How to Learn Everything About Your Consumer Audience With Affinio

Welcome back to Marketing Marvels! I’m joined today by Tim Burke, CEO of Affinio—an incredible piece of software that helps marketers understand what makes their audiences tick.
If you’ve been thinking of your audience as one, homogeneous unit, you’re wrong. As Affinio will reveal, your audience is actually made up of many diverse segments, connected by a few key interests. Now, thanks to Affinio, you can uncover exactly what those segments and connections are.
Affinio’s analytical capabilities and data visualizations mean your brand can create more relevant content, improve ad targeting, connect with the perfect influencers, and understand your audience better than ever before.
Want to see Affinio in action? Watch the video for Tim’s demo!

Jay: Hey everybody, it’s Jay Baer from Convince & Convert, and this is another episode of Marketing Marvels, where I bring you the very best marketing technology. Technology that you should implement in your organization, technology that I believe in and use, and I hope that you’re going to use it as well.
Today, a very special guest on Marketing Marvels, my friend Tim Burke who is the co-founder and CEO of a terrific company called Affinio, A-F-F-I-N-I-O. I’ve worked with Affinio for quite a while now, we use it almost every day on the consulting side of our business at Convince & Convert and, also, on the media side. One of the only software tools out there that we use on both sides of the company. You’re going to see why in just a minute.
What does Affinio do? Here’s how it works. You’ve got a ton of followers on Twitter, and Pinterest as well, perhaps, and those followers have commonalities. They have ties that bind. They have things that make them birds of a feather. But, what is interesting is that your audience, on any social network, is not homogeneous.
You may think it is homogeneous, that the people who follow you are like this. But, as it turns out, that’s not true. There’s a bunch of different micro-communities of cluster segments of your audience that follow you for different reasons, and each of those clusters has commonalities that are really, really important to understand.
When you understand those commonalities, when you understand the micro-segments of your audience, you can do better targeting, you can do better partnerships, you can do better influencer outreach. You can do a lot in social media and content marketing when you really know what makes your audience tick. That’s what Affinio does. It gives you a fast, simple, incredibly powerful and amazingly illustrative way to understand what really makes your audience tick, and we’re going to see it in just a second.
Tim Burke from Affinio, thanks so much for being on Marketing Marvels.
Tim: Very excited to be here this morning, Jay, thanks so much for having me.
Jay: You bet. So, you are one of our favorite Canadian companies as well, so that is fantastic. That’s why we like working with all the Canadian companies because they’re so nice.
Tim: Yeah, we say “Eh” and “Sorry” a lot, but other than that, Jay, it’s a good story, yeah.
Jay: What did I leave out of my introduction, anything that I didn’t talk about? I know there’s a lot going on behind the scenes from a horsepower and big data perspective to make Affinio work.
Tim: Yeah, I think what’s interesting in our approach overall is that, fundamentally, our thesis is that people’s interests and passions is something that, up until now, hasn’t been something that you’ve actually been able to segment audiences based on. Right? So traditional segmentation strategies: demographic, geo-location, age groups, what we believe is that those are old strategies for segmentation.
What’s really, really compelling when it comes to marketing and, specifically, content marketing, comes down to understanding why people are consuming what they consume. Why do they consume the content? Why do they read something, why do they watch a certain video?
Realistically, we firmly believe that it’s foundational interests and passions of these individuals that basically drive them to consume that content and so, with a lack of understanding of that, you’re sort of shooting in the dark. You’re creating content without necessarily understanding why they’re going to consume it or if they’re going to consume it.
What our approach is, is basically looking at these very subtle signals in social, and although it’s a marketing intelligence platform, so social’s just a very, very rich signal for us. Looking at the things that they follow, what they like, as opposed to what they’re saying on social as this underlying understanding of those interests and passions.
When we do so, when we’re able to segment based on that, we find ourselves with a really, really compelling platform, very unique in its nature and very, very compelling in terms of content strategies.
Jay: Yeah, it sure is. As I mentioned, we use it all the time, both for our own work and also for clients. You can use it as well, Marketing Marvels viewers. At the end of this short episode I’m going to give you a special URL that you can use to go get a free analysis – a custom analysis – of your own audience, so you’ll get a little taste of Affinio for your own business. I’ll give you that URL in just a second.
Tim, are you ready to take a look at the platform?
Tim: Yeah, absolutely, let’s just jump right in here.
Jay: Fire up the screen share.
Tim: Fire up the screen share, and for today’s viewers we’ve got the . . . We’re looking at Jay Baer himself, his audience on Twitter.
Jay: Uh oh. Probably mostly relatives, right? Is that . . . ?
Tim: Mostly relatives, yes. We’ve sat and we’ve found a few, actually, probably, arguably more important and more valuable than yourself, Jay, but we’ll see if we can find them when we actually dive in a little deeper.
But, super excited to actually show this as a presentation. Obviously there’s going to be a lot of familiarity with what we present today, both to Jay and your viewers themselves. The context, I think, will be super compelling in terms of this is a demonstration, but . . .
Jay: In order to get this report, what do I have to supply, as a person or a brand, to get this output? Just, here’s my Twitter account information and it’ll do it?
Tim: Exactly. I mean, we put a lot of focus and a lot of attention into building a platform that’s not driven by data scientists. It’s driven by individuals like yourselves and myself that, effectively, what you’re looking for is the information to be aggregated with the power of this algorithm in the back end.
In this particular case, all we did is simply point it to your Twitter handle as a starting point, and told the engine to go. With doing so, basically the algorithm then goes to work. And what’s unique about our approach, as I indicated early on, is that what we’re doing in this particular report is looking at over 200,000 of your social followers on Twitter as a starting audience segment that we’re analyzing.
What the algorithm does is looks at each of those individuals and, beyond following Jay Baer, looks at what else they’re following on Twitter specifically, in this particular example.
What it’s looking for is similarities or “signals,” that identifies individuals based on the similarities of their follower patterns. So what it starts to do is looks at their follower patterns and then aggregates them around a multitude of these different clusters and segments, based on the similarities of those patterns.
When the algorithm first comes back, these are our unlabeled segments, they’re segment one, two, three, four, five, but what it’s done is the algorithm’s identified, it’s found a group of individuals, as a subsegment of your audience with similarities in their follower patterns, doesn’t know exactly what that signal is.
When we dive into the platform you quickly identify how the naming convention of each of these segments become very, very easy. We’ll start to see how those signals start to get pulled to the forefront, and understand why the algorithm actually identified these unique segments within your audience, overall.
What you’re seeing is, as I scroll down here a little bit, highly visual presentation of this audience overall. Looking at, you know, how these different segments break out in your audience, the overlap of those audience segments, visually, actually represents similarities in their interest patterns at the core.
Then, on the right-hand side what you’re seeing is what we highlight as an important factor here. Again, instead of looking at what people are talking about, as a key indicator of what they’re interested and passionate about, what we’ve identified early on within the development of this platform is that most people don’t say anything on social.
These green bars represent the people within each of these segments that are talking or tweeting regularly in this particular social audience, and the reality is that most people don’t say anything.
So what we’re able to do with our, sort of, analysis of what the engine’s actually doing is look at those people in that audience who may not be tweeting frequently, but looking at their underlying follower patterns, as a key signal as to what they’re interested and passionate about. We’re able to leverage that signal so that we can, essentially, build better content and build better creative that’s hyper-targeted based on their interest patterns.
So although they may not express themselves or even necessarily be sharing content that they like, you fundamentally know what they’re interested in and, therefore, have a solid understanding of what content they’re already consuming and if your content that you’re creating is going to align with, basically, those interests and passions as a whole.
Jay: It’s amazing, every time I run a report like that I discover something new. This time, I did not realize that it had a full segment worth of Toronto followers, and maybe . . . actually, I know why that’s true.
Two weeks ago I was in Toronto for the Uberflip Experience conference and keynoted an event there. Uberflip’s another company that’s been on Marketing Marvels, and a company that I’m invested in. Of course, it was hundreds and hundreds and hundreds of highly relevant Canadian, mostly Toronto, content marketers. And so, I think that’s where that segment emerged from.
The last time I ran this report, which has been a couple of months, that segment wasn’t there, so I think that’s where it came from. So it’s fascinating to see that you go do a conference in a particular location and, all of a sudden, it creates a relevant segment in Affinio.
Tim: It’s pretty remarkable. We’ve done analysis of a lot of brands, direct enterprise audiences, and oftentimes they’ll actually be able to . . . we’ll identify audience segments that seem obscure to us but they’re highly relevant, it turns out, to be highly relevant to the brands themselves because of, oftentimes, I’ll, like, “Oh, that was an event that we sponsored last year,” or we did, basically, a collaborative campaign with that other brand, you know, a year ago.
So, you oftentimes are actually able to tangibly see the ROI associated with many of those marketing efforts, based on this type of analysis, which I think is also super compelling.
Jay: Well, what’s amazing about that day to day is that, let’s say you’ve got the Toronto audience in my case. I might have a piece of content that I want to share that I know is going to be relevant to the Toronto audience because maybe it’s a Canadian slanting piece of content, but most of my audience is not that audience, right? The Toronto group is a small subset of everybody.
What typically holds organizations back is they say, “Well, if I share this right now, I know that only a few percentage points of my overall audience is going to be interested in it, so let’s not do it,” because you don’t want to turn off 92% to turn on 8%.
But, what you can do in Affinio is you can actually press a button and export each of those segments as a custom audience, and then use Twitter ads or Facebook ads to promote that piece of content only to that segment.
Let’s say I’m coming to Toronto again and doing an event that has an admission fee. I could just go into Affinio, press a button, export those Toronto people as a custom audience, import that custom audience into Twitter and/or Facebook on the paid side, and then run ads only against that segment.
That is how you use micro-targeting to your advantage.
Tim: Yeah, and I’d take that one step further, Jay, is that the opportunity here, and oftentimes what we’ll receive this push-back when we first present this data to people is, “Well, that’s only a very small segment,” and, you know, that may be several hundred people or several thousand people.
The interesting thing, compelling thing, though, is that what we’re doing is we’re leveraging the signals of that interest-based group or the interests and passions of, let’s say, your Toronto group. We’re able to blow that up into a look-alike on Twitter, so that you can reach beyond those people in Toronto that are following you, to those people who look like those in that segment.
Jay: That’s right.
Tim: That who’s now scalable. So, like you said, for your conference, you’re not reaching thousands of people who are already following you, you could be reaching hundreds of thousands, in that region, whose interests and passions mimic those that are already following you as a hyper-targeted audience for your next conference in Toronto.
Jay: Yeah, and you can do look-alike audiences now, of course, in Facebook and Twitter, but a lot of times what you’re using as the starting point, as kind of the sourdough starter, the seed corn, if you will, is not nearly as relatable and identifiable and accurate as what we have here.
This isn’t just what Facebook believes to be people interested in social media marketing, these are people who actually follow me in social media marketing, and so when you use that as the beginning point of a look-alike, you’re in much better shape.
Tim: Yeah, no, and that’s exactly right. I mean, when people say, “Well, how does your look-alike, you know, differ from others,” oftentimes what’s capable on things like the platforms like Twitter is a look-alike against an audience, right? I mean, we could build a look-alike off of Jay Baer’s audience.
The reality is, though, that they won’t have the nuances that we’re discovering here, and what the benefit of this, sort of, hyper-niche targeting is the ability to basically build hyper-targeted content, for every one of these segments, that you know resonate at a different level.
That’s what we think is super exciting in the world of personalization, recognizing that, I don’t think we’re ever going to reach a point where one-to-one targeting and one-to-one content is scalable.
What we’re providing is a niche method of basically achieving some level of personalized content in scalable audience segments that, once you discover, you can start building better content for.
I will jump right in here and, certainly, this happens to be one of the most exciting screens within the dashboard. What I’ll jump into is our first segment on social media marketers directly, clicking over here to our all segments. So many familiar faces, I’m sure, to Jay’s audience as well as Jay himself, who sees many of these individuals.
What you’re seeing here in this particular segment is effectively, first of all, upper left-hand corner, Jay Baer, we ran the audience of everybody who is actually following you, so not surprisingly you rank number one in terms of the affinity to this particular segment.
What you’re seeing, in descending order from left to right, are the other individuals who, statistically, are effectively most relevant and have the highest affinity to this segment that we’ve identified.
So the people in the social media marketers doesn’t say that they all follow these accounts, but statistically this becomes, sort of, that follower DNA that we talk about, and it’s the foundational basis of our interests and passions.
We know that the people who are in this segment, these are the influencers that they’re following, these are the thought leaders that they’re interested in. Just to give you some perspective on, sort of, how this looks segment by segment, if we jump over, you’ll quickly see sort of a dramatic shift in the follower patterns of the individuals in, say, a technology BI, which now sets heavily towards technology. Far more media heavy in terms of their front-end interest patterns as opposed to people patterns.
So looking at, you know, a lot around, sort of, the content patterns themselves. Like you said, if you jump down to unique segment of Toronto PR and Comms, suddenly you get something that slants heavily towards a Canadian reflectiveaudience, where CBC News and everything, I’m sure, if we look far enough you’ll find a Tim Horton’s logo in here, and some hockey as a foundation, Toronto baseball.
I mean, again, this is what’s compelling and most powerful for our platform, and usually is the wow factor, is that when people first see our platform, this is what gets them really, really jazzed and really, really excited in the framework that, suddenly, they have a deeper understanding of what the interests and passions of their audience members are, or of a target segment are.
So that, when they are writing content and creating content, this is in the back of their minds. Those insights become super powerful when you know how to slant your content, even just based purely on the interest patterns that you’re looking at.
Jay: How we use it on the corporate side, when we do this for big brands on the consulting side, we’ll say, “All right, we want to work with some influencers. What influencers should we approach?” Well, we have this whole array here that says, you know, what other people does this particular audience tend to resonate with.
Then, on our side, on the media side of Convince & Convert, we use these kind of reports to help figure out who we should approach for guest blog posts, where I should try and write posts on other publications that aren’t my own, and even who we should invite to have on our different podcasts.
Tim: Yeah, I think what’s really, really interesting and, certainly, over the last few years as we’ve developed this platform out, the most compelling thing that we’re identifying is how rich those insights become. Simply based on what, as a premise, is a very simple concept. The signal that you’re able to extract based on people’s likes and follows, to your point, influences what influencers they’re listening to and basically consuming content from, what media channels they’re reaching out to.
Like you said, when it comes down to, even, brand affinity, which ones should be advertisers? I mean, we’ve got a number of large media companies working with us right now who, simply by clicking into sort of the high over-indexing brands associated with any one of these segments, can quickly identify the affinity of brands to their media. So suddenly, when it comes time for sponsorship, advertising, this can drive those decisions and help quantify them.
Traditionally, we’ve talked to media companies and asked them point blank, “How do you identify your best-suited advertisers for the audience that you’ve attracted,” and for many of them there is really, you know, not a strong signal to help quantify the . . . Oftentimes, they tell us, “We just start with Fortune 1000 and work our way down.”
I’m saying, “The data will tell you, basically, A), what audience you’re attracting from your media account, but more importantly what brands have high affinity to those audience segments that you should be able to leverage from an advertising and sponsored and native content perspective,” which, in this day obviously, with native and sponsored content growing and rapidly emerging, becomes a very compelling use case and a very powerful tool to help quantify that opportunity for these media companies.
Jay: Now, you can also pull out of Affinio reports content and hashtags that resonate with those audiences as well, yeah?
Tim: Absolutely. Once the algorithm is actually done its job, segmented these audiences into these natural clusters that we’ve identified, based on their follower patterns, what it then does is it starts to look at the content that’s being shared within each of these segments.
Again, we don’t start with content or the fire-hose as the foundational data set. What we’re doing is, again, we’re segmenting based on interests, likes, and passions. Then, once segmented, we’re taking a deep dive to identify exactly what makes each of these audiences tick.
What’s interesting is that, once we’ve pulled that back, what we can identify is, rapidly, things that are over-indexing content-wise within each of these communities. Everything from, who were they mentioning more than anybody else, segment by segment, what hashtags are they using, what content are they consuming.
This is an example in this particular case, obviously in our technology BI group, where in their use of the hashtag cloud IOT innovation is heavily over-indexing in comparison to the rest of your audience, Jay. Those people in this particular segment are obviously sort of pounding those hashtags more frequently than anybody else.
Then, beyond that, we go in and we can look at things like the domains that people are looking at. When you start speaking of display ads, banner, targeted content, even branded content, again, from a native sponsored content perspective, you can see where people are consuming their content.
So you can actually say, if I want to reach and expand my technology BI segment within my audience, where else are they consuming data? Where else do they spend their time? Where are they finding articles which they are sharing on social, as the foundational signal as to where you should place your content.
Where you should be doing, maybe, sponsoring native content, or maybe where you, like you said Jay, maybe want to be guest posting or guest blogging, to basically help grow your brand itself, to help grow that particular segment.
Then, beyond that, we just released, most recently, what we call our Topic Explorer. Looking at the content that these people are sharing at an article or blog level, what we’re doing is actually analyzing each piece of content that is being shared, and aggregating that together to try to identify those over-indexing signals.
For example, in this particular case, you can see a cluster forming content-wise for this technology and BI group, and when you dive into it you can start to see that what’s over-indexing is this talk about Facebook and their feeds and video ads.
Again, the whole premise is that we’re trying to establish, effectively, a content and strategy cheat-sheet at the core, so that when you actually want to go targeting you understand what they’re consuming, what content is over-indexing and resonating with them. What they’re interested in, what media channels you want to place that content.
It really becomes a very, very holistic approach in terms of strategy and, as a result, that’s why we got such a massive adoption with digital strategists all the way through to analysts and researchers, who are now powering our platform and using it to power, essentially, their strategy and front-end, understanding these audience segments.
So what I think is really interesting in terms of this marketplace now and what we’re seeing as a shift, is that audience has always been traditionally identified and talked to, talked about, in the format of media buying. Which is sort of the execution or final stage of the campaign.
Typically, a fake persona is generated content and they created around this persona, and then the media buying is sort of the only real discussion around audience, right? So it’s like, “Let’s go find the audience that fits this demographic or age group that we’re trying to target.”
What we’re doing is flipping that on its head. We’re saying, if we have a deep understanding of the audiences that we want to go after, and if we can understand their interests, passions, content consumption patterns on the front end of strategy, it’s just far more compelling.
It will lead to better content, it will lead to better content strategies, lead to better strategies in general, such that when it comes to media buying you’ve already pre-defined that audience segment.
To your point, now, in this particular case, next time you do a conference in Toronto you know exactly where that starting point is, you know what they’re interested in, you know how to slant that content or that ad toward something that they’ll be interested in. And you know where to find that audience on social when you want to amplify whether it is an ad, a piece of content, or conference promotion in that format.
That’s what we see as super exciting in terms of the framework of what we’re seeing as a shift in the marketplace. Where this audience first approach is becoming a very, very compelling means of developing strategy, all the way through to the creation of content and the execution of that content at that scale.
Jay: And you can also do some interesting competitive analysis in here as well.
Tim: Absolutely. I mean, you know, in this particular case we’re analyzing your audience. We’ve got a number of users on our platform that do deep dives into competitors’ audience.
We have the capability to actually run more than one handle simultaneously, in this particular case, so you could actually find the white space between you and a competitor. You can find segments that your competitor dominates that you’re not, you don’t even necessarily, show up in at all, so you can start to formulate strategies.
In this particular case, Jay, you may, against a competitive nature, you may have identified that technology BI is a segment that you’re growing that your competitor doesn’t show up in, that slants in a direction that you may see it as an opportunity to start building more compelling content. To help continue to grow a segment that is not-, being ignored, or not taken advantage of, from a competitive perspective.
Jay: That’s awesome, fantastic, great demo. Friends, to get your own free custom analysis, give you a taste of the power of Affinio, I want you to go to this URL: product.affinio.com – A-F-F-I-N-I-O – product.affinio.com/marketingmarvels, product.affinio.com/marketingmarvels and check it out for yourself. You are going to be amazed.
Tim, how do you guys charge for the platform?
Tim: So our current pricing is seat-based licensing, so we structure is as a SaaS service. The platform continues to expand. We just released our most recent product update as of yesterday. What you see in front of you is Twitter, we’re adding other platforms as we go. Some exciting news coming down the pipe in the next few months for us in terms of additional platforms that we’re adding. The utility, basically, is a SaaS-based product on a seat-based licensing is how we charge.
Jay: Let me ask you this, I suspect you’ve got to have a fairly sizable audience for this to really make sense. If you have just a small number of followers in a particular social channel, it may be more horsepower than you need.
Tim: You know, it’s remarkable. Most people make that assumption, Jay. It’s phenomenal to me to see what you can see as a core signal from very small audiences. In the platform, we typically do a cut-off at around 500 followers, 400 followers, as sort of a foundational signal to leverage up. But even at that level it’s amazing what you can start to see, which typically aligns perfectly with what somebody’s audience is.
So even as small or immature an audience may be at that scale, those signals of who you’re starting to attract and what that persona or DNA is, is already very strong. We’ve seen it work very well, even for smaller audiences as well as ones of your scale, overall.
Jay: Yeah, that is smaller than I thought. That’s pretty interesting. Okay, so remember, product.affinio.com/marketingmarvels.
A couple more questions for you, Tim, before we let you go. How did this happen? I mean, you co-founded this company, how did you get to Affinio?
Tim: So, interestingly, my background is, I’m an engineer by trade, so was my co-founder. We happened to be serial entrepreneurs, we’ve been working together for over 12 years.
On our previous B2C company, we had a mobile company a few years back, and they had actually stemmed directly from an experiment that we wanted to play with, with that audience.
We happened to be looking at our Twitter audience for this previous stuff, and we had 6,000 followers and I just happened to ask Steven, my co-founder, I said, “Listen, instead of looking at who’s talking about our brand, what if we just look at what else our followers are following?”
In an afternoon experiment it just blew our minds. The amount of advertising and marketing intel buried inside the social graph that nobody seemed to be taking advantage of, was a ‘wow’ moment for us.
We sort of looked at the opportunity, couldn’t see an end to the potential of these types of insights, saw it as a massive opportunity and jumped all over it. And so, here we are three years later, and really excited to be able to show off the product that we built over that amount of time.
Jay: Yeah, it’s pretty slick. We love using it, that’s for sure. Tim, what’s a marketing marvel for you? Obviously you love your product. I love it, too, I think Marketing Marvels viewers are going to love it. But what’s a marketing marvel for you? You think, you know what, that’s pretty amazing stuff.
Tim: You know what I think is most intriguing to me is the emergence and growth of the DNP networks. There’s a number of them, I mean, I look at things like everything from Oracle where BlueKai is now pairing up with AddThis, which is sort of strengthening the value of that framework.
Crocs, we’re big fans of as well, and looking at the exciting things that they’re doing. I think the movement in the DNP space, I think, is extremely exciting. They’re amassing, obviously, large amounts of data against which these insights are going to become more and more tangible, and more marketable, readily available for many of the enterprises that we’re working with.
From our perspective, I keep my fingers on that pulse directly. I think it’s an exciting space, and the growth of big data is actually starting to get, you’re starting to see the power of it in the context of these that are continuing to grow rapidly.
Jay: Yeah, because if I can use the same kind of Affinio filtering and clustering and discovery technology against my first party data as opposed to only through my social graph, then all of a sudden I’m like, “Oh, now I can run Affinio against my database.” That’s pretty interesting and it opens up a whole new set of opportunities.
Tim: That’s exactly where we’re heading with the product. So we’ve got a number of thoughts and experiments that we’re working on with some of our biggest clients right now, heavily slanted toward first priority integrations.
Leveraging exactly what you’re referring to, Jay, is the foundation of our core intellectual properties around being able to analyze massive graph data sets. When you think beyond social and a social graph, to everything from content graphs, literally what people consume at scale, to transactional graphs, what people are buying at scale.
Everything can be analyzed in a very similar format and, when you can identify and segment at that scale, those insights become very tangible, become very visual, and start to make for really compelling and interesting value within these organizations, and how they action them. When we’re able to also overlay things like social intelligence on top of those segments, it gets really exciting fast.
Jay: Yeah, the future is here right now. It is going to be amazing, you guys are right on top of it. Thanks so much for your partnership, we love working with Affinio, great job as always.
Remember folks, go to product.affinio.com/marketingmarvels to get your own taste of the power of this kind of network analysis and segment identification. Tim, thanks so much to you and the whole Affinio team for continuing to iterate and put out a fantastic platform.
Thanks to all of you for watching this episode of Marketing Marvels. As you know, we do Marketing Marvels episodes every three weeks or so. To make sure you get all of them, subscribe to the channel. Go to bit.ly/marketingmarvels, that’s bit.ly/marketingmarvels, to make sure that you do not miss an episode.
Marketing Marvels is, of course, a production of Convince & Convert Media. We have a number of other shows that would appeal to different segments of my audience: Social Pros for social media marketers, Content Pros for content marketing marketers, Influence Pros for people interested in influence marketing, the Convince & Convert podcast, The Business of Story, and many more. Go to convinceandconvert.com/podcasts for the entire lineup, or find them on iTunes.
Until next time, I am Jay Baer from Convince & Convert, and this has been Marketing Marvels.