Dan Misener is Head of Strategy and Audience Development at Pacific Content. This guest column was originally posted on the Pacific Content blog (on Medium).
A few months ago, I decided to draw a map of podcast neighbourhoods.
To begin, I looked at the top 400 shows in Apple Podcasts (US). For each show, I drew a point on my map.
Then, for each show, I looked at the “Listeners Also Subscribed To” section in Apple Podcasts. For example, listeners of Armchair Expert with Dax Shepard also subscribe to Getting Curious with Jonathan Van Ness, Ellen on the Go, and Conan O’Brien Needs A Friend. This helped me understand which shows have audiences in common.
Then I drew lines, connecting shows with related audiences.
Here’s the result (click for an interactive version):
Suddenly, I saw Apple’s charts in a new way. I could see distinct neighbourhoods. Clusters of shows with common audiences.
As soon as I saw this, I became obsessed. The map looked cool, but what could I do with it?
Understand where the audiences are
Listeners of a feather flock together.
This type of map (AKA network graph) is useful for identifying the relationships between individual shows. I used a community detection algorithm to analyze my network graph, and identified a number of distinct neighbourhoods. Let’s look at a few.
Public radio and public radio-esque
This neighbourhood includes Freakonomics Radio, Planet Money, Hidden Brain, Radiolab, Revisionist History, This American Life, Fresh Air, Invisibilia, 99% Invisible, and Wait Wait… Don’t Tell Me!
Wondery-flavored true crime
There’s a Wondery-heavy neighbourhood that includes Dr. Death, The Shrink Next Door, Crime Junkie, The Dropout, Over My Dead Body, and To Live and Die in LA.
Obviously, there’s something about this particular flavor of true crime that sets it apart from other neighbourhoods of true crime shows that appear elsewhere on the map. I suspect much of this is attributable to in-network promo.
Finance and money
We also see a neighbourhood of shows related to money, finance, and investing, which includes The Journal, Snacks Daily, several WSJ shows, Marketplace, Economist Radio, Rich Dad Radio Show, and more.
On the outskirts, we see a small group of kid-focused shows, including Story Pirates, Brains On!, Wow in the World, and But Why: A Podcast for Curious Kids.
Six Degrees of Joe Rogan
One of the most interesting things I discovered when looking at my map was the concept of a “bridge” show — a podcast that connects seemingly unrelated neighbourhoods.
For example, how do you connect Joe Rogan to Malcolm Gladwell?
The answer: Dan Carlin.
According to Apple Podcasts, Revisionist History doesn’t appear on the “Listeners Also Subscribed To” list for The Joe Rogan Experience (or vice versa). But Dan Carlin’s Hardcore History lists both shows.
You don’t have to be connected to other popular shows to appear in Apple’s top charts
I was encouraged to see the ring of shows orbiting the dense neighbourhoods in the center:
To me, this is evidence that it’s still possible to appear in Apple’s top charts without a direct connection to an existing popular show.
When looking at these network graphs, it’s important to remember two things:
- Shows are not grouped together by their Apple Podcasts categories. They’re simply grouped together by common audiences. My map has no understanding of which categories (e.g. Business, Comedy, New, etc.) these shows belong in.
- If a show on my Top 400 map has no connection to other shows, that doesn’t necessarily mean it has an empty “Listeners Also Subscribed To” section. Rather, it means the show isn’t connected to any other shows that also appear in the US top 400.
More to come…
I first started to explore this type of podcast network analysis a few months ago. Since then, we’ve discovered a number of ways to use this data in our day-to-day work at Pacific Content. We use this same approach to:
- Show existing clients which neighbourhoods their shows live in
- Deep dive into relevant Apple Podcasts categories (what do the various communities within Automotive look like? Design? Technology?)
- Explore networks of shows related to relevant search terms
- Identify shows for paid media buys
- Find shows to collaborate with through promo swaps, feed drops, and podcast guesting
In the coming weeks, I hope to share more examples of how we’re using this type of data to understand podcast audiences and help our clients market their shows.