web analytics vs media attribution: debating the role of web analytics tools

In this week’s episode of Digital Banter, Scott Konopasek and Brandon Beck from Mint Measure engage in a lively debate about the role of web analytics in media attribution. Join us as we challenge the traditional approach of using web analytics tools for measuring website performance and media impact.

In this episode, we discuss how we have been brainwashed by Google Analytics to believe that this free tool is the single source of truth for all web analytics reporting and media attribution. We also dive deep into the future of attribution with detailed discussions around the future of 3rd party cookies, google antitrust lawsuits, and alternative analytics tools.


Podcast Transcript

From Dragon 360, this is Digital Banter, a podcast focused on modern marketing tactics and driving real business results. And now here are your hosts…

[00:00:14] James: Do you think that it is more difficult to bench press if you have longer arms? So do you think it’s easier for a person with shorter limbs to lift more weight than somebody with longer limbs?

[00:00:28] Brandon: So I don’t have to think about that. That’s just physics guys.

[00:00:31] James: Thank you.

[00:00:32] Andy: I agreed with that. No, wait, hang on. It wasn’t about, the bench press piece wasn’t squats where we were, we were arguing.

[00:00:38] James: I don’t know what you were, you said that squats were valid, but I, I don’t think that that’s true either cuz as Brandon said, it’s literally just physics.

[00:00:45] Scott: Well, it’s like, it’s the length of the bones, but it’s also the muscle mass ratio because

[00:00:52] James: Oh gosh. A muscle mass guy. Sorry

[00:00:56] Scott: because like if I have short bones but I have like bulging muscles, that’s gonna be different than if I have like long lanky arms and twig twig muscles.

[00:01:12] Brandon: Like, oh yeah, it’s a muscle to bone ratio. It’s a muscle to bone ratio. I would also say leverages matter because like muscle or fat, right? The total distance, if your chest is bigger, bar like lock out down to chest is shorter. Um, and same thing like you get some, like in the crook of your arm, you get some like extra spring back if you’ve got some more meat there.

[00:01:30] Scott: But like if, if all things are relatively equal, then it shouldn’t matter because the angles are the same. If, if the angle is going from all the way in to all the way extended, doesn’t matter if I have long arms or short arms, right? Like the angles are the same. So isn’t it not the same effort though?

[00:01:50] Brandon: Doing more like the physics, you’re doing more work. So like force times distance, you’re covering greater distance, so you have to do more work.

[00:01:59] Andy: Scott walks into the gym, like with that meme in the back of his mind, like all the symbols and everything else. Like, oh, this is how I’m gonna bench today.

[00:02:06] Scott: I do, dude, you have no idea. When I go to the gym, I’m like, ah, form. And I’m like, literally the fucking math meme. I’m like, hmm, where do I put my own little, yeah, fucking for real, for real.

[00:02:17] Brandon: Question I wanted to ask, cause I know James, you coach high school football. I want to know if you ever like have tried running 5 31 if you guys are, if you’ve even even heard of the program.

[00:02:25] James: 5 31. Um, I haven’t heard of the program, but no.

[00:02:32] Brandon: Okay, so it’s, yeah, it’s like a, it’s the program that I do, but it’s, uh, popular, like invented by a popular football, like high school football coach out in Ohio. So that’s, I was wondering if

[00:02:41] James: We do BFS bigger, faster, or stronger. We’ve actually, I don’t know, we do other stuff now, like we have an agility day, and so it’s a little bit more in depth than that.

[00:02:52] Andy: So, because we’re talking about math and agility and bench pressing, let’s talk about attribution because that’s the purpose of today’s podcast. Right? So joining us on today’s podcast, we have Brandon Beck and Scott from Mint Measure. Uh, Scott is the CEO and founder of Mint Measure, while Brandon is the co-founder and his secondary title is The Dumb Sales Guy. So he gets you on calls and tries to convince you to buy for Mint measure. Um, guys, thanks for joining us.

[00:03:20] Scott: Thanks for having us.

[00:03:21] Brandon: Thanks.

[00:03:22] James: For what’s worth, I think Brandon’s one of the better tech sales guys I’ve come across. If you ever talk to him, it’s worth it.

[00:03:29] Brandon: And I’m gonna toot toot for a moment. Brandon didn’t sell tech before 12 months ago, 13 months ago. And so, for someone who doesn’t know Ad Tech, who didn’t know Ad Tech, who hadn’t ever sold to ad agencies, he’s, he’s learned that an incredible amount.

[00:03:46] Andy: Now I’ve been on enough Mint Measure. I’ve listened to enough mint measure podcasts and live events with you guys to also know that Scott’s cat is probably the best salesperson out of everybody on the team. So when are you going to actually hire him full-time and make him the company mascot?

[00:04:02] Scott: She’s already right here wanting to get into my lap. This is Maggie short for magnet because all she wants is to be on my lap and to be touched and to have affection.

[00:04:13] Andy: So, so as we are about to talk about multi-touch and single touch in every other attributional touch, in a second, do you guys wanna do a quick kind of intro about mint measure and what you guys do, Scott?

[00:04:25] Scott: Yeah. Mint Measure, was the result of 10 years of ad agency frustrations as a media buyer getting so aggravated that I needed an analytics or I needed a this or that. And so just broadly, we are an attribution tool made by media buyers for media buyers to give the agency the insights that they need to be able to prove to their clients what’s working, ask for more budget and be able to improve results.

And there’s some unique things about how we do it, but at the end of the day, like most of the time ad agencies struggle to be able to look at all of their data to make sense of it and turn that into a compelling story for their client and so we’re helping to not only provide the data that they need, but help them to turn that into a story and know what to do about it. And there’s really the kind of like that gap of analytics knowledge at the agencies that we, try to fill.

[00:05:25] James: I mean, I can tell that story firsthand. I think one of the first things we’ll get into is, how we’ve been trained on the analytic side, right? So I know a big part of what Mint Measure does is talk about how the different channels work together and what all advertisers want agencies want, um, is they, everybody looks for this single source of truth when it comes to attribution and measurement. And I think we can all agree that doesn’t really exist because there’s biases built into every single attribution model that’s out there. The major flaw in where all of this comes from is our good old friend Google Analytics. So it’s funny, we just got off, internal training on Google Analytics, Google Analytics 4 specifically just kind of running through updates on that. And I think that, I’ll be honest, I think that Google Analytics four and its clunkiness and not greatness is gonna open up a lot of opportunities in the attribution space. So I mean, one of the major flaws of that is always based on like last click attribution so I think I’d like to, first get off your thoughts and hear your events around your experiences with Google Analytics last click attribution and um what you see the future looking like.

[00:06:49] Scott: Yeah, well, I’m sure that to this audience, I don’t need to, complain too much about the woes of last click and why that is just not enough. Why does everybody use Google Analytics? We know that the way that they report on stuff sucks. We know that it’s not the full picture. So why do we use it? It’s free, exactly. So Google has done like this really crazy thing where they have made the value of website analytics $0. Nobody values web analytics. And, if you’re not like a really high volume e-commerce or lead gen site, you’re not gonna invest in a tool like a HotJar or anything else to be able to see how people are engaging on your site. 90% of brands are on Google Analytics and they’re just going to be fine with whatever Google Analytics provides them. So I think that there’s two parts to this. First is that people are using a web analytics tool. To try to do paid media analytics. It is not the tool for that. I’m not gonna go to the mechanic and ask them to fix my knee, like my knee might operate the gas pedals. But like, it’s not the thing that that person’s gonna fix. And so Google Analytics shouldn’t be doing paid media analytics. I think like the first major problem is that like people look to GA to do this thing that it was never designed to do. We only have UTMs because Google bought urchin urchin tracking modules, what UTM stands for, they bought that company and we’re like, oh, well we’ll just slap it in here. Or we can read that into our tech. Right? So I think it’s just important for everybody to know that like Google Analytics has a job to do on your website. It has nothing really to do with your paid media. So I think like that’s like the big hurdle, right, is people have to kind of like take these two things apart. Cost of Google Analytics being free. So, do you guys remember the world of coffee before Starbucks?

[00:09:00] James: No. Didn’t exist that I’m aware of.

[00:09:03] Scott: Right. Like you could buy a cup of coffee on the street corner for 25 cents or 50 cents or really giant cup of coffee for a dollar, but nobody ever thought that you were gonna be able to charge more than 50 cents for a cup of bean water. Like people just, it’s coffee, beans and water. And then Starbucks came along and said, we’re actually gonna just judge up the experience, give you something different, and now we’re gonna charge three for $7 for a coffee. And what happened is the rest of the coffee category around it evolved. And so you have other, like more fancy drinks, but also the baseline cost of a cup of coffee is a lot higher now because it was reinvented. And so I think that web analytics and Google Analytics has to have its Starbucks moment. You know who, who’s gonna pay for web analytics? You talked to like your average brand. And even if you would be like, Hey, a hundred dollars a month for web analytics. No. Why? Why would I do that? Google Analytics is free.

So what’s gonna happen in my opinion, is someone’s gonna come along with a much better solution that has a better UX thing about all, like the pain and difficulty with GA4, right? It might be powerful if and when you get it all set up, but like who on God’s green Earth knows how to set it up properly? Right? There’s like really big consultancies that are forming just around the implementation of GA4. So I think that there’s this opportunity for some other web platform to come in and assert Google Analytics. And so we can talk about like, what that would kind of require, but I, we go down that rabbit hole. I wanna call that out as a rabbit hole.

[00:10:45] James: Like you guys talk a lot about, The value of using a web analytics platform. And that’s where I feel like people just haven’t made that connection yet. You know, a, a web, a, a media attribution tool, right? Call it three to 10% of your media spend. And it comes down [00:11:00] to Brandon, you point this out all the time. Like, if we can’t save you 10% using this tool, then like, what the heck are you using it for? So I think the other piece that I think is a struggle for a lot of people is the, the problem that you get with the introduction of all new tech is that people don’t know how to use it.

[00:11:20] Scott: Yeah. Yeah. And that’s always tough and so there’s a lot of hesitation when somebody is introducing a new data source. We’ve dropped a couple of like articles and like podcast episodes on this, but like if, if your client is using Google Analytics and let’s suppose that they have decided to introduce another set of data, whether it’s any sort of attribution tool, whether they’re testing a different web analytics tool, no one’s gonna be like, oh, throw out the GA data.

Like, let’s look at the new stuff. It’s gonna be, well, let’s look at these two side by side. Do these make sense? Are they close enough? Do we believe the new data? I think there’s a really big mistrust in the data. And we hear from people all the time, oh, I don’t think attribution data is trustworthy. I don’t know if I can like, really trust the answers that I’m getting. and so I think like those are, those are like two of the main problems around that.

[00:12:12] James: I think a great thing to call out there though is people right now are doing the side by side of GA and GA four. And quite frankly, like that data is, I mean, I’ve seen upwards to 30-40% different in scenarios.

[00:12:27] Scott: There’s better to do in this world. Who knows?

[00:12:29] Brandon: Shut up, use data driven attribution and just buy all of your media through Google.

[00:12:31] Scott: Yeah. Because the antitrust lawsuits definitely show that Google is above board and they don’t do anything shady and it’s definitely in your best interest Right.

[00:12:44] Scott: What was, uh, what is it like, don’t be evil or what was they have that like painted on their wall. Right? That was the early motto of Google.

[00:12:52] James: Yeah. And now they grade their own homework.

[00:12:55] Andy: You know, you know how I know you’ve been in tech for 10 years, Scott, is because you mentioned urchin and you could have [00:13:00] just kept going through all the legacy GA codes. Right? Urchin, GAT, GAC. Right. I remember those days. I remember just throw the Peppers Farm meme in front of me. I remember.

[00:13:13] Scott: Yeah. Peppers farm remembers. Exactly. So it’s easy to like kind of shit on Google Analytics and be like, it’s tough and it’s hard, but like I would challenge you guys. Can you guys name even just one web analytics alternative

[00:13:30] Andy: Mix panel, Adobe Analytics? There’s another one that I looked at that it was like European based, but it is very inaccurate. But yeah, you’re right. And I think you brought up a good point with like the Google analytics experience and it. Before GA four, it being somewhat seamless, intuitive, right. And if you compared that, go back, what, three, four years, James, maybe be too Adobe, clunky as hell. You couldn’t find anything. The customized setup, everything associated with that. Yeah, it was enterprise in, in nature, but like the, the experience itself was so difficult to actually navigate through. So I think you bring up a great point there, but yeah, who, who in today’s world is actually using anything, but yay. They exist, but they’re an afterthought.

[00:14:11] Scott: Yeah. So I think one of the, the things I always ask people is, okay, let’s suppose for a moment that you were to, to find something that wasn’t Google Analytics. What would that tool do for you? And are you buying a web analytics tool or are you buying a media analytics tool? And like, what’s the job to be done? What are the missing things that you would want that Google Analytics doesn’t currently deliver?

[00:14:37] James: Yeah, I mean that one to me is clear. That’s a media, on the media side, it’s the, the ability to measure all channels equally. Um, I mean, typically if you’re using Google Analytics, you’re also reliant on only the Facebook Pixel, only the LinkedIn pixel, only your programmatic pixel, like basically each channel and it’s silo. And then there’s always somebody on the backend saying in Google Analytics saying, Hey, how come our Facebook conversions don’t match Google Analytics? Or like, where’s the view through data? Or shit like that, right? And that’s the piece that is inherently missing in my mind.

[00:15:15] Andy: But I think also, we’re talking about two different stakeholders in a lot of organizations, right? We’re talking about paid media as far as channels that we’re talking about specifically. And then we’re talking about ownership of the analytics side. It does not typically lie with anybody that’s handling paid media, whether that’s agency or in-house web analytics handled by marketing ops CTO, CIO. Especially as you move up the ladder of organizational size, it becomes more bifurcated. And I think I don’t wanna go down this rabbit hole, but it’s a great example of people not talking to each other and understanding how one tool set impacts the other areas of responsibility.

[00:15:54] Brandon: I think a lot of this also stems from the fact that the intent of paid media analytics and attribution has always been around justification. And so tools are built to serve a CFO and a CTO and the non-marketing stakeholders in that equation where the marketing stakeholders in that equation would say, yep, James just said exactly what I want and the CFO would say, why the hell does any of that matter? I have all the data in GA.

[00:16:20] James: I hate the people who try to also measure, like UX stuff in GA. This is a pet peeve of mine for a long time though. Let’s tag each element on the entire website. You mentioned using HotJar before. Like if you wanna know how people are interacting with your page, why don’t you have a nice visual representation rather than tallying up events?

[00:16:40] Scott: Like there are tools designed for each of these jobs, but people have treated Google Analytics like a Swiss Army knife. Oh, it does my web analytics and my product analytics and my UX and my paid media and, and, and, and it just, it’s not made for that. And you’re trying to put 20 pounds of shit in a five pound bag and like it’s just not gonna work.

Yeah, I think like the, the biggest thing, if I were to just define a solution in a vacuum, I would want something that tells me all of my channels, how they’re working post view, not just post click. And I want to be able to have like drill downs into specific things, right? I wanna be able to see a media mix or how a channel is like driving net new results. I think just defined in a vacuum absence of, is it web or anything else, like this is, this is the solution, right? This is the thing that a paid media person would need to be able to really explain and justify now, like which pages have high dwell times and low bounce rates and stuff like that. That’s a job for Google Analytics. It’s perfectly great at that. But I think, Andy, you brought up a really good point. It’s something that we see all the time. The person who would make use and benefit from the analytics that we provide are not usually the same person who is making analytics choices. And so we oftentimes will start by talking to somebody who is like a media VP or a media director, and they’ll see what our tech does, the insights, blah, blah, blah and then they say, hey, I have to go pull in my analytics director or my VP over there. And it’s kind of like the secondary step. And then the question becomes, well, where does this get paid for? And so this is something that we used to be like, well, wherever you wanna pay for us, is it out of an analytics budget? Is it out of a media budget? And the thing that we ran into is, no clients at all have a budget for this. You say, hey, you should pay X extra dollars amount for analytics. They’re like, we don’t have an analytics budget. And so we’ve figured out that like the sneaky way for us is to say, well put us on a line item in your flow chart, and we’re gonna guarantee ROI. You’re expecting a three to one ROI out of each of these different things you’re spending on. Great put us on there. We will have to like align with that. And that like still doesn’t solve the like cross department, conversation. And, and to your point, the bigger the organization, the more difficult it is and the more stakeholders there are and the more clearly defined the use case has to be for why and how this is all gonna be beneficial.

[00:19:20] Andy: The funny thing is, is like it’s an operational expense and really it’s in the same realm of a Marketo, a Salesforce, Eloqua, HubSpot, etc., as far as concept is concerned. But you’re right, like to have it on a line item of a P+L , whether that’s departmental or or executive level, it’s like, well, why am I paying for this crap when I can get this other thing free? I mean, it’s an easy cut, just like a lot of other marketing expenses. So it’s a great point.

[00:19:44] Scott: Right. Why am I going to pay $3 for a couple bean water when 50 cents is all the market would bear? Right. So you know, the thing that’s really interesting though for us is that there’s so much skepticism, doubt, concern, worry that like, hey, this analytics just might not be useful, might not be insightful. And so once we like have a chance to work with people and like show them what is contained and like how it gets applied, what we found is actually most of those barriers go away. And the excitement and the like trust in the solution increases quite significantly. So I’d love to understand, like from your guys’ perspective, like how do you coach clients when you’re saying, hey, you should be transitioning over to this other thing, or hey, here’s this new data source. Like how do you get them to take that first leap and then how do you typically like continue to build that trust and confidence with them?

[00:20:41] James: Were you talking specifically about like attribution tech?

[00:20:44] Scott: Or anything in general? I guess like this, this doubt and proving it and building that trust like that happens across any of the disciplines that we do as agencies.

[00:20:55] James: Yeah. I mean, I think it comes from being honest on what the situation is, right? So I don’t, there’s the quote, like, 50% of your media spend is wasted, which half is it? I think as agencies, like it’s our job to figure that out and reduce which half that is. And if we’re able to admit the shortcomings in the process and then work toward, I mean, like anything a client wants to know, they, obviously everybody wants things to be all good all the time, but it’s about focusing on, what isn’t working and the three things that you’re doing about it right? So if you were to take an attribution tool into place, it’s like, okay, the problem that we’re having is like, we’re struggling to justify spend across these different channels because we’re not easily able to say what’s incremental, what combination of channels is driving results, and you’re gonna get a better ROI if we have this tool in place.

So that’s where it becomes part of the tool set to solve that specific problem. And I think that’s like anything, if you’re pitching new creative, new landing pages, [00:22:00] um most typical agency upsells are gonna come from trying to solve an existing problem.

[00:22:09] Scott: But also stems from a partnership in the relationship that you build and then identifying what those expected impacts are. So if we’re talking about a three to one ROI because of implementing a tool. Okay, so what does that flight plan look like and what’s that test and learn methodology? Round out to be, are we talking about a three, three month pilot where we’re gonna side-by-side the data? How are we gonna measure that data? How are we gonna compare it, and how are we gonna report out on those results? It’s mapping out those scenarios when we’re talking about either short-term investment or a long-term shift that has to come out of that short-term investment. I mean, it’s all about, and then also because of our client base being a very strong mix of B2B clients, there’s an element of quantitative versus qualitative data, right? We are never gonna have a single source of truth that has, well, number one, we’re never gonna have a single source of truth. We’ve established that on multiple podcasts now but we’re not gonna have any source of truth that’s gonna combine those into one single view. It just doesn’t exist because it’s two very disparate data sources and data context. So quantitatively, if we’re talking about a 3% ROI lift based off of a new measurement protocol, great. There’s the quantitative piece of things now qualitatively, how do you create the buy-in to leadership that has to therefore expenditure that, and whether that’s a line item itself or baked in as a markup, right? There’s, there’s a level of veil of transparency there, but also there’s a veil that has to get lifted there when it truly comes to fruition. So you have to speak to that at the same time about, yes, we’re looking at clear cut numbers of ROI, but here’s the qualitative path to proving that out and here’s the hypothesis of where we’re going to end up.

[00:23:47] Scott: Yeah. And it’s a lot of pre-planning, it’s a lot of managing expectations, it’s a lot of coaching and teaching and you know, letting them know what they can expect. And you know, we oftentimes will do like a mid max, hey, in a worst case scenario, this is what it looks like in a best case scenario, could look like this. And the interesting thing is, uh, when you are doing this type of work with clients, all these things that we’re, we’ve talked about that you’ve mentioned, Andy, have to be done upfront because if any of this happens on the backend, it just sounds like pure excuses. It’s like, oh, well of course you’re saying this now, it’s not going well. Oh, of course you’re gonna bring this up now. Like, right. And so a lot of this is just managing that client expectation. Making sure that they know what they can, the potential pitfalls, hey, there’s a potential pothole over here and over here. Like we’re gonna try not to step in it, but we might. right? And I think it comes back down to that partnership and having that trust and rapport with them so that they can trust that like even if you do step in the pothole, that you’re not going to tip the whole car.

[00:24:52] Andy: So I think the other piece too, going back to that quantitative versus qualitative aspect of things is there’s a quantifiable justification and then there’s also the risk aversion about what does this new protocol mean as far as revealing the truth about our paid media makes. And then at an individual stakeholder level, what does that reveal about type of job that I’m doing on behalf of the brand. And I would love to hear from you guys, like what do those conversations kind of, how do those play out? Because I’m sure you’ve seen the full gamut there from leadership down to peers that are, are implementing these different types of attribution, whether it’s a mince measure tool or different kind of aspect of things that are getting unexpected outcomes, let’s say.

[00:25:37] Scott: We typically see one of a couple of different scenarios. Oftentimes, we are brought in by somebody who is struggling to prove their value. Often times that’s a programmatic person, like a programmatic trader or trading director. And their client is like cutting budget and reducing X and Y and they’re like, guys, like, I need a job. I gotta figure this out. How do I prove my stuff? And so we’re solving like a hair on fire problem for them, but then the rest of their team may or may not be on board. And Andy, this is like such a good thing to bring up. Like people are very averse to change and when somebody sees the opportunity to like shed new light on things, like it sends ripples throughout the organization.

There was, a really big FinTech company. They had built, they had hired a PhD to build an in-house attribution model. They’d hired like three staff 18 months later, they’ve not made great progress. And everybody inside the organization had learned to game the internal attribution model. They knew the inputs, they knew how it was graded. And so they optimized their campaigns, like social campaigns they optimized towards clicks and a CPC because that’s how they were graded. Not on ROAS, not on net new transactions. And so this was happening across several departments. And so this, uh, PhD left and the organization said, oh shit, I guess we probably need to buy an external tool now because this wasn’t so great. And so what happened is, and we actually saw this on one of the sales calls, a big consensus call the individual.

Channel members got very nervous because they saw that there was no gamification, that there was no bias, that there was no way that they were gonna be able to keep doing the same shit that they were doing under this new model. And so on the call, there was one person in particular who was very confrontational, very challenging. Well, what about this? What about this? Well, that’s not good enough. And trying to like poke holes, they’re like, well, you guys don’t do post view in social, so that’s not any good. I’m like, well, neither just Google Analytics. Are you using Google Analytics? Well, yeah, okay, well we’re gonna report the same as Google Analytics. Oh, well, uh uh, right? Like, so you could just see that there was this difficulty. Now that’s with the channel members, right? Because what if a new analytics tool comes in and says, actually social, you are delivering half of the impact that you thought you were. Um, that’s scary. But even at this company, the higher ups, there was a VP who saw what was going to potentially happen, saw that all of this time and money that was invested in hiring somebody to build an in-house attribution model. If they bought us. They were basically saying that they had wasted that money for the last 18 months. And even not alone, senior management wasn’t really ready to let go of that. They saw that this was happening. They saw that like there was a lot of, uh, potential for them to see new things, but afraid of how it would reflect back on them and the choices that they’ve made.

And so look, this is human nature, right? Like everyone is nervous about this. And you know, the biggest thing that we run into is, does the, does the agency have the gumption, the internal drive to say, yeah, I’m willing to take on a new project, figure out this new tech, advocate for it to my customer and then implement it, learn it, make like changes and like actually apply it. It’s a really high bar to clear there’s a lot of inertia, there’s a lot of, well, it’s fine. My client isn’t asking for it and so yeah, I think like the aversion to changes everywhere at every level in the organization.

[00:29:38] Andy: You talk about poking holes, I mean, that’s, that’s my job in calls. I’m the, I’m the bad cop and James is the good cop. Brandon, are you bad cop? Are you good cop in your, in your sales calls?

[00:29:46] Brandon: I guess it depends like when someone’s trying to sell us. If it’s sales tech, I’m definitely the bad cop and I’m the one poking holes if they’re trying to sell us any. Oh yeah, go for it. James.

[00:29:57] James: I looked up your personality on my Lavender. I don’t know if you’ve used Lavender at all. I’m sure you’ve heard about them at this point. But they have like little personality profiles and it, it literally says that on your personality. I’ll send it to you later.

[00:30:14] Scott: I’d like to see what mine has to say.

[00:30:15] James: I’ll send you guys both. It’s, it’s a, they’re shockingly accurate in most cases.

[00:30:22] Scott: Yeah, that’s funny. Change in fear of change has like always been a headwind. And I think if you look at the marketing industry as a whole, we only evolve when we must, like third party cookies is a perfect example. Like Google said, hey, this is the thing, and now all of a sudden everybody in the ecosystem is shifting and changing around that.

And so I think like whether it’s implementing new data source or evolving the way that you like buy your ads and do your targeting, like we are an industry that like requires something nipping at our heels to be like, oh, okay, oh, I’ll keep going, I’ll get moving. And so, um, yeah, I don’t know, like how do you guys see the evolution with third party cookies, third party data, like it’s been on, on our minds for about three years now. It’s gonna happen.

[00:31:18] James: Honestly, this is one that I really wanted to pick your brain on cuz I, I think my answer is quite frankly, I don’t know at this point. I’ve gotten to the point where I feel like if you asked me a year and a half ago, I was all about it. I was looking at like how they’re modeling for this data and like expecting the change to happen. And at this point I’m like, do I need to do anything at this point? Like they’re just gonna keep pushing it back, pushing it back, pushing it back, and it just like doesn’t feel evident. I dunno.

[00:31:48] Andy: But I think strategically we’ve embraced the fact that they’ll go away at some point. And first party data that is being pushed through our targeting tools and therefore becomes first party data is much more powerful both as far as reach and quality of reach is concerned, but actual results are concerned. So like when third party cookies goes away, yeah, there’s gonna be an impact as far as exponential reach outside of those targeting tactics and strategies. But is it gonna have that big of an impact on us from where we are today versus where we were, let’s say two, three years ago? I think it’s very minimal at best first.

[00:32:24] James: Yeah. Cuz I, I remember the big push was first first party data, first party data. Back then, I was like, well, how do you get first party data? Like I’m not just gonna just have a newsletter sign up and target those people. I’ll just email those people. And now tools like Clearbit Metadata, Versium are ones that we use. They become first party data I guess when you purchase the list. ZoomInfo. But those have been a key change in our targeting for all of the channels. Like Facebook targeting now is junk. Programmatic targeting for the most part is junk. LinkedIn is still okay, but we’re even learning there where you put Metamatch audience in and it outperforms what’s already in LinkedIn.

[00:33:11] Brandon: So yeah, I think this is probably one of the benefits is that most ad tech tools in-house teams and agency teams are using, they’re gonna be the ones doing a lot of the heavy lifting on transitioning from third to first party data. So there are things that in-house and agency teams have to worry about, but a lot of the other things, ad tech’s kind of taking on the brunt of that work to be the most attractive company in the market to make sure that they’re the one that you want to buy.

[00:33:39] Andy: Well, I know you guys have talked a lot about server side tracking as kind of one of the topics that roll up to this transition. I mean, point blank, what the hell is server side tracking and why should I care about it?

[00:33:51] Scott: Yeah, so the easiest way to describe server side tracking is, we take a third party tag, our third party pixel, and we make it a first party pixel. So our tags, they are mintchip.mintmeasure. We call our cookies, mint chip cookies. So that’s why the URL is that. So what we do is we, I know it’s, so we take our URL and we make it mintchip.brand.com. And when we make this change, the tag is now a first party tag, and then we set it up so that the, cookie, the first party cookie that gets generated is stored outside of the browser. So server side tracking is actually the solution that Google offered up when they announce the death of third party cookies. They said server side tracking is the way to go. It’s safe, it’s private, it’s secure. Now the big problem is that when you take this type of approach, The brand begins to build their own first party data set with their own first party cookies. But that’s only as big as the brand is. So if the brand only has 25,000 website visitors a month, that’s the maximum size of your identity graph. So it’s great for web traffic and maybe remarketing to those people, but it’s not great from an acquisition standpoint. It’s not great from a prospecting and like growing your business. So server side tracking certainly has a place. It is typically really helpful in fortifying and improving attribution data, but it’s not like a panacea, right? It’s not like a a oh, well if you just get this first party data and the server side cracking, like it’s all fixed. Um, it’s one part of the equation to improve the underlying data for your, your analytics.

I think around death of third party cookies, I actually today am not convinced that third party cookies will ever go away. And if they go away, they will be replaced by some other interoperable ID. And so, James, I think I was very similar that if you’d asked me like a year ago, I was like, yeah, it’s, it’s fucking happening. We got a plan for it. Let’s build, let’s go. We gotta, right? But Google’s getting antitrust. They have, court dates this summer. There’s two different lawsuits. And the lawsuit is about breaking up its publisher side and it’s buy side. So the entire reason why Google announced the death of third party cookies is they own everything. They own the publisher side, they own the intermediate technology and they own the buy side. And so there’s really kind of like these three, three pieces, right? There’s pub. The exchanges and the tech and then the buy side. So if the, the anti-trust lawsuit is really only focused on the publisher’s side. So if they have to divest one third of their business, how are they going to know which impressions to bid on? How are they going to see a user on a publisher’s website get data from them to be able to send it to their DSP to bid? Well, if not third party cookies, there must be some other interoperable ID that will allow the publisher to communicate with their, their buy-side tech. Now there’s other lawsuits and conversations about divesting some of the like assets specifically like search and YouTube away from the buy side tech. So that would be like separating out the ad exchange from DV360. So in that case, those three pieces are all independent. Again, how is Google going to know that in your browser, in your Gmail, you saw this thing and then they want to take that and push that out to the publisher, right? There has to be some form of interoperable ID. Now, third party cookies actually are kind of one of the best solutions out there because it is anonymous, it’s never going to put at risk my personal information. It’s only ever going to be some anonymous ID. It has a limited lifespan and I have the ability to like clean or refresh or like remove those from my computer. So there’s been a lot of, first party identity graphs that have come to light after this third party or third party cookie announcement, but I actually argue that those are worse. Way worse because if something is built off of hashed email addresses Live Ramp, and let’s suppose that there were ever any sort of a privacy or a hack or whatever, which we know in our modern day happens, well then actually my information, it’s my actual email address, my actual phone number, my actual whatever data that they have on me that’s getting leaked, that’s getting like out there. So I think from a risk standpoint, first party identity graphs are actually far more risky than third party identity graphs. And, look, third party cookies might not be the long-term solution, but there will have to be some sort of interoperable ID. And so there’s just no way that Google can divest one third of its business publisher side from the buy side and not have to play nice.

And look, maybe that’s where like a UID comes in. Or an ID5 or Lotame or any of these other solutions that are out there, maybe there’s a completely new solution. You know, I’m a big believer in consent and I think like when we look at the entire ecosystem right now, the big thing that’s missing from all this is users having consent. Like how do I as a user have the ability to control my data or manage my data? Like I don’t really have that ability. I go to every single website. I have to manage every single website separately. So you know, I’m a big proponent of you what I call like, it’s like the USB drive for your website visitation. Imagine you go to a website and this little virtual USB plugs in and says, hey, I’m willing to share with you that I’m a male, ages 18 to 49, that I live in Salt Lake City, and that I make between 50 and a hundred thousand dollars a year. And that’s my default. And I manage that consent on every single website.

Now if I wanna go read free articles on New York Times, New York Times, lemme say, hey, give us some more of your behavioral data. We’ll, we’ll serve you some ads and then you can read this article for free. So this is just like one idea, but like this is rooted with the user at the center. And so I think that it’s not just a matter of is there a tool or a company that can offer this, but I think there also has to be legislation that goes along with this and assigns rights to users for their data and to control and like consent their data. Ala GDPR called a digital bill of rights. But like it’s my data doesn’t matter where I am or what platform I’m using, it’s mine. And you know, the current world that we live in is I will give away 100% of all of my data and all of its rights and all the monetary upside in exchange for using your service for free. Facebook is free. They get all of my data, they read everything off of my phone. Gmail is free, but they read everything I’ve subscribed to and turn around and sell that. And so I think that this world structure is going to change. I would actually, personally, I’d pay $3 a month for Gmail if it meant that they didn’t get my data, or I would pay $5 a month if it meant that I got a rev share with them on any idea of mine that they sold.

Or how many people would use Facebook for two or $3 a month if it meant that there were no ads or very limited ads, right. We’re seeing these types of, like business models arrive, Netflix is ad supported tier, all these different streaming services, right? Like, I think it’s not too far in the distant future where there’s the opportunity to pay for a service and limit the amount of data that that company is collecting versus free service for, signing away my data rights.

[00:39:59] Andy: How much would you pay for TikTok?

[00:42:01] Scott: So I would actually, I would pay $5 a month for TikTok. I’m dead serious.

[00:42:06] Andy: To release your information or not to release your inform?

[00:42:09] Scott: For, for them to use my data only to improve my experience, but to not sell my data anywhere else or to like, not sell, serve me ads.

[00:42:19] Andy: In the US or outside the us?

[00:42:22] Scott: Yes, because like, look, I actually think that TikTok is, makes really good use of the data that they get. So, okay, last night, I text my friend and I’m like, hey, I’m gonna skip my workout tomorrow morning, my lower back is out, not 10 minutes later does a fucking TikTok cross my feed and it’s like lower back pain, do these exercises. And on the one hand I was like, okay, this is creepy. But on the other hand I’m like, but, but actually though, like my back hurts. So like I stood up and I did a couple of them. I’m like, wow, okay, that was good. Like that’s a great UX. Like sure it’s a little bit creepy that you’re reading my text messages, but like actually damn, it helped. And so like as a user, that sort of customization is great, but what I don’t want is now to see ads for the next four days from Icy Hot and Bengay being like, oh, ice your pain, right? Like that’s the part that becomes annoying to me. And so like, yeah, what would I pay for TikTok or any other service? Like think of all the things that we use for free every day. How much would you pay to private to privatize that, to not share your data? And I think most people would pay something.

[00:43:37] Scott: Scott, I really thought you were gonna say that. You got the Papa Swolio TikTok. Go to the gym. Go to the gym.

[00:43:46] Andy: So I wanna circle back to server side tracking, but in the context of educate me about server side tracking as it relates to every other attribution model theory from mixed media modeling to incrementality, [00:44:00] to multi-touch bimodal, the whole nine yards. Like where does it, wrapped into it? And is its, is it its own thing? Is it in conjunction with those? Like how does that play out?

[00:44:10] Scott: Yeah, all right, so let’s, we’re gonna have to unpack this a little bit. And so let’s try and, lemme see if I can lay this all out in a way that makes sense. So, broad topic and we kind of have to think about things, in three different buckets. So there’s what I’m gonna call just reporting. So server side tracking can really help with just reporting on your paid channels. It’s like a conversion API is like the most standard thing, right? You’re gonna set up server side tracking, you’re gonna send that data back, that’s gonna improve the data that you have and the like ability of the algorithms to improve. I would actually lump Triple Whale into this reporting category. They do have a pixel, it is server side, but really all they’re doing is reporting on it. They’re not really doing super advanced attribution or, or other things like that.

So then there’s what I’m gonna call modeled attribution. So model attribution is any analytics methodology that starts with some baseline data and then extrapolates or estimates cross-channel performance or credit or anything like that. So, Rocker Box is a great example of this. They also have a server side pixel and so their primary methodology is ingesting API data log level data, and then they use the log level data with the server side tracking, and then they run their models and algorithms over top to estimate channel contributions.

And then the third category is what I’ll call direct measurement. And so, Leads RX used to take this approach before they got acquired. And then I think we’re the only other technology that I know of that takes this approach where you measure every single impression conversion and server side tracking is one of those direct measured inputs. And so the biggest difference between this approach and the modeled attribution is that in the direct measurement, you are just cleaning your data and then there’s no extrapolation or modeling. And so we de-duplicate users across channels. So instead of trying to estimate how many people saw Facebook and then search, we’ve measured every click. So we know that it’s X percent that saw Facebook and then clicked on search. And so, um, the role of server side tracking in all these is really to fortify the underlying data.

And from a reporting standpoint, right, you’re just trying to like, get back to full, get back to like what’s real. Um, on the model attribution side, you’re trying to get a better baseline sample in order to be able to do your extrapolations and your estimations. And then in the direct measurement world, you’re again trying to like get back to that like full picture but with the lens of analytics and performance.

[00:44:56] Brandon: One thing I’ll add in here, especially on the B2B context with server side tracking is unlike browser side tracking where we have max a 28, 30 day cookie window, you can set the lifetime of those cookies within your servers. You can have a lifetime cookie on a user. This is especially useful I think, in B2B attribution and tracking where sales cycles are long and you’ve got multiple people involved in a purchase decision. So I think there’s an opportunity, I haven’t seen anyone do this, I’m not sure it’s on our product roadmap for the immediate future, but I think there while third party tracking is still alive and there are identity solutions that can identify what company a user works at when they visit your website, that married with server side tracking, I think could create a really interesting attribution or intent tool for brands internally.

[00:47:47] Andy: I think that’s a great point that you bring up though too, in the sense of, given the changes that we’ve seen, so James and I have talked about this before and you guys know this, that we use Clearbit on our site and we advise clients to have Clearbit on their sites to get a, another source of data about who’s visiting the site from an account perspective. And if all of those platforms, and whether that’s on your pro product roadmap or not, are using IP lookups as the basis of those data sources. I mean, the match rates are so low at this point with remote work, hybrid work, etc., that I don’t know if there’s that much value coming out of it, especially when you look at different industries, whether it’s cybersecurity, healthcare, those that are uber privatized, are uber privacy centric and privacy focused. It’s a great point that you bring up Brandon.

[00:48:37] Brandon: Yeah, and I, I was actually thinking about this the other day, that this might be strengthening that lookup and that identifier from whatever the PII identifier or like device ID it is you’re using to identify that user. There might be a use case for some companies to bring back gated content for this reason, to tie users back to the accounts they work at in B2B.

[00:48:56] Andy: James has left the podcast, you said gated content. He’s out.

[00:49:01] James: Nah, I’m all for it, man.

[00:49:02] Scott: So guys, this is so interesting because I actually think that tools like Clearbit have less than a 90 day lifespan.

[00:49:11] Andy: As far as how long the wait, give some context to the 90 day lifespan.

[00:49:15] Brandon: The tech will break after 90 days.

[00:49:18] Scott: So starting July 1st, there are five states that have privacy regulations. Having this sort of data capture on your website from a third party tool, And doing all the lookups and whatever. Starting on July 1st will be considered a sale of personal data. IP addresses, PII we just went through this whole rigamarole with Hyundai and that’s a whole fucking thing. So if you’re using IP address to look up somebody’s company and their other PII, email, phone number, where they work, right? That’s already problematic. Under these new privacy rules that start, any third party provider who is doing that on behalf of an advertiser has to have a special type of arrangement and legal processes and documentation of their relationship. Otherwise, that is considered a sale of personal data.

Mint Measure tracking a conversion event on an advertiser’s website is considered a sale of personal data without the right paperwork in place. So there’s all of this stuff that’s happening that is currently kosher for the next 90 days, past that you’re gonna start seeing these states start to crack down, and you’re gonna start seeing companies requiring new sorts of paperwork in adaptations and whatever else. And like the scary part is like, how do you keep up with that? Who’s gonna tell me what I need to do? Who’s going to be the one that comes in and says, hey, actually this thing is okay today, but it’s not okay on July 1st. And something that like we’re trying to figure out and like prepare our clients for, but it has like really massive implications.

I don’t think people have really wrapped their heads around the impact of these privacy laws in these five states, but also like the full impact of like what happens when you don’t have unlimited right to sell people’s data without their knowledge and like their, their consent like so, sorry, sidebar on, on all these things, like these tools have a very limited lifespan and if it’s not 90 days, by the end of this year, you’ll either have to change how you work with them or they will probably be fine.

[00:51:37] James: That’s so interesting because they’re popping up everywhere too though. Like, was it Apollo is pretty new, Clearbit, ZoomInfo has been doing it for a long time. Right?

[00:51:48] Scott: So ZoomInfo is like a plugin into your like email inbox and it reads everything that comes through. But like I send you an email and you sell that to ZoomInfo and you’re getting a value exchange from ZoomInfo based on your relationship. You’re selling my personal data. How does, how does that fly under these new regulations? Right? Like ZoomInfo’s entire way of gathering information is without my consent. Your consent in your inbox, but I’m not telling you that you can sell my data. So like I think that the, the house of cards that the ad tech ecosystem has built is like one good regulation, one good, like crackdown away from falling like house of cards.

[00:52:34] James: All right? I’ve got a prediction here. You brought up revenue shares before. Revenue shares from Facebook, TikTok, ZoomInfo, Clearbit. I’m all in for these revenue shares because ultimately we’re gonna have to create the universal basic income because of AI, right? We’re all gonna lose our jobs to ai and that’s how we’re gonna make money in 10 years, is based on revenue shares with tech platforms that were built by AI.

[00:53:04] Andy: So don’t encourage ’em, Brandon, just don’t even, just don’t even engage. Don’t bite on it. Don’t bite.

[00:53:10] Scott: So, James, I mostly agree with you, but I think. How UBI gets paid for is probably more of an and approach. So like, I need to get paid for my data that’s being out there. The company who has the technology, who has a 99% margin because the AI built it and they don’t have to staff anybody, right? they need to pay a portion of that. And then the uber rich fucks who have millions and billions of dollars, they need to be taxed. And so I think it’s an and solution, but like, look, at the end of the day, the last 15 years of capitalism in the United States has been predatory on individual consumers data. And any sort of shift has to begin with like changing that dynamic. We have just given away everything, the farm to everybody in exchange for our free thing. And I think people are starting, like the culture is starting to be like, wow, actually, like there is a lot of information out there on me, or, hmm, maybe I don’t wanna give my information away to everybody. And so I think they’re starting to be this cultural shift of people caring more about their privacy, right? Think about how many people are like digital natives or are like all of us probably grew up analog and switched into digital as the years went on, right? Like we understand better what our digital footprint looks like than the people who came before us.

So I think that like there’s an element of like time and cultural evolution around this, but also like, No company ever said, yeah, let me just let go of this asset worth billions of dollars a year. No third party gate company is ever gonna take this laying down, right? Google’s not gonna take this laying down, Facebook’s not gonna take this laying down. And so we can, we get into a really interesting conversation about what is the role of government in setting boundaries for, capitalism and tech companies and what’s the role is there a “moral role” or a “minimum standard” required that the government has to put in place or should the free market just do the free market thing? So I don’t know if you guys wanna go down that rabbit hole.

[00:55:28] Andy: No, that’s for our other podcast, Political Banter.

[00:55:31] Brandon: Political Banter.

[00:55:33] James: Listen, I’m down for that episode whenever it happens.

[00:55:37] Brandon: Scott would? Yeah, I think Scott and I both would eat that up and we would, yeah, you’d get to see the tension between Scott and I on all those issues.

[00:55:46] Andy: I wanna come back to your explanation of server side tracking, Scott, just to make sure I understand it. So basically, in the realm of Mint Measure, right? So server side tracking basically is being enacted by having your mint chip pixel and therefore subdomain basically being the redirect that all traffic is flowing through. Capturing the impressions, capturing the clicks, right? Things like that. So in the most basic terms, if I go into my Google Ads campaign and I’m setting up a tracking template, I’m inserting mintchip., insert my brand name as my custom domain, and all traffic is flowing through that, which is very similar to, let’s say, a Pardot, a Marketo, a HubSpot, just in a different way rather than appending UTMs and other parameters to the end of it. Have I got it right so far?

Okay. So all data is flowing through that, which then creates the foundational data source that you are then able to use when you are giving the data to their clients to analyze both from an incrementality, a media mix modeling, media mix modeling perspective, but also extrapolating that into a broader view of how various channels and tactics are either working in conjunction to drive someone through to the path of purchase or ultimately not influencing that purchase behavior.

[00:57:06] Scott: Correct. Yeah. So that’s 95% correct. I’m gonna, I’m going to make one distinction here is we don’t extrapolate or need to model the data. And so, you can think about it as there’s kind of like two different data sources. There’s your server side tracking for all of your onsite behavior. So, we can still like pass through UTMs and whatever for your Google Analytics, but like, from paid media, like who came, like what are the like IDs that are associated with those websites, and then there’s the offsite data. And so we take the post view and the post-click data from offsite and we marry that with the onsite data to do the analysis. And that’s why there’s no extrapolations because we’ve measured every single impression and click and the website visit. So we’re able to just kind of pair those together.

[00:57:55] Andy: Now I know we were kind of chatting before we hopped on here. Have you seen any patterns as far as impacts are concerned, certain channels playing well with each other and influencing each other versus those that just live in a silo over here?

[00:58:10] Scott: Yeah, so we have this insight about how ads deliver results and we preach this from the mountaintop. So every ad channel for every advertiser will always deliver results in two ways. There will be some users who only see a single ad channel. Let’s suppose they only see Facebook. And they might convert from that. But there are gonna be some users who see Facebook and then click on Google search and then get remarketed to. And so in order for me to quantify how Facebook is performing, I need to quantify how often it’s reaching users by itself and converting them and how it lifts the conversion rate when it’s part of the media mix, and how often that media mix is happening.

So all ad channels always do both of these things, and no other way that we have found is able to analyze and quantify both of those halves. And so when we’re thinking about ad performance and how do we evaluate, it’s always through this lens, how well does a channel reach and convert incremental new users and how does it support the conversion rate as part of the medium mix?

[00:59:15] Andy: So I think many people’s interpretation of that is going to be, and don’t shoot the messenger here because it’s, it’s obviously well beyond that, but it’s basically multi-touch attribution at a larger scale.

[00:59:27] Scott: That’s a, that’s a fine way of thinking about it. Let me put it this way. Every attribution methodology starts with the same data. Every single company, there’s probably a dozen or so of them out there that are pretty prominent. We all have the same data. So what’s different is how we process and look at that data. And so yeah, we start with a little bit bigger data set because we’re measuring every impression instead of sampling or using API data. But yeah, it’s really just in the way that the data is processed and analyzed.

[01:00:00] Andy: Now as the CEO of an agency that does more than just paid media, what about the non-paid side of things? How does that come into play? Or does it not come into play with the data trends and analysis that measure outputs?

[01:00:13] Scott: So let’s quickly say like, when you say non-paid, are you talking about like direct and organic traffic?

[01:00:18] Andy: Yeah, let’s use that. Or just organic social as another method too.

[01:00:20] Scott: Boy, organic social, I think is a waste of time and energy for 95% of brands. And I will tip my hat to Avinash Kaushik and his sharp POV on that. But like, okay, so for directing organic traffic, right? From a web analytics standpoint, the way that we look at it is we are gonna tag every single paid channel, email, text message, programmatic search, etc. So if we see a website visitor that did not see any of those paid media channels, cause we’ve tagged everything that’s either direct organic traffic.

Now what happens if we see that person come through, but we actually look back and we say, oh, well they actually saw a programmatic impression, right? So we’re able to then see the volume of traffic that’s coming through attributed as direct and organic, but be able to look back and say, actually it was influenced by this or, they come to the site for the very first time, through no paid media, but then they’re retargeted by email and by text message and by programmatic, right, we’re able to parse that out and help the brand to understand where that’s fitting in and if it’s starting as direct or organic and being closed by paid media or vice versa.

[01:01:33] Andy: What about dark social and our boy, Chris Walker, can you measure that?

[01:00:35] Scott: You can’t, no. So the best thing you can do is UTM it or tag it. Look, there’s a certain amount of untrackability. There’s just not a ton that you can always do around that. So I think like, look, we’re super transparent with our limitations and I think you just have to know as you’re buying a piece of technology, like what, what those limitations are.

S0 let me go back to your question about like what are some of the macro trends that we see within our dataset? So, everybody thinks, or we hope maybe, that channels are working together to lift conversion rate. And so we’re often like, yeah, but programmatic, it’s so helpful and it lifts search. But like, has anyone ever seen that data? If you haven’t worked with us, probably not. But, so like one of the things that we do is we just help people to say, hey, search by itself converts at two and a half percent. That’s a great conversion rate, especially blended across brand and non-brand. Oh, but you add programmatic to the mix and the conversion rate actually goes up to 12%. So you’re getting this massive lift in conversion rate. So almost without fail, we have seen a few instances where programmatic plus search or programmatic plus social almost always converts higher than either one of those channels by themselves. And we typically see it in the realm of like three to 10x higher when you add programmatic to the media mix. And so I think that’s like pretty consistent.

The second meta trend that we see is that a shockingly low percentage of conversions come from, uh, a multi-channel medium mix. For any given campaign, it’s probably between, I’m gonna say unoptimized, when we first start working with somebody, anywhere between like 15-25% of conversions are happening in a medium mix or said another way, 75% of conversions are coming from a single ad channel. And so as we start to look at this, we’ll see that like any specific combination, like digital video plus search, like that combination is probably not representing more than like three to 6% of all conversions. So, most conversions happen from a single channel, but the conversion rate across channels is almost unequivocally higher.

So like we manage client’s expectations. We say, hey, by the way, and we’re gonna like show you this data and just this is what we typically see. So when they see that data, they’re like, oh, okay, great. So then what we do is we go through and we say, great, well these are the three media mixes that have the highest conversion rates. How do we optimize your ad delivery to do more of those things? And so we’re trying to get this video plus search from 3%- 6% or to 7% or to serve ads to people who clicked on a search ad and then left the website and go find the channels. They’re gonna have the best conversion rate in that medium mix. And so, yeah, very few conversions tend to happen, or low number tend to happen across channels, but it’s always better across channels, when you deliver it.

[01:04:46] James: So do you guys find yourself or your clients shifting their optimization to be like more focused on add exposure retargeting? So say CTV plus banner plus social, right? You’re essentially building retargeting lists based off of the exposure audiences you built. Cause that’s something that we’ve done quite a bit without, quite frankly, without this data to support it. Do you find that that’s like the direction that they’re going in order to force that desired behavior?

[01:05:15] Scott: It is. And so we’ll offer custom audiences. So whether that’s search people who went to the website via search and wanna serve that as a remarketing audience to programmatic or, and these other things that like, kind of you described, it is certainly possible to do it. There’s more than one way to, to do that. But yeah, from like a, a practical, tangible standpoint, I guess like, I didn’t get up on my soapbox for a moment. This is like one of my biggest problems with data and like attribution. Like, what the fuck are we doing? Why? Why are we spending money on this?

[01:05:47] James: Right, cuz we’re like being in this space, we’re focused on level one and then level three, like it’s how are we shifting money between channels based on the individual results of those channels basically and what creative, what bids, like all that minor stuff, right?

[01:06:55] Scott: But, okay. So the last major thing that we see across campaigns and advertisers is a gross misalignment of frequency delivery. So what we see is that most users only see a single impression and I think on every campaign except for two that we’ve ever ran, the conversion rate is higher with a two frequency than a one frequency. And so, like, there’s also this really big balloon of users on the other end who’ve seen like 50 impressions over two weeks. And so, like shifting my budget, shifting 10% of my budget from video to display might be helpful in a broad sense, but I’m actually gonna get a lot more gains by just capping and saying, hey, if you saw 50, you don’t see anymore. And if you only saw one, I’m gonna try and make you see two, right? The incremental gains that you get from making that type of adjustment within your channel, not moving budget across channel is far greater than any cross-channel optimization, because if I’m wasting my spend, only serving a single impression, that’s wasted, that’s not impactful. Well, shifting more money to that, it’s only gonna guarantee that I’m doing more of that wasteful spend. You know, it’s the nature of ad platforms to try and buy the lowest cost impression. That usually is an incremental new user. Because if I’m targeting an audience of 50 million people, well for the other 49 million people I haven’t reached, that’s a much lower competitive set than if I’m trying to like bid on that 1 million who already saw a single impression.

So like, I know this is getting pretty nerdy, but like these are the inherent biases built into the platforms and like it’s just this different way of thinking of like shifting budget, yes, but let me change how my campaign is delivering and if I make a 3% improvement today over here at a 5% improvement over here and those carry forward to next month, that’s 8% in a single month.

Now does it hold true and does, right. So like this, we call it the our, our marginal gains theory, right? 3% here, 5% here, 1% here done on a monthly basis is going to yield over the course of the year for one of our clients, a 75% reduction in CPL. They’re delivering four times as many leads for the same budget 14 months later after they started working with us. And like that’s just how much waste is in there. James, I think you said at the beginning, like half of my ad spend is wasted. I just don’t know which half. In today’s world, it is unacceptable for a brand not to know where they’re wasting their digital ad spend. It is a hundred percent possible, and it’s just if you don’t know the answers because you’ve put your head into the sand and it’s not because you can’t find it out.

[01:08:48] James: I did steal that quote from you by the way, like four days ago.

[01:08:54] Andy: Well, that actually is from, from how long ago? So let’s give credit where credit is due here. Yeah, so as we bring today’s episode to close, guys, I wanna throw it back to you, Scott and Brandon. We always like to ask our guests and ourselves, what are two to three actionable takeaways that someone can do? Now, we talked about a lot here, right? We talked about Scott’s scientific approach to bench lifting. We talked about James’s short arms on how they impact or don’t impact his bench press, all the way through the TikTok and Scott’s ability to pay for $5 just to keep his data privatized. So what are two to three actual takeaways that are outside of those topical discussions that you guys would give our audience when it comes to Mint Measure, but also kind of attribution and all that that encompasses?

[01:09:41] Scott: Yeah I recommend people go check out Open Web Analytics. It is basically Google Analytics. It is free. The only thing you have to pay for is the data storage on your own server. So you download the software and you’re gonna pay something like 12 to $36 a month depending on how much website traffic you have.

It’s a great alternative to Google Analytics. It is GDPR and CCPA compliant and it’s open source. So whenever there are updates to it, there are people who are doing like major projects or fixes or anything else, like it’s free, the only cost you pay is is your web hosting. So for anybody who is looking at GA4, who’s maybe looking at crazy consulting fees or whatever else, like open web analytics is a really, really good alternative and it’s worth checking out.

Maybe you don’t use it, maybe you can’t condense that CFO to get off of your, Google Analytics, but like, it’s, in my opinion, probably the best free alternative out there.

[01:10:48] Andy: Brandon, you got any

[01:10:49] Scott: Do you have a piece of tactical advice takeaway?

[01:10:52] Brandon: Um, yeah, other than like, go follow Papa Swolio for a good laugh every day. I would say that, mine’s more of a mindset shift piece of just start asking is this data that I’m looking at, does it come from modeling? Does it come from measurement? And understanding the difference there, as well as knowing when to graduate from last click attribution or any attribution that only gives you a single channel answer.

[01:11:19] Andy: So I’ll add one on top of what you guys are saying then too, is taking the step and identifying whether you’re a risk taker or whether you’re risk averse when it comes to the data that you really want to have to move the needle for your job’s performance, but also the business. Cause I think, Scott, you, you made great points when we were talking about that in today’s episode, and that’s just a key point of either you’re gonna take the next step or you’re just gonna continue to hide behind the veiled curtain of Google Analytics and every other non-transparent, non worthwhile tech platform that lives out there.

So, cool. Well, Brandon, Scott, thanks for being on today’s, episode of Digital Banter. How can our listeners connect with you guys, either individually on LinkedIn or anywhere else you guys are, or learn more about Mint Measure?

[01:12:08] Scott: Yeah, you can find us on LinkedIn. I’m super active on there. So is Brandon. And then we also have our podcast where we talk about similar things. If you are an agency person or a marketer, uh, that podcast is designed to give you real practical advice. If, if you learned anything today, that that podcast would be really helpful for you. Otherwise find us on mintmeasure.com. Would love to talk and help you guys get smarter.

[01:12:33] Brandon: Yeah, if you’re on mint measure.com, you should click in the top right corner. There’s a button that says Book a demo. Really cool things happen when you click that button.

[01:12:43] Andy: There’s our dumb sales guy. All right guys. There you go. Great CTA. So for our listeners out there, check out Mint Measure, connect with Scott and Brandon on LinkedIn, and until next time, we’ll catch you later.

Thanks for listening to Digital Banter. If you enjoy today’s episode, please be sure to like and subscribe wherever you get your podcasts.

Entertaining Content with Purpose

Interactive Demos are Better Demos

Beyond Creation: Maximizing the Impact of Your Content

People-First Playbook AMA Edition Part 2