← Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
How Bad Data Corrupts AI Bidding and CAC Performance
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- [00:00]
A
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Jordan Cooney.
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B
I'm Jordan Cooney, and joining me today is Chester Scott, Chief Strategy Officer at unio. Chester, welcome to the Voices of Search podcast.
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C
Hey, Jordan, thanks for having me. Appreciate you taking the time.
- [00:55]
B
Yeah, thrilled to be diving into this conversation. You know, we don't get enough folks on the show that are here to talk about where data's going, how data's changing. I mean, we've been seeing so much change to attribution and traffic. But before we get into all of that, tell us a little bit more about Lunio. Tell us a little bit more about what you guys do and, you know, give us a little background on your role there.
- [01:21]
C
Yeah, sure. So maybe I'll start with myself and then I'll move into Lunio. So, as you're aware, I'm Chester, currently the Chief Strategy Officer at Lunio. But first and foremost, I'm a marketer. I spent the bulk of my early career agency side, both independent, before moving into big network, worked on some of the biggest accounts across the uk, European, Middle Eastern and APAC markets. Managed a bunch of different channels inside of that arena. And I've also though, had the pleasure of working client side as well, and so worked in two different retail e commerce conglomerates. One in Southeast Asia at Central Retail and then the other in the UK in a global role at the Hawk Group, looking after their beauty effort. And in addition to that, I have also spent time platform side, an early employee of TikTok across the middle East, Turkey, Africa and Pakistan, leading their Pure play e commerce vertical. I was the person that took their auction solution into the market in that region. I've launched a bunch of interesting commerce solutions. Live Shopping was one of the first pieces that we launched, TikTok shop, so on and so forth. So I had the luxury of working, let's say, in all three areas, which sets me up for having a relatively unique vantage point, I would say, in my current role at Lunio. So if we come on To Lunio specifically, Lunio was actually previously known as PPC protect 7, 8 years old. As an organization, we became Lunio as we grew up. As an organization, we became multi platform in 2022. And first and foremost we started out as a click fraud and some might refer to as invalid traffic solution. Ingesting clickstream data from search platform, predominantly social platforms. As we became Lunio, we classify that traffic through proprietary detection algorithm to understand the difference between high quality traffic and traffic that is maybe of lower quality or at the very worst end fraudulent and that has no propensity to go on and purchase. But the reality is that that was our starting point. Click fraud was our starting point. We have always had this ambition to eliminate paid media waste. And paid media waste is obviously a much bigger arena than click fraud itself. And so we're starting to make inroads on that strategy right now. And 2026 is a big year for us and as we move into 2027 as well, and we will venture into those additional waste vectors and sort of help add value to customers beyond just click fraud and invalid traffic.
- [04:19]
B
So I think this is a largely misunderstood topic by a lot of our listeners. Right. I guess SEOs, for better or worse, just take traffic at face value. I think paid search marketers have become highly reliant on Google to help them make many of these decisions. Why is a technology like Lunio helping brands understand where fraud is happening, where quality actually sits in terms of the Internet's traffic?
- [04:50]
C
Yeah, you know, I think there is a necessity for advertisers, both sort of brand direct and agencies to leverage third party solutions, whether that be from kind of a traffic verification, whether that be from a data perspective, whether that be from a measurement standpoint. Because I think that by leveraging third party solutions, you ultimately are taking back control. You're not passing back, passing over the control ultimately to the platforms, you know, that are incentivized to drive high quality results for themselves. And so I think first and foremost it's about making sure that as an advertiser you are taking as much control as you possibly can, that you're validating everything that you possibly can. And ultimately that should drive improvements in performance and long term marketing effects as opposed to, you know, potentially driving short term marketing effects or efficiencies, I probably should say, and forgetting about actually the overall effectiveness of what you'll buy and how everything kind of integrates together. So I think it's more around, you know, verification, due diligence and just taking as much control as you possibly can to make sure that you've got your own interests at heart. I don't think we can rely on, you know, other parties to, to, to do that for us.
- [06:12]
B
Yeah, no question. And like you've got this like vast experience across marketing channels. Right. Like I mean you've done social, you've done some paid, you've got a lot of different like experiences when you look at these. I think a lot of marketers think that there are certain verticals that are more susceptible to fraudulent traffic. The two that really come to mind are social media and affiliate type traffic sources. But that's not necessarily true. Fraud probably happens in all different verticals. Tell us more about that and how you all have in your own experience as well. And lunio, you all have looked at this traffic challenge by vertical?
- [07:00]
C
Yeah, sure. I mean like if you look at the kind of channel mix and the traffic quality that you get from each respective channel and you know there are varying levels of fraud for sure. Right. You see higher levels in fraud potentially within social platforms, within black box or AI native platforms such as the Performance Max. Advantage plus remains to be seen because it's still relatively new Advantage plus so and so forth. You tend to see higher levels in more traditional solutions such as Google Search as in text ads. Yes, we do tend to see lower volumes of invalid traffic, but it doesn't mean to say that it's completely squeaky clean. And I think that where we potentially come unstuck is looking at a relatively small percentage and not thinking that actually trying to improve that relatively small percentage can't have big impacts in absolute terms. And that's because of the volume of investment that's going through those channels. And I think there's also a reality that, you know, fraud within the former of what I just suggested, the social platform, search, shopping, sorry, PMAX shopping, etc. Is quite obvious as to why there might be fraud. Where they might be. It's quite easy to understand. Whereas when you think about sort of traditional search or you think about an LLM, you know, where the incentives for fraud maybe feel a little bit more abstract, they're a little bit harder to get your head around as to why somebody might be doing something because there's not a real kind of immediate obvious incentive. But that's not necessarily the reality. And you know, I think as marketers sometimes we're guilty of trying to find the big thing. What's the big thing that's going to move the needle when in reality we're all probably just trying to find what are maybe the five, 10 things that can drive marginal improvement that add up to that big thing. And so I think that that's the really key thing, particularly within the most sophisticated of advertisers, is they're ultimately trying to find marginal improve improvements in absolutely everything that they do in order to drive big effects when compounded together. And, you know, there's tons of sporting analogies in this, right? Like inch by inch, mile by mile, like. And it's relatively similar from a paid media and a performance marketing perspective.
- [09:24]
B
Yeah, no doubt. I mean, one of the big things that I think is really interesting is we really don't fully grasp the bad data problem. And, you know, we're in a very tricky moment in digital marketing as, as an era, which is, as we've seen, AI now become much more prolific. We now have new channels of discovery. ChatGPT now has an advertising platform. We all have seen what is the organic discovery track for ChatGPT and these AI models. The reality here is that we're dealing with a bit of a vacuum because we don't have great data, we don't have great insights. But when you think about the data problem and you think about the measurement problem, how much of this challenge is scrupulous bad actors versus bad data versus just the evolution that we're seeing in the market?
- [10:28]
C
Let me try and break this down into three core areas, I think. One, because I think everything that you just said, Ben, there, I think that there's probably multiple challenges that sit inside of that. So let me try and break that down into something that's a bit more digestible for me to be able to answer. Firstly, we've got a significant amount of diversification in channel mix. We've gone from having a relatively straightforward kind of meta, traditional search, maybe bits of shopping and maybe a bit of Snapchat and TikTok. But what we're now seeing is a significant diversification across that mix. There are more channels than ever today that we need to think about how we plan, buy, optimize. And I'm talking about traditional search, social search, shopping, PMAX, AI Max, you've got the new entrants from ChatGPT, you've got TikTok in the mix, you've got Bing and all of their respective ads, you've got social search. And so that kind of ecosystem is bigger than it ever has been before. And so we're no longer trying to understand the signals and then the downstream impact of, of one channel and one campaign type. We're actually trying to understand the signals and then the impact of all of this diversification from a channel perspective, that's happening as well. And so I think that that is definitely a significant challenge. How do all of these channels, platforms, campaign types, how do they play nicely together? So that's the first challenge. The second challenge is ultimately about data volume. I think that once upon a time it was all about how can we collect more data, how do we get as much data as we possibly can? Because that's going to make us better at everything else that we do in terms of our planning, our decision making, our optimization, our measurement. But I think maybe to some degree we've gone so far the other way. The pendulum swung so far the other way. The real challenge that advertisers have now is how do we separate signal from noise, what data points and signals are actually important in order for us to be effective and what actually is nice to have, but it's not business critical and it's cluttering those decision making processes. So that's equally a significant problem. And then you've got the measurement challenge right. And I think you might have used some examples there in your question around GA4 and other solutions. It's now using model data. I think that measurement for the largest part is still relatively unsolved. The industry, from my perspective, has maybe been a little bit complacent in accepting and, you know, a fundamentally flawed approach in last click attribution as an example, in multi touch, multi touch attribution. And that might be because, you know, those big platforms that maybe don't have always our best interests at heart of, you know, they've largely been the big innovators in this space. And so I think that that is definitely a big challenge. And so the great thing now though is that I think that advertisers are now starting to ask those questions because of what's happening in the macro. And now we really are starting to see some really strong innovation from a bunch of different vendors in the market as well as from the big platforms on how do we innovate from a measurement perspective. And I think for me the gold standard is trying to understand what is the causal proof. How can we develop a scalable system that leverages causal proof? The large challenge with that at the minute is that causal proof, and that could be in the form of market mix modeling as an example, is largely a long term directional indicator. But we know that on a daily, you know, as when you're activating, you need something that can give you insight into how to optimize on a daily basis. And so that's the real challenge now is how do we find a system that uses causal proof that's able to balance the long and the short term. And I think that's largely where the, where the challenge comes. And so when it comes back to your kind of original question of, you know, where is the data problem? I think that it is massive increase in diversification of channel. How do they all play nicely together? It's the volume of data, how do we separate signal from noise and how can we help each other to be able to do that? And the measurement component, the more innovative and more robust, the more sophisticated our measurement capability is, the better signals we're going to be able to pass back, because we can pass those signals back into AI driven solutions, into decision making processes with a level of confidence that we're making the decisions based on a measurement framework that has the robustness to do that in the right way. Does that answer your question?
- [15:19]
B
Actually, I think it does. And not only does it answer the question, I think it exemplifies the challenge that digital marketers are facing, which is that this is a hyper complex, it is a, it is a both business decision level of complexity in terms of how you're measuring as much as it is a tool platform tracking challenge as well as a channel and marketing challenge. And I think this is, this is where, you know, marketers are grappling with this, which is, it is, it is, it is so difficult to isolate where the problem is. And so the main question I have for you off of this is how big of a problem is fraudulent traffic? And is that in the order of all these challenges, a high priority, mid priority, low priority? And does it change based on who and what business we're talking about?
- [16:14]
C
John, I think that's a really good question and I quite often like to, at the start of any presentation that I do, I quite like to use a quote from a guy called John Wanamaker. You might be familiar with the guy and one of the original marketing pioneers from the late 1800s was famous for inventing the price tag, but he's also extremely famous for inventing or for making the quote, which is half the money I spend on advertising is wasted. The problem is I don't know which half. I think the real ironic thing about that quote is that that was made in the late 1800s, but that quote is as true today as, as what it was back then.
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B
Right.
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C
And so when I think about kind of the specific question that you, that you had around fraud. If you look at Lulio's own data, which is based on a meta study of over two and a half billion clicks per year, and that's increasing each year, we're able to estimate that roughly 8.6, 8.7% of traffic that is coming from paid channels is due to invalid and fraudulent traffic. But the reality is that the waste problem beyond just click fraud and invalid traffic is much bigger and we're probably getting up towards kind of 50% when you add in all of the additional waste vectors. And I'm talking about kind of low intent users, I'm talking about non incremental investment, I'm talking about poor creative execution, audience saturation, so on and so forth. Right. And so I think that that's the really key thing is that click fraud, invalid traffic is a big problem in itself, but actually there is a much bigger waste problem that we should all be paying attention to because that's no longer a marginal gain. That's, that's. That you're getting the whole cake there.
- [17:59]
B
Yeah. I want to transition to something that I know SEOs and organic marketers are going to love to hear about because it is fundamentally a challenge to communicate what is happening in the market today as it pertains to bot traffic, as it pertains to these AI agents that are now more and more becoming a prolific way of doing discovery and seeking out information. How are, how are you thinking about that particular part of the ecosystem which is. In the last 24 months, we've seen a huge increase of bots and the complexity of these bots is becoming even more challenging because it's not just that ChatGPT and Anthropic have one bot, they have a multitude of bots that are doing certain types of activities. How are we as marketers to think about the bot piece and the AI agent piece of the traffic that's hitting our sites these days?
- [19:05]
C
So I think there's a couple of realities. Right. I think you're absolutely right. There's been a lot of talk in our industry about agentic traffic, agentic commerce. And I think that sometimes what we have to be really conscious of is, is that we're in a bit of an echo chamber and that actually we maybe talk about these things before they become a real consumer reality. And so I think that for me, what's been really interesting is that whilst there's been a lot of conversation about this type of thing happening and where the industry is moving to, and that has been real, I think over this Past couple of months we're really now starting to see that become a reality. And I'll unpack that a little bit in a second. But up until this point we're still seeing a relatively small amount of agent led traffic that comes to customer sites. Based on what we're able to identify, do I think it's going to remain a small amount? Absolutely not. That's definitely going to grow, right? Exponentially. And so I think it's about how do we be aware of that evolution that's happening but equally not make any knee jerk decisions and kind of changes because it's maybe not 100% necessary right now. But what has happened over this past couple of months that has been really interesting from my perspective is that we've seen the announcement of Alexa for Shopping. Alexa for Shopping is the collapsing of Rufus, their on site agentic capability into their Alexa offering. And for me that announcement was probably the first time where I thought, wow, we're probably really now going to see agentic shopping and traffic at real scale. And the reason that I say that is because, you know, Amazon have done a great job for the past 25 years in building consumer trust and confidence in their shopping experiences. And I think that if there is a business that is able to get the consumer comfortable with leveraging agents on their behalf to to make purchases, to research, to make bookings, I think they're probably the closest to it. Now what's then also happened in that time frame? We've had Google Marketing Live and Google I O and as part of Google Marketing Live and Google IO they've announced Google Ask Advisor which is equally an agentic capability for the consumer, which could also see real scale of agentic traffic. And it may be even agentic commerce that becomes a reality.
- [21:39]
A
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C
So then the second question or the next sort of challenge or thought that that raises is then, well if a genetic traffic becomes a reality, then how do we begin to understand the quality of that traffic that's coming to site? And so if you think about the fraud industry, and that is the big incumbent players that have been in the market, the IASs, the double verifies the moats of old other players in our space. Well, it's largely been built on the understanding that if you're able to identify what is a real human, then you have a good understanding of whether that user is a value or not. Because if it's a real human, it's a value. And if it's not a real human, then it is either of low value and low propensity as a chance of low propensity to purchase or it's fraudulent. And so that quite binary definition has essentially been the backbone of kind of verification and fraud solutions within the apptech space. Now with the rise of agentic commerce and us now accepting that that's going to become a reality, you know, that completely changes the picture because we're no longer having to understand what's a real human and what's not. We now need to understand what's a real human, what is a bad bot, a fraudulent bot or a fraudulent user. And what is a good bot, a bot that's maybe there with real human intent. And that ultimately means that the old way of thinking collapses to some degree. Now as I said, that doesn't mean we all need to panic, right? This is not changing immediately, but it does mean that we need to think differently. And so if I think about the early thoughts around this from a linear perspective and what we've started to work on, it's all about intent. Irrespective of the source of the entity of that traffic. What is the actual intent of each individual click, of potentially of each individual user that comes to site. If we can understand the intent of each individual user, of each individual click. That ultimately means that we are then able to have an understanding of the value and the propensity that that user is then going to go and complete a desired action. And ultimately that means that you can then remove out the bad. And potentially it also means that you can then sort of change the position to some degree so that you actually focus on the good stuff, you double down on the good as opposed to maybe only focusing on the bad.
- [25:47]
B
I, I love this and I think I, I have a major question for you because this is where the industry is going to go. I mean, you brought it up. Your Amazon example is, is spot on. It's exactly, exactly where consumers are going to be going, which is we're going to be leveraging agents to perform a lot of these activities. Shopping can be a very cumbersome, time consuming process and if I have an agent working on this in the background, it can help me move my decision making much quicker. My main question here for you on this, and I think the challenge that everyone is trying to understand is what happens if my agent is lazy? What happens if my agent is a bum and is just doing really crummy things on a website and isn't legitimately shopping on my behalf? How do I monitor? How do I track? And then even worse, how as a marketer do I validate or invalidate that as a real source?
- [26:44]
C
Another great question, Jordan. I think that you kind of hit the nail on the head with this question, right? I think that I mentioned right at the start that at times in our industry we can be in a bit of an echo chamber. We're excited about all of this latest innovation and we want to take advantage of it, we want to be an early mover on it. But there is a reality that quite a lot of industry press, et cetera, Ms. And that is that all of this innovation and agentic commerce, capability, shopping capability, agents for consumers, all of that innovation is all well and good, but only if the consumer adopts. If the consumer doesn't adopt, then actually the innovation is irrelevant. And so I think that what's going to be really interesting over this Next, you know, 12, 24 months is how much does the consumer adopt? And in order for the consumer to adopt, they have to trust, they have to have confidence in, in that the agent is working aligned to them as a human, that they're doing as good a job as what they could do as a human. And if it's maybe not doing as good a job, maybe in terms of identifying the right price or from the right provider, then it needs to be offering enough convenience. And so I think that we're probably going to see this in stages. I think that we will see some adoption, but I think it will be on relatively menial tasks to begin with. So it might be research projects, it might be, you know, then it might transfer into finding me the best holiday deal, the best flight price, then it might be booking a reservation, then it might be making its first real transaction, but a low value transaction, then it might get into a bigger transaction, so on and so forth. And so I don't think this is a kind of an on off switch. I think it's a process that as an industry and from a consumer perspective we're going to go through and it's going to take time for the consumer to build trust, to build confidence that the agent is acting and delivering in a way that is aligned to exactly what I need. And I don't have any of those concerns. And so, yeah, I hope that answers your question. It does. A long way around.
- [29:03]
B
I actually want to dive into this just a little bit further and talk about events. Right. Because I think as marketers with this lack of data, this lack of trust and transparency in our measurement tools, this level of confusion with all these different channels, what we've done as digital marketers is we've gone down to like the root event as our primary metric and only metric. If a signup doesn't happen, if a click doesn't occur, if a purchase doesn't happen, if a request for more information isn't transpired on the website, it's just not real or useful. And that's a very, very narrow set of your visitors, users, audience, if you want to call it prospects, if you want to call it on your website. And so my main question about this for you is, how does Lunio and how does the demything or myth busting aspect of a Lunio product help marketers better understand what is quality? When all of our minds are focused
- [30:14]
C
on events, it largely depends on optimizing for the right metric. If we are optimizing on metrics that could largely be considered to be vanity metrics, a click as an example, maybe a form download, a form submission, well, they're all things that a bot itself can achieve. And so we need to be able to understand what is the actual business goal and how do we set ourselves up to be able to deliver that business goal or business outcome. And that might therefore include ensuring that we've got all of our upstream capability in terms of site quality, sorry, traffic quality in order. And a good example of this, right, might be, you know, you've got an organic search versus a paid search and you might be wanting to drive as many sales as you possibly can. And so you've got, obviously got a paid search team that are bidding on every single user that comes to site irrespective of whether you're in the, whether, whether anybody else is in the auction or not. It could be non incremental. And so, you know, do you really need to be in that auction? Probably not. Right. And so it largely depends on what you're optimizing for and how everything sits together, how the ecosystem works. It's, it's all about systems as opposed to siloed efforts. And I think that that's, I'm not sure that that analogy really helped it but ultimately what I'm trying to say is it's about ensuring that we think about the way that we plan by optimize as a system, not as individual singular efforts.
- [32:06]
B
I fully agree. I think that the systems based thinking is something that a lot of digital marketers have lost themselves on. And you know, there was a whole era there where digital marketers were building their own paid search systems and their affiliate tools and all these other ways to better evaluate and determine what is a, a, a useful channel. And we've kind of just like, I don't know, for lack of better terminology, become lazy and relied on entire like ecosystems that are provided by the advertiser themselves. Right. Google's GA4 Google search console. Right. And so there has to be this middle ground where we're using third party, our own capabilities as well as the, the advertisers actual technology to help us elevate how we, we perform in digital marketing. I want to transition kind of to our last kind of topic which is talking about the marketers themselves. I mean you're, you're a marketer yourself. You, you've worked in a variety of different environments and, and types of brands and businesses. And you know, we've talked throughout the show today about the struggles. I want to talk a little bit about the opportunities, what are the opportunities that marketers have today? What makes it exciting to be a digital marketer right now in this era?
- [33:24]
C
I think for me like we're going through kind of uncharted territory. Right. And so to some degree that's leveling the playing field for everybody. And what I mean by that, that's leveling the playing field to some degree for Advertisers, it's leveling the playing field for agencies, it's leveling the playing field equally for vendors like Lunio because everybody's. All the rules are being rewritten. And so that creates huge opportunity and excitement. Right. We're being challenged in a completely different way. And that requires us to think differently, quite critically, on how we actually solve for those problems. So that for me in itself is really exciting if I then think about kind of, you know, media and marketing in general. For me, everything is trending towards a black box first approach. The buy side, to me, feels like it's collapsing to some degree. You know, we previously had a ton of different platforms that were all planned, bought, optimized in very different ways. But because everything is trending towards black box and kind of AI first, it feels as though that buy side is all starting to look quite similar. And so what that means is that the challenge, the problem that we're trying to solve within those platforms is changing. For me, it's all coming down to the data quality that we are ultimately able to build. It's therefore the signal quality that we're able to pass back into those smart bidding algorithms that is really going to give us the edge and then to kind of close that off. That's why measurement is so important. Because if we don't have the right measurement culpability, then ultimately the signals that we're passing back aren't verified, aren't maybe as high quality as what we believe them to be. Therefore, the signals that we're passing back, we're not able to deliver the outcomes that we'd like to. And so I think that we have as many challenges as what we had yesterday, they're just changing. They're different. And so the advertisers that are going to get the edge, the vendors that are going to get the edge, are the ones that embrace that. We accept that we're changing. We don't try and kind of cling onto the past, and we accept that it's changing, we take advantage of it. And that's what I'm really excited about at Lunio is how do we take advantage of that?
- [35:52]
B
And I'm, I'm curious to get your perspective as, as marketers shift into, into this opportunity. Right. And taking advantage of the future of where digital marketing is going, what are some of the risks that we should be aware of? Like, what are some of the, the pitfalls, maybe even some of the experiences you've had in your career that we should avoid?
- [36:14]
C
Yeah. So I think that, you know, if we don't get that data component, right, and we are passing back, as we would refer to at Lunio, dirty data, you know, non cleaned signals into the bidding algorithms, then ultimately those agents are going to be incredibly good at finding more and more rubbish. And so that is a problem, right? We have to solve the data quality problem in order to be able to get the outputs that we want from agents. And I'm a firm believer that we need to embrace agentic capability, we need to embrace AI first solutions. But we also have to recognize that if we don't get the signals and the data inputs right, then the waste problem, the invalid traffic problem, the fraud problem compounds. And that is a big challenge. And that is then equally linked to the measurement piece, right? If we don't have measurement sophistication, if we don't treat measurement as our first priority, then ultimately everything upstream of that is to some degree irrelevant because we are basing decisions then on flawed measurement solutions. And by the way, I just want to preface this, which is, I'm not saying that there is a perfect measurement solution out there or there isn't a perfect measurement capability, but I think that we should be treating it as our first priority. Because if we treat it as our first priority, everything upstream of that can be better. Whereas, you know, the way that I think about measurement, by the way, is, is probably a bit of an insurance policy. Nobody wants it until they absolutely need it. But when you need it is critical.
- [38:04]
B
Absolutely. I think it's a great statement around how we should be thinking about our capabilities in digital marketing. If we don't actually have good quality data, if we don't actually have really good quality inputs, everything else downstream fails. And you need to build in the systems around that to ensure that we're reaching that capability. So it's a really valid point, but I want to transition to my favorite part of the show, which is what we call our lightning round. So Chester, I'm going to ask you five questions from the themes that we discussed today and we have you give me a 30 to 60 second answer on each. Sound good?
- [38:43]
C
That sounds good. Awesome.
- [38:44]
B
All right, first question. What is the most overrated marketing metric today?
- [38:52]
C
So I'm actually going to give you two, I'm going to give you, I'm going to give you a really obvious one which would be clicks Optimizing for click is one of those vanity metrics that I, that I spoke about in a previous question that is not a marker of what's good and what's Bad. It is a, you know, it's a leading indicator, but one that is fundamentally compromised. So that's definitely one. The second one is a little bit more controversial and that is roas. And the reason that I say ROAS is because it is probably the North Star metric for most advertisers, but it disguises the truth. And I believe that as an industry we should be focused on marginal roas. What is the diminishing returns when we invest in a certain channel, platform, campaign, audience, creative, in order for us to be able to drive overall effectiveness. So marginal ROAS would be the focus. Roas for me is the enemy.
- [39:56]
B
Love it. Excellent. So next question for you, Chester, is what's one thing marketers are trusting too much?
- [40:07]
C
I would say their own measurement capability. And that's not every advertiser. But as I said in a couple of earlier sections, I think as an industry we've been complacent around measurement. We've largely relied on last click attribution. It's fundamentally flawed. It over credits certain channels, it undervalues others. And actually for me, in order for us to be able to drive overall effectiveness, for us to be able to drive real improvements, we first have to solve the measurement problem. If we solve the measurement problem, everything else upstream can be more effective.
- [40:45]
B
Absolutely. What's a major misconception with AI marketing, specifically paid or organic?
- [40:56]
C
I think that it's going to solve all of our problems. And I think the second part is that the human is no longer needed. I think that couldn't be further away from the truth. I think the reality is, as we've spoken about throughout this podcast, is AI is absolutely incredible at delivering on against the objective that you give it. But if you're optimizing towards a compromised outcome because your measurement is not right in the first place, or it's being fed information that is fundamentally flawed in the first instance, then it's useless. The data input, the prompt, whatever it might be. But the input is critical. And that's where the human comes in. That is the role of the human moving forward is how do we fine tune the input in order for AI to give us a better output? But human oversight and augmentation for me is still business critical.
- [42:02]
B
What is a major marketing signal that digital marketers should be paying more attention to?
- [42:10]
C
I kind of answered this in my previous question. I think I would say marginal roas and incrementality. I think that we need to try and do more as an industry to get closer to that answer in order to be able to drive overall effectiveness. And I think the overall effectiveness ultimately is what will drive growth and market share wins over time. And so I think, yeah, marginal roas and an incremental contribution as we look
- [42:38]
B
ahead, maybe two, three, five years ahead. What is a measurement that marketers wish they would have been tracking sooner?
- [42:51]
C
I keep these questions are leading into, into each one, Jordan, but I, I would absolutely say obviously marginal roas, because that, that's, that's kind of what I answered the previous question and it's directly tied to this one. But, but ultimately, I think if you look further upstream of that, I think it's about trying to validate, verify the quality of the input and tracking that quote the quality of the input, not just accepting that all traffic is equal because it's absolutely not, not accepting that all signals are equal because they're absolutely not. And doing more to understand the quality of each individual input which allows you to deliver the better outcome.
- [43:35]
B
Absolutely. And that wraps up this episode of the Voices of Search podcast. A huge thank you to Chester Scott, Chief Strategy Officer at Lunio, for joining us. If you'd like to contact Chester, you can find a link to his LinkedIn profile in our show notes or on the voicesofsearch.com you can also visit his company website, Lunio AI. If you haven't subscribed yet and want a daily stream of SEO and content marketing knowledge and your podcast feed, hit the subscribe button in your podcast app or on YouTube and we'll be in your feed every week. Okay, that's all for today, but until next time, remember, the answers are always in the data.