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The Audacity to Podcast® - tips and how to start and grow profit in podcasting
438. Podcast Metrics Explained: Downloads vs. Plays vs. Audience
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The world of podcast metrics can get confusing. We've got downloads, streams, plays, audience, listeners, and maybe even more terms than that. Here's what the most important terms actually mean. Thank you for joining me. For the Audacity to Podcast, I'm Daniel J. Lewis. In podcasting, we try to measure our audiences and our reach, and for many years the benchmark term was downloads. How many downloads were you getting? But even in that, there is confusion about that. And where there are these multiple terms, downloads, streams, plays, listens, views, audience, listeners, viewers, watchers, community members, all of this stuff, all of these terms, I think come down to three different basic labels. Downloads, plays, and audience. So here's what those mean. If you want to follow along with the notes, they are a simple tap or swipe away. Look at the Chapters powered by podchapters.com or go to the audacitytopodcast.com this is a fun one. D v p v a that stands for downloads versus plays versus audience. D v p v a number one downloads. This is what we've had for so many years. When we're talking about the size of a podcast, we talk about how many downloads it gets. Also somewhat interchangeable with this is the term streams. How the file is delivered is different between a download and a stream. In many podcast apps these days, you can just go into a podcast, find an episode, press play, and you'll be able to start listening to it or sometimes watching it within a few seconds. That is considered to be a stream, where really what it's doing is it's progressively downloading the file. Once you press play, it starts downloading it and it buffers ahead of time in the background. But due to the speed of many connections, we see it as press play and it starts playing almost immediately. That is still a download because it is still downloading in the background. It's just downloading progressively. Some apps or even data networks might limit how much can be downloaded in individual chunks, whether that's 50 megabytes, a hundred megabytes, or something like that. So if you have a really long episode, it might get downloaded just half of it first and then the other half when you get to that spot, if you've just simply pressed play on that episode. But some apps just download the whole thing and it depends on if you're on your mobile data connection or if you're on Wi Fi, how your battery percentage is, how the app behaves in general. But all of this is still a download, where it is downloading the file from the Internet onto your device. Sometimes it does that before you press play, sometimes it does that when you press play. It's still a download. And this is the most fundamental measurement of a podcast's reach, if we could call it that, or maybe audience, if we want to call it that, because we're tracking at the server level how many downloads a certain episode gets. The problem is there are many ways to manipulate this. Many bots out there that will download these things. Lots of systems, good bots and bad bots. Downloading this information from your podcast to get more information about your podcast, maybe transcribe your podcast, maybe cache information about it. All of these things that happen automatically, many of them are not actually powered or triggered by humans. And so many of these podcast hosting providers Captivate, Buzzsprout, Blubrry, Libsyn, Transistor, Pretty much all of these places have their list of what they consider to be a bot of some sort, and they will not count those downloads. They will usually serve those downloads anyway to those systems, because if they didn't, then sometimes that breaks certain things. But they try to filter out what is seen to be unhuman downloads or bot downloads, something that is not triggered by an actual human. Even then, even if we had a perfect system measuring exactly how many downloads are being delivered to humans, that still doesn't tell the whole story. In fact, I've done episodes about this in the past about the myth of monthly downloads, where if you say, oh, we got a hundred thousand downloads this month, that actually says nothing about how big your audience is. Think of it like this. This is an extreme example, but just to illustrate this point, if you had a hundred thousand downloads last month, you could have released 100,000 episodes downloaded by one person, and your audience is one. That's an extreme example, but it does illustrate the point that the downloads per time, as I call it, downloads per day, downloads per week, downloads per month is really a meaningless metric, so don't depend on that. The only place, though it's really somewhat useful is if you have dynamically inserted, maybe even programmatically inserted ads across your entire catalog. And then when you're looking at downloads per month or downloads per day, any period of time, you're looking at that through the perspective of potential ad placements and how many ads could be downloaded and served. But if you want to know your audience size, downloads per month or downloads per any time is not going to be effective. What you need to look at is your downloads per episode after 30 days. That is the classic benchmark that's used for advertising. It's often used for just understanding the audience better because that's that does capture both your most loyal audience who downloads within the first few days, as well as some of the stragglers. And we could say that really the most loyal audience will download in the first three days. But first 30 days is a good metric here, and it is only the downloads for that episode that's 30 days old. So if you have an episode that you're tracking its downloads and it hasn't been 30 days since that episode released, then you don't have the full picture yet. And you need to compare episode to episode using the same amount of time since that episode was published. And I suggest that that be 30 days. Many podcast analytics tools, OP3 Captivate, Transistor, BluBrry, Buzzsprout, many of these tools will now display different benchmark periods. Whether that's 1 day, 3 days, 7 days, 10 days, 15 days, 30 days. All of them though do 30 days, since that is considered a benchmark and that's what it's been for many years. That's far more reliable than your downloads per month or downloads per day or anything like that. And this also tries to take into consideration the different ways that things are downloaded. I had this conversation recently with a podcaster where they were quoting their stats that they thought they had a monthly audience of a certain amount. But that certain amount was not exaggerated, it was just misunderstood. Because what happens when that larger amount was calculated, the per month is not only does that calculate if the same person downloads multiple episodes, however, most systems will tag that as one person. So if we look at it as just IP addresses and even that by itself has issues. What about a College where maybe 2,000 people at the college are downloading the podcast and it's coming from one IP address? Many of the systems have ways that they filter around that properly. But if we're looking at just IP addresses, it's still possible that you as one audience member could count for multiple IP addresses per month, maybe even multiple per episode. Because it could be like this if you are streaming the episode where it is downloading progressively, or maybe it is truly streaming. You start it at home, on your home WI fi, and then you get in the car and you run an errand and then maybe more of it downloads. That's now coming from a different IP address. While you're driving around, it's possible that your IP address changes depending on the cell towers and different things. Maybe you momentarily park near a coffee shop that you frequent and your phone automatically switches to that WI fi, even though you don't get out. Maybe you're just going through the drive through, but your phone turns on its wifi and connects to that coffee shop WI fi. While downloading this episode, that could be yet another IP address for the same episode. So you see, you could have multiple IP addresses for the same episode. And thus, if you try to count your audience by counting IP addresses within a month, that gives you too big of a number. And even if we're not talking individual episodes and how the IP addresses could change for a single episode, it's very possible that you could download an episode on one IP address. In the next episode, you're downloading through a different IP address, whether that's your home, IP address, downloading at work, downloading in the car, anything like that. So even though you are one person, you might represent several IP addresses. So any system that tries to say your unique audience, unique listeners, or unique IP addresses for this month is this number. That is a potentially helpful number, but it can be very easily misunderstood because that's not your actual audience, that's the number of IP addresses. You could maybe get closer to a more reliable number if you divide that number by the number of episodes you've released in the month. But then in some cases, that might be too aggressive and cut it down too much, because one person who always downloads from the same IP address might not be counted properly. So all kinds of math problems with trying to look at unique IP addresses like that. That's why if you look at downloads per episode after 30 days, that's more reliable, because once a person downloads the episode, they're probably not going to download it again, regardless of what IP address they're on, the same IP address or something else, even if their IP address changes while they're downloading the episode. Many of these podcast hosting providers and analytics providers are smart enough to see, oh, it was downloading at this time, and it cut off at the 10% mark. And then here's another IP address, the same user agent, which is how an app identifies itself to a server. So it's the same app downloading it, but picking up right at the same time that the 10% stopped, and it's picking up at the 10% mark at about the same time. So it's probably the same person. There are these kind of algorithms out there that try to calculate this kind of thing like that, and that's the way that they're trying to be more accurate about this. But in all of this to say, and I hope you feel a little bit of stress about this because this is a stressful thing. And that's why we need other ways to measure an audience reach and the size of the audience listening to a podcast. Downloads are very reliable, but they're not very accurate. They need a lot of filtering and they need a lot of algorithms and they have to account for all kinds of scenarios. So then we have plays. What is a play? Well, that's maybe the question Shakespeare would ask if he was a podcaster today. To play or not to play a play? In some places, like Apple podcasts, a play is when the episode is played. That doesn't mean when it's listened completely, that doesn't mean when it's finished, that means when it's played. And if you look at your stats inside of Apple Podcasts or maybe even Spotify and other platforms that count plays, you might see something like this where you know, you have a certain number of followers for your podcast and you look at your downloads and then you look at your plays and. And you see your plays are actually much higher than your downloads. And the reason that can happen is because it's counting every play. If someone pauses the episode and presses play again, that's now another play. Or in some cases, if someone has their voice assistant on their phone giving them driving directions. And the way that that system works in some apps and some devices is that it pauses the playback while it gives the voice directions and then it resumes that playback of whatever other media content, whether that's music, podcasts, audiobooks, or anything like that. So that could count as another play. So if you're driving around town getting turn by turn directions and it's interrupting your podcast episode, you might have accounted for dozens of plays on the same episode. So plays is another one that's. It's interesting but not very reliable. And that's where this super secret group that's been working behind the scenes like a man behind the curtain, the alliance for Measurement in Podcasting, or AMP A M P For short, is trying to define what a play is. I don't like how they're going about this and how secretive they're being and who they are not including in their group. But I do like what they're trying to do, and that is trying to define what a play is in podcasting. I think that they're going to go beyond this and try to define other metrics which perhaps have already been defined or need to be redefined as things change in the industry. But if we could all agree on what counts as a play, that could be really beneficial and if that could even apply to the non podcast platforms like YouTube. Consider this for example. On YouTube it is assumed there is no factual data that backs this up. This is just assumed from observation. YouTube has never said this, but it seems like on YouTube when someone presses play on a video, they don't count as a play until they've played at least 30 seconds. In podcasting, the IAB, that's the interactive Advertising Bureau download guidelines for podcasting, says that a download. So here we are different terms, but they say that a download is when 60 seconds of audio has been downloaded and that's 60 seconds after any kind of header information, which would be the chapters, the metadata that's in the ID3 tags, the image that's in the file too. So it can't just be the first 60 seconds of bytes, it has to see the first 60 seconds of audio and then that's counted as a download. But a play, what AMP is trying to do is define a play as 30 seconds. They might also start defining that a play can or can't include what happens after you pause an episode and you resume it. Because that can be really confusing to see that where downloads are measured at the server and thus a podcast hosting provider or a third party analytics service can measure the downloads. They can't see what happens inside the apps and that is where the plays happen. Happen. Yes, some apps won't download until you press play and sometimes that play will look different to the server from a normal download, but sometimes not. And sometimes it depends on the speed of the Internet connection too. So you can see again, there is confusion. There are multiple ways to define this, multiple ways to measure this. That's why I like what the alliance for Measurement and Podcasting is trying to do to clearly define this is what a play means. And this could also be something for the Podcast standards project PSP to do and what I've wanted to do with podcast standards for a while. And it's why I own a lot of the domains related to podcast standards and podcasting standards. But we need to have a standard way of measuring things. And when we use a term, it would be great if the majority of platforms and apps all understood that term to mean essentially the same thing. That's what the IAB download guidelines for podcasting tried to do with download metrics. But there is still some wiggle room with that. And there are certainly also ways that that can be manipulated and faked, even though they're trying to make it so it can't be faked. Before I move on to the next point, I want to thank a few members of my audience. Ralph Estepp Jr. Invited me onto Podcast Morning show to talk about podcast engagement, chapters and more podcasting topics. Had a great conversation with him. It was really fun. He and I are new friends. We've known each other for a little while, but our friendship has been deepening more recently as we've been working together on some stuff and collaborating on some things. And it was really exciting to be on Podcasting Morning show to talk about these things and just talk about podcasting in general as well as he gave me some opportunities to talk more about podgagement and pod chapters. I've got the link to Podcast Morning show in the notes for this episode. I'd love for you to check it out. You can listen to it or yes, you can even watch it both from the website where you can watch me and Ralph talk about podcasting there. I'd love for you to check it out. Also thanks to Martin Lindiskog, who streamed 706 satoshis over the last few episodes and also sent a boost of 1,111 satoshis, saying I broke your quote rule with my first podcast, Ego Netcast. It is a caricature of yours truly. That's Martin who's saying that. Created by editorial cartoonist and fine artist John Cox. I'm wearing a baseball cap and here's why he says he's breaking the rule holding a retro microphone and a radio. Thoughtful advice about not wearing headphones. I have found a pair of very comfortable headphones, the AKG K92. And thank you very much for that feedback and that support, Martin. By the way, akg, if you're looking for comfortable headphones and I don't get paid to say this, I might have an affiliate link in the notes. But I found that the AKG headphones are some of the most comfortable. They might not sound the most natural, they might not be the most portable. But in my testing of multiple headphones several years ago, and multiple headphones I've tried since then, I just continuously find that the AKG headphones seem to be the most comfortable if you want the big over the head kind of headphones. So thank you so much for that support. If the Audacity to Podcast means something to you and it's inspired you, it's provided value to you, would you consider giving back whatever you feel it's worth to you? You. You can do that through the audacitytopodcast.com giveback. And I would really appreciate that support. Now, number three, audience. Some people would use the term listeners, but the reason I don't like the word listeners is because podcasting since the beginning, not just recently, but since the beginning, podcasting has also been video. So to say listeners implies only audio. And I know that generally when we're talking about podcasting, it is audio podcasting, and therefore the audience is generally listeners. And I like the term audience better because it could mean your listening audience or your viewing audience. So however you want to label that, I'm going to go with audience. This is the third major way to measure the reach and the size of a podcast, is how many people are actually listening, viewing, consuming, pressing, play, downloading, whatever it is that they're doing. How many people is your podcast reaching? And unfortunately, there is no universal way to measure this everywhere. There are all kinds of privacy concerns around this, because if a podcast app can track what you listen to by some kind of ID associated to you, then they could build some kind of picture of what kind of content you consume, and they could sell that picture to advertisers, to whoever. I don't know who might want that information, but it does present privacy concerns. And some podcast app developers have just outright said, no, I'm not going to do anything that in any way identifies a person or reports the number of people consuming a podcast. So we've got that little challenge. But this is also information that has to be tracked inside the app. And despite the fact there are privacy concerns, there are still some ways that this could be done. And one of those ways has been talked about by Dave Jones behind Podcasting 2.0, and it's the universal listener ID or universal audience ID. So UL ID or UAID. The idea behind this is that a podcast app would generate some kind of random ID strings or a list of letters, numbers assigned to a person and send that with the download request. So anytime that person, regardless of what IP address they're on, tries to download that episode or any episode from the podcast, it will be tracked as that person, or at least that ID string, not necessarily a person associated with that. But there are privacy concerns with this, because first of all, the app, if it uses the same ID across multiple podcasts, then that means that certain analytics tools could start to see what else you listen to, and that starts to invade your privacy. It could be that podcast apps will then randomize it so that you have multiple IDs. So for each podcast you listen to, you have an ID that is unique to that podcast, and it is globally unique. So no one else has the same id. So if you listen to podcast A, you have one ID that's reported there. If you listen to podcast B, you have a different ID that's reported there, and then that's sent to the server. The problem with that, then yet more privacy concerns. And some of these privacy concerns are not so much what people are actually doing, but what people could do with these kinds of things. And the privacy concern here is that even if you have a unique ID from my podcast, whenever you download that, I could, if I was malicious about this and tracking the server information, I could then start building a picture of where you go, all of the IP addresses that you use with that id. And then I start to see, oh, you go here and here and here and here. I wouldn't do something like that. Many of the podcast hosting providers out there wouldn't do something like that. But advertisers would love to do something like that. Many of your social media apps are basically already doing that. And that's how, by the way, this whole thing of my phone is listening to me because I had a conversation with someone the other day about this thing, and then I started seeing ads for that thing. My phone is listening. Well, no, the actual truth is much scarier than that. The actual truth is the algorithms behind that, that they see you and this other person frequently visit this same location around the same time, and you have these other connections, these other relationships and social circles that are also going here. So it's very likely that you two occasionally talk. And so it starts to build this algorithm that tracks you in ways that are far scarier than my phone is listening. Because your phone is not listening. The algorithm is watching. It's the big brother of the 2000s, the algorithm. And advertisers love that. And I hate that advertisers love that. And I get why they want it, because with more information, they can target the right customers. They can target the customers at the right time with the right offer and all of this. Yes, I understand that. But the frustrating thing is that that kind of targeted advertising, or just reach for whatever it is, even if it's not advertising, just any kind of reach, it means building a profile of someone without their consent. Very possibly. And although in these social apps, Facebook and such, you are giving your consent for these algorithms to be built, your consent is buried inside of the terms of service that everyone just check marks to say, yes, I read it And I agree to it. We're giving away our consent with that stuff. And podcast apps don't want to have to ask for that same kind of consent Apple Podcast does, because Apple Podcast does generate a unique ID for you. And you can at any time, inside your iPhone or your Mac, you can go and reset that id. But who's going to go do that? So we have this difficulty that we as podcasters, we really want to know how many people are listening to our podcasts, but we just can't really get that information without invading privacy or without getting every app developer on board and enforcing certain rules of what you could or couldn't do with this information. Like it would be totally possible with the idea of the UL ID or the UA ID to be able to implement that and ensure that it respects everyone's privacy. But the app developers have to commit to respecting that privacy. And there is the possibility sometimes, and this has happened with Chrome Extensions, where when people start using it, they've agreed to certain terms and certain privacy policy that the developer at that time put out, but then the developer sells it to someone else, that someone else changes the privacy policy, they change what is tracked, and now they've got all this personal information about the users. That has happened with Chrome extensions, Firefox extensions, WordPress plugins, and other things like that, where it's changed hands and the new people don't respect your privacy. So what are we to do? The best thing we can really do is, and the reason why I did this episode is to help you to understand the caveats to these different things. Sometimes I joke about, I'm like, Mr. Caveat that I just instantly see caveats to all kinds of things. And I really, really want the caveats to be known about things. I don't know how many caveats I've given in this individual episode itself. But when you better understand how downloads are measured, how plays are measured, how audience is measured, when you can better understand these things, then with the understanding of the caveats, you can better understand the actual reach of your podcast and you won't have to rely on the latest metric or the latest standard. And when you understand these things, you can go into these different platforms like Apple Podcasts, where they provide more detailed information about your podcast consumption because they are tracking it inside the Apple podcast ecosystem. So they can provide more information like that. You can go in there with a better understanding of okay, plays, is how many times that episode was played at all. That's not necessarily my audience size, but you can still gain information from that. Like you might expect to see if you do really long episodes, that you're going to have a lot more plays for those episodes, because it's more likely that people will pause and resume a long episode than a short episode. That does not mean that a long episode gives you a bigger audience, because plays does not equal audience. Downloads is closer to equaling audience. But still, once an episode is downloaded, that does not guarantee that people are actually playing that episode and consuming it. In general, yes, there's been some survey data shared before from Edison Research that in general showed that most people who download an episode ahead of time listen to most or all of that episode. That could be changing though, especially as these apps and services might not actually download. And how many of these people know the difference, the actual audience? People know the difference between what has been downloaded ahead of time versus what's available for play as a progressive download or a sort of quote stream, unquote. And because of that, I think even some of the survey data we can't completely trust. And it really comes back to what is important for you. Is it really the number? Some of these numbers I think are more important if you have sponsors or advertisers or there's some kind of business behind the podcast where the business needs to know These metrics, the KPIs, the key performance indicators. Yes, there's a need for that in places. Other podcasters, though I think it's not the numbers themselves that give the podcasters excitement or fuel to keep going with their podcasting. Just like my previous episode where I talked about podcasting with a small audience, I think for many of us it's not the size of the audience, but the depth of our relationship with the audience. So the more that you can foster that relationship, the deeper you can go with the audience you have. That will be the number that matters more. How many people emailed you this last week? How many people commented on your episode? How many people have not just left a rating for your podcast? And this is something cool that I show inside of Podgagement is not just your total number of reviews, but the number of ratings versus reviews. Because at least an Apple podcast you can leave a rating without writing a review, but every review you write must include a rating. So you can see more engagement on podcasts where they have a larger number of reviews and a smaller gap between the number of reviews and the number of ratings. You'll always have more ratings than reviews because a lot of people go in and they just leave a few stars and that's it. They don't write anything. But the smaller that gap is, the more engaged the audience is. Like for the Audacity to podcast, it's a much smaller gap for the total number of ratings versus reviews compared to a podcast like let's say serial, where they have a huge number of ratings, but a big gap between the number of ratings and their number of reviews. So maybe reviews is an important metric for you and you should definitely be using Podgagement to track that for you over@podgagement.com but even reviews, that's one review per person, they can't leave a new review every episode. So it's one review per person. So that's why maybe something like feedback or engagement in your community or reposts on social networks or anything like that. Think about what really matters to you for your podcast. Why are you doing your podcast? The profit, the P R O F I T, popularity, relationships, opportunity, fun, income and tangibles. And which metric measures the why for your podcast? Is it the size of the audience? Is it the number of plays? Is it the number of downloads? Is it something else? That is then the most important metric for you is that metric that connects to your goal, whatever your goal is. If fun is your goal, well then it's this very simple thumbs up, thumbs down metric after each episode. Did we had fun? Yes. Then we met our goal. And it doesn't matter how many downloads, plays or audience we have, it's up to you to decide what is important for your show. If this has resonated with you, I'd love it if you would comment on this episode or share it out wherever you like to share things. I'm most active on X, but I know a lot of people are active on other social platforms too. So you can go over to theaudacitytopodcast.com dvpodotod which sounds like maybe a cheer from cheerleaders, but the audacitytopodcast.com DVPVA, that stands for downloads versus plays versus audience to share that episode out. Or maybe inside your podcast app you have the sharing option there. Now that I've given you some of the guts and taught you some of the tools, it's time for you to go start and grow your own podcast for passion and profit. I'm Daniel Jones Lewis from the audacitytopodcast.com thanks for listening.