The episode itself is well worth listening to (as are their other episodes), but during the show, Sophie asked if Mathew or Jayme had heard anything about when the best time to publish a podcast was?
They didn't really provide a conclusive answer, but there is a good reason for that. It's complicated.
Let me explain.
First of all, we do actually have studies that have looked at podcast consumption and time of day. One of them is from Megaphone. They were wondering: "First, what days and times are most (and least) popular for podcast releases. And second, is there a way to determine a correlation between release schedule and downloads?"
The result was a number of interesting graphs.
The first graph is this one illustrating what day of the week most podcasts are published (of the podcasts using Megaphone). And here we see a very clear focus on the weekdays.
Next, they also looked at what time within each day the podcasts were published. Here we see a very strong focus on overnight publishing, to have the podcasts ready for the morning listeners, with another spike in the late-mornings.
Here is another way this could be visualized to give you a more detailed look (weekdays only).
Okay, so this is when the podcasts are published. But when do people listen to them?
Well, here we have a slight problem, because the way this is usually measured is by looking at the downloads. And the downloads may not actually represent when something is actively being listened to. We know, for instance, that there are many podcast players that enable auto-downloads.
But if we just look at the graphs that Megaphone provided, it looks like this:
Okay, there are a number of interesting things to unpack here. First of all, you will notice a lot of overnight activity, peaking at around 5 AM. Megaphone does not provide any insights about this, but I suspect that a lot of this is automatic downloads.
For one thing, we know that on average, people mostly get up at 6, 7 and 5 AM (in that order), so it's unlikely that the spike we see at 5 AM would indicate when people actually listen to this. This, of course, is one of the many problems with podcast analytics.
You will also notice a midday spike, and an evening spike. But notice how different it is each day. Tuesdays and Thursdays look very different from Wednesdays.
Why is that? Well, I suspect that might not actually be because this is how people behave. Instead, it's likely because that's just the kind of shows that are available on those days.
In other words, if you were to take all the shows published on Tuesdays and instead put them out on Wednesdays, Wednesday would probably have the same pattern.
So, I don't really think this data is that useful. And I could say the same about pretty much every other study.
For instance, there are studies that claim that the ideal length of a podcast is 22 minutes, and that Tuesday is the best day, or that weekly is the best frequency. But then we see other studies where the most popular podcasts are 40-60 minutes, and many of them don't even have a regular frequency.
So, all this data is useless.
What I want you to do instead is to think about podcasts in a different way. Podcasts are very different from other forms of media for two reasons.
First, because podcasts are usually long (20-60 minutes), it's a macro-moment. It's not something you can just snack on. It's something that you actually dedicate some time for.
However, podcasts are also something you can listen to while doing other things. In fact, the trend around audio is really defined by this factor. It's something we use between all other media moments, while we engage in other activities.
Let me give you an example.
Imagine that you are going grocery shopping. Normal content wouldn't work for this. You can't read an article while buying groceries, and you can't watch a video either. You could snack on some posts from Instagram, but only really during that minute or two that you are standing in line at the checkout.
But podcasts are very different. If you know that you are going out grocery shopping, you would put on your headphones as you leave your home, and then listen to your favorite podcast for the duration of your trip.
Think about how amazing this really is. Podcasts create this entirely new form of media consumption. It's kind of like radio, but it's far more mobile and on-demand.
So, here comes the question. How do you get people to pick just your podcasts?
Well, there are two ways.
The first option is what Megaphone advise. They wrote:
Our theory to explain this [why podcasts are downloaded more in the early hours] is that an episode gains a competitive edge by being at the top of the list in a user's podcast app at the time they commute to work. This is supported by the finding that episodes published in the afternoon do best when they post just before the afternoon commute (in the 4 PM hour).
And then they conclude this:
Still, we have enough information to see patterns emerging. There are many reasons for choosing a particular time slot for your podcast, but if you're able to publish on Tuesday at 5 AM, these data suggest that wouldn't be a bad choice.
Okay, I can kind of understand what they are trying to say here. They are saying that when people wake up in the morning and they see this long list of podcasts that they are subscribing to, you want your podcast to be the one at the top, so that you get the maximum focus.
However, think about what behavior you are actually optimizing for. This is what we call a low-intent audience.
If people go to their podcast app and just pick whatever is at the top, you are talking about an audience that have no idea what they want to listen to, and have no real connection to any specific show.
This is not a good audience.
Even more than that. It doesn't necessarily work this way. Megaphone writes about this too:
But this theory is based on the assumption that podcast apps tend to list the newest episodes higher in a listener's feed. We did some testing, and that's generally true, although it's not quite as simple as that - in the Apple Podcasts app for example, the algorithm tends to favor newer episodes unless you haven't listened to a particular show for a while, and in a few other situations. Nonetheless, Apple Podcasts and most other apps we tested did indeed give newer episodes an edge.
But when I tested the same thing across my podcast apps, none of them listed shows by date.
So, just optimizing to be at the top of a list means optimizing for a disinterested audience.
Instead, you need to optimize for the moment instead.
People are diverse beings. We have different interests at different times of day, and our list of podcasts reflect that.
In the morning, you might want to listen to a specific type of podcast. Whereas in the afternoon, on your way home, you are really just trying to relax and so you choose to listen to an entirely different type of podcast.
So, my advice to you is to focus on creating the right moments instead. You shouldn't care about whether you happened to be at the top of the list just because you published at a certain time. Instead, you should care about whether people choose to pick you.
Create a podcast that people love so much that when they get up in the morning, they will specifically pick you. They will scroll down their list of podcasts to find you, or they will specifically say: "Alexa, play [this podcast]".
It's the same thing if you publish weekly. What time you publish doesn't matter. What matters is if people love your podcast so much that during each week, they all pick a time to listen to you. Who cares whether you published it on Tuesdays at 5 AM or some other time? Your loyal audience will listen to it whenever the moment is right for them ... and the success of that is whether they do this as a habit.
Most importantly though, focus your efforts on reminding people about your podcast and promoting it over time.
What I mean is that, if you look at the analytics for most podcasts, you will see that most of your downloads don't actually happen on the same day that you published.
Instead it might be a day, several days, or even a week before people get around to listening to it. This doesn't mean people aren't interested in your podcast, this is just a reflection that people have busy lives.
Take my podcast as an example. If I look at my download analytics, only about 9% of my listeners download it on the same day that I publish it. The rest download in the days afterwards, or even weeks or months after.
In other words, 91% of my listeners are in the 'long tail'. And you will probably see this a lot with podcasts that don't publish daily (and even many who do).
With this type of audience, suddenly it no longer really matters whether you publish on a Tuesday or Wednesday. Instead what matters is how good you are at reminding people over time to listen to each show.
So what you want to do is to keep up the momentum of exposure over time, so that people don't forget about you.
You do this by promoting each episode continually. For instance, when the Media Voices Podcast published their latest episode, they posted this:
And then a few days later, they posted this:
This is how you keep the momentum going and bring out the viewers from your long tail. And this is far more important than anything else. Because podcasts are macro-moments, and because people listen to them on-demand when the moment is right, doing things that remind people is going to have a much bigger impact.
And this is how you should think about podcasts. If you create a daily show, focus on building a daily habit, and design it for a specific moment in people's daily lives. If you have a weekly or more irregular podcast, focus on creating something that people love so much that they will specifically pick it over anything else, and focus on keeping up the exposure over time by reminding people to check out the latest episode.
BTW: If you want to learn more about the future trend of audio, I have written three big articles/reports about this:
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"Thomas Baekdal is one of Scandinavia's most sought-after experts in the digitization of media companies. He has made himself known for his analysis of how digitization has changed the way we consume media."
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