Brad Hill: Daryl, it’s great to see you. Thanks for making time to do this.

Daryl Battaglia: Thanks so much, Brad, for having me here.

Brad: You’re welcome. This is going to be interesting. For as long as RAIN has been offering content related to podcasting, measurement has been at the center of it all. Podcast publishers need to know how many people are listening to the shows, podcast advertisers need to measure how effective their campaigns are. And you, in your position at Triton, have your arms around the whole thing. So, I’m going to start you off with a broad question, and you can dive in from whatever angle you like. Daryl, what kind of trends have you been noticing recently in podcast measurement?

Daryl: Sure, Brad. We measure a lot of data for a lot of different publishers, a pretty big cross-section of the industry. The most recent analysis I did was with the US, so we’ll talk about that. The method that we do to go about doing an analysis like this is we try and do what we call same-store sales. Additional publishers are getting measured, that, of course, is going to drive growth in the numbers, but we try and tease that out and just focus on sort of an apples to apples what’s the growth in podcast listening and downloads. And so, what we’ve seen is close to a 10% increase in the last year, but in particular in the last few months, it’s been picking up. I’m not sure if that’s because more people are commuting, more people are going back to work, but has been picking up even more.

Brad: That actually seems counter-intuitive to me because there was such a rise in podcast listening and creation during COVID.

Daryl: It’s hard to say, I think that rise would have happened anyway. But what we’ve seen, and studies like The Infinite Dial have shown this as well, is podcasting has been on a multi-year trajectory of kinda steady rise. I don’t think that… I think that we’re still waiting for the next breakthrough hit that is going to really create a step change for podcasting.

Brad: Yes!

Daryl: Sort of what Serial and Joe Rogan and podcasts like that have done. But it’s been very, very steady. And one thing we’ve noticed from the data, we did an analysis where we looked at… We basically bucketed the podcast that we measure into brackets by those with 10 million plus downloads a month, one million to 10 million, 100,000 to a million, and 10,000 to 100,000 and so on. And what we’ve seen is it’s not really the big mega podcasts that are driving the growth, the number of those have pretty much remained constant. What we’ve really seen is under a million and especially in that 10,000-100,000 range, we’ve seen a lot of growth there. We’ve seen, I think the latest analysis was just kind of apples to apples, same-store sales, we saw a 31% growth in the number of podcasts that fall in that range.

Brad: So, that’s great news for podcasters and publishers in the middle of the long tail, isn’t it?



Daryl: It is. There’s a lot of opportunity with podcasting. There’s something for everyone, I think, and there’s more content that is being produced than ever before, and they are driving a solid audience, those additional podcasts that are being produced, and perhaps they’ll grow to be something more than that, but the diversity and the amount of content that we have that is driving an audience is just increasing more and more.

Brad: Alright. So general trend upward and not just the top 100 hits or the top 50 hits continuing to accrue ever more gigantic audiences, but the effect is trickling downward at least to some extent. That seems healthy to me somehow, does it to you?

Daryl: I agree, I think it’s very healthy. I think it’s not one thing that’s driving the results, it’s a lot of different podcasts, a lot of different publishers, and consumers are… And listeners are finding more things to listen to. Yeah, I agree, and it’s not a one-year fad, so definitely healthy, that’s steady, healthy growth.



Brad: Alright. So, what’s the current state of measurement? And you can angle into this from whatever perspective you want, from the publisher’s viewpoint of needing to know about listener numbers, from the advertiser viewpoint of needing to verify campaign results. What’s the state of podcast measurement?

Daryl: I think we’ve come a long way. I think podcasting originally, when you talk about download and listener numbers, there wasn’t always a standard for the measurement of that. The IAB Measurement Guidelines and Certification has been widely adopted and it’s helped bring those numbers very closely aligned, which is great. It’s still primarily based on IP address and user agent and downloads, and that’s part of inherent with what data gets returned back when the listening is occurring on third party platforms as opposed to publishers’ own platform. But I think that it’s going to continue to grow the tools to make use of the data. Certainly Triton, and I’m sure others, are working on supplementing that data with other sources and using new methodologies in order to measure, so it’s continuing to develop. And on the attribution side, we have attribution now, I think that’s definitely a positive, to be able to track the impact that an advertising campaign is having. It’s still primarily on website visitation to the brand, which makes sense for some brands but not all.

Brad: No.

Daryl: Not for every brand is the goal to drive people to the website. There are Brand Lift studies to help understand, update the campaign, help to drive brand awareness or favorability. And I think just the analytics will continue to improve to really be able to pull out the insights regarding what exactly did this campaign drive not just that there was, “Here’s how many impressions of the advertising occurred, and here’s how many of them went to the website.” But to what extent did the ad campaign truly drive that result?

Brad: Yes.

Daryl: Those analytics will continue to improve. And I think that making that data then actionable, having that feedback loop into advertising to be able to say, okay, based on these learnings, what changes am I as an advertiser going to make to my sponsorship investments and to my advertising plan?

Brad: Yes. All right. You’ve articulated a whole landscape of podcast measurement, and it’s an uneven landscape, it sounds like, kind of craggy in some places and smooth in other places. Is it fair or unfair to say that there is a common currency in podcast measurement today?

Daryl: I think the IAB standards have gotten us part of the way there, but we’re not all the way there. We are very much still in a place of publisher self-reported numbers, the numbers that they can provide can vary, it’s… They’re not wrong. They’re just, every publisher is providing numbers within their own context, their own timeframes, their own geographies, new episodes back catalog and the data that the ad agencies and the brands have to work with, it’s not always clear what that data represents. It’s not always sourced from a third party. They don’t have all the tools to make use of that data. And the data that they do get from the publishers is usually very summary level, not all of the detail that they’d like to see.

Daryl: So I think that the measurement data that exists is much more trustworthy than ever before, but the utility of that data, the access to that data for buyers of advertising needs to improve, and that will benefit everyone. If you make it easier for brands to evaluate “where should I advertise?” and they can make those decisions in a way where they’re informed, and they’re confident with the information that they’re working with, it’s going to mean more investment in advertising. So I really think I’m moving more towards a… I think a less fragmented ecosystem, less publisher self-reported numbers, it’s just going to increase that confidence and help fuel advertising.

Brad: Okay. What is the pathway to that? You’re speaking as an expert in Triton Digital and some of your colleagues/competitors in the field have their own methodologies as well. So it seems to me like there’s kind of a built-in fragmentation here, or am I looking at it wrong?

Daryl: I think there’s a built-in fragmentation because the data really is controlled by the publishers. It’s essentially their data. There’s a lot of different hosting platforms, a lot of sources, and there’s not really one central place to access information about every podcast and publisher out there. I can speak for what Triton is doing. We are IAB certified, one of the first to do that. We are providing IAB certified numbers and measurement. Our methodology is public. It’s on our website. Quite detailed document that talks about exactly how we do everything. We’ve been out and have met with ad agencies and walked them through. Particularly, even a couple of years ago, they really weren’t familiar with where does the data come from? How does measurement work? Why is it the way it is? And so we’ve worked on educating ad agencies. So I think that transparency and education is a big part of it. The other thing that we do is we collect data, raw data from different sources apply that same measurement, apples to apples measurement, so that the potential exists to get data across publishers and podcasts in one consistent way in the future. So we’ve been building tools to enable that as well.

Brad: Okay. Speaking of building tools, I want to lead us into an upcoming product that I think you’re developing, but I don’t know much about it, I’ve just gotten hints about it, so I’m going to query you about this. I think you are on the verge of merging census reporting, which is measuring on the server side of podcast downloads and mixing that with survey work. Do I have the outlines of this project right? Correct me if not, and tell me everything about it.

Daryl: You are right. I’ll definitely expand on that. So to just to back up a second, there’s a lot of great things with the server side download data that we measure today. It is a huge data set. We measure, just in the US alone, we measure over a billion downloads a month that’s contributing to that Demos Plus solution and many more around the world. And it gives you daily data by episode, by listening platform, by device, so there’s a lot of great things about it. There’s also…

Brad: And by the way, anybody who’s watching this can go to and look at summaries of those reports. It doesn’t have all of the client information in it, but it does… Again, of course we cover those on RAIN News as well, too. I just want to make sure everybody knows that this is not a completely walled garden, you can actually go and look at reports on your website.

Daryl: Right? Yeah, we have a public ranker report of the top participating podcast networks and podcasts, exactly. Yeah. So, the download measurement, the server side measurement, big data set, what it’s lacking is an understanding of who is the audience. IP address matches for household IPs are widely used for advertising, but there’s some flaws from a measurement perspective. And the biggest need that we found started with understanding person-level insights of who the audience is. Age and gender, and other socio-demographics about the audience. You can expand beyond that to purchase behaviors and interests. But it really started with… There was no central source for demographics about the audience for every podcast.

Daryl: And surveys, on the other hand, can give you person-level insights. People take a survey, they tell you about who they are, which is great. There are some surveys which are, “Hey, listeners of my podcast, please click on this link and take the survey,” which is great for getting feedback. The challenge is that it’s not necessarily representative of the whole listener base, and there are biases regarding who’s likely to take those surveys, including are you getting the same response rate for the surveys from diverse audiences? And then there are other surveys that are representative of a population where you’re recruiting a balanced sample of survey responses, but it only has enough sample to be able to break out demographics for the very largest podcasts.

Daryl: So what we’ve tried to do is combine the download data with the survey data using some data science layered on top of it. We work very closely with a company called Signal Hill Insights who’s done an amazing job managing the survey and putting the methodology into place.

Brad: That’s Jeff Vidler and his company.

Daryl: Jeff Vidler, exactly. And so what we do, we have a representative survey of the population, we have quotas by age and gender, race, ethnicity, region, education. It’s 12,000 monthly podcast listeners a year that we make use of. But even with that sample size pretty big, it’s very common that you’ll have only a couple of people who say they listen to an individual podcast, maybe zero. So what we’re doing to overcome that is that we form a neighborhood or essentially cohorts around each individual podcast using the download data. So out of the tens of thousands of podcasts in the Triton database that we measure, what are the 100 that have the most overlap in listening on an index basis with your podcast? And podcasting is so niche, there’s so many different types of podcasts that there is a very tight association between a podcast and those others in its neighborhood that have a similar… Similar listeners. And we’re able to bring in the survey results from the survey for those neighbors as well, and attribute the profile of the audience back to that original podcast.

Brad: I’m a little confused about the neighborhoods concept, although I’m familiar with podcast neighborhoods. But how does that apply to your new product?

Daryl: Sure. So what we’re trying to do is scale the results of a survey so that the survey requirements aren’t in the 100,000 or a million range in order to measure the audience for every individual podcast. And so we’ve had to embrace new, innovative approaches in order to estimate the audience for every individual podcast. And part of what we’re doing is we’re saying, “There are other shows that have a very similar audience based on the download data.” They have common listeners at a high rate. There are so many podcasts out there, and there are many different niches of podcasts about different content that have a common audience.

Daryl: And so we’re using the download and listener data from that big data set to identify what are the podcasts that have the most overlap or common listeners with your podcast? We’re then separately going to the survey, and we’re pulling the survey results for those podcasts as well. And it’s an ingredient into understanding and estimating the overall profile of the audience for your podcast. So those other podcasts that have similar listener base are contributing to the results and helping to scale the results of the survey. We are not surveying the individual listener that you had, we’re not trying to re-contact them, they’re two different steps, but this is our method of applying data science and a new approach to estimating the audience.

Brad: Oh, that is so interesting, even thinking about some work that was done a couple of years ago that gathered podcasts into neighborhoods, and this sounds like the listener version of that. You’ve got listeners in neighborhoods around podcasts. That’s fascinating.

Daryl: It is. It is amazing, when you look at the neighborhoods, how much they make sense, but it’s all data-driven. We have IAB… I’m sorry, we have Apple Podcast categories like true crime and comedy that are very broad, but this is a data-driven approach to saying, “Here are the podcasts that have a common audience, and let’s use that information as part of the ingredient to understanding your audience better.”

Brad: Okay.

Daryl: So I’m a basketball fan, I listen to basketball podcasts. Well, the other podcasts in the neighborhood often are other basketball podcasts. The data tells us that it’s not based on any subjective expectation. But it’s not always a basketball podcast. It could be another sports podcast or it could be something outside of sports. We’ve found, and Jeff at Signal Hill has done a lot of this research that different types of true crime associate with each other, that there’s not just one type of true crime, and that the profile for some true crime podcasts are different than others, and this method helps to tease that out. And really what I’m getting at with this is that within the measurement space, we need to adopt new data sources, new methods, not just like tech to say, “Here’s the data as it is in its current form,” but applying data science or modeling on top of that to help fill in the gaps that exist in measurement.

Brad: Will there be a public expression of this the way there is of the existing Triton Podcast Reports?

Daryl: That remains to be seen.

Brad: Okay.

Daryl: I mentioned that the data, and particularly the download data really comes from the publishers, so that’s not a decision that Triton plans to make on our own, but we are talking to publishers.

Brad: Okay.

Daryl: I do think it would help grow podcast advertising if the buyers of advertising had a source where they said, where can I advertise to reach the audiences that I’m most interested in? Let me help… Let me understand those audiences better, and so that’s what we intend to build. I mean, separately, I also think it would help the publishers to understand the broader landscape and not just their own podcasts, but where they sit in a broader landscape, but that’s at an evolving discussion and not something that we would do unilaterally.

Brad: So at the starting point, this will be a subscription product for advertisers?

Daryl: Correct, but it would be a subscription product for publishers, they will be able to make that data available to advertisers.

Brad: Oh, okay, of course.

Daryl: And they’ll be able to use it in their sales materials or just for their own internal understanding. So, it is a source that we intend to be public and to be used to help make advertising decisions by the advertisers, but the subscription occurs with the publisher.

Brad: Got it. Okay. And it is called Demos Plus?

Daryl: Correct. It’s part of our podcast metrics service, and it’s an extension of that which we call Demos Plus.

Brad: Speaking more broadly, what is needed now in podcasting to further the measurement intelligence for all stakeholders?

Daryl: I think it really comes back to listening to the needs of the buyers, of the brands, of the ad agencies, at least from an advertising perspective. If publishers provide them with what they need, then it’s going to grow the publisher’s business. So, it really starts with that, what is the data, the education, the tools that they need to help them plan their advertising campaigns, measure it, optimize it, putting all those pieces into place for the brand based on what they’re saying they need, is really going to grow the whole business.

Daryl: There are other aspects to measurement that have nothing to do with advertising, helping to understand your audience, and to develop great content for them, helping to promote your podcast, so that you can grow that audience further. So measurement isn’t all about adDarylvertising, but from an advertising perspective, it’s “Let’s listen to the brands and the agencies and develop what they need to help grow the business.” I think we’re in a good place with podcasting and with measurement. I just think we need to continue innovating, like podcasting has been doing for so many years, you know, keep evolving, but we’re heading in the right place.

Brad: Daryl, thank you so much for making the time to do this and for this very informative make me smarter kind of [chuckle] show. I really appreciate it.

Daryl: Thanks so much, Brad, I appreciate it as well.