Solving Podcasting's Linear Listening Problem

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Over the weekend I was listening to Desert Oracle and learned of an Arizona-based band called Giant Sand. Ken and his guest had high praise for the band and used some descriptive language that piqued my interest. So I opened up my very popular music streaming app, searched for the band by name, and started listening.

And I was hooked immediately. Their sound is right in my wheelhouse. The perfect kind of weirdo stuff that I and weirdos like me like. While I was mentally making a list of all the people I wanted to yell out for not introducing me to this band when I first arrived in Arizona back in 1997, I was struck by the experience I just had. Specifically, I started contemplating the difference in how music is presented to new listeners in 2020 versus how podcast episodes are presented to the uninitiated.

Here’s what the music streaming service didn’t do: They didn’t present me with the most recent track released by the band. A band that’s been around for some 30 years and has an evolving sound. The service also didn’t present me with a list of the 30-ish albums the band has released over the decades, forcing me to pick one to listen to in its entirety. Because this isn’t the ‘70s and I don’t even own a record player.

Instead, this app mined its own data built from roughly 260 million users and presented to me a list of songs by that band ordered by popularity. Their assumption is solid: there’s a very good chance that the more popular a track is, the more likely that a new listener will not only like it but then keep listening to more content.

No, That’s Not How It Works In Podcasting Today

On the surface, it seems like podcast listening apps do that for us as well. If you hit the home screen, they’ll show you the most popular podcasts, and that’s probably based on internal usage data.

But then... what is the listener to do next? All the app has done is line up the shows themselves by popularity, which is akin to showing the most popular albums. Sure, die-hard fans often have a favorite album. But normal people just have favorite songs. And most of us would struggle to name the album that featured their favorite song.

To which episode should a person brand new to that podcast listen? And which episode after that, ensuring that the listener keeps listening? How should episodes be presented to help new listeners better enjoy content from that podcast on that app?

Put yourself in the shoes of a brand new listener for just a moment and imagine a podcast app working more like the music streaming apps. Someone tells you of a new show and you search for that show by name on your listening app. This imaginary helpful app then presents to you the very best episodes of that show for you to listen to first, allowing you to quickly confirm the recommendation given was right for you.

That’s a very different world than we live in today, podcasters.

One Size Does Not Fit All

No, this is not going to work for all types of podcasts. And yes, I can already hear the pearl-clutching of many of my conventional podcasting friends as they envision a dystopian world where an app has the audacity to present their content differently. The horror!

This concept won’t work for time-sensitive content, where you’re covering “the news” or current events. It’s possible that an episode of The Daily from 2018 or Geek News Central from 2007 is quite popular, but those episodes have a shelf life that expires in single-digit days. Regardless of popularity, those episodes are stale and shouldn’t be presented as a first listening experience.

It also completely breaks for serial podcasts like, well... Serial or with podcast fiction/audio drama content like Valence. Serial podcasts, by definition, need to be consumed in order from the first episode. By way of example: Even if Amazon could somehow determine Chapter Six of a Kindle novel was most enjoyed by readers, they’re still going to start new readers at Chapter One.

Those notable categories aside: This concept is perfect for timeless, evergreen podcast content. Content that is neither immediately newsworthy nor builds toward a conclusion in a linear fashion.

A Better World For Evergreen Podcast Episodes

A huge portion of the podcast landscape is evergreen, with episodes that maintain their relevance months and years after they’ve been published. Shows like Sleep With Me or The Snooze Button are good examples. The former will clearly have “favorite” episodes that help you fall (and stay) asleep by listening. The latter almost certainly has stand-out episodes with great information from guests on how to get a great night’s sleep.

For those shows, and the vast swath of the podosphere that is also timeless content, the forced adherence to linear episode consumption in apps runs counter to what music app developers have figured out.

As I've said previously on this show, the most recent episode of your podcast isn't necessarily the best episode of your podcast. Yet it’s the one that the apps present to new listeners and would-be subscribers first. See the disconnect?

Podcast Downloads And Ketchup Packets

What data can be relied upon to determine the best episodes of a timeless podcast? Popularity is what the music streaming apps use, but getting to that is a problem in podcasting.

Downloads aren’t helpful in making this determination. Sure, you can run an easy report that clearly shows which episode gets the most downloads. But that’s not a good data set, and I need to use the metaphor of you running a fast food restaurant to explain why.

Let's say you, in your new role of fast food restaurant manager, want to understand which of your three condiments -- ketchup, mayonnaise, and mustard -- is most popular. If you count up the number of packets of each leaving your store every day, you’d probably learn that ketchup is by far your most popular, so you better order more ketchup, right?

But there’s a lot of noise in that signal, and it comes from drive-thru and take-out orders. To maintain the efficiency of your quick-serve (the term they prefer) restaurant, your staff has been trained to automatically, and with almost every order, toss 2-3 packets of ketchup in the bag. Conversely, it’s exceedingly rare for mayo or mustard packets to be added without the express wishes of the person who ordered the meal.

Because of that noise in the signal, you can’t rely on the total volume of packers moved to base your decision upon. It’s really only the volume of packets moved through the on-the-counter condiment receptacles that you can count on. But because of efficiency, you use the same packets in both places, so your end of the month analysis of condiments is a fool’s errand. So you just order more ketchup?

Many (most?) podcast episode downloads are like ketchup packets tossed in a takeaway bag. They often (usually?) happen automatically, which is why you see a spike in downloads the day your release an episode. Having episodes automatically appear in podcast listening apps or other distribution channels is one of the magic powers of podcasting.

But that magic adds noise to our data. Because a download does not equate to a listen. And podcast hosts remove additional downloads/streams when they detect them to preserve the integrity of “unique downloads”.

Play That Podcast Episode Again, Sam

Podcast hosting companies could give us this data if they wanted to. (And wanted to invest the development resources.) They could do deeper analyses and identify when something out of the ordinary happens on one of your episodes -- a possible popularity signal. They could also better report on repeat listening, connections between episodes downloaded by the same unique user, and other insightful data that could help us build truly timeless episodes our listeners want to listen to over and over again.

Podcast apps should have a better time at this. Forgetting for a moment the fractured reality of podcast listening apps, they at least have actual-play data. And because podcast apps require an account to keep track of subscriptions and listening history, the apps have great insight into actual listening behavior that could be shared with the creators of the content. At the very least, the apps could use repeat-plays as a strong signal to help present the best timeless episode to a brand new listener

Blame It On IAB 2.0

Actually, blame it on us. We’re the ones who decided we wanted accurate download counts more than anything else. We didn’t see any value in knowing how many times a single user listened to the same episode. We didn’t understand how that behavior at-scale might give us incredible insight. We couldn’t see why we might want to use that data to present our episodes in a better way for people who’ve just discovered our content.

Maybe, as we approach the third decade of podcasting, our attitudes will change. Maybe this concept will percolate through the listening app community. I’d actually bet money on that, as the much-more-data-savvy app makers now listing podcasts in their apps will force that change.

They’ve solved the problem for music. They’ll solve it for podcasting too.

If you enjoy my pontificating, please go to BuyMeACoffee.com/EvoTerra. You can not only buy me a virtual coffee (please and thank you), but you can also become a member. Membership, as you’ve no doubt heard, has its privileges. I just announced some special perks for members over the weekend and have a few more ideas as well.

If that's not in the cards for you (thanks, impending global economic collapse), then pretty please, with sugar on it, tell one other podcaster you know about Podcast Pontifications today. Even if you've done so previously, please mention the show to somebody new you’ve just discovered is podcasting.

I shall be back tomorrow with yet another Podcast Pontifications.

Cheers!

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PPS3E12: Solving Podcasting's Linear Listening Problem - Transcript

Evo Terra: [00:00:00] Podcasters tend to obsess over discoverability, but a bigger problem might be impenetrability a problem that neither the podcast media hosts or the apps listeners use are helping us to solve yet.

[00:00:20] Hello and welcome to another podcast. Pontifications with me, Evo, Tara. So this weekend it podcast, I was listening to great desert. Oracle introduced me to a Phoenix band called giant sand, which I'd never heard of. And I moved to this state in 1997, anyhow, decided to discover giant sand and went to use a very popular music streaming app, which I won't mention because it's not important to the story right now.

[00:00:50] And I was hooked immediately. Great sound right in my wheelhouse for the kind of weirdo stuff that I like. But it was that experience of discovering, as in someone told me about a band, I went to the app and the app did an exceptional job of giving me. A good experience. Here's what they didn't do. Here's the last song that that band has been around for over 30 years record, because a, who thinks that's the best.

[00:01:29] It also didn't say, okay, here's a list of all the albums begin playing those albums, starting with track. Number one. I don't know if you've noticed this or not, but it's not the seventies anymore. We don't have to do album cuts. No, this app somehow took it upon itself from the roughly 260 million users.

[00:01:52] It has on a regular basis. Use the data. From those listens from how that content is being consumed for that band specifically, and then presented songs to me from that band in order of popularity, the assumption being what's most popular might be something you should listen to. If you're brand new to this band, and you're on the listen to music from this band side.

[00:02:20] Now you might be saying, hang on, Evo podcast apps, all do that. They will always show you the most popular podcasts. Yes, they do. But I don't know that that's the best, but back to the question of how do you listen to that content? Again, these apps presenting in an album form or most recent episode from that, that's not the best again, what this app did was presented the contents to me.

[00:02:47] Based on some algorithm that they determined, help people listen to and consume things better. That's where we're falling down. Imagine for a moment, if podcast apps worked that way, when you typed in the name of a podcast, you were looking for it, didn't show you the most recent episode. It showed you the best episode.

[00:03:14] The episode that based on the data, they have tells you what you should be listening to first presents that to you first. So you can go, wow. That is every bit as good as person X told me. I'm now going to subscribe. Now, first off, there's some flaws with this plan, this, this is not going to work for all types of podcasting.

[00:03:36] Like if you were doing a news program, if you're covering. Events that happened right now, like, like the daily or like geek news central, that content is going to be worn out very quickly. It doesn't have a long shelf life if you will. So for them probably the most recent news is much better than something from 2007.

[00:04:01] Also, this won't work for cereal content like a well cereal, obviously the big gun or valence. Hey, Saifai drama. Audio drama, storytelling podcast made here in Phoenix, Arizona. Those serial shows need to be consumed from episode one. From the beginning. You don't want to read a novel starting with chapter six.

[00:04:21] So presenting those episodes based on popular doesn't make any sense at all. However, it's perfect for timeless content. Content that is neither newsworthy immediate required listening now or building on prior things in a serial fashion. Sure. And by the way, a lot of podcasting out there is timeless.

[00:04:42] This show is timeless, sleep with me or snooze button to just do two random shit. Those that are sleep related are both. Doing timeless content sleep with me. You want to fall asleep. Here's what episode you help listen to the snooze button interviews with experts to find out why sleep is such a problem, right?

[00:05:00] Then those timeless all the time. So if you're making timeless content, the most recent episode, as I've said previously on this show, isn't necessarily the one people should be presented first. It should be your best. So how do we figure out which one is the best? Well, we can't rely on downloads. You cannot go to your podcast, hosting company and assume that you'll get a nice report that told you which episode is most popular.

[00:05:27] Oh, don't worry. You'll get an understanding of which episode gets the most downloads. But yeah. Let me bring to you the fast food problem. Let's say you run a fast food restaurant and you want us to figure out which of your three condiments, ketchup, Mayo, and mustard are most popular. The problem is if you just count up, well, how many packets of mustard and Mayo and Keppra catch up?

[00:05:52] Did I go through, you're probably going to see catch up more than the rest of them. Why? Because catch-up is what you automatically throw into the bag for all of your take through, take out stuff and your drive through things. No one asks to have ketchup put in or taken out, or very few people actually do it.

[00:06:10] It's an automatic response. However, people are at the counter grabbing packets. That's a different, we don't have a way to isolate those. And what I mean by that is in podcasting. Most of the people that are listening to our content are just getting it. It's automatically sent down to them. Our podcast hosting companies count, counted, download as a download, not necessarily a stream, not necessarily somebody who wasn't subscribed.

[00:06:36] They don't give us any of that information. They could give us that information. It's not easy. I get that, but they could do some deeper analyses and tell you, Hey, these episodes, something's weird. It's not part of your regular RSS distribution or this particular file was listened to 74 times by people.

[00:06:59] Listen to versus actually play now. That's hard for them to get to. I understand it's not an easy thing. They can't turn on and turn out to flip on this apps. Certainly could be helping with that because they do have play information. Every single app out there has access to whether or not a person, an individual has played an episode multiple times.

[00:07:19] And if they see that kind of thing happening over and over, they could provide a very strong signal. To the podcast creator that this episode's popular. Maybe we should rank that a little bit higher up in the list. But part of the reason they don't do that is we podcasters have been so focused on getting downloads, right.

[00:07:38] We want an accurate download count. And if somebody listened to the same file, 17 times, we don't want to know about that. Wait, we don't really, I think that's kind of key who you might want to know that because if we knew something like that, If we knew that we could then present to people who have been introduced to our show from somewhere else, someone else, the best episode for them to start with, but as it is, we're stuck doing it on our own.

[00:08:09] Now, you know, who knows, maybe that'll change over time. Maybe some of these ideas will percolate up and as better app developers get in and more data gets consumed, maybe they'll find a solution for this. I sure hope so. Or maybe, maybe the new players that are entering the market spending lots of cash.

[00:08:25] Who've already solved this problem, but for music, maybe they're going to solve it for podcasting too. Now, if you enjoy my pontificating checkout, buy me a coffee.com/evo Terra. That's where you can not only buy me a coffee, please. Thank you for the virtual coffee by the way, but you can also become a member and I'm doing some special things for members over there.

[00:08:49] Go to buy me a coffee.com/evo, Tara, and a something to become a member. It's really cheap. And you'll get some special things, special things. And if that's not for you then pretty please with sugar on it. Go tell one other podcaster about podcast pontifications today. Even if you've done it previously, do it again to somebody else brand new that's helped.

[00:09:10] Yeah. Maybe somebody you just met who needs to discover more about podcasting and needs this particular episode. That's fine. Send it away. I shall be back tomorrow within another podcast. Pontifications cheers.

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