142 – Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)

Experiencing Data with Brian O'Neill (Designing for Analytics)
Experiencing Data with Brian T. O'Neill
142 - Live Webinar Recording: My UI/UX Design Audit of a New Podcast Analytics Service w/ Chris Hill (CEO, Humblepod)
Loading
/

Welcome to a special edition of Experiencing Data. This episode is the audio capture from a live Crowdcast video webinar I gave on April 26th, 2024 where I conducted a mini UI/UX design audit of a new podcast analytics service that Chris Hill, CEO of Humblepod, is working on to help podcast hosts grow their show. Humblepod is also the team-behind-the-scenes of Experiencing Data, and Chris had asked me to take a look at his new “Listener Lifecycle” tool to see if we could find ways to improve the UX and visualizations in the tool, how we might productize this MVP in the future, and how improving the tool’s design might help Chris help his prospective podcast clients learn how their listener data could help them grow their listenership and “true fans.”

On a personal note, it was fun to talk to Chris on the show given we speak every week: Humblepod has been my trusted resource for audio mixing, transcription, and show note summarizing for probably over 100 of the most recent episodes of Experiencing Data. It was also fun to do a “live recording” with an audience—and we did answer questions in the full video version. (If you missed the invite, join my Insights mailing list to get notified of future free webinars).

Want the full audio and video recording on Crowdcast? Head over to: https://www.crowdcast.io/c/podcast-analytics-ui-ux-design

Highlights/ Skip to:

  • Chris talks about using data to improve podcasts and his approach to podcast numbers  (03:06)
  • Chris introduces the Listener Lifecycle model which informed the dashboard design (08:17)
  • Chris and I discuss the importance of labeling and terminology in analytics UIs (11:00)
  • We discuss designing for practical use of analytics dashboards to provide actionable insights (17:05)
  • We discuss the challenges podcast hosts face in understanding and utilizing data effectively and how design might help (21:44)
  • I discuss how my CED UX framework for advanced analytics applications helps to facilitate actionable insights (24:37)
  • I highlight the importance of presenting data effectively and in a way that centers to user needs (28:50)
  • I express challenges users may have with podcast rankings and the reliability of data sources (34:24)
  • Chris and I discuss tailoring data reports to meet the specific needs of clients (37:14)

Quotes from Today’s Episode

  • “The irony for me as someone who has a podcast about machine learning and analytics and design is that I basically never look at my analytics.” - Brian O’Neill (01:14)
  • “The problem that I have found in podcasting is that the number that everybody uses to gauge whether a podcast is good or not is the download number…But there’s a lot of other factors in a podcast that can tell you how successful it’s going to be…where you can pull levers to…grow your show, or engage more with an audience.” - Chris Hill (03:20)
  • “I have a framework for user experience design for analytics called CED, which stands for Conclusions, Evidence, Data… The basic idea is really simple: lead your analytic service with conclusions.”- Brian O’Neill (24:37)
  • “Where the eyes glaze over is when tools are mostly about evidence generators, and we just give everybody the evidence, but there’s no actual analysis about how [this is] helping me improve my life or my business. It’s just evidence. I need someone to put that together.” - Brian O’Neill (25:23)
  • “Sometimes the data doesn’t provide enough of a conclusion about what to do…This is where your opinion starts to matter” - Brian O’Neill (26:07)
  • “It sounds like a benefit, but drilling down for most people into analytics stuff is usually a tax unless you’re an analyst.” - Brian O’Neill (27:39)
  • “Where’s the source of this data, and who decided what these numbers are? Because so much of this stuff…is not shared. As someone who’s in this space, it’s not even that it’s confusing. It’s more like, you got to distill this down for me.” - Brian O’Neill (34:57)
  • “Your clients are probably going to glaze over at this level of data because it’s not helping them make any decision about what to change.”- Brian O’Neill (37:53)

Links

Transcript

Brian: Welcome back to Experiencing Data. This is Brian T. O’Neill. Today I have my friend Chris Hill from HumblePod, who is the man behind the show, the man behind the curtain. Chris, how are you?

Chris: Doing well, Brian.

Brian: [laugh].

Chris: Happy to be here.

Brian: Yeah, it’s nice to talk to you because we email, like, every week. We’re on email, and we don’t see each other too often. But Chris runs HumblePod, which is a podcast production company. So, they help hosts like me handle a lot of the logistics of producing a podcast, and audio mixing and, show note writing, and pulling out interesting quotes and things like that, and that’s been a huge help for me to keep the velocity of the show going.

But you also have this analytics thing. So, you’ve been working on, like, how can we use podcast data to help hosts grow their shows, or maybe know what to change about it. And the irony for me as someone who has a podcast about machine learning and analytics and design is that I basically never look at my analytics. I kind of don’t care. I look at [laugh] qualitative reviews.

When someone reviews the show, I really care more about that because frankly, I don’t know what to do with the data. It’s mostly vanity stats. I’ve never changed anything about my own show because of what numbers said. It’s just I don’t do the show to try to just grow it. I hope it spreads organically, and it’s quality listeners versus just, like… I don’t need five thousand tire-kickers. I’d rather have a thousand hungry people that really find the work and the content helpful.

But maybe there’s an opportunity here, and so I’m going to kind of try to play this role of advisor and helper here, and I’m going to be trying to take off my own hat. And part of this is, even though I think you’re going to be using my [laugh] data, which I haven’t seen, in this—so it’s super meta—you’re going to be using my show’s data, as I understand it, when we look at—

Chris: Mm-hm.

Brian: —your stuff, which is fine. But for the audience here who are working on data products and designing stuff, part of being a good designer is not—this is not art, and it’s not about what I think is right. And so, being empathetic means I need to put on my user hat, which means I need to think about everything from the role of other podcasters—which ideally would be informed by user research—and knowing that sometimes it’s informed by a mediator, which would be Chris talking to me about the people that come to you. But I’m really actually going to try to take myself and my own interest as a podcast host out of this a little bit, and if I do go into that, I will tell you, like, this is my perspective. You know, that’s a selfish ‘me’ perspective, which may be indicative of what other people want.

But that’s a skill that is really important is to take ourselves out of it when we’re designing solutions because it’s not about us, and chances are we’re just bringing our own selfish biases into things. So, anyhow. So Chris, welcome to the show. Where should we start? Do you want to just jump right into the presentation? Do you want to give some context about this thing?

Chris: Yeah, I think that’d be great to get some context on what we’re doing here and what I’ve developed. So, the problem that I have found in podcasting is that the number that everybody uses to gauge whether a podcast is good or not is the download number. You know, how many downloads do I have per episode? How big is my show? It’s the most ubiquitous standard there is. The IAB uses it to say this is how effective your show is, and there’s all these things around the downloads. So, that’s what everybody looks at first.

But there’s a lot of other factors in a podcast that can tell you how successful it’s going to be, that can tell you where you can pull levers to, if you want to grow your show, or engage more with an audience. And I think having a better picture of what your audience actually looks like when you create a podcast is helpful on a number of levels. First and foremost, it just helps you as a podcaster know who your audience is. Because it can get lonely podcasting. You can feel like you’re some days, talking to nobody, and that nobody’s ever responding.

And then one day, you may—say you send out a typo in a newsletter. All of a sudden, you got a thousand responses, and you realize everybody’s actually paying attention. How do I actually get them to engage with this beyond the typo? And so, that’s where this Listener Lifecycle comes in. Also, I’ve got a background in marketing.

I’ve got an MBA, I’ve got, you know, experience working at agencies, and you know, other companies, and you’re always looking for, like, a Customer Lifecycle or Listener Lifecycle, and so the idea here is to kind of take that idea and put it into practice in a way that you can say, “Okay well, if this is my funnel, or if this is my lifecycle, where does most of my audience sit right now?” And so, that’s what this does. There are five phases to it. There’s the Curious Phase, that’s the phase where people are just like, “What is this show? What is this thing that I’m doing?”

And then there’s the Explorer Phase where people start to go, “Okay well, I’ve sampled on the website, or I’ve listened to the trailer, or I’ve seen a social media post. I’m going to go listen to an episode.” And they may listen to that on Apple or Spotify, but they’re not quite committed yet. They’ve not hit follow button. And once they click that follow button, they’re a follower, so that’s the next phase. Once they’re a follower, you now have the ability to go into Apple or Spotify and say, “Look, I have this follower count. These people have actually clicked the button, and they’re going to continue to follow my show.”

And then from there, it’s about engaging that audience and getting them deeper into your content. I mean, Brian, to your point, like, I’d rather have a thousand people that really love what I do and are more focused on it than have ten thousand people that are just clicking a follow button and following me. So, how do you get there? Well, one of the ways to get there is to actually be able to know that audience at a deeper level. Clicking a button is one thing, but signing up for a newsletter is a deeper level, and that’s when we get into what I call the Subscriber Phase.

And in that Subscriber Phase, that’s where you’re actually going through, and you’re signing up for the email newsletter. If you’re a different type of podcast content creator, you may have a Patreon, something where people can go and pay money to have either additional engagement, additional content, deeper dives, whatever that is. Like, the minute you volunteer your personal information, that’s a deeper commitment to the podcast, deeper commitment to the content.

And then in the final phase is the True Fan. And the reason True Fans are so important is once you do have a hundred, two hundred, a thousand of those people that actually love and engage with your content, you’re able to grow exponentially beyond that. There’s an old blog post called, I think, 100 or 1000 True Fans. Just go Google it. I’m sure most of y’all probably know what I’m talking about here.

But the idea is, you know, if you have a thousand true fans, and they each pay you $100 a year to stay committed to you, then that’s going to be $100,000 a year that you can make as an independent content creator. So, using that philosophy kind of helps with podcasting because you want to build that core fan base. And so, what I’m trying to do is also help podcasters think through how do I get people from just being interested in the show to actually being a core fan of the show, and what I would do with them?

Because the other problem I see, too, is a lot of times, true fans are ignored. “Oh, there’s that guy that always posts.” “Oh, there’s that guy that always shares.” Like, well, you should probably reach out to them, engage with them because they’re the ones that are sharing your content, and they would probably be excited if you said, “Hey, thanks. I’d love for you to help me with this.” And then all of a sudden, they’re sharing that content too. So, that’s what that true fan piece is about.

And then of course, from the marketing sales perspective, if you want to make money, if you want to do advertising, and you want to be able to be in a niche market, you may not have what would be a traditional CPM level for being profitable. You need thousands and thousands of downloads per episode, in order to really grow if you’re doing a CPM model for paid advertising. But you can do flat rates, you can do custom rates, you can do feature guests, you can do all these things, and if you’re able to say, “Look, here’s my total audience and here’s the grand picture of what I am,” that’s a much more powerful and compelling message to an advertiser. So, for all those reasons, I’ve developed the Listener Lifecycle Assessment. And uh, yeah. Can’t wait to get into it more.

Brian: Yeah. Are these HumblePod stages, or is this generally accepted jargon?

Chris: They are HumblePod created, and I am trying to align them to what the industry defines for—like, especially the Follower Phase. That used to be the Subscriber Phase when I first started because Spotify was doing follower, Apple was still on subscribe when you clicked the button in Apple, and most people were saying, ‘subscribe.’ If you listen to podcasts, it’s a subtle thing, but just pay attention, you might have noticed that some podcasts now say follow instead of where they used to say subscribe and that’s because that lingo has changed for most players. But yeah.

Brian: And I find that confusing because they’re all—

Chris: [laugh].

Brian: the data differently, and it’s almost like when you know too much about data stuff, it’s just like, who know—when you start combining all this stuff together, what’s being double-counted? Like, what’s a listener? Well, listener is they had 29 minutes, on average, the last seven days. And it’s like, and Spotify calls it this other algorithm, and it’s like. I kind of like, my eyes glaze over at some of that stuff there.

I can tell you one reaction that just immediately is, I wonder even about some of those labels, like ‘fan’ for example. So, when I think of, like, a content authority-based podcast, I wonder if that’s, like, how hosts think about their listeners.

Chris: Mmm.

Brian: So, I also have my—you know, I’m a professional musician as well, and I have a band called Mr. Ho’s Orchestrotica that I run, and we have fans. And that’s art. And that’s, like, they’re fans of the thing. But I wouldn’t relate to that—and I’m putting my Brian hat on for a minute, but I’m also extrapolating this out to other people that are probably doing podcasting more for trying to be a source of content and help for people as opposed to—like, it’s not about Brian; it’s about like, how do I help people design better machine learning and analytics user experiences, and turn them into valuable data products.

I don’t really care where you learn the information. The information is more important than me, and I wonder if that lingo—I don’t know. It’s just, that—

Chris: Yeah.

Brian: —that was a reac—I’m giving you a reaction that I wonder how that would, when you have, like, a business-type podcast come in, if that’s how, like—like, we do a PR disaster consulting, whenever, like, your CEO screws up in touched the wrong person. We’ll come in and help you with your PR nightmare. Do they have fans of that podcast [laugh]? Like, you know, so just, I’m just kind of thing out loud that the labels—and I understand the model of what you’re saying and I think the listeners right now will understand enough of the model here. But even the labels that we use, these create emotions and feelings about stuff, just from a design standpoint. Those are all things that I would be thinking about. Is it both clear, but it’s not just about the clarity, it’s also about the feeling that it gives sometimes with certain kinds of things. So, just something to think about in terms of your labeling. So.

Chris: Yeah. No, I mean, that makes a lot of sense. It used to be when I first started this, it was the True Fan Continuum, as the name of it, and it evolved in the Listener Lifecycle because I wanted to focus more on, that’s really what it’s about is listeners, your audience, what you’re doing. True fan, I agree with you. I think that I have run into, you know, manufacturing clients that we have that produce podcasts, and you know, they may not have a diehard fan for that show. But you’d be surprised. I mean, people really get into the content in these specific areas. And they do have people that are dedicated and follow along, and could be defined by every metric as a fan.

Brian: Yeah.

Chris: So that’s—

Brian: It’s a good enough analogy, too, for now.

Chris: Yeah. But I like it.

Brian:But just—

Chris: Something to noodle on.

Brian: —I’m just going to give you stuff to noodle on about as you think about the positioning of that lingo, you know?

Chris: Yeah.

Brian: Or are you telling me I need to start having merch for Experiencing Data? Like, I need shirts, and—

Chris: [laugh]. I mean, why not?

Brian: Like—okay. All right. Well, if I see in the chat everybody wants orange Experiencing Data shirts, then maybe I’ll think that they’re fans. But right now, I’m not seeing a lot of that, so [laugh]. So, let’s dig in. Share your screen. Let’s take a look at this—

Chris: All right, yeah.

Brian: —bad boy, this is this MVP, as I like to call it. And my understanding, Chris is, like, this is a quote, live tool. It’s not live on the internet, but it is a report that you are using this currently with prospective hosts that come to you for production help.

Chris: Yeah.

Brian: This is not a work in progress. This is kind of a real thing—

Chris: Yeah.

Brian: —for people that are watching. So, Chris is sharing his screen we’re looking at, I don’t know, I forget which tool you built this in, but we’re looking at, like, a dashboard that has a bunch of, it looks like my show’s data in it.

Chris: [laugh].

Brian: I have not looked at this stuff. And we… we were looking at some data right before we logged on, as I was giving Chris permissions to access some of my analytics down the road so he doesn’t have to ask me to pull numbers, I really don’t pay attention to this a lot. So, I’m seeing this stuff for the first time, just like everybody else. But I’m really going to be having the not-Brian hat on as much as I can. I’m going to be trying to think about your customers and your prospects there and framing it that way. So, when I think about design, too, with these kinds of solutions, like, right now, this is not self-service, right? So, this is something you provide to them on a call and they get—

Chris: Right.

Brian: Handed-delivered to them, correct?

Chris: Yeah. Yeah, we’re trying to make it to where they don’t have to think about it. I’ve got someone on my team that, you know, I’ve trained and have worked with to help pull this data, and understand, and analyze the data for me, and outside of that, of course, I do it myself. And in this case, today, I did this all myself. But how I have this set up is it can be used one of two ways: either for a report like you see here, and of course, there would be data in the executive summary, in the action items, we could fill that in in a more robust detail with more information that we have.

Like Brian, I got a little bit of extra data for you right prior to the call, but like, if I had more time, like, if I could get the Google Analytics data and see how the web traffic is flowing, and all that information helps us get a better picture for the podcast, and where things are being effective and where they’re not. But I’ve also got a prospect option up here too. And again, similar thing, but the idea being that we can do a quick assessment that doesn’t have as much data, doesn’t have as much information, and I can come in, and I can basically run through—this is where we start is with a checklist and just ask these basic questions. You’ll be surprised at how many of these get unchecked when you’re working on a newer podcast. Sometimes they don’t have a website, sometimes they don’t have these things, and all these things combined, basically, you can just go through, and I can have one of my team members just go through and go through the checklist: look at the website, look at their podcast, make sure they’ve got all these things done, and check off the boxes, literally as we go.

So, that’s the big thing there is just getting to make sure that they’ve done best practices. This is kind of like a best practice checklist. And then over here in social media, same thing. Like, what social media channels are you on? Okay, that’s cool, and then what are you doing within those channels? So, Brian for you, you’re doing a good job. You’ve got counts on Twitter and LinkedIn, you don’t need to be everywhere, but you know, that’s a good start and good places to be. You’ve also got a very big audience on LinkedIn. So…

Brian: Is the idea that this rolls up into a scoring—

Chris: Mm-hm.

Brian: —of rubric of some kind? So—

Chris: All this data rolls up into this scorecard right here.

Brian: Got it.

Chris: So, this is calculated in the scorecard. So, I’ve got a formula in here that I can go in and count, and—

Brian: Oh, I see. Okay.

Chris: It says, here’s the score, and then it tabulates down here so that you have the stars to look at and say, “Okay, where are we at?” And you can see, overall you’re doing pretty good. And it also shows us, like, I can see by the website, website everything, you’ve got best practices on, social media, some things could be—

Brian: quite lousy [laugh].

Chris: [laugh]. Maybe a little bit. Some things can be improved there. But there’s things to improve with that calculation, too, that I need to do at some point. But this is just a good estimate for everything that’s happening. And then podcast data, like, you can see where you’re doing well, where things could be improved.

And then the podcast content itself, you know, if there’s something missing, there’s missing in the Curious Phase, which we can talk about when we get down there. But yeah, just gives you an idea of where the gaps are in the service. And then what I’m able to do is come in and look at this, and if someone on my team did it, I can just come in and look at it and go, “Oh. Yeah. Wow, Brian, what’s this thing in the Curious Phase in the podcast score that we’re missing?” Well, let’s scroll down here. Well, it turns out, I don’t think we have a trailer for Experiencing Data. I could be wrong on that.

Brian: What do you mean a trailer?

Chris: A trailer. Like an actual designated trailer for the show. Like a minute or less.

Brian: There is—

Chris: This is the podcast—

Brian: —that guy, that guy works in music. The guy—

Chris: That’s the intro. But do we have? Do we have that as a trailer? [pause]. I don’t think we do.

Brian: What’s the difference? I don’t even know what that means.

Chris: Yeah [laugh].

Brian: Sorry, I don’t know what—

Chris: No, no, no, you’re fine. I don’t want to get into the weeds on it now, but it’s basically like, “Hi, my name is Brian. This is the Experiencing Data podcast. We talk about these things. I think you would love the show. Come and listen to it.”

Brian: Oh, okay. Well, we have a guy that—I had a voiceover guy do it, and I’ve thought about changing that. And at some point, we talked about that, but—

Chris: Well—

Brian: Anyhow, we don’t have to get—

Chris: Yeah.

Brian: —I don’t want to focused get my show here because I want to focus on the reporting here. What I would suggest is, I want you to treat this—instead of demoing it to me think about me as a prospect. So, let’s focus on the actual data and the insights and not so much on the capture piece. Because unless the capture is something that the host would need to deal with, I mostly see that as plumbing—

Chris: Yeah.

Brian: —and chances are, they’re just like, “Chris, just tell me what I need to improve.” Or, “Where am I at? Where do I need to go? How do I get there?”

Chris: Gotcha.

Brian: That’s what most people want from data, right, is they want decision support, they want insights on what do I need to change? What needs my attention right now? So, whenever I do, like, an audit of a dashboard or something like this, my first question for the person is generally around, like, what are the scenarios and the use cases? Like, somebody was doing something before they got on a call with you or they logged into this tool. They had some question or need. Like, they don’t just wake up and randomly come in here.

They’re thinking about stuff. There are scenarios. They’re either working on a project, they’re doing a monthly coffee check on their stats, or I don’t know what it is, but that usually starts to inform, like, well, what should we put, and where should we put it, and how much of it and compared to what, that’s kind of the beginning here, so. But you didn’t say this is more of a handheld storytelling kind of experience, as opposed to self-service. Is that—

Chris: Right.

Brian: So, how would you give this to me? Just on the insights piece, how would you deliver that experience?

Chris: So, we have a landing page here. So, you wouldn’t actually see that quick assessment, and all the man-behind-the-curtain type stuff. We can hide all this data and just narrow it down to where you just get this landing page. Now, this is for you, as a client, so it’s a little more detailed than what we would give them, but we would have a detailed executive summary here that would just say, “Based on all the information here, here’s the things you need to work on, here’s the action items.” And then the sales piece for HumblePod is, “Hey, we can help you in these areas. We’ve noticed you’re really weak. You don’t have a trailer. You don’t have a good intro and outro. You know, your content is struggling here.”

We might be able to pull that data. Or we can say, like, “You’re not promoting yourself,” you know? “You don’t have a website.” We can help with all those things, and we can show them the areas of weakness that we can help them improve on. So, that’s really the core and where we use this when it’s a sales tool.

When it’s a client, it’s, “Hey, what are we doing good? What are we doing bad? What can we improve on?” And so, you know, like, your action item here would be ‘convert more followers,’ just based on the stuff that I have seen. I would put more in here if I had more time, but you know, as we look down through your phases, if we look at the Curious Phase, I can tell you that just based on the Listener Lifecycle overview, you got a lot of people in the Curious Phase when it comes to your audience.

So, what we’re talking about there is, like, your LinkedIn, your social channels, all that stuff that drives people to listen to the podcast or gets them curious about the show, all those things drive there. And then we have a lot of explorers as well. We have a lot of people engaging with the show. But you’ll notice that the Followers and the Subscriber Phase is really small compared to the others. And even on the downloads per episode where we actually look at the downloads and compare that data, you’ve got a good number of people—about 42% of those people—are what we call Explorers to every show. So, they’re coming in, they’ve not clicked follow, they’re not dedicated listeners, and that’s really, like, a different percentage there.

But you can see there’s more of those. So, how do we convert more of those people into followers because it looks like you’ve got the potential for a much larger audience for your podcast than what we’re seeing on downloads per episode. And so, that’s where there’s opportunity, right? And that’s where I can help guide you and say, well, we could do these things. You could try paid ads, you could try maybe doing more call-to-actions in the content you’re creating, or maybe we ramp up some of the social media promotion, all these things to do. As we get to the Explorer Phase—

Brian: Let me pause you for a second. So, let’s go back to the top here, just slow down for a second. I’m going to throw some tac—low level tactical stuff at you, and maybe—

Chris: Go for it.

Brian: —ask some questions. First one, so if you scroll all the way to the top here, your graphic here, I understand there’s some branding there, I would highly recommend taking that out, or—

Chris: Oh.

Brian: Making it really small. It’s fairly hard to read, but it’s occupying a lot of noise, and it’s static information that’s not going to change. So, whenever you think about data, we’re always thinking about kind of signal-to-noise ratio, and since that’s not dynamic content, it’s effectively decoration. So, as a host, I would imagine people are, is it safe to say most of your prospects are rarely looking at their stats and are data knowledgeable, or are they, like, chasing these numbers all the time, and they love looking at their numbers, and they’re really hung up about it? How would you characterize the average host, or the kind of client you want to attract, in terms of their appetite for data and analytics?

Chris: Great question. I think most of the clients that we’re dealing with don’t care about their data that much until they take a glance at it, and they’re like, “Why are these numbers so small? This isn’t what I expected.” You know? And it’s like, “Well, we’re doing these things. What can I do to improve it?”

Brian: Got it.

Chris: And also giving them perspective on where they stand in the market too is helpful. But if there are those questions, obviously, we don’t want to get to the point where they’re freaking out about numbers, and we’re caught unaware. We set goals and targets, things like that so that doesn’t happen. But I think most of the time, you’re right. I think I run into a lot more of, like, eyes glazing over if I get too deep into the data. Because I get excited about it. I love this stuff.

Brian: [laugh].

Chris: But you know, I definitely have gone through, this is probably the third or fourth iteration of this, and this is way whittled down from what it used to be. Because you can get way deep with this if you need to. And that’s where the problem lies for me, personally. So, I really appreciate that feedback. Anything I can do to make it easy to glance at and action, that’s what we’re trying to get to.

Brian: So, nobody wants machine learning and analytics. Nobody gives a shit about any of that, right? They just want insight. Like, tell me what to do about it. How do I change? What’s my status? These kinds of questions. I would say if your audience is fairly data unaware about their own shows, then a barometer about, like, “Well, where am I?”

And this gets into this classic analytics question, which is, “As compared to what?” You have 652 widge-a-ma-call-its. As compared to what? The as compared to what question—this is a Tufte, Alan Tufte  question, which I love—well, what are your clients interested? Is it, I want to benchmark against people like me, shows like me, industry like me, my own performance in the past?

I don’t know what the answer is, but it may be helpful here to give them a sense of, like, [neeer bonk] we just put a stake in the ground. Here’s where you are relative to New York City: you’re 500 miles south-southwest, or whatever it may be. Where am I, to get grounded. And then there’s this idea of, like—and I’m not even sure if that means, like, half my listeners are curious, or I’m doing a 60% job on satisfying Curious Stage listeners, but this also gets into the as compared to what. Like, what are we trying to compare that to such that I would know what an improvement or—what’s the opposite of improvement? Going worse? Getting worse [laugh]—whatever you call that, like, going downhill would be. So, thinking about all this stuff in terms of comparisons can be really helpful, especially when there’s not a known standard by which everybody—

Chris: That’s a good point.

Brian: Measures everything is against. It’s all relative to something, and that can help people feel grounded. The second thing—and I like that you start—if you scroll down a little bit—some of the listeners will know this. I’ve had this episode on the show before, and I have a framework for—a user experience design framework for analytics called CED. It stands for Conclusions, Evidence, Data.

And if you think of this sort of as, like, a triangle, but the basic idea of it is really simple: it’s lead your service your analytic service with conclusions, or as one of my past guest’s called them, they’re more opinions because, like, our—you know, we have a machine learning algorithm that comes up, and it looks at stuff, and it gives an opinion. He’s like, “I don’t like calling it a conclusion.” But—and I’m like, well, you’re getting hung up in being perfect and accurate. And so, this is all estimates, right? All measurements are wrong, some are useful here.

But this idea that, let’s deliver the insight, which is what needs to change, or maybe what am I doing well? Normally, I don’t focus on what are we doing well because most people don’t, once they’ve seen that, it’s old news. It’s really the deltas. Like, where are you trending up, or where are you underperforming? But you want to start with that conclusion, and then only as necessary do we provide evidence for the conclusions.

Because, frankly, you know this stuff really well. I would trust that the evidence behind your conclusions is solid. And that’s where the eyes glaze over is when tools are mostly about evidence generators, and we just give everybody the evidence, but there’s no actually analysis about how is this helping me improve my life or my business? It’s just evidence. I need someone to put that together.

And it’s very hard because sometimes the data doesn’t provide enough of a conclusion about what to do. But this to me is when it gets into, like, well, you’re a podcasting professional and expert. This is where your opinion starts to matter because even though, like, what do we do with you have 74? It’s like, well, as compared to what? Well, compared to this show, it’s this, compared to that, but my question to Chris would be well, is that not enough? Or, what am I trying to do with that? This is where I think some of the opinion comes in, and you have to estimate and use some of that human judgment from your qualitative experience—

Chris: Yeah.

Brian: —comes into this, right? But I really wanted to see that conclusion here when I come in here. And maybe it’s just, like, “You are kicking ass at these three things. Overall, your status is underperforming on engagement. Well, what does that mean? Here are your action items.” Like, no one clicks out of the show, you’re not capturing any listener data, and blah, blah, blah. I don’t know what it is. That’s like, if you need to focus on three things to grow your show—where grow means increased downloads or whatever that—I mean, now we get into, like, well, which metric are you actually trying to change? But it’s that kind of conclusion thing that we’re trying to surface here as early as possible in the experience. Give me a read-out, the executive summary, that kind of, like, actionable thing. Then maybe I will drill in.

And I hear drill, like analytics people, and I’m sure some of the listener—that people on the show here will relate to this, it’s like, “And you can drill down into all this stuff.” And I’m like, did you know that drilling down is mostly tax. It sounds like a benefit, but drilling down for most people into analytics stuff is usually a tax, unless you’re an analyst.

Chris: Yeah.

Brian: And so, we actually want to reduce the need to do that. It doesn’t mean we take away the ability to do it, but to me, almost like the game with these kinds of analytics tools is, like, eventually, maybe you never need to log into it because it just—maybe it sends you a message when there’s signal that matters, based on what you need, and the rest of the time, you don’t need to look at the dashboard. You don’t need to drill down because there’s nothing to see here. Go home.

Like [laugh], or, you know, it’s just the lines are trickling, okay, look at my trend line. Okay, it’s basically up and to the right, slowly. Yay. It was like that a year ago. There’s eight minutes of, like, play time with my kid that I just lost, right? A lot of times it’s tax, it’s not benefit. But the conclusions piece, that’s the tough part is how do you turn all that data into some decision support, so I can make a decision. Like, “Chris, you’re right, like, I need a trailer.” Or, “Chris. All right. These people are bored sick of the show, and I’m not surprised they’re bored sick [laugh].”

Chris: Yeah.

Brian: It’s about analytics.

Chris: Exactly.

Brian: What do you think? I’m just kind of throwing a bunch of stuff at you.

Chris: I—man, you’re hitting the nail on the head for the challenges that I’ve faced. I mean, I’ll be very honest with you. Like, there’s no copy right here for the executive summary at the moment, but that is where that would lie, and that is exactly why I do that. I’ll just tell you, this is an embarrassing moment for me, so but it was a great evolution of this product, and it was something that I think you’ll understand completely once I get through the story.

So, had this setup, had the executive summary, was going through—kind of like we were just doing—all the details, everything going on with the show, and after about 30 minutes of the call, the client just looked at me, and he said, “I don’t care.” He’s like, “What do I need to do? Just tell me what I need to do.” And I’m like, “I’m telling you. In detail.”

Brian: [laugh].

Chris: And he’s like, “I don’t care. Like, what are we going to do to get our show to this number?” And I’m like, “Well… okay, well, we need to do these things. That was going to be my conclusion. You didn’t let me get there yet.” And so, I realized after that, like, oh, gosh, I really should have taken this approach of like, just directly saying, “This is what you need to do and this is all you need to know from this presentation.”

What was interesting was he was on there with some of his marketing team and the marketing team after he left the call—because he had to leave early—after this guy left the call, the rest of the team said that was really good. That was really helpful. We really needed all that data. So, I heard it from both sides. And that’s the challenge with this stuff is like the marketing people are going to want to see this, but the executive or the host of the show a lot of times, who tends to be both—in our case—very often, it’s just going to want to see okay, is this doing well? Are we doing good? As compared to what? And where do we go from here?

So, I try to keep those things as simple and direct as possible when we get into that level of detail with things. But yeah, I—that’s just really good feedback to keep in mind is how do I continue to make that simpler and more direct? Because, yeah, the data and stuff that supports all this information is cool, and it’s useful, but—at least, I hope it is—and it’s a lot.

Brian: It is. And, yeah, you’re welcome. But I’m not saying get rid of it. I’m talking about how do we experience—

Chris: Oh no.

Brian: The analytics, the information? We want to experience, conclusions, summaries, we need grounding, and we need some advice about what to do with it, especially because you said these people are not living in their data. They don’t look at this stuff. If eyes are glazing over, than we can make the assumption by default for the host, it’s a tax, not a benefit, until we can find ways to turn it into a benefit. But the benefit will never be going in and looking at all the raw numbers or even some of the rolled up numbers. That will not be the benefit.

The benefit will be what do I do about it? Can I take action? Should I take action? And then if you even get to that point, it’s how is my progress against the action that I took? Now, I know—that’s my trailing performance. Am I losing weight or not lose the weight or whatever your goals are? Someone—and I think, Sam, someone had asked a question, “How would you categorize your clients in terms of goals? Are they doing podcasts for fun? To make money? For PR? To support their businesses?” I’m guessing it’s a mix?

Chris: It is a mix. I would say we don’t have many people that are just doing it for fun. In fact, I don’t think we have anybody who’s just doing it for fun. Most of who we deal with are professionals, people like you, who use it as part of their consulting practice, or what have you.

Brian: I’m professional, Chris.

Chris: Do what?

Brian: I’m an unprofessional.

Chris: Unprofessional—well, yes.

Brian: [laugh]. Unprofessional.

Chris: [laugh]. But still, like, all those types of folks, like, that’s a lot of who we deal with. And a lot of times their goals and objectives are not just big podcast download numbers. And in fact, for perspective, something to your point of, like, as compared to what—there is one piece of data in here that I can pull from—so the average downloads per episode, so for Experiencing Data, like, the median for downloads per episode, which is the first metric I was looking at, it’s 145 downloads per episode. Looking at Experiencing Data over the past 11 episodes or so, your average is about 783. And to get into the top 20%, you only need to be at 1200 downloads per episode. That’s it. To be in the top 20% of all podcasts, that’s where you need to be.

So, that bar is very low, and that’s why we talk I try to talk about when I talk to businesses, it’s like, you don’t need to make that your goal. Like that is a great benefit if you get there, but it is not an end goal. Because even at 1200 downloads per episode, the average money you would [make off—you’d say 00:33:12] wanted to monetize this and do CPM, I think the industry average right now is about $35. So, you get 35 bucks an episode. Yay [laugh]. So—

Brian: Life changing.

Chris: It’s life changing. So, you have to have a different strategy and a different approach, and that’s why we set up other goals for folks when we do this.

Brian: Yeah, yeah. And [Sheila 00:33:32] makes a good point. Like, when you design this dashboard, which customer did you have in mind? I’m assuming that the sale is largely to the host, not to the marketing team, but I don’t know. Is that—

Chris: Yeah—

Brian: —the first sale is to them, and convincing them that you’re the right fit?

Chris: Yeah.

Brian: Yeah.

Chris: And a lot of times, like, this level of data is not what we show prospects because we don’t have that data. We have to get their download information. We do have some data points. I’ve got a partnership with Podchaser—which, if anybody’s looking for podcast data analytics, reach out to me, I can put you in touch with them; they’re good people—but Podchaser does give us some estimates. So, we have some rough numbers before we come into a conversation; we kind of know where they sit. But that’s about all we have. So, we can guesstimate, but that’s about it.

Brian: Yeah. A little tangent here too. As I’m looking at this—and I’m now putting Brian podcast host hat on for a second—

Chris: Uh-huh.

Brian: I feel like a liar because on Listen Notes, it says, oh, Experiencing Data is in the top 2% of all podcasts worldwide, and according to this, I’m not even in the top 20%. I don’t give a shit, to be totally honest with you. I really don’t care. I have promoted episodes on the show, which you know, we’ve done the first of those. They care about that stuff.

And this gets into the data qual—where’s the source of this data, and who decided what these numbers are? Because so much of this stuff, as I understand it, is not shared. Apple doesn’t release—they release some stuff, they don’t release other stuff, and then people are trying to extrapolate this from other third-party data, and it’s like, on one show, you’re doing this and another show you’re doing that. As someone who’s in this space, it’s not even that it’s confusing. It’s more like, you got to distill this down for me, Chris, either through the tool or through you because I get 15 different stories, frankly.

And it’s not that, Chris, I’m worried that you’re trying to tell me something that’s not true. It’s more like, well, where’d you get your information? I don’t actually want to know. I don’t care about the source of the information. I—because like, oh, Buzzsprout. I don’t know anything about Buzzsprout. I don’t know if that’s the right place or not—

Chris: [laugh]. No.

Brian: —I don’t care because it’s only about the Delta. Like—

Chris: Well, that’s, that’s—

Brian: — for me, right? How do I want [move it 00:35:38]. But maybe some of your marketing people do, but I don’t know.

Chris: So, let me give you a little more detail there. So like, on the back-end, when people are filling out data, pulling in data and stuff, I am constantly hunting down what these average numbers are, where the best places are for this data. There are two places that have given these data points out in recent years: Libsyn is the most consistent being the oldest podcast host, one of the biggest, they’re going to have some of the best data on this stuff. And so, anytime Libsyn—and if you ever go to a podcast conference, Libsyn is there. Hang around for their—they do an annual report at every conference they go to—and it’s basically like the State of Podcasting for 2024.

And they’ll give out these numbers. This is 2023’s data from Libsyn, and then as I was researching, I also found Buzzsprout, so you can, kind of, gauge the two. Because, like, Buzzsprout will say, based on Buzzsprout data that downloads in the first seven days—which is a completely different metric than downloads, the 30-day download number—that it just varies depending on the source. So, there’s a lot of stuff. But Libsyn has been the most consistent, so I use it, but I try to keep the rubric in here and keep data sources in here.

That way people know, like, if they have that question, “Well, where’d you get that data? What is that compared against?” Like, we can tell them, “Hey, it’s based on these platforms that provide this information.” Because it’s a very complicated field when it comes to, like, how to gauge your podcasts and everything else. But this is generally the accepted standards that most people go to. And if you Google it, you’re going to find Libsyn’s data pretty much up front. So yeah.

Brian: Now, you’re getting into that, as I said, right, everything’s about something as compared to something else—

Chris: [laugh].

Brian: —and what I imagine is the hosts don’t even know what I should be comparing my stuff to, let alone do I care about the data because there’s not these stan—or if there are these standards, they seem to change. Because like, you just showed me a bunch of stuff about total downloads or whatever. In the ether, I’ve heard oh, it’s—“No, it’s average downloads the first seven days.” “No, no, no, it’s average downloads the first 30 days.” “No, no, it’s the total downloads.” “Downloads don’t matter. Devices download shit all the time. It’s the number of people that actually listened on average to the show.” “Well, how much?” “Well, only if it’s 75%.” You see where I’m going with this? On and—

Chris: Yeah. Oh, absolutely.

Brian: On and on and on. And again, your clients are probably going to glaze over at this level of data because it’s not helping them make any decision about what to change. So, even if your solution is somewhat opinionated, I think it’s okay, if it has an opinion about it. Maybe you’re like, “Well, we aggregate all three sources of truth about what it means to be in the top 1%. We came up with our own, it’s an average of those three.”

Kind of doesn’t matter because they’re talking to you, but I think if you can make a compelling story there with the data, it’s okay because all this stuff is wrong. It just needs to be right enough to help someone make a decision. All these numbers are technically wrong—

Chris: Oh yeah.

Brian: —and we all know they’re wrong because they’re incomplete, depends on who you ask, depends on what they said when they said listener. Well, what does that mean? That’s okay. It just has to be right enough to make decisioning. So that’s, to me, the key thing with this is we want to surface that decision support. What will help Brian, or whoever the host is, make a change?

And they can’t change everything, right? There’s probably a million dials and levers I could change to make the donuts look different. I want a chocolate, so I want subscribers. Or [laugh] is that red? I guess that’s red.

Chris: Yeah.

Brian: Strawberry . The point they’re being though, I think distilling this down, especially on an early call with a new client, to me, and I imagine for a lot of them, it’s not going to be impressive the breadth of the data that’s under this. What will be impressive is distilling it down into actionable insight for them. Just like, “You need to work on three things. I’m assuming your goal is X, which is I want more subscribers because subscribers turn into paid newsletter people, so let’s work on that. If that’s your goal, here are three things: your current is this, your goal should be this, and here’s what to change about it.”

And if you could distill this down into just that, I think just three things—is three the right number? No because all numbers are wrong, but you get the idea here, right, which is, like, the average host is probably not going to care or be able to process all of this stuff in here, but we want to inspire some action. And it’s just like, your gym trainer: the good ones will tell you it doesn’t really matter which exercise you do. Can you do one? Like, just get up and do one sit up.

And the thing is like that the activation energy to just get from zero to one, that is most of the pain is the activation energy. It’s not going from one to ten setups; it’s zero to one, so to just inspire the action and to get them thinking about, “Oh, we’re not that far from this goal,” or, “Oh, you know, if I just started talking differently, I could convert these people that I already have into paid customers,” or whatever. And Chris knows how to do that. Now, there’s a business, I’m not just paying you for production help. I’m paying Chris and his team because they’re going to help me grow the show, not just deal with the technical part of the podcast, which is more about cost savings, and just getting rid of labor that the host doesn’t want to do. You’re actually now in an ally. The data in HumblePod, but the data has now become an ally instead of a tax in that sense. What do you think?

Chris: You’re, again, hitting on all the right notes, I think. I mean, this is where the are really—

Brian:I’m a musician.

Chris: I do, I do. Pun intended. There we go. When I think about my action items, like, we had a recent client we did this with, and I’m feeling some regret right now as you’re saying this because, like, the action item list that I have for him as insanely long. Now, granted, they’re an active client, so some of that is, “Here’s what we’re going to work on, here’s what you need to work on, here are the things we can do to improve.”

And I try to pull everything into a setup to where it’s easy to break down in your head. Obviously, I didn’t get this fully completed, otherwise, you would have an executive summary here, some very clear action items here. And I do agree with you, like, three or less. Like, keep it simple because if you’re talking to an executive, they might look at this for five minutes and move on, you know? When we propose deals, I use PandaDoc and I can go in and see how long people look at my proposals. And typically, if it’s more than five minutes, I’m surprised.

Brian: Yeah.

Chris: They’re either downloading it as a PDF, or they see the numbers they need to see, and they wait to make a decision later.

Brian: Did you see Joel Swan ? He’s also in the DPLC. Bottom Line, Up Front: BLUF. That is fantastic. It’s so much better than my work. But like you—he just nailed it. That’s such a good acronym. Thank you, Joel. So, I’ve never seen that Bottom Line, Up Front. It’s fantastic.

Chris: I like it.

Brian: You know? [laugh].

Chris: I like it. Yep. And that’s—

Brian: Yeah.

Chris: Yep. And that’s the goal of the executive summary there is to have that there so that you can say, “The bottom line is this, and here are the things you can do about it.” What I also tend to do is I would put a summary here. So, curious would be like, whatever conclusion I come to for the Curious Phase, I’d put a few words there to say what to do just as general reminders on each one, and then allow them, as the user, to be able to drop down and look at this report, if they need to dig into it more. And say they have a marketing team. They can take it back to the marketing team and better evaluate it. So—

Brian: Got it. So, you’ve created effectively, like, a data model here, this Listener Lifecycle. This is, like, your mental model of how a fan moves through the experience of going from curious to fan as you call it, or whatever. Can you scroll down a little bit, so we’re—

Chris: Yeah, sure.

Brian: For our listeners, we’re looking at this dashboard. And Chris has five headings: Curious, Explore, Follower, Subscriber, True Fan. These are headings. You can expand them, there’s content inside each of them. You’ve exposed your model here, which is okay, but as a host, my use case is not necessarily attached to your mental model of how fan’s lifecycle works.

So, what do I mean by that? I mean, what do I need to do today with explorers? I am not asking that question. My guess is your clients are not asking, like, what do I do about followers this week? No, they’re saying, “What do I need to do to grow my show?” And the answer might be, “You need to focus on moving subscribers to true fans. That’s the number one thing. Yes, you could move curious to explorer, but the reason why and here’s the data: it’s because x. I want you to focus on that. Don’t worry about the other stuff right now. You’ve already got enough. Because your goal is to make some money. You’ve proven that you will make money if you get fans. 80% of your fans are still in Subscriber Base, but you have a ton of subscribers. Let’s just move them over to the fan category. Don’t worry about top of funnel.”

We didn’t lead with the model. We lead with the main insight is, go focus on this part of the model. But my goal isn’t I want to check in on how I’m doing at each stage in the Listener Lifecycle. That is not a goal. And this is actually a regular thing I see with—so when I come in and do an audit, I’ll ask the head of product or whoever’s overseeing the strategy for the solution—the application, the dashboard, whatever—what are the use cases?

And a lot of times what they do is they take their existing solution and they back-end the use case into it. “As a user, I want to look at my curious count, explore count, follower count, subscriber count, and true fan count so that I know what to do about it.” And it’s like, okay, so you took your solution, and then you created a use case out of your solution.

Chris: [laugh].

Brian: That doesn’t sound l—and usually you can tell, like, just doing a gut check, like, does that sound like the way podcast hosts talk? No. Even though I haven’t talked to any podcast hosts, my guess is they don’t talk like that. That is probably not a real use case. It doesn’t sound like that’s informed by talking to hosts, so let’s not pretend that that’s an active use case because it’s not.

So, we’ve exposed the model, the system model here, which is, if your thing is loosely based on these five stages, we’ve kind of literally exposed that in the UI, but that’s not always what we want to do. And I see this with other tools were—like with analytics—where in the data model, there are objects, and objects have stats attached to them. You have a network, you have stores, you have departments, inside departments, you have rows and shelves, inside shelves, you have containers. Inside containers, you have products. So, they expose all of those nouns into the UI, and then they provide stats on top of them, and they say, “Look, you have access to all your data about your network, your stores, your whatever, your whatever, your whatever.”

And the head of Albertson’s is like, what do we need to change to drive more sales or to stop wasting fruit on trucks because it’s not selling right? I don’t know if it’s a container, a shelf, a drawer, a network. I don’t care about the data model that’s underneath it, but we’ve exposed that to them in kind of an artificial way. And I’m not saying it’s always bad, that you never need to have stats on that stuff. It’s just about when is the right time and experience to expose that model to them, or to give them stats on that.

Because leading with it creates a—we call this a mental model in the design space, right, which is my mental thinking about how this works, does that match up to the way the system is modeled or not? Most of the time, they are not lined up because the way the engineers built it and captured data is not based on the mental model of how someone actually thinks about it. They think about it very differently than the system architecture is designed.

So, you’re kind of in this middle stage where there’s a lot—this is logical from a marketing standpoint for a show host. It’s not, like, just pure system data, and it’s not an artifact of engineering decisions. Like, you put intention behind this model to be helpful to hosts, but I think my need is not to come in and understand how I’m doing against the model. It’s, I don’t even know what part of the model to care about yet. That’s how illiterate I am about this data about my show, is I don’t know where to even start.

Even if I had to do five things, I’m already a little overwhelmed because it’s like… well, should I be getting people into the Curious Phase? Should I be migrating people over? Those all sound like really hard things to do. I’m already, kind of like, freaked out, you know? And I’m just mimicking—I’m not freaked out, but I just want you to be thinking about it from that standpoint, which is their goal is not to get exposure to the model and check off how they’re doing against the data model.

It’s, what’s in it for me? What’s the bottom line? Once you’ve showed them that landing page, here’s where you are, it’s now about deltas. What’s the change? What do I need to do differently? And don’t overwhelm. Like, let’s just focus on small movements, getting them going. And if you can do that, it’s just like—you know how it is. You can get that trainer can get someone to just start going just to do that first push up, the rest of it is so much easier. Anyhow, that’s my experience, at least, with just generalizing on the analytics, the way I think about this. But your mileage may vary, and maybe you’ve seen difference. I don’t know.

Chris: I think you’re right. I mean, as I’m thinking about the places where this has been a success, it’s been in using it as a prospecting tool because I can pull it in, and I can say, here are the core things, the three core things, that I identify as your problem with your podcast. And a lot of times they look at me like I have some type of, like, psychic knowledge about their show that they didn’t even have. And that’s great validation for the tool because I know I’m doing something that’s providing me with results that allow me to be able to guide people properly.

Brian: Right.

Chris: And I think that’s where I’ve gotten a lot of excitement for this in the past. The first time I used this tool [unintelligible 00:49:10], it was with some folks that are very heavily data analytics oriented to begin with. They were so excited, they wanted to invest in the business [laugh]. So like, I know it has value. But yeah, figuring out that happy middle ground for all those people, and figuring out how to make this an actionable tool that also doesn’t take a whole lot of time to do. That’s the other challenge with this is something that I’m really striving for. So, this has been really great feedback, Brian. This is really good.

Brian: Good. Yeah, I noticed too, we’re at the top of our hour, so we could probably call it here for the podcast. And I know, thank you for people that are listening to the audio version because you weren’t able to see some of this GUI. This will be available on crowdcast.io. If you just look up my name, or actually just join the mailing list, I’ll send out the link designingforanalytics.com/list, you can hop on there, and we’ll be sending out this recording. So, I think on the podcast side, let’s sign off, [laugh] and say thank you, and good luck with this. And thank you for—

Chris: Thank you.

Brian: All your work on my show. It’s just fantastic to just be—I love just like, back to you. I don’t want to see this until, like, just —

Chris: [laugh]. So.

Brian: —like, it’s so—it’s just—it’s so good. I just love—

Chris: Thank you.

Brian: —handing that part off. Yeah [laugh].

Chris: Yeah. Our goal is to make podcasting easy, so it’s clear that we’re doing our job.

Brian: Yes. Well, thank you so much, Chris.

Chris: Great. Thank you, Brian.

Additional show notes, full transcription, and quotes for this episode are coming soon.

Array
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe for Podcast Updates

Join my DFA Insights mailing list to get weekly insights on creating human-centered data products, special offers on my training courses and seminars, and one-page briefs about each new episode of #ExperiencingData.