The Power of Industry Data x Marketing Analytics, with Charlie Grinnell | Sponsored by SearchMaster

Speaker A: Hello and welcome to the Marketing Times analytics podcast. I'm your host, Alex Safranis, and today we're on with Charlie Grinnell. Charlie, would you like to introduce yourself?

Speaker B: Yeah. Thanks very much for having me. Charlie Grinnell. I'm the co founder and CEO of RightMetric. We're a marketing outside firm and so we help consumer brands harness all of the external data around them, primarily digital data. So think things like deep audience research, competitive intel, content insights, cultural trends, et cetera. I got my start in marketing through video production of all places. I fell into marketing ass backwards. And I worked in house at some big consumer brands like arteryx, Red Bull and Aritzia. And I co founded the company that I run now about eight years ago.

Speaker A: Awesome. So tell us about the value of understanding a, uh, company's outsites. I like that term.

Speaker B: Yeah, we had this, my co founder and I, when we first started the company. Both of us worked either on the agency side or in house at big brands, and we always had a really good understanding of things that were happening in our own ecosystem. Right. So when I was at Red Bull or when I was at Aritzia, I knew who was coming to our site, I knew who was subscribed to our email newsletter, who followed us on social, etc. Etc. And so the kind of inkling for this external viewpoint actually came from a very uncomfortable meeting that I took when I was working and leading social at Aritzia. So we had every week we'd have to go in front of the CEO and talk about how are things going. And so at the end of each month we were giving a monthly update. And so I came in and all of our metrics were awesome, our views and engagement were growing, our traffic was growing. And I come in one morning and I go, yeah, here's the updates for the last month. And I think we were up like 20%. And uh, the CEO, uh, I'll never forget it, he said, how much did the category grow? And I said, I don't know, I don't have access to that data. And he goes, because you're saying we grew 20%. If the category grew 80%, I should fire you, but if the category grew 5%, I should give you a raise. And while that was a very uncomfortable conversation, he was right. And so that kind of got my mind going, being like, hey, there's probably these data sets out there that if I got my hands on them, that would give us a more complete picture of what's going on and how well we're doing and primarily at that time, it was through the lens of competitive benchmarking. But then as I started to get my hands on those data sets, I started to realize, oh, wow, we can learn about audience behavior here, we can learn about content learnings here. We can apply this to targeting. Like, there's so many different areas in marketing that we can apply that to. So, yeah, I had this aha, uh, moment where I was like, holy moly. Most of us are just so focused on looking internally that there's all this stuff happening externally. And previously the data sets either hadn't existed or they've been really fragmented, or it's been really difficult to pull all that into one place and figure out what to focus on and what not to focus on. So that's how fell, uh, into it. And I think what's really exciting with what we're seeing in the AI space now is it's getting easier and easier to count and pull meaning from things that were previously quite, quite difficult to count and pull meaning from.

Speaker A: Yeah, that makes a lot of sense. I'm curious, how does the data capture work? How much of it is modeled? What are the data sources?

Speaker B: Yeah, so we're not private about where our data sources are from. We work with, I think, over 30 different vendors now. Some of them, you've heard of similar web pathmatics, SparkToro, SEMrush, Tubular Labs. Like, where we've built, our niche is we go out and we buy access to all those data sets, we pull them all into one place and then that's where we have our methodologies and our processes of, like, how do we clean, organize, visualize and start to generate insight from there. Now each one of those tool providers has their own way of capturing things, right? Some of them are scraping, some of them are API, some of them are panels, like you name it. And I think that for from our perspective, how we think about that is no data set is going to be perfect and even brands own internal data. Right. When I was logging into our Google Analytics of our own website at Aritzia, yeah, I felt good because, yeah, this is how much, uh, people were coming to our web. This is how many people were coming to our website. But no data set is really perfect. And yeah, from our perspective, what we wanted to do was, hey, can we get our hands on, uh, a breadth of all these different data sets with the understanding that it's not going to be x equals 10, it's going to be directional. And some of the discrepancies in these data sets are anywhere from 5 to 10, 20%, depending on the source. But that direction can be really good context because that context can go, are we talking about us versus our competitive set? Are we talking about in the thousands, hundreds of thousands, millions, dozens of millions, et cetera? And so, yeah, there's a lot of different ways that data can be pulled in. I think what we're trying to do is how can we get as many of the right data sets pulled into one place and then how can we actually look at them through the same lens and normalize them and make sure that they are. You're not just focusing too much on one area and missing out another.

Speaker A: That makes sense. What type of insights do you find across your clients is the most valuable to them? So out of. We could look at traditional SEO traffic metrics and keywords. I know there's AI traffic.

Speaker B: Yeah.

Speaker A: Uh, what are people gravitating towards these days?

Speaker B: Yeah. So I think there's no shortage of questions. And that's a lot of customers come to us with different questions. That's where it all starts. Hey, we need to understand XYZ audience or hey, our sales are down and we need to figure out why. They might have a hypothesis. They might be trying to understand an audience, they might be launching in a new market, they might be launching a new product, they might be losing market share, like, you name it. That's usually where it starts. Some of the stuff that we're getting a ton of requests for is content analysis and, and specifically video content analysis. And when we think about the vast behavior of people on the Internet, like Internet equals video. Like we all just hoover up short form, specifically short form video and longer form video, but short form video. And historically, when we think about marketing budgets, content as a line item, specifically video content is a big number. But we've been like, ah, ah, give that to the video producers and off we go. Hopefully they produce something that like looks on brand. And I'm using air quotes here because I'm like, I feel like that's a bit of a crutch. Just give it to the creatives, they'll deal with it. And like, I've, uh, ticked the box and off we go. What we're able to do now is we're able to analyze not only the performance of our own content, but the performance of content in a category and then isolate what are the things, creatively speaking, on screen, that are linked to performance metrics. So I'll give an example. We worked with a pet food company about a year ago and they came to us and said, hey, we need to reach cat owners in the uk, in England. So we were like, okay. And they're like, we want to get an understanding, uh, of what do cat owners in the UK spend time watching. Because we want to figure out what do we need to create to show up there and earn their attention and provide something of value in a content format. So we went away and we go out and we throw a big lasso around all, all of the social platforms and all of the content with cats. So we do a bit of keyword research to pull in a big boolean string with cat related content and then we also seed it with accounts. So we go, okay, can we pull in a whole bunch of competitors? Can we pull in creators who focus on all of this, etc. Etc. Cool. So we land all of that stuff in one place. Then what we're able to do is we can sort it by performance. So we can sort it by views, engagements, watch time, et cetera. Where we've taken it a step further is once we've sorted it by performance, we actually start to watch those videos. Now, five years ago we watched those manually and we filled out a rubric, which our customers told us was insane at the time, but the output is so valuable and I'll get there in a second. So we watched these videos manually and we'd fill out a rubric and that rubric would be, is this video? How long is it? 10 to 15 seconds? 15 to 30 seconds? 30, 45 plus and so on. Is the video live action or animation? Does the video have subtitles? Yes or no? Does it have music? Yes or no? What's the first frame on screen? Is it a close up of a product? Is it someone's face? Is it a wide establishing shot? How many cuts between shots? In the first three seconds? In the first six seconds. And so getting really into these kind of like qualitative type creative decisions and mapping those things out against the performance data, because then what you can go is, oh, wow, this is where the audience started to drop off what was on screen. Okay, don't do that. Let's put that in a creative brief. So when we think about content as a line item, so many brands are spending so much money on producing content and previously there has been no way to figure out, like, how can we make our briefs more effective and how can we give our brand a chance to produce something that is going to earn someone's attention and ultimately drive performance? So that is the number one thing that most Companies coming to us nowadays. And I suspect it's just because, again, the amount of money being hammered into content is huge.

Speaker A: Yeah. That is really fascinating and reminds me of how Mr. Beast worked on his videos. It's almost engagement hacking, in a sense. Yep, yep.

Speaker B: Um, it's so funny you bring that up. You're bang on. I think about. We work with big financial institutions, big CPG companies, and they talk about content and they'll say, we want to be the Red Bull of whatever. We want to be the Mr. Beast of whatever. I love the ambition. I love it. Mr. Beast has a thumbnail team. Not a thumbnail person, a team. And all they do is thumbnails. And so like that, I think, is like the level of detail that goes into reverse engineering what earns attention. And so it's so competitive today.

Speaker A: Right.

Speaker B: Like we think about Net, Netflix has, is releasing crazy amounts of content every day. Everyone now is a creator. We've seen these AI video tools like Sora start to pop up where people can prompt and create these insanely entertaining videos. There's no shortage of supply and the demand has plateaued. And yeah. Makes it so that to stand out, what are you willing to do that others aren't willing to do? And so in Mr. Beast's case, I'm willing to have a thumbnail team because he's realized thumbnails matter. And then he's probably also realized the way that we edit the videos. What's the first 3 second hook? How are we continuing to re hook people along? Because once people start watching something, they're just waiting for an excuse to pick up their phone or swipe away or go somewhere else. And so this kind of like deep psychological thinking and aligning that with what are we actually producing is the way that it's going. Whereas it used to be, hey, you're creative, go make something creative. Now it's no, uh, we have to engineer this and be very strategic and specific about what we're creating so we can continue to earn someone's attention as they're watching the video.

Speaker A: It's the cocomelonification of content.

Speaker B: Exactly. And, uh, Cocomelon. We've done a whole bunch of research actually into Cocomelon. We worked with a company that was looking to get into that space. And yeah, you're bang on. Cocomelon are like the original gangsters of how can they engineer that attention?

Speaker A: Yeah. So what have you learned? What are some things that the audience can take away for their content?

Speaker B: So, number one, I always joke, like, I run a research company. And I have the luxury of sitting on all of these data sets and have a team of analysts and strategists that are way smarter than I am, pouring over all these different industries. All of my stereotypes and biases that I would have to an industry are completely wrong. So I always say, I don't know shit. That's what I've learned is you might have an assumption of, oh, yeah, this checks out and then you actually get into the data. And the data that we use is all behavioral, right? So none of it is, oh, we asked a thousand people or whatever. It's literally, this is where people are spending their time. And so it's like the ultimate arbitrator of truth, so to speak. And yeah, number one, I've learned that if you have a, if you have a gut instinct of something, even if you tend to be right, you'll still learn a lot by going through the process of looking at this stuff. So that's like, number one. Number two, I think this idea of hooks, we, like, I feel like the marketing world has just started to go, oh, yeah, we have a written hook. What's the caption? Written hook. But there's also visual hooks. Right. And so what are the first, what's, uh, the first second on the screen? What's the first three seconds? What's the first six seconds? 10 seconds. And this idea of rehooking people, I think that's something that, that most marketers and most creatives specifically don't really think about. They're like, oh, yeah, we roughly storyboard something out like this, blah, blah, blah, blah, blah. But they're not actually thinking, is this actually going to retain someone all the way through? And again, it sounds simple when I explain it like that, but again, it is. It can be a decent amount of work to do that. So I think this idea of hooking and rehooking is something that I've learned and it's across industries and it looks different in industries, right? Like hooking and rehooking someone in the pet food space is very different than the financial services space, which is very different than in the sports space. But it's that same principle and it's just how is it applied in a nuanced way to different industries.

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Speaker B: Absolutely. So something that I've been I feel like yelling about a lot on to our customers as well as more broadly with my content that I produce on LinkedIn for marketers is really around. I think many marketing teams have deprioritized organic social media and I think that's a huge mistake. And I think it's been deprioritized because we've gotten addicted to the crack that is performance marketing where it's like I can put a dollar in and make four. Maybe that was optimistic in the old days where you could put a dollar in and earn four. Maybe it's a little bit less than that now. We had this kind of illusion of control with performance marketing and I'm actually a big believer in that. You actually need to over invest in Organic Social with The assumption that 98% of your content that you put out on Organic Social is going to perform like shit. But the point is you should be flooding the zone organically. Establish what a baseline performance, what baseline performance looks like, whether it's views, engagement like whatever you're trying to do and anything that over performs that should automatically get ad dollars put behind it. And after it has ad dollars put behind it, if it continues to go, you should double down and take that concept and scale it across your other channels. So let's say I make a video that starts to pop off organically. Cool. I put some ad dollars behind it and then it starts to really perform well as an ad and it's reaching and finding a larger audience. I should then go, huh huh? Could I do a dedicated email send to my audience referencing this video or taking this video and turning it into some sort of interesting email to read? Could I take this and put this onto my.com as a long form blog post? Could I take this and Continue the lifespan of it. But the problem is most organizations think of organic social as like later on. Oh yeah. By the way, put that on social and you don't get any reach or anything. So you don't really matter. What really matters here is like the ad dollars where we're spending and it's no, that actually needs to be at the front. That needs to be your petri dish and your R and D lab because that is the closest signal that you can get in real time. And then anything that pops there, that's where you start to pour gas. And so it's insane to me that big companies spend tons of money producing creative and then pumping ad dollars into it without actually posting it organically first. It's insane.

Speaker A: That makes a lot of sense and I think it is that that habit of wanting to just put it out basically get that return and that control over the income or the revenue stream.

Speaker B: Yeah.

Speaker A: And that like you said there's been a sort of inflation and possibly like a shrinkflation effect on advertising.

Speaker B: Totally.

Speaker A: Where businesses, a lot of different ad ah platforms have started to expand their inventory. So they now have I call it like the armpit of the Internet placements. And they can drum up impressions.

Speaker B: Yep.

Speaker A: But it doesn't materialize and they sell it for the same price as their prime time, what you used to consider their only placement type. And oftentimes they hide it and so you don't even realize, oh, that's what the ad network means or that's what the extended network is. That's not good. And but by the time you figured it out, you spent a year paying a premium price for a watered down product and or service. And that's what I think you're describing in the sense that we no longer see that same roi. It takes a lot more skill to get to that level these days. Not to mention all of this new attribution methodology which is creative to put it nicely. And that way you can really make anything look like a 3x return. Uh, even if it does overlap with other platforms and stuff. So I love the idea of organic being the petri dish, the R and D lab.

Speaker B: Yep.

Speaker A: So on that thread would you look at, and you mentioned this earlier, would you look at other competitors and see their what not only what pops for your business because maybe you don't have a lot of really good content. Would you look at competitors and mirror what works well for them? How close would you get?

Speaker B: Yeah, really good question. So yes I would look at competitors but I would look at Them in two ways. I would look at competitors, and I would also look at creators. And I'll get into the creators one in a sec, but let's talk about competitors first. So when I say the word competitor, there are direct competitors and indirect competitors. So when I was at Aritzia, Aritzia is a women's fashion company. It's like Zara, right? So a direct competitor of Aritzia would be like a Zara, a J. Crew, a Lululemon. They. They sell a very similar product or service. Cool. That's a direct competitor. So you absolutely should be looking at them. So when we talk about indirect competitors, I would look at a skincare company, l', Oreal Glossier, Aesop. They don't sell the same product or service, but they sell to the same person who buys that product or service. So you should look at direct competitors and indirect competitors. That's number one. And then we have this framework that we use called mma Monitor, Measure, Adapt. So when I said you should look at competitors, yes, you should. But this is, I think where most marketers fail is they don't measure. So they just look at it and they go, oh, competitor X is doing this. Competitor Y is doing this. Cool. But they don't anchor that in performance. So when they look at that stuff, they're not going, oh, Zara did this. Is that performing well or not? I don't know. They're just usually making it, oh, uh, I like that creative, or I don't like that creative. That's what they're doing. Whereas now, with all the data sets available, you can actually see, is that post overperforming or underperforming for them? And is it overperforming or underperforming for the category? So now you have it, like, anchored in context, and then you can actually have a conversation of, do I like the creative or not? Is this on brand, et cetera, et cetera. So that is, like, where I would start is if you're going to look at competitors, look at it through that lens. So monitor, measure, and then adapt. Have the courage to be able to test something. Now, I'm not saying go out and copy your competitor, but if they have done something that shows performance that you also think you can credibly show up in and put your own brand spin on it without diluting the meaning of it and getting super far away from that concept, test it. Like, adapt and test it. So that's what I would say about competitors. Now, why I brought up creators, I think this is really important. For content folks to understand, for creators, their content is their product, their content is their product. So they are just better at content than we are, full stop. And I don't mean that. So I think that so many brands can actually learn from creators now. I can hear some brand people being like, they can do things that we can't do as a brand. You're absolutely right. They can do things that you can't do as a brand. However, you can, uh, they can also teach you things that you're probably not doing as a brand. Whether that's strategically, tactically, how things are edited, how things are shot, a new style or format. Like, these are all things that you could absolutely do. And you could either work with that creator or you could be like, hey, I'm going to steal that theme, but we're going to do it with one of our internal ambassadors or one of our employees or one of our existing partners, et cetera. So I just go. Creators are such a good leading example of what good looks like, because at the end of the day, that's their product. Their product is content. That's what they sell. That's how they make money. So they spend more time obsessing over it than you do. That's how I would look externally from, uh, a content perspective. And all of that stuff flows into that petri dish of Organic Social, which then is ultimately going to flow into your advertising.

Speaker A: Yeah, that makes a lot of sense. What do you think about overwhelming Organic Social? Is there a risk of that?

Speaker B: I would say no. And I would want a clarification on what you mean by overwhelming. So I think this kind of whole, oh, I can't possibly increase my post cadence. What if my audience gets pissed off? 2% of them. What's organic reach on Facebook now? 2%. Probably less across the platforms. Here's the thing, 99% of people aren't going to see your stuff. So that's number one, so you don't really have anything to lose there. And number two, the stuff that they do see, they're seeing it. Welcome to marketing. That's good. And so I think about, like, there is no downside to flooding the zone, because number either, worst case, they don't see it and you both continue on. Best case, they do see it. And if it starts to perform well, cool. You can take that and scale and go from there from an advertising perspective. And so, yeah, I think that it's more. So do you have the courage to, one, invest in it properly and not like half ass it and two, stomach that 98% of it isn't going to perform very well. And that's okay. Like we, we would do this at Aritzia, at ah, like we were posting when I left red bull in 2017. So when I left, uh, we had 400 social media accounts, 200 million followers, and a thousand people around the world at Red Bull who could publish on a social account. And we were publishing 650 times a day. That's in 2017 when I left. Now it's much bigger than that. The kind of punchline there though is that you think those 650 posts a day that everyone was a banger. Absolutely no way. Sometimes there were days where I was like, yeah, uh, yeah, that was. Nothing really popped off today. Sometimes there was a week where like nothing really popped off. And that was okay because the organization was willing to do that because for every day or week where something doesn't pop off, eventually something goes huge and it makes it worth it. And I think about almost like VC investing, right? Oh, we're going to make a hundred investments, 99% of them are going to go nowhere, but one of them is going to a hundred x and it's going to pay for the all of that. I think about that from a content perspective. It's a good mental model to apply.

Speaker A: Yeah.

Speaker B: But it does take some courage from an ego perspective to be like, hey, the stuff that I'm producing might not hit. And that's okay.

Speaker A: Yeah. So when you talk about the category that companies in and you're looking at other companies in that category, is that like an industry? And like what numbers are you looking at? Are you looking at average percentage engagement for the category, total followers or average followers per company in the category? What, what is the actual number that you're looking at for a category?

Speaker B: Yeah, so I want to start like high level of what is a category first and then we can get into like the specific metrics. So oftentimes when a customer comes to us, they go, we're in this category. Okay, great. Who do you compete with? We ask them that and they go, we compete with A, B, C, D, E, F, G. Okay, cool. Anybody else? They go, nope, that's basically it. And then I bring up that concept that I talked about earlier about indirect competitors. And I'm like, don't you actually compete with them too? And they go, huh, I didn't really think about that. And I was like, you do, you might not be competing about are you going to sell them this shirt or not? But you're ultimately competing for their time. And if people only have two eyes, one brain, and 24 hours, like there is a supply or, uh, a demand constraint there. And oftentimes we start with that and we work with customers to establish what are the kind of guardrails. Because some people say, no, I only want this. Fine. But hey, explaining that you're actually competing with more than what you think you're competing with, that'd be number one. Number two, then we would actually go to business question, where we take their business question and that actually determines the different metrics that we're going to look at. So a lot of the stuff you mentioned, followers, engagement, video views, traffic, app usage, returning visitors, like all we can see all of that. But the purpose of going through this process and mapping things back to their business question is you have to have, no pun intended, the right metric mapped back to the business question. So they might come to, uh, us about a trying to reach a new audience. So in that case, what I would actually want to understand is where does that audience spend time from a platform perspective? Okay, then on those platforms, who does that audience follow? Who has influence over that audience? Follower overlaps, video views, engagements, behaviors, affinities, all of that sort of stuff. That's where I'd want to start. So that would be like an audience example, right? Because we're measuring attention. Then I would want to get an understanding of, uh, okay, let's say, hey, so we know how this audience behaves. How do they behave in relation to our category? That's where I would start to layer in that competitive set that we have established earlier. And I'd want to go, okay, based on this audience, how many of these competitors are effectively reaching this audience? And we're able to look at those competitors and their audiences, right? So I'd look at competitor 1, 2, 3, 4, 5, 6. How much of this audience do they have in their own analytics? So I'd start to look there and then I'd want to go, okay, are they effectively reaching these people? So I'd want to go, how much are they spending across all the different platforms? How much traffic is that generating for them? How many impressions or video views is that generating for them? How is this growing? Is this growing or shrinking over time? Is there cyclical nature, et cetera, et cetera? And as you start to do all of this, you start to just build out a map to go, okay, this is how this person behaves. This is the category who's trying to reach that group of people. This is what they're doing today. This is how well or how not what's working? And then from there you can start to go, okay, are there white spaces here? Are there overlaps? There might be things where it's like, hey, yeah, uh, this works really well but it's super competitive. All the, everyone's there and they're all spending a ton there and it's like a full on dog fight. That's okay. There might also be like, hey, none of the category is investing in this one area, but this one brand is actually like over here and it seems to be doing really well for them. So again by looking at all this stuff, you can start to map out and see the whole board, so to speak. And then you can use that to start going, okay, where are we trying to go as a business and how can we start to chart a path ahead?

Speaker A: Yeah, yeah, okay, that makes a lot of sense. You're sitting on a lot of data. I think it's really interesting, let's say that like of the data providers, I'm just curious about your perspective because you're very familiar, you must be very familiar with them. What are some of the best data provider. What is like the state of marketing data from your perspective? Is the industry in a tough place? I think the industry's in a tough place because I think there's a little bit of a, a reputation challenge with data. I think there's, when you mix and model data with real data and you have data from all these different sources, I think that it's start, I think it's gotten to the point where people don't know how much is snake oil, how much is real. What's your take on that?

Speaker B: I agree with your assessment. I'll start there. I agree with the assessment. I think that there's a couple things. Number one, we as marketers over the last 10 to 15 years have been conditioned by the digital platforms that x equals 10. I think the answer like some, like everything in life is it's complicated and it depends. And so I think Meta and Google have basically made it so that no, you put a dollar in and here's your ROI and like off you go and like X equals 10. Now that made sense at a point in time. But as there's been this massive diversification and fragmentation of platforms to your earlier point, all of those platforms are trying to get it on the credit because what do they want? They want you to spend more money with them. So I think that I, uh, agree with you that it's like hard and then on top of that we have the whole like start stop of cookies going away. Not going away, just kidding. They're going away. Just kidding. They're staying actually. We don't know. So you have that whole ping pong match and then you're also having new platforms come out of nowhere. And so I think about five years ago everyone was like, what's a TikTok? And then four years ago was like, have you heard about this TikTok? And then three years ago it was like, should we be on TikTok? And then two years ago it was like we're on TikTok help. And now this year it's you're not on TikTok. What? So we've had these kind of like progression through of all these new channels. So how I bring that all back to the different data providers is I think we need to think less x equals 10 and more directional. We're not going to have this x equals 10 anymore. And I think if people who are running attribution companies, they're probably going to be pissed off hearing me say that. Prove it to me. But you can't because it is too complex. So I just think that doesn't mean that there's not value in this data. It means we have to loosen our grip a little bit. And instead of being like x equals 10.2, it's like, hey, x could be between 9 to 11 or between 8 to 12. That's still directionally better than not even being able to look at it. So I think that it's like there's a mindset shift that needs to happen from us as marketers because we've been trained to have all of this stuff at our fingertips in this way and our internal bias is actually looking for just give me the exact answer. That's why ChatGPT and all these AI platforms blew up is because people can type things in and they get an exact answer back. But to your point, is that answer right or not? Don't know. Same type of thing. Like we as humans have this bias. So that's that from a data provider perspective, all the data providers that we work with, whether it's SimilarWeb, Tubular Labs, SparkToro, SEMrush, all of them are very transparent about how they collect things and they all collect things using a variety of different methods. And so for me, I actually like that I don't want to a provider to only collect a piece of information in one way because I think that there's potential for risk and bias and all of those different things. So I actually think the strength is in that kind of like diversification, like an investment portfolio, both from the data sources that we buy, but also from the way that providers are buying data. So for example, similar web, uh, they're getting web traffic data and app usage data. The way that they get that data is so broad. They're buying anonymized data from Internet service providers, they're scraping things, they're using panels, they're building apps and websites that they own behind the scenes to be able to get. They have data sharing agreements. So is it perfect? No, but they're diversified. And so I think that again, we as marketers need to stop thinking so literally and just start loosening our grip a little bit and be like, hey, this is actually going to be a signal, not the actual answer.

Speaker A: I like that. Uh, have you heard of data for SEO?

Speaker B: No.

Speaker A: So it is primarily, it's an API based data provider and I use it for a tool that I created called Searchmaster, which does keyword traffic analysis as well as AI traffic analysis. And I picked it because first of all, it was pretty, uh, it's like a pay as you go situation. So I could build it on the back end and not have to over invest in licenses and so forth. It scales as the user scale. So it made a lot of sense for backend for the tool.

Speaker B: Super cool.

Speaker A: Yeah, I think it's great. They have a crazy amount of APIs.

Speaker B: I'm cruising their site now and they

Speaker A: have AI and LLM, um, APIs as well for like meta, I guess, like just broader stats around AI usage. I would say it's a little bit nation, but it's, it's definitely, they'll definitely keep adding to it. They seem to be pretty on the ball in terms of capturing new data.

Speaker B: Super cool. And this is what I think though is so exciting. There's no shortage of. Okay, as the world gets more and more digital, like, I'm a big believer in that. The Internet is eating the world and I think you probably are too. The Internet is eating the world. More and more things are happening on digital devices, which means there are going to be more and more data points farted out the back end that we can count and pull meaning from. That's exciting. But, uh, there also is a need for solutions like this and others. There's going to be a whole explosion of companies out there that are helping people harness and pull meaning and organize and whatever. And one of my favorite guys to follow is a guy named Anand Sanwal. I don't know if you are familiar with Anand. He has a. He started a company called CB Insights. And CB Insights is a big, like, they started in finance and they do a whole bunch of different types of research. Anyway, he's brilliant and he's been on a few other podcasts, M M, the My First Million podcast. And he talked about these unsexy data businesses that are popping up because again, people are doing all this behavior and it's, oh, if someone can build something to count and pull meaning about that stuff in the back end, there's a business there. And so I just think about whether it's this thing you just showed me, whether it's something that's not even online yet. I think we're about to see a whole bunch of different things pop up. That again, three, four, five years ago would have been like, oh, that sounds cool, but there's no way that's possible. I think that's about to change.

Speaker A: Oh, uh, okay, final question on, uh, that note. What's your prediction? What is one type of data that we don't have yet that you expect will come out in the next five, ten years?

Speaker B: Oh, one type of data set that will come out in five to 10 years. That is a really good question. I think what we're going to start to see is, I think we're going to start to see less data needed to be able to create really awesome personalized experiences. So what I mean by that is think about the TikTok algorithm. What's amazing about that algorithm is you almost have to give zero input and the personalized experience it creates is wild. Now if we think about, compare that to Facebook or Instagram, right? So when, you know, I signed up For Facebook in 2008, I was 18 years old, so I was in, what, first year university at that point. So I signed up maybe 2007. And so I signed up, I had to create a profile, right? Then I had to go in and be like, what do I like? And then I have to be like, oh, Alex is my friend, hit connect. And then I have to hit connect with all these people. And then from there, cool. They didn't even have newsfeed at that time. But then once they have newsfeed, cool. Now I see this algorithmic feed of content. I had to actually input all of this data for this experience to be created. With TikTok, you literally create an account name, phone number, email, that's it. And you're dropped into a thing and within like a swipe or two you're seeing something that you didn't even realize that you would like and then it pulls you down a rabbit hole. So I think this idea of it used to be like, how can we get our hands on as much data as possible? I still think that's very valuable behind the scenes, but how does that actually create the experience for people where it's completely seamless and the input is so small? So I think from a data perspective, what we're going to see is even more data sets going in behind the scenes, but nobody, uh, really even noticing. Like an iPhone, right? Like an iPhone is super complex. But then you're like, oh, I'm just tapping this kind of glass thing and it just works. What's happening in this device is insanely complex. I think we're going to see that from a data perspective, translating into personalization.

Speaker A: Yeah, I love that. And uh, along that thread I also have a prediction which is that we will have more biofeedback data to work with to understand what resonates with people. I got the new AirPods. There's a heartbeat monitor in my ear. And how long is it before that can be used to determine if I'm excited by a, uh, particular piece of content or bored by it or, uh, calmed by it? And how do we use that to, to make content more engaging? I don't necessarily think it's good. I'm just saying it's a piece of data that's out there that's not really being used.

Speaker B: Absolutely. And I agree. I think, yeah. Does your heart rate increase when you're watching something? I also wouldn't be surprised if, if you think about, I had a friend show me a photo recently where it's like he, he was on a subway car and there's 30 people all standing there looking at their phones. What's on that phone is a front facing camera. And is there going to be a, is there going to be a point in time where people go, hey, you know what? I'm actually okay for my phone to look at me if it's creating a better experience. And I think this brings together that really interesting conversation of. It's that balance between privacy and convenience. And we all talk a big game about privacy, but we all choose convenience by carrying these devices around and spending time there. Um, and yeah, whether it's heart rate. You're talking to a guy who wears an Apple watch, who sleeps with an aura ring. I have an eight sleep mattress topper on my bed. Like I'm such a dork when it comes to this kind of technology, but because I do value the data, both from a health perspective, but to your point, I actually do. For me, I do think it's going to create, hopefully better net new experiences. Whether that's helping me with my health, whether that's helping me enjoy certain things, learn certain things, connect with people on a deeper level, et cetera, et cetera.

Speaker A: Yeah. My opinion is if you work in marketing, you have no right to have all the privacy blockers and all of that, because that's the only reason you have a job, is because that data is being shared.

Speaker B: Bingo. Bingo.

Speaker A: Awesome. Um, thank you so much, Charlie. This has been a great conversation. I really appreciate your time and I know the audience learned a lot and so did I. So thanks again.

Speaker B: Yeah, thanks very much for having me. Hopefully we'll do another episode soon.

Speaker A: I'd love to. Awesome. Um, and thanks, everyone, for listening. We'll talk to you soon.