Growth Beyond Google: Diversifying Spend Without Losing ROI (w/ Matt Shenton) | S3 EP5

Speaker A: On TikTok you can buy in different formats. You know you can pay, you can pay cpc, CPM or impressions. There's different buying mediums. Like what's the one that makes sense from like a volume and audience perspective?

Speaker B: Hello and welcome to the Paid Media Lab. My name is James, I'm the content lead here at Lunio and in this episode I'm joined by Matt Shenton, Biddable Director of Crowd, founder of Paid Search NYC where he hosts both a podcast and and an in person event series for performance marketers based in New York. Matt's podcast is great with really high production value and brilliant guests including a few who've also been on this show. So if you enjoy these kinds of conversations, I definitely recommend you take a look through the back catalog of the Paid Search NYC podcast. Link to that will be in the description. Matt has been a Crowd for the past eight years and moved from the UK to their New York office in 2021. In that role, he's worked across almost all industries and developed paid size strategies for some large multinational brands including Amazon Prime Video. And in this conversation Matt shares his tips and guidance when it comes to diversifying ad spend beyond the Google Ads ecosystem, which is something a lot of mid sized businesses think about when they're trying to drive additional growth and scale up to that enterprise level. Even on this podcast we are definitely guilty of over indexing on the Google Ads side of things, but it is important to consider the other channels that are out there and how you can tap into them effectively to drive incremental growth. Whether that's the big players such as Meta, TikTok and Microsoft or the more niche platforms like Reddit and Pinterest. Matt shares his framework for fairly testing new platforms without burning through cash and killing your roi. The biggest mistakes he sees marketing leaders make when it comes to budget reallocation and the tools and metrics you can use to judge the the true impact of any new channels you decide to add into your mix. If you're planning to diversify your paid media strategy in 2026, there is a lot to take away from this one, so let's dive in. Matt Shenton, welcome to the Paid Media Lab podcast. It's great to have you here.

Speaker A: Thanks James. Yeah, great to be here.

Speaker B: Uh, you've had some great guests on your own podcast, Paid Search nyc, which people should definitely go and check out. One of your recent episodes was a deep dive on AI Max with Kirk Williams. Kirk is a former guest on this show all round. Really nice guy. Uh, so well worth a listen and I think, like us, for you, Matt, it's now approaching a year since you first launched the podcast, so I was wondering, first of all you could just let us know about what that experience has been like.

Speaker A: Uh, yeah, sure. Well, first of all, happy birthday to uh, both the Lunio and paid search podcasts. Uh, I didn't realize it had been a year. That's crazy. Um, the process has been really fun. It's a great way to connect and speak with really, really smart people in our industry. And honestly I've approached it almost like a, uh, I get to learn so much more these people. Um, obviously it's great when the content does well and people watch it, but just from the host perspective, it's great to talk to people and become really curious and ask really cool questions, um, and speak to these really cool people. So yeah, it's been a lot of fun. Carry on doing it as I'm sure you guys will. Um, yeah, it's, it's, it's been great.

Speaker B: Yeah, for sure. Um, sometimes I get a little bit out of my depth in the conversations because you, you have more hands on experience with paid media than I do. Like I come from an SEO and content background, so sometimes I'm kind of like I'm really trying to brush up on my knowledge in some of these conversations. But you're, you're based in New York, but you were, you, you moved there a few years ago from, from the uk. And one of the things I'm interested in, just selfishly curious, like, do you see any differences in marketers between those that work in the UK and the us? Is there a kind of different mindset in the marketing world or is it very broadly similar in the way people approach things?

Speaker A: Yeah, it's a good question and um, definitely one I've asked before, I think, to be honest. Pretty similar. Broadly similar. You could, you could maybe say that um, in the US there's a greater appetite to test and move a bit quicker, but even that feels a little bit unfair. I would say they are broadly similar. Um, yeah, if anything, maybe the appetite for a bit more risk.

Speaker B: Mhm, for sure. So on that note, today we're going to be talking about hedging your bets and reducing the risk and diversifying your spend beyond Google Ads, which I'm excited to get into because it's something that I'm hearing crop up more and more in my role speaking to different people in the industry. We have some big clients at Lunio who are considering moving some of Their budget that traditionally would have gone into Google, they're now considering, do we want to move that across to meta? Multiple people have been telling me about how underutilized Microsoft ads are as a channel, especially in the B2B space. And when you couple that with the fact that year on year CPCs are rising fairly significantly on Google, I think it's a good time to explore what other options are out there. Even on this podcast, we've been very guilty of being very heavy on the Google Ad side of things. Um, but these diversification conversations are becoming a bit more common, uh, especially I think for mid sized companies who are looking to scale up and drive that additional growth on other channels. So to kind of start things off, uh, if you were speaking or if you were dealing with a client that was heavily invested in the Google ecosystem and they were spending, um, the vast majority of their budget through that particular channel, uh, obviously the answer depends and you can't be very prescriptive about what you'd be doing here, but in terms of the initial advice that you would give them and when the mindset that they need to approach diversifying this bandwidth, are there any pointers that you would give people to steer them in the right direction?

Speaker A: Yeah, for sure. Uh, I'm glad you covered off on the Depends caveat before I did. Um, because even in your sort of build up there, you talked about the fact that Microsoft apps are B2B or mid sized companies. It depends on your vertical, the size, maturity. Um, so that caveat out, the way I think, the way I think about it is, uh, a couple of different layers to it. So first of all, there's almost always a portion of Headroom from a setup perspective. So if we're talking about do we diversify in Google, the first question I would ask is like, well, are you set up in the best way? Because there could be an opportunity to get more out of your existing media within that platform. So this will make sure best practices are all in place. Um, and we know it's very easy to have the wrong setting in place or not have the best structure. Uh, uh, and sure the return on these things is lower than it used to be, but there's still a benefit to following these best practices. So first of all, are you set up in the best one on the platform? The second one is Headroom. So again, and I'm not quite getting out of, I'm not quite answering your question of like how diversify yet, because I'm kind of like there are two parts to that First. So first is setup, next is headroom. If you're just in core Google search then uh, and nothing else, no pmax, no demand gen, then you're probably going to be hitting headroom pretty quickly because you're just going after core search, um, you know, very, very high intent, high commercial terms. There is an opportunity to expand within Google to the rest of Google ecosystem. Like Google isn't just Google search, Google is YouTube, um, which in itself is massive. And I think most clients uh, don't come to us and say we've maxed out YouTube. Um, so I'd say there's always like an opportunity to expand in the Google ecosystem. And even if you're a mid sized business, there's probably more you can do to grow within the Google ecosystem. And I think it's important to make that point because that is still one of the most sophisticated media landscapes available. Like from a uh, sort of variety of inventory, video, image, text to the quality of the platform data, the first party data, like how much Google knows about you and that obviously feeds the targeting. Like you can scale across the funnel in multiple formats all within the Google ecosystem. So that's my don't sound too pro Google but like I think it's important to think about things in that way before diversifying straight away. I think once you've kind of done search, you've done YouTube and you're running YouTube in a way that's like multifunnel. It's not just all CPA based. You're sort of getting that into consideration. You're running demand gen. You're probably then in a position where you've got assets that you could then repurpose into other channels like meta. Um, so I think if you can do demand gen in Google and that's working for you, you'd probably be able to get it to work in meta just because those products are relatively similar and the creative is the biggest part of what dictates if it's good or not. So one's good and probably do well in the other. Once you've kind of done that and you've got Google running well, you know, across the, across the core uh, ecosystem you've got meta running well. You've then got like a more sporadic choice of platforms and I think then you probably start to think about well where's the audience I'm going after over indexing, you know. So I'll give you an easy example would be like TikTok frim beauty. That audience over index is from a discovery point of view on that platform. Um, or it could be you're going after, uh, I'm trying to think of an example. It's not quite as good, but maybe like B2B SaaS, you might go Reddit because there are people researching those crowd terms. So you kind of have to be a bit more deliberate, uh, in terms of like, where's the audience over indexing because you don't necessarily need too much like Google and Meta because those platforms have mass appeal. Um, and then beyond that, it's a case of where you can buy media on these other platforms in different ways. You can do it on a cpa, you can do it on a cpc, sometimes it's a cpm. So you factor that in and then you've got. Well, what's the platform's quality of first party data? Uh, Google's kind of the gold standard. It's got all this data, uh, people use all the different platforms. If you compare that to a Reddit, Reddit doesn't have a ton of first party user data on you compared to Google. And so the targeting isn't as good as, and so the CPAs might be higher. So you kind of get, as you get into these other fragmented platforms, you have to sort of look at these other considerations from a diversity perspective. Um, so that's how I'd look at it. Very short answer. Not really.

Speaker B: It was a very challenging question to open with. So I think it's a very, um, fair and balanced answer. So to uh, think about just budget reallocation specifically. So thinking about, you know, we've got clients at Lunio who are considering. Okay, does it make more sense to, to pull budget out of Google and put that into Meta and drive that upper funnel awareness and then see if we can capture more of that later down the line. These are obviously quite big commercial decisions and there can be a lot riding on them. And you know, in your experience working with different clients in the industry, have you seen any mistakes that tend to crop up when people think about these budget reallocation decisions? Like making too big of a change too quickly or not giving the new channel enough Runway and kind of data to get up to full speed? Is there anything that you've come across when people uh, are making those reallocation decisions that get them into trouble?

Speaker A: Yeah, to the top two are certainly pulling the budget before you've really given it a chance to prove itself. I think one of the most interesting things that's happened within the industry from an education point of view, is this whole idea of the learning period. I think the platforms have actually done pretty well at helping uh, practitioners understand the benefit and the value of that. And ah, that learning period, you can probably break it down into both for the algorithm to collect data and understand, okay, these are the people you want to go after, but also the, the time between media and action, uh, when that period of time is a little bit longer. I think that's really important because not everything is instant payback. So the learning period's been a really good way to educate the industry. But you've got to be aware of what that is, platform to platform as you go into it. Otherwise you could pull it too soon and you've not given it the best chance. So that's the first one. I'd say the second one is probably uh, maybe, maybe along the lines of like trusting the platform's data a little bit too much. Uh, um, so you know, putting, putting money in you kind of the opposite problem. It looks really good, does really well put more money in and you realize, oh, actually based on a sort of impartial source of truth. This isn't actually doing as well as I, as I thought it was. Um, it's funny because like the um, the crazy thing is usually the cheapest conversion is sometimes the least incremental. So uh, you've always got to be, you've always got to watch out for that. So those, those are the two main, main reasons. But um, I probably see more the first than the, the latter.

Speaker B: Yeah, yeah. It's funny how each platform will engineer its own analytics to make themselves look as as good as possible. You know, that's, that's the nature of the game. But you're right, it do need to be careful. We'll get into those attribution conversations uh, a little bit later, uh, in the interview. But right now just, just speaking about, just I guess for a sense of you personally, um, you know, everyone has their own preferences and um, the things that they're interested in. But for you right now, um, which platforms excite you in terms of you think there's a big opportunity there that people maybe don't see and aren't using as, as well as they, they could. Um. What, what's exciting you at the moment?

Speaker A: Yeah, uh, it's a really hard, hard one to, to answer because I kind of, I'm kind of excited by everything for different reasons right now. Um, like I'm, I'm excited about platforms that have been around for a little bit for, for, for a while but are just getting easier to execute on. So uh, something like, you know, CTV is getting easier and the barriers to end trigger getting lower there. Um, TikTok shop has been around for a while now and I have, you know, I know people are actually killing it on TikTok shop which is incredible. Um, the growth in micro and nano influencers and UGC content, like being able to scale that more. Super, super interesting. So there's like this kind of continuous development and innovation in all these channels is really, really cool. I think the one that maybe is a bit more emerging and uh, this is the first time we said AI in this podcast. I think so, yes.

Speaker B: Well I said AI Max in reference to your episode with Kurt.

Speaker A: But second, yeah, second term, um, emerging is probably more the AI stuff and I guess to qualify that a little bit more. What I'm really interested in is this idea that AI search and conversational search is just different ball game. I think we know now that it's not necessarily a zero sum game. It's not that ChatGPT has come in and has x percent market share and has taken that all from Google. It's an additional way we're using the Internet. It's an additional way we're showing up digitally. And uh, in some ways Google is even growing as a result of this because it's released its own AI product and conversational search experience. But what I find really interesting about that is if you take the way Google's made money in terms of very high intent commercial terms, and I think I was looking at a study a few weeks ago from a while back that showed that Google is 14, 15, 16% of searches. Google searches like really high intent, high commercial. And then OpenAI released that um, study a month or so ago and I think they had commercial terms at like 2 or 3%. And so the percentage of uh, you can't say directly comparable because those two studies probably use different methodologies. But let's just say for argument's sake that they were somewhat comparable. The amount of commercial terms that are comparable to Google OpenAI are a lot smaller, which kind of makes sense because the way we're using OpenAI ChatGPT LLMs for me personally is not so much, uh, Adidas trainers, it's longer form stuff. Some of it's not even um, searching for products. It's like productive work rewriting content. So if you think about what AI search and conversational search is and what it could be, it's almost like there's an opportunity to collapse the search funnel because you can take Someone who's having a conversation about something that's very informational, discovery based and then take them on a journey through that conversation to the point where they then buy a product. And it's most evident in probably chatgpt because whenever you converse with it it always says at the bottom, would you like me to do this, this as a follow up? And you can just go, yeah. And then so it carries on the conversation. And so if you imagine if they engineered that uh, experience to guide you to a point where they then put something in front of you as a product or service, it's almost like a salesperson, I've got you talking now, Uh, I just need to find out how to sell to you. If they re engineered it to do that, then their ability to convert those terms uh, increases massively. And that's huge because then you're basically collapsing the search funnel if they do that. That's a game changer. Um, I think it's probably pretty difficult because Google just had to interpret, interpret a lot of data points but in terms of what you're searching it's pretty simple. Whereas these LLMs have to interpret really long form text content and that's really difficult to do. So I think it's gonna take a bit of time. But I'm just really interested to see how that shows up as like another medium because it probably will be another medium. Like conversational search will be a new, potentially a new skill set. Um, probably pretty much is already a new skill set versus like traditional search. So I'm excited for that to become its own thing. How we buy that, how we show up there, like how advertisers get in that space. Um, very, very interested by that.

Speaker B: Yeah, for sure. I think, um, if we fast forward a year, I would be astounded if ChatGPT hasn't monetized through ads. I know Sam Altman's pretty reluctant and keeping his cards close to his chest about what they're doing and what that model is going to look like. But there's no way that they can monetize that or you know, become a profitable business unless they run ads. It just doesn't work. Um, so they're going to need to develop something and like you said, you know, I'm excited to see what that looks like. It's still a gray area. One thing that I was paying attention to more recently, I don't know if you caught the headline about perplexity turning down and not accepting new advertisers and it seemed like I'll Be honest, like I haven't read into it in great detail, but from the bits of the story that I did catch, it seems like already advertisers weren't seeing great results and it kind of seems like they're taking a step back and rethinking about how they're approaching things over there. I don't know. Have you ever used Perplexity? Do you ever dabble in that kind of offering that they put out there?

Speaker A: Uh, not, not the ad product, but just from a user perspective, I've definitely, um, I think I went through a period of maybe trying like perplexity, Claude, Gemini, ChatGPT all at the same time.

Speaker B: Which was the winner for you?

Speaker A: I think different ones for different things. So I really like Gemini for coding and data analysis manipulation, ChatGPT for writing and just general, um, ideation and then Claude more so for like long form analysis of like text documents. That's what I found. Uh, Perplexity kind of fell by the wayside for me a little bit. Like I felt like I got more from the other ones. I didn't need to throw in a fourth one. Um, yeah, the ad product stuff's really interesting because you could make that parallel to like. Well, if you're trying to monetize conversational search, it is actually really difficult to do that because of the things I mentioned before. Um, and also they're smaller than ChatGPT and then ChatGPT's commercial terms are smaller than Google. So you're kind of like coming down and so you can understand how like advertisers might go on there and be like, yeah, the performance isn't great and the volume small. Um, I don't know that for sure, but just reading between the lines, it sounds like that was probably the case. Uh, so yeah, it's a hard problem to crack.

Speaker B: Yeah. But we will wait and see what uh, plays out over the coming months. So if we think about. So we've got a marketing team and they've decided they are going to diversify their spend and um, maybe they're pulling some spend out of Google and they're putting that into uh, Meta and TikTok. Um, if they're in the E comm space, is there any framework or guidance that you would recommend people follow to test those channels fairly and without preferably burning through a load of cash in the process?

Speaker A: Yeah, I think part of it is that kind of audience, uh, audience analysis in terms of where do they ever index on these platforms? So audience analysis and then for some platforms, depending on where you're going there's also like just from a uh, market size on that platform. Like some platforms you only have a finite amount of time. You'd only running 10 different platforms. So it's also worth just making sure the platform you're looking to advertise on still has the volume in terms of your market. So volume, audience. Uh, and then I think it's worth thinking about like the, the level of data they have available and, and kind of how they are as an ecosystem. I kind of made that comparison like Google's an ecosystem and lots of different products. There's loads of data that makes the targeting really good versus like a Reddit, versus maybe like a TikTok. So you can kind of infer roughly what that looks like. Um, and then I guess it's like how do you buy media on that platform? Um, on TikTok you can buy in different formats. You can pay, I think you pay CPC or you can pay CPM or impressions. There's different buying mediums. What's the one that makes sense from ah, a uh, volume and audience perspective? Um, to give you an example, you could do TikTok search, but if the search volume on that platform is very small then it's not really worth it. Um, but you could buy an scpm, but does that make sense from where you are from other platform opportunities? And the last thing I'd probably say is um, just think about how you then judge the success of that platform. So once you put money in the early stages, you're probably going to be looking to try and benchmark engagement metrics with somewhat similar platforms that you're running as that kind of early read. Is this working obviously bearing in mind learning periods, et cetera. And then as time goes on you can kind of move to those sort of harder metrics, um CPAs, um ROAs, et cetera. Uh, that's how I think about it.

Speaker B: Yeah, it's a great question. That was going to be like my follow up question about those kind of metrics that you'd be looking at to measure the success of those new channels. And you know as, as you said previously you do need to be careful there because you know each channel has a vested interest in making the performance metrics look as good as possible on their end. I'd be interested like uh, in, in the past we did some content around market and efficiency ratio and rather than looking at platform specific roas, you know, taking your total revenue, dividing it by total marketing spend and it kind of gives you a more holistic review of all your marketing efforts. And um, I think the example that we had in the article at the time was that you might have, for example introduced Reddit ads and you might be looking and the metrics that you're seeing on roas for Reddit specifically might uh, not be that impressive and you're not that happy with it. So you pull the budget from um, Reddit and then all of a sudden you know, that impacts Google or it impacts another channel where you might not have thought, uh, that that was going to be an issue. Like is market and efficiency ratio something that you look at? Or, um, is there any way to kind of get a more holistic view of how these channels are working together to ultimately drive the business outcomes that you want?

Speaker A: Yeah, I think the way that you do that now in the most accurate sort of data savvy way is mmms and um, geo switch off incrementality studies. That feels like, especially in that example you gave, where one channel is influencing another, um, mmms usually the sort of methodologies that surface that relationship, obviously that takes a lot of data and can be expensive. It's getting easier to run those things. Um, you know, the barrier to entry to those tools is getting lower, requiring less data. But that's probably the best way to figure that out.

Speaker B: You know, we've been talking about other platforms, but the reality is like Google is still going to be an essential component of any kind of modern marketing strategy. And for the majority out there, that will be where the bulk of the budget goes. And as you alluded to in your early answers, that there's so much scope for growth even within the Google ecosystem that people probably aren't tapping into. But if we think about the search side specifically and some people feeling frustrated that these margins are getting tighter and tighter each year with the CPCS going up. We've done some podcasts in the past here in the Paid Media Lab about combating rising CPCs and changes that you can make to your account structure to help combat that. But is there any advice that you give to marketers when it comes to maintaining that efficiency of their spend through, you know, search specifically?

Speaker A: Yeah, uh, I remember back when there used to be hacks, um, that was kind of how we answered that question. And it was, it was pretty fun. I don't think there's many hacks left these days. No, which is unfortunate. Uh, I mean, best practice, account structure, um, there's loads of information out there now. I will say there's probably like five or six different ways you can probably set up an account and they're all pretty correct. Um, but there is generally like a good way of doing things. Um, so outside of structure it then just becomes the data you're feeding in, you're bringing in really good first party data, you're bringing conversion data that is as close to actual business value. But this is all best practice stuff. I think once you've exhausted all of that, it's then a question of incrementality. And this is probably the one area that still is more often than not an area of opportunity for optimization. And I'm talking specifically like brand search. Like you're not. We don't find many clients these days who aren't M bringing in like good um, CRM lead data with LTVs or E COM with LTVs or profit data like M. Most sophisticated advertisers are doing that now. There's still quite a few that are like heavily invested in brand search because they haven't quite figured out how to come out of that space in a way that everyone's happy, um, and doesn't damage the numbers within a silo. Even though if you take search holistically it looks fine. Um, that's probably still the one area of optimization. Um, there's incrementality, but that feels like that's probably the place you get to with every platform. Eventually you optimize every platform to a cpa. And the final question is, well, if I just turn this off, would they, would they still count? And that's, that's kind of where we are.

Speaker B: Yeah, it's a really interesting point like that. And you know, before we switched on the mics, we were talking about that brand element and the incrementality from that. And in those kind of. If you've worked with clients and you are looking at that brand spend and wondering, you know, is this really, is this driving incremental value? Is it a case of you would just pause all spend on the brand campaign or you just significantly reduce it? Or like if you, if you want to test right. How much value is our brand campaign actually driving here, uh, on search, like what would you go about doing to assess that?

Speaker A: Yeah, first, first step is just getting a reporting view, bringing that data together, paid and organic, um, and getting a view of the landscape. Like where's the overlap? Where am M I P1, where am I spending heavily? Um, and at Crab we've developed those tools, we have reporting suites that bring those two data sources together. So that's a starting point. There's then I Guess two different ways you can approach testing coming out. We have solutions that help advertisers come out, um, on a kind of, I don't want to say non real time but like more of an action you would take on a less frequent basis. So you come out and then reassess maybe after like a couple of weeks or a month. That works for advertisers who are in a less fast paced volatile market where you've not necessarily got new competitors coming in all the time. So we develop those solutions to help advertisers come out, save money in brand, maybe put that into non brand for advertisers that work in a space that's more dynamic, more fast paced. I think maybe finance. Certain parts of finance There are automated third party tools that can scrape a syrup every 5, 10 minutes. Is there a competitor? Are you P1 in organic? Put a negative keyword in, pull the ad out, save the money, reassess. Um, and that's like a more effective real time solution but again it's more for advertisers in volatile fast paced environments. Um, so I'd say yeah, reporting first and then approach testing coming out based on how dynamic the marketplace is.

Speaker B: Yeah. Okay, so I got, I got two more questions and it's, we've spoken about, spoken about attribution already and um, yeah, this is, I'll be honest, this is over my head and it's, it's, it's beyond my abilities and technical expertise. Um, but I'd just love to hear you maybe talk about your uh, your thoughts, your mindset when it comes to attribution. We, we did a podcast earlier in the series with Ron Fishkin and he's kind of um, he thinks like attribution can, can lead people astray quite a lot of times. Um, especially obviously you know, he comes from a big SEO and content background and because it's very easy to put paid media and um, you know, Google search and meta and whatever, you can put that into a spreadsheet. You can say we're going to increase span by this much and this is expected to deliver this result and you can put that in front of your CFO and your CEO and it's easy to get sign off on that. And then when it comes to content and organic and brand building activity, it's much more difficult to put that into a spreadsheet and say we're going to put X in and we're going to get why. And then this leads to this like over indexing on the paid and performance side. And then kind of forgetting that oh, the brand and the content and how you're perceived out there does matter and that does affect how the paid side performs as well. Um, but fairly broad question, just your thoughts on attribution and it is a bit of a mess right now and it's very challenging and people, uh, I'm not even running campaigns but I feel like I'm tearing my hair just trying to understand it. So God only knows how difficult it is for those people who are in the trenches. Uh, really hands on with this. But yeah. How do you think about attribution today?

Speaker A: Yeah, great question. I wouldn't necessarily say I feel like an expert either, um, just given how quickly things change. But just before I dive in the point, I'm actually organic and how organic is influencing paid a subject from the time maybe, but that in itself is a really interesting thing that's changing in the industry right now. But that's not the question you asked me. Attribution, um, uh, I think the way I think about it is within walled gardens, particular ecosystems, there is uh, an argument that you want as much data attributed within that as possible because that helps fuel the algorithms, helps the machine learn. And so you kind of want a good level of credit being taken there from a data perspective. So if you want that to happen, it happened in all these different platforms you're working. So you can kind of get the most out of these platforms from like a, uh, media effectiveness from their perspective point of view. And then you obviously have this third party attribution which probably based on you uh, know, methodology like last click, you then obviously come up with a bit of a problem in that uh, you're probably overweighting or down weighting certain types of media based on that methodology. I think to be honest, the only way to really get around it is MMMs and incrementality and I think that's probably where we're going to get to. I think the way I think about it is the platform data is like I said, you want as much as possible to feed the algorithm, it's directional. So hopefully if platform conversions are generally increasing, overall conversions are increasing. So platform data is important, it's directional. And then to really understand the true impact, you're running MMS and incrementality studies because otherwise you're kind of having to create this model of reality which is really hard to match reality. There's loads of these attribution systems you can build and put together, but there's going to be so many gaps in the Data that the success of your overall marketing effectiveness is M. Obviously the success of overall marketing effectiveness is going to be partly dictated by your model, your attribution model. And if you get that wrong, then you can fall into a place where you're spending money on stuff that isn't incremental. But I think it's so difficult these days to get that model exactly right that you kind of have to go with your best guess and then use these longer term systems tools to validate the true value and then adjust the model. So I guess one way you're kind of working in different time time zones like platform is today hourly. Um, your source of truth is maybe more on a weekly monthly basis and maybe then your mmms and incrementality is like a monthly or maybe in quarterly basis. Uh, um. And so if you do that all, well it all kind of feeds each other and you optimize in a smarter way over the long term whilst also not losing the ability to optimize to a certain extent in the short term. If that makes sense. Yeah, hopefully that answered.

Speaker B: Yeah, no, I like that framework of thinking about it in different timeframes. And yeah, like the data that you get in out of the platforms is a kind of day to day stuff, but you do need to look beyond that as well to make sure that you are going in the right direction. Yeah, that makes sense. Um, so before we finish going, uh, to go for the crystal ball question, obviously this is just speculation. It is interesting. Anyway, no one knows what's going to happen next month, never mind a few years from now. But if I'm going to force you, Matt, to make a prediction. Initially I was thinking five years, but that seems like an incredibly long time for today. So let's wind up back to two years from now. Um, do you think that Google is still going to dominate the paid media or will the landscape be a bit more evenly distributed? The one thing that I will say is that the recent antitrust ruling where Google kind of didn't have to sell off Chrome would suggest that there's not going to be a massive redistribution. But I'll pass it over to you for your thoughts on what you think the landscape will look like if we project two years ahead.

Speaker A: Yeah, I mean the short answer is, uh, yes, I do think Google will still be in the current position, if not even more dominant. And the reason for that is, uh, as you mentioned, the regulatory risk feels like it's fallen away. And if we were at peak Google dominance, we've kind of passed that uh, now with all these other ChatGPT LLM players in place, feels hard to believe that if you can't break it up just as these are emerging once these guys have got fully up to speed, that it feels even harder to break up. But I'm not a regulatory expert, but just based on that, seems like that's less of a risk, um, from a user behaviors perspective. Like I said before, we kind of know now that it's not a zero sum game. These LLMs are, ah, additional ways. They're not taking from Google, so Google isn't losing massive market share. So regulatory, less risk, user behavior, less risk. Um, and then if you just think about who has the tools, the data and uh, the experience to figure out how to monetize this new way of searching, who are you going to choose? Are you going to choose the company that's been doing it 15, 20 years? Um, probably. Um, are they on the back foot a little bit with conversational search and AI? Yes. Have they caught up? Yes. Do I think they're in the best place to figure out how to monetize it? I think so, yeah. And so when you think about it like that, then it's hard not to believe that they're at least going to maintain their position, if not slightly grow it, but they're also growing it. Uh, even if they do grow, it's growing into like a bigger pie. It's not like they're taking away from the LLMs, it's growing because the pie is getting bigger. So that's also an important consideration in that their marketplace is growing like the world they operate in is getting bigger as well. So that's obviously a tailwind for them.

Speaker B: Yeah, yeah, I'd have to agree with you. If I was in the fortunate position to have loads of cash to invest, I wouldn't feel hesitant about putting it into Google, to be honest. Maybe one day. But yeah. Uh, I just want to say thank you so much for joining me today, Matt. It's been great to connect a lot for marketing teams and marketers out there to think about beyond Google, especially as these kind of conversational search experiences are becoming more and more common. And yeah, we'll be interesting to see how that plays. But before we wrap up, is there anything else you'd like to mention, uh, or let people know about?

Speaker A: No, uh, just like to thank you, um, and Lilia, for the opportunity. Love that you guys are in the podcasting game. Love all the content. Keep it up. Um, and, uh, thanks again for having me on.

Speaker B: Cheers, Matt. I'll see you on YouTube.

Speaker A: Sounds good.

Speaker B: That's it for this episode of the Paid Media Lab. Thanks for tuning in. If you found it helpful, hit the subscribe button to get the next episode as soon as it's released. Subscribing costs not, and it helps us bring you the best possible guests in the next season. And if you want more advice on how to boost your return on ad spend, head to Lunio AI, where you'll find loads more free tools, resources and guides. Lastly, if you know any other marketers who benefit from the tips in this episode, you can do them and um, us a favor by sending them the link to the show. That's all for this one, and I'll see you in the next episode.