distributors.
But there may be changes afoot. Apple is about to provide podcasters with much more in-depth information about listener habits. This will certainly benefit advertisers — they’ll be able to know how many people hear that mattress ad — but it may also benefit the distributors as well as the end listeners.
With this new data, there are lots of new opportunities for podcast discovery apps to improve the ways we can discover new content, and for the discovery apps to discover new content to potentially distribute.
Here are some ideas:
Show me new content based on what the audience thinks:
- Show me podcast episodes that at least 80 percent of people have listened to all the way through.
- Show me podcasts that people mainly listen to at a certain time of day.
- Show me episodes that are under a certain amount of time that at least 80 percent of people have listened to all of the way through.
- Show me podcasts where the majority of people who subscribed this week or month have listened all of the way through.
- Show me particular episodes that perform much better compared to other episodes of the same podcast. (In other words, which episodes command more listener attention?)
- Show me podcasts where people listened to at least five episodes in a 72-hour-period. (In other words, what are the binge podcasts?)
- Show me podcasts that are not published by a major distributor that meet any of the criteria listed above. (In other words, show me the undiscovered gems. This also is likely a way for distributors to find new content to distribute.)
Show me content based on the current news cycle or by the content on the app:
- Show me all podcasts under a certain amount of time that are still relevant to a specific topic.
- Show me every podcast in this category that came out within the past week.
- Show me every podcast that has interviewed a certain individual.
- Combine this with some of the stats mentioned in the previous category. Which podcast had the most people listen all the way through?
- Show me all of the new podcasts that came out on Mondays.
Show me content based on other factors:
- What episodes did people I’m friends with on Facebook listen to all the way through?
- What podcasts are listened to all the way through by people who also like a page I like on Facbeook?
- What podcasts are most associated with this emoji on Twitter?
- Show me all podcasts that have fewer than 50 episodes and 100,000 listeners. (In other words, show me the series.)
- Show me every podcast that was listened to multiple times.
- Show me podcasts where the majority of the audience is from a specific country or state.
What’s missing? Email me email@example.com or write a response! There are likely 100s of more ways we can improve discovery mechanisms.