Why Demographics Are Not Meaningful Audience Insights

Phil Renaud, VP of Engineering at Affinio, joins the Content Experience Show to help us find the most meaningful data through unlikely audience insights.

In This Episode:

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Audience Insights That Matter

Using audience insights to target advertising is in no way a new concept. However, traditional demographic data might not only be irrelevant but may also be causing you to miss important trends with your customers.

While you may be able to find some overlap amongst people of a certain age group, location, or gender, the fact is that not all 20-somethings, or women, or residents of a certain city care about the same things. As consumers become more protective of their data and ad blockers become more prevalent, your business needs to find more meaningful audience insights to give your customers information that they find relevant and actually want to see.

By disregarding traditional demographics and digging deeper to learn about your audience you can advertise in a positive way and help create a world where your customers actually want to turn off their ad blockers.

In This Episode

  • How to find relevant audience insights
  • Why data can teach you more about your customers than the customers themselves
  • How the film industry uses data to determine who should star in a particular movie or show
  • Why demographics no longer matter for audience segmentation

Quotes From This Episode

Pay attention to non-demographic things.” — @phil_renaud

Don't build an advertisement around something that is a person’s fear. Click To Tweet

Resources

Content Experience Lightning Round

What is the one thing that you miss, that you can no longer now get back in Canada?

As funny as it sounds, the thing Phil misses the most is the wide variety of American chain restaurants!

What has been your best thing to come back to in Canada?

Phil’s favorite thing to return to has been the Canadian weather. After living in Arizona, he says he’s happy to have more than just t-shirts in his closet!

See you next week!

Episode Transcript

 
Randy Frisch: Welcome to Conex, the Content Experience Show. I'm Randy Frisch. Anna Hrach is here with me.
And this week, depending on where your mind goes, you could be a left-brainer, a right-brainer, but all of us these days, we have to deal with data. It's a reality. It's sitting there in front of us and it's really a question of what we do with it. And today's guest, Phil Renaud, from Affinio, is really gonna help us understand some of the possibilities, some of the possibilities today, some possibilities where we're going. We had a great time having him here on the recording this past week, and I was motivated.
Anna Hrach: Yeah. Phil gives so many good ideas with how to use data in this episode. I think everybody, to your point, whether you're a left-brained, or whether you're a right-brained, it doesn't matter. You're gonna be so excited. You're gonna wanna get your hands on some audience data and you're gonna wanna just put it to work right away.
Randy Frisch: Yeah. I mean, some of the stuff that we ended up hitting on, which is, you know, I knew some of the people who listen to this podcast are, you know, either B2B marketer, B2C marketer, and they're trying to figure out content marketing. So stay with the podcast as it goes, because we start off kind of, you know, hitting on more media examples. But I found them, personally, fascinating, like where you ... The low aim for the ... that we talked about was this idea of Netflix and how it came from data to figure out that that would be ... that House of Cards would be such a watched series for them. And that we're now able to do this if we don't have the billions of dollars that Netflix has through platforms like Affinio and other ways to do it.
But then we got into some other real cool marketing use cases. I don't know if any of them stuck in your mind.
Anna Hrach: Yeah. I mean, everything from, you know, like what Phil was talking about, how movie studios are using it to select actors and actresses, you know, to just making content incredibly relevant for just everyday people. And I think that, at the end of the day, Phil really hit on exactly, Randy, what you and I are passionate about, and what the show is about, which is creating better content experiences. And he's able to help us do that through data and help us understand it a little bit more.
It's so ... It was just such a great show in general. And I think it's a topic that we don't get to touch on really heavily. We've touched on it in a couple of previous episodes. But today it's really all about that data, and how to make it relevant.
Randy Frisch: Okay. And I'm gonna give people the reason to listen all the way to the end of the podcast. And as, sometimes you get to that 20-minute mark and you're like, "All right. I get the gist of it," but he really hits also on some of the trends that are coming around data protection, right? And how to think about that, and I'm gonna leave that as the cliff hanger here. And then we can kind of just like roll with this and let you brain-fill in this past week, and we'll roll the recording right from here.
Anna Hrach: Hey, Phil. Thank you so much for joining the Content Experience Show today. It is so great to have you here.
Phil Renaud: Hey. Thanks for having me.
Anna Hrach: So you're from Affinio, and I know you pretty well. But for those of you out there who don't know Phil, Phil, would you mind just telling everybody a little bit about yourself?
Phil Renaud: Yeah. Hi. I'm Phil Renaud. I am living in Toronto, Canada, and I work for this company called Affinio, where I am the VP of Engineering. Basically what we do is we build out all of sorts of really great mathematical models around how different groups are clustered or not clustered together.
Anna Hrach: So the coolest thing that I love about Affinio, and you gave just a really really quick overview, but the thing that I love is that it gets really really insanely deep, visually beautiful, most importantly, audience insights. And the the thing that I love most about Affinio is just how beautiful and usable it is.
So you're basically responsible for all of that and the team that manages it, correct?
Phil Renaud: Yeah. That's more or less that the engineering team here is fantastic. We have data visualization people, data scientists. Really anywhere from top to bottom of the stack, we've been working on this thing. We're in love with it, and it's a lot of fun to work on.
Anna Hrach: So one of the things that Randy and I talk a lot about on this show too is just how valuable audience insights are, and how it really is like personas are kind of like the Holy Grail for content creators and content marketers, and just how there's so much data sometimes. You know, people don't really know what to do with it.
For anyone using Affinio, can you kind of give them a walk-through about sort of what it does, how it collects data, and kind of the results that they can expect, and just really the insights they can glean from it?
Phil Renaud: Yeah, absolutely. So Affinio looks at a lot of different kinds of datasets, and I think the whole idea of this being content experience and not just the content without the experience is really important. The big thing that we talk about is relevance. So when we look at different datasets, we look at enormous datasets. We're not looking at just small things, but we might be looking at something like the entirety of the social network graphs. Something like Twitter or Facebook.
And, you know, I know how often I tweet, and it's probably too much. And I say a lot of things that aren't necessarily descriptive or definitive about who I am. However, there's all sorts of other signals about, you know, who I am on Twitter. And that has to do more with, let's say, who I follow. So if I follow the Phoenix New Times, that might be indicative of that I lived in Phoenix for some amount of time. If I follow the Detroit Free Press, that might be indicative that I lived in Detroit.
Well, okay now, let's take a look at the other 600 things I follow, and also take a look at another, you know, 10,000 people that look an awful lot just like me. Now you have a really really large dataset and you can start to make some conclusions about how these people talk, where they like to shop, what they're interested in, things like that. So that's one half. That's the social side of things.
We also do all sorts of very interesting stuff with data that doesn't come from social. So, you know, sometimes through the data, census data, internal data from really big brands about how people use their products or even view their sites, read their papers, things like that.
Randy Frisch: So I find this really interesting, and it's fun to have. I was actually out talking earlier this morning at a local event, in Toronto nonetheless. Amazingly we're in the same city. But ... And people were asking a question. They said, you know, "How do you know what content is gonna work in different stages of the buyer journey?" is they were asking. And I said, you know, "Really, before you've [inaudible 00:06:24] out what's work, you gotta understand who you're speaking to." And I think a lot of what you're hitting on here, Phil, is all about better understanding personas and better understanding audiences.
Maybe you can kind of give us a little bit of the thesis, if you will, around where you think things are going like five years from now. I mean, we're in a exciting point where we're starting to use that data? But why are ... the engineering team that you have motivated around this. What is the view that you see down the road?
Phil Renaud: Yeah. I really like answering this. So we come from a background where people wanna know really basic questions like, "Tell me about an audience, and tell me how they differ or are similar based on demographic traits, based on their gender, their age." You know, you just had a great podcast just the other week about somebody who's doing audience segmentation at the university level. And I said to myself, "I wonder what some of the data points they're working with. What might those look like?"
You know, it turns out what you're majoring in, or what the status of your tuition looks like, things like that. These are all possible data points. The point is, the further you get away from really concrete things like somebody's age group, a lot of good demographics, the harder this stuff gets to be in terms of being able to structure it. But that doesn't mean that the data gets any worse. In fact, we find that it gets a lot more interesting.
So the way that we look at data is a very complex set of connections, almost like a network graph, nodes connected one to another, about how people have things in common. So, just because you and I maybe shop at the same grocery store doesn't necessarily mean we know each other. But if you take that we shop at the same grocery store, that we use the same type of credit card, that we have an address that's in the same neighborhood, that we both follow the same newspaper, and that we both listen to the same radio show, we're getting a little bit closer together. If we could pair that with other things like we're both interested in the same baseball team. Well, now we're getting a lot closer to an idea of a picture of a cluster.
Now this doesn't even start to talk about the content necessarily working here. You said how do I know what kind of content's gonna work in that group. Well, the good news is that between social media and just basic data handling, we have all sorts of data about how people talk and how people respond. There are certain kinds of advertisements just in terms of medium, right? Like we can tell when a video will work and when text would work better than video. We can tell when in a video is something that's relatively silent and non-abrasive is going to work better. Because this is how we see the content being shared for this group in the first place.
Anna Hrach: That is so cool. I love it. I love that so much. And it's so funny, Randy and I were actually just talking to another guest coming up about just how important it is to listen to your audience and, you know, one of the things that I've noticed after doing a ton of audience research is first-hand collecting of audience data first-hand and/or interviewing them is absolutely essential. But there are sort of the things that audiences don't know that they do? So there's the things they know they do and that's where a lot of that valuable first-person feedback comes in. But then there's a lot of the stuff they do where, you know, they don't realize they do these things. Like they don't ... That's why we have tools like keep mapping on sites, 'cause they don't really realize what they're looking at. They get so focused and I love that Affinio can help sort of reveal some of those things that customers don't even know about themselves, so marketers can get so much more details, so much more nuance, and so much more targeted.
What are some of the cool things that you've seen Affinio customers do with all of this data?
Phil Renaud: Yeah. Great question. I think that the thing that you're talking about here, that I like the best, is these unconscious things that we do, right?
Anna Hrach: Yeah.
Phil Renaud: These come into play in all sorts of different ways. Most recently, for myself, and I look at myself inside of a lot of different audiences just to try and test out a tool, and test out some new feature that we're building. So one of the things that I find is, going a little bit partly for me, is I'm starting to enter myself into these clusters of much older people than I feel comfortable with. I'm no longer in like a hip and young cluster. I find myself in like the old people cluster that tell people to get off their lawns.
Randy Frisch: It's all relative. It's all relative. Give yourself a break. Come on. Come on. You look-
Phil Renaud: The point is, I'm not saying anything, I'm not deliberately going out there. It's not like I followed, you know, the whatever the Canadian equivalent to the AARP is, but the fact is that I started following things that a lot of other people, like they're older people, will start to follow too.
So there's some things that you could do with this data. If you looked at this and said, "Ah! This is a segment of people that are demographically older than average," that would be the wrong way to think about it, because now I've entered this cluster. I'm not necessarily a particularly old person. I've gotta hope. But the point is that I have all sorts of different likes and relative things that I care about that I don't know I'm doing. Or at least I'm not consciously doing it, but I follow two or three different baseball teams, and I follow a couple of different newspapers. And I follow a couple of conferences and, you know, five years in my industry that were more from when I was growing up than they are from now. And these are all indicative of somebody who has a diverse set of interests, that doesn't necessarily gel with a young person on social media.
So for people that are kind of building content around this, you know, that's something to look at. You couldn't just say this is a group of old people, this is a group of people that live in a certain location. You can say, "I can aggregate the content around this and build something new."
Randy Frisch: It's really exciting. Yeah. I mean, it's funny too because one of the things we say in our company, just for [inaudible 00:11:54], just from our core value perspective, and it's gonna come out sounding terrible, but when we explain it, is they did it, the customer isn't always right, right? And what we mean by that is not, you know, the offensive like, you know, "Tell our customers to go you know where," but it's more that when we talk to our customers, which we need to do, we need to listen to them, but we also have to try and find the things that they're not asking for yet. Or they're not verbalizing, right? Or that they don't know that they need, but that their behaviors or suggesting as such.
And it comes back to your earlier point, Phil, I think around relevance, right? We need to find out, not just what's relevant now, but what is relevant to people that they don't even know yet? And that's, that to me, is what's really exciting about all this data.
We're gonna take a quick break here from some of our sponsors here on Conex, the Content Experience Show. we're gonna be back with Phil Renaud, and hopefully we can dig into some real-life examples, where we're seeing how this data is going to use.
Be right back here.
We are back here with Phil from Affinio, and we're talking all about the amount of data that's out there and the reality today that we can use AI, that we can use these clusters of data to start to make decisions for our content that we put in front of our audiences. And the really exciting thing, Phil, is some of the customers you're already starting to do this with and making real-life, real-time decisions on things like who's gonna star in a movie.
Phil Renaud: Yeah, that's right. We have quite a few different media and entertainment space clients. So anything from a movie studio to a television network and movie providers. We have a pretty diverse set of clients, and they ask a lot of weird questions. So, you know, when we talk about Affinio and in context, a lot of times I like to say we're trying not to push the envelope on a specific use case, but a really common one that comes up a lot is, well, what do people wanna watch? What do people wanna hear? What's relevant to people? You know, what is the non-noise that people really emotionally connect with?
And movie studios and television networks care about this more than anything, because the amount of attention that you need to watch a movie or to care enough about it, a movie to go see it in theaters, for example, is pretty high. You know, my immediate diet is not particularly heavy. I may be able to watch a show a week, or a couple of Netflix shows a week, or something like that. But there's not too many things that I'm, you know, running out to see in the movie theaters. But there are some things.
So we get these companies that come to us and they'll say, "Look. We've identified a really really great script that we want to make a movie for," and in the case of some networks, one of our clients is the BBC, for example. They're kind of a television network and a media network of record in all sorts of different countries around the world. They might come to us and say, "We really want this to be a hit in, let's say, New Zealand or South Africa," or some country where they really wanna make much more of an impact where they previously had.
So a question you can ask Affinio is, for the people that are interested in this particular script, or in any movie. Let's say that we're talking about you wanna make a Jaws remake or something, right? They can go and look at people that care about movies like Jaws that live in South Africa or New Zealand, and what other kinds of movies do they like? More importantly, what kind of actors and actresses do they like? Is there anybody that maybe isn't so popular but is still extremely niche and important to them that they would really connect with emotionally? These are all the kinds of questions that we can answer.
Randy Frisch: It's really cool. You know, I think back to ... And I know a lot of people probably know this, but one of the most famous recent-year pieces of content on the media side that was all around data science was Netflix's House of Cards, right? I think ... did you guys all know that story that I think the way it worked was that they were able to use big data because they knew that the version in Britain of House of Cards had a huge audience. There was a movie directed by David Fincher that had a big audience, but then ... And it was also people who watch the British version also like Kevin Spacey films, so they ... And they also liked movies directed by David Fincher.
So there were able to kind of like pull everything together and say, "This will be successful," which is essentially what all of us are trying to figure out when we put pen to paper, tried anything from a blog post to an ebook. Like, what of our work is gonna bear fruit.
Phil Renaud: Yeah. I agree. In this case we think about the Fincher and House of Cards situation quite a lot. And it's exactly the kind of stuff that we like to do a lot too, right? You know, there's all sorts of different use cases where you wanna know who the celebrity du jour is going to be.
So we do this in music as well, right? So it's pretty ... I think everybody uses some variation of Spotify nowadays. If it's not Spotify, then it's Apple Music or anything like that. The music that you listen to helps determine what else it's going to suggest to you, but that hasn't really broken out into the abstract outside of songs and albums and things like that very well.
Amazon does this very well with what products they recommend, where they do a search cell, I guess. But there's all sorts of different cases where we haven't really hit this point where people can recommend things based on what I've unbiased and unconsciously declared as relevant to me. So for actors, actresses, directors, movies I wanna watch, networks I like to pay attention to, those are all things that people can be doing.
Vacation destinations, what kind of soft drink or beverage I wanna be drinking. These ... We have all sorts of signals that indicate what these could be that people aren't really tapping into yet.
Anna Hrach: Honestly so unbelievably relevant. I mean, just in general, because, you know, relevance is the key to creating the best experience possible for customers and for audiences. But also even just with the shift and social algorithms lately about how really it is about creating that relevant content. It really is about creating such highly tailored content, and the data that you're providing to customers, you know, whether they be massive movie studios, or down to five-person agencies, that data can really allow them to do things and provide experiences to audiences that they couldn't before.
So based on all of the clients that you've worked with, do you have a couple of like top tips for how people can really start to use data, and how to really start to make content more relevant for their audiences? How they can actually put this stuff into play?
Phil Renaud: Yeah. I think I have some tips for that. I guess the things I would say to pay attention to are non-demographic things. There are all sorts of different signals that would indicate my interest and stuff like that. But I don't really want to be pigeon-holed into who I am, you know, just along the equivalent of whatever I would fill out for the census, right?
If somebody just put my census in though, and tried to advertise me based on that, the most that they might be able to get out of it is, you know, what's my area or zip code and what restaurants in that area or zip code that people of my gender or age might be interested in. And that stuff doesn't really fly any more. I know that a lot of our big clients tend to just disregard that kind of info altogether. They might try to pare it back at a later date to make things a little bit more custom, but they don't look at it when they come down to clustering and segmenting. And so you can eliminate demographics as a potential signal, is one big one that I care about a lot.
But another thing that I also really care about is doing this in an unsupervised way. So this is ... I'm not gonna get very mathematical here, but one thing that we really look at a lot is AI that provides you with data that you can try and go and try to fold yourself. Or AI that provides you with data and does it all for you. A big part of Affinio is, you know, the reason for existing is that we wanna make it as easy as possible for people to not have to decide what the lines in the sand are. So we use what's called unsupervised learning on these very large datasets to figure out how would these clusters break out.
It's a lot of different clustering algorithms that exist out there. And some of the ones that we've tapped into have done this very well for different use cases. So if you ever are presented a dataset and the equivalent spreadsheet that has a need for columns and you could say, "Let me pick the one that I think I wanna separate these people by," whether it's by nature or zip code or gender, I'm gonna say try to look away from that. There are clustering algorithms that will take all of those into consideration and come up with really ... They seem complicated at first clusters, but the way that they're presented with Affinio is just very straight lines that say, "Yeah. This is a group of people that," I don't know, "it's like people that live in Manhattan and often talk about going out to eat. And here's a different group of people that are in Brooklyn and will literally talk about going out to eat." Or, "Here's a group of people that like the Los Angeles Lakers and this different group of people that like the Los Angeles Clippers." There's no real indication about their demographic, their age group, anything like that. It just of like comes out in the wash.
Randy Frisch: This is wild. I mean, there's so much opportunity as you highlight the amount of data out there and the way that data is now being able to be processed through companies like your own.
I'm gonna shift though to kind of the fears here, right? You know, the big brother, if you will, 1984 type of mindset. And there's also a lot of things that are threatening our ability to get access to this data. I know one that a lot of marketers are dealing with right now is becoming compliant for things like GDPR, right? Which, if people are not familiar, is coming out of the UK, I believe. And the new area I focused on, how we enforce, you know, general data protection. So what data people have access to.
How do you see that kind of shifting, the way data is made available to companies, to deliver these better experiences in the coming years?
Phil Renaud: Yeah, great question. Well, almost everything about the access is a personal choice. You know, people on ... I'm just gonna use Twitter as an example, because they do this pretty well. People with a private account on Twitter aren't really opting into anything that they don't think that are. A lot of advertising that they might get from Twitter would be, in one way or another ... Depending on what they post, but nobody else ever gets to touch that kind of stuff.
Because of this, we find a lot of people are using ad blockers and things like that, in addition to the GDPR, and this is another major concern for advertisers, right? The prevalence of ad blockers in major browsers, and on Smart phones now. But one of the big goals here should be to get around these things. Potentially, what we should be thinking about is how to make people not want to use ad blockers? How do we make people not feel like their data should be threatening to them in terms of advertisements?
I think the point of this is to think about what data they're comfortable to give at first, but to take that data and use it in, I guess, a socially responsible way. And I don't build anything like an advertisement around something that is a fear that a person has or a ... that don't put that actor towards somebody's prejudices, I suppose. You see this stuff in the news a lot from last year. There are all sorts of different prejudices that were preyed upon around advertisers for different political gains. This isn't something that I think should be anywhere near how advertisers approach things in the future.
If you show people that what you're showing them in terms of advertisements is something akin to what they like, what they respond to emotionally or in a positive way, I think that they're gonna turn the ad blockers off and be a lot more okay with seeing ads that are relevant to them.
Anna Hrach: That is beautifully said. Advertisers, in order to get that trust, have to be relevant, and they have to use that data in a responsible way, which in turn will sort of bring about trust from the general public and give them data.
I think, honestly, that I feel like this is sort of the most ... I don't even know how to say it. It's like one of the most beautiful poetic things I think anything has been said on the show recently.
But no, it's true. You know what I mean? Like it's really about responsibility and using data carefully and, you know, respecting the customer, and respecting our audiences and I love it. I think it's beautiful.
Randy Frisch: I think there's an opportunity for a new t-shirt, by the way, Anna. It can be like, Data Makes Poetry, or something like that. I feel like you've got the licensing rights.
Anna Hrach: I love it. Yes, we're gonna start that t-shirt campaign now. We'll start the GoFundMe here in just a minute.
Phil, so unfortunately our time is coming to an end, but we would love for you to stick around to get to know a little bit more about the personal side of Phil, if you're up for it.
All right. So we have Phil here from Affinio. We just heard about all of this amazing data collection, all of this amazing audience inside information that we can do. He gave some great tips. Now we're gonna get to know a little bit more about the personal side of Phil.
So Phil, are you ready for some lightning rounds of questions?
Phil Renaud: I'm ready. Yeah.
Anna Hrach: All right. So you, at one point, were actually living in the US. You were actually here in Phoenix, in Arizona, with me.
Phil Renaud: Yeah, that's right.
Anna Hrach: What is the one thing that you miss, that you can no longer now get back in Canada?
Phil Renaud: Good question. I really miss ... I'm gonna say it, American chain restaurants. This is gonna sound insane. I go ... I travel to the States quite a bit, and I think people think I'm crazy, but I'm completely in love with so many American chain restaurants. Not just for the portion size, which is insane, [inaudible 00:25:00]. But also for just like the food content. There's an insane blend of different types of food that none of which go together, and I'm thinking of every Southwest egg roll I've ever had. There's the P.F. Chang's and the Gordon Biersches of the world. And there's even just burger places that we don't have in Canada. We're lacking In-N-Out Burger and Rally's and Checkers, and Sonic. I could go on for another day if I had to.
Anna Hrach: But to be fair, we don't have Tim Hortons.
Phil Renaud: Yeah. Tim Hortons isn't so bad. There's a couple of hockey arenas state-side where you'll find a Tim Hortons embedded therein, but that's basically because they're Canadian embassies.
Randy Frisch: Yeah. To be honest, like we have way better things than Tim's. Like Tim's is good. It's a staple. But at the same time, I mean, like Phil, I mean, you know, it's like you're painting Canada as this desperate place where we don't have any great food.
What has been your best thing to come back to?
Phil Renaud: In Canada generally? I really missed weather. That was a big one to come back to. When I lived in Phoenix there was a lot of summer all the time. I don't believe that I have a wardrobe that isn't just t-shirts again. That's really fun for me.
No, I love the way that ... So I've lived in Canada, in Halifax and Toronto, since moving back from Phoenix. And I love the way that these cities handle the weather. There's giant, crazy, underground mazes in both of these towns, and people just kind of live in them and not like mall people, but they get around just great. Because it's wintertime and it's crazy outside and how else are you gonna live, you know?
Randy Frisch: I thought you were gonna say you were just happy to have a baseball team, you know, back in your home city.
Phil Renaud: I'm very happy to have it. I'm happy to have.
Randy Frisch: Absolutely. I mean, Toronto's got a pretty, you know, competitive team last summer of yours. And I know you're a big baseball fan.
Phil Renaud: Yeah, that's true. I'm very happy to see the Blue Jays doing well. I grew up around Detroit, so I'm still a Detroit sports fan at heart. There's kind of a funny thing with Canada and sports, in general. It doesn't really matter where you live, you have to like whatever Canadian team is doing best. So whatever Canadian hockey team makes it furthest in the playoffs, you kind of put aside your grudges and you say, "Ah, all right. I'm gonna cheer for them." At least most of the time.
Randy Frisch: So true. So true. Awesome, Phil. It's been great to have you on the podcast. I wanna thank everyone for tuning in and listening to Phil. If you wanna learn more about his company, you can go to affinio.com That's A-F-F-I-N-I-O .com Learn all about how you can use data to make some of the choices you're leaning towards in your content creation, and the ultimate content experience you put in front of people.
The Content Experience Show is part of Convince and Convert. You can learn all about the podcasts on convinceandconvert.com You can also find all of the other podcasts that we have from these episodes on Spotify, on iTunes, on Stitcher, on Google Play. It's hard to keep track these days of all the places, but we are there to be found. And please leave us a review on what we can do to make these more engaging until we have the data to tell us to do that.
Until next time, I'm Randy Frisch. Thanks, as always, to Anna Hrach, and Phil for joining us.
 
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