How to Align Google & Meta Ads With Your Business Goals

In this episode, Nathan discusses how to align Google and Meta campaign builds with business and financial goals. He emphasises the importance of understanding the incentive structure of big tech advertising platforms, which is to maximise advertiser revenue. He also highlights the complexity of customer journeys and the need to allocate budgets strategically. Nathan shares insights on SOP creation, emphasising the importance of data-backed SOPs and having a mental model that explains why. He provides specific strategies for Google and Facebook campaign optimisation and emphasises the correlation between performance and top-line revenue.

Takeaways

• Aligning ad campaign builds with business goals is crucial for maximizing profitability and scaling.
• Understanding the incentive structure of big tech advertising platforms is essential for effective ad campaign management.
• Customer journeys are complex, untrackable, and require a deep understanding for efficient ad campaign optimization.
• SOPs may not be suitable for high-performing teams due to the constraints they impose on competent individuals.
• Values and fundamental understandings of ad platforms play a significant role in decision-making and campaign optimization. Understanding the customer journey is crucial for effective digital marketing.
• Strategic testing and budget scaling are essential for maximizing ad spend.
• New customer acquisition plays a significant role in determining the success of ad campaigns.
• Attribution models and budget allocation require careful consideration and testing.

Chapters

00:00 Introduction: Aligning Campaign Builds with Business and Financial Goals
05:24 The Complexity of Customer Journeys and Strategic Budget Allocation
12:12 Creating Data-Backed SOPs with Clear Mental Models
21:05 Optimising Facebook Campaigns for Success
27:28 Correlating Performance with Top-Line Revenue

Transcript

0:00

Welcome back to the Blues Brothers podcast. In this episode, we'll be running through how to align Google and Meta campaign builds with your business and financial goals. Now the issue with trying to design a podcast episode like this is that it goes out of date very, very quickly because the platforms are constantly changing. SOPs are changing the way that you should approach scaling. The platforms always changes. However, in this episode, we'll be providing you with fundamentals that should always exist within the platforms. so that you can come back to this piece of content in two years from now where maybe met on Google have died and now it's just TikTok. And you can still apply all of the core principles that I'm running through in this episode into your campaign builds over there. So we'll be starting off with the two core fundamentals to understand if you ever want to build a Google or Facebook campaign that actually aligns with your financial goals. We'll go through the principles of SOP creation. We'll go into Google, we'll go into Facebook. and then we'll go into correlating performance holistically across multiple different platforms. Starting off with the fundamentals. If you are going to build a campaign on Google or Facebook or any digital marketing platform, there are two fundamentals that you need to understand, which most people either don't even know about or completely neglect. Number one is the incentive structure of big tech and number two is customer journeys. Let's start with number one. Understanding the incentive structure of big tech advertising platforms is the fact that the job of Google or Facebook is to maximize advertiser revenue. And the reason for that is that I believe the statistic is that Google's revenue, 80% of it comes from AdSense. And so advertisers coming and advertising on Google is the only reason why Google is one of the biggest companies in the world. It's the same for Facebook, 90 % of or 80% of revenue of Meta comes from advertising placements on the platform. And so they are so heavily reliant on advertisers going to the platform and spending that the entire incentive structure of these big tech companies is to either number one, drive more advertisers onto the platform or number two, squeeze more ad spend, more revenue out of existing advertisers. So get existing advertisers to spend more. Now they do both. You've seen a lot of advertising for Google and Facebook where they're trying to consistently get new advertisers on the platform. They're constantly running offers. get $500 free on Google ads. If you spend $500, that's just an offer to try to get more advertisers on. But the number two is that they try to get you to spend more. Now they do that through Google reps. They do that through meta reps. Their job is to really just get you to crank up spend in the platform. But they also do it through increasing attributed revenue. One of the easiest ways to get a brand to spend more on Google or Facebook is to simply say, Hey, you do a hundred K a month in top line revenue. And guess what? 80K of that is attributed to Google. And so now you as a founder will make the assumption that the best decision that you can make is put more cash into the vehicle that seems to be driving all of our revenue when in fact it is because these platforms love to over attribute because it means that advertisers will spend more. Now, sometimes there is actual good, there's good reasoning as to why something's in place. For example, I could sit here and argue that. Facebook's one day view attribution is a complete ploy by Meta to just over attribute conversions that has nothing to do with the platform so that you spend more as it looks like Meta is driving better results than it is. Sure, you could make that argument, but also you can make the argument on the other side that view through conversions are a really important part of getting better conversion modeling because you have higher conversion volume getting attributed through the campaigns and therefore that enables better targeting. And Meta does argue that. Meta argues that you see better incremental revenue when you have one day view within cost cap campaigns because you have larger data sets that the campaign can use to work with. And so when you start to look at really any argument that you could make for an incentive structure based ploy by one of these big tech campaign, companies, there's always going to be a counter argument that is somewhat valid, but it's still important to really understand this fundamentally. Google's job is to attribute as much revenue as they can. Facebook's job is to attribute as much revenue as they can. Clavio's job is to attribute as much revenue as they can because they can then price as high as possible. If Clavio is sitting here going, we're responsible for $2 ,000 in revenue per month and they're charging you $2 ,000 per month, you will cut Clavio. That's a very easy, rational decision to make. However, if you sit here and Clavio is claiming 20K a month, then you can sort of rationale it. And so that's why these platforms need to attribute. They need to attribute really, really badly. And one of the ways they do this for context is conversion modeling. So a lot of the conversions, or at least a fair few of the conversions within your campaigns aren't actually real. They didn't actually ever occur. Google and Meta use machine learning based models to determine how high intent a user is that clicks through. If they deem that this user will probably convert, they'll attribute it as a conversion. And so that's a whole nother video on itself, but that's fundamental number one. Fundamental number two is understand how customer journeys work. To keep it really simple, they're long, they're complicated, and they're almost untrackable. And this is where a lot of advertisers really mess up across trying to scale Google and Facebook, is that the customer journey is way longer than you think it is. If you think that your consumers are buying within one hour of first impression with your brand, they're not. Most brands that have... average order values above $250, you generally have like a 10 day conversion window. From first interaction to purchase, it takes a very long time for a consumer to actually decide to follow through and spend $250 with you. And so if you can understand that, you can then get a better idea for number one, how to structure and build campaigns. It'll reiterate the importance of heavy retargeting throughout that entire funnel. But number two, is it allows you to better allocate cash across campaigns depending on how the campaigns are attributed. Let me run you through an example. What I see very, very common, and we actually have a case study of this at the moment. I think it went up on the Blue Sense Digital YouTube channel yesterday on the 9th of June, which was that we brought on a client who was really struggling to scale. Their previous agency tried to 2X budgets twice, never worked. No incremental revenue uptick. from the doubling in budgets. Now, the reason for that is because that agency was looking at the customer journey as if it was simple and as if users bought immediately. And what I mean by that is they were looking at performance max and advantage plus campaigns, and they were going, that's where the highest return on ad spend is. That's where we should put the budget. But what they were neglecting is the fact that, and if you're listening to this, I apologize because I'm going to try to articulate this with my fingers here, which is, This is the first 80 % of the customer journey, and then this is the last 20%. And the last 20 % gets all of the attribution. The front end, the top of funnel awareness generated about the product, never gets attribution. Because either that was generated through a view through, or it was generated through a click, but then that click got diluted over time over multiple different browsers over a very long customer journey. And these platforms are very bad at attributing this far backwards in time. And so all the attribution. attribution goes to the last few clicks. Even if you're using data driven, you're using all of these different attribution models, it still ends up predominantly just being a last click attribution model because these platforms aren't that good at distributing all the way back to first interaction. And so what ends up happening is a lot of agencies, a lot of internal marketing teams, they're trying to scale up campaigns, but they don't realize that they're trying to scale the bottom of the funnel, but there's no new users coming in through the top. And so when you look at an account structure where there's a shopping campaign at a two ROAS, a performance max at eight, then on Facebook we have CBOs, let's call it ABOs with some audience suppression that's at a two ROAS. And then we have an advantage plus campaign that's in there at an eight. The obvious assumption is, okay, let's put all the budget into that advantage plus campaign and that performance max, because that's where the returns are. Wrong, won't work. once Gale, you'll see no incrementality on top line revenue, given a few assumptions baked in that. However, if you go and put the budget into the shopping campaign and the ABOs, after about five to 10 days, you'll suddenly see incrementality on top line revenue. How is that the case? It's because the shopping campaign was driving actual real new customers to site. So was the ABOs. The advantage plus and the performance max was the bottom of funnel campaigns that was scooping up the traffic that was being generated from the other campaigns and claiming all of the conversion. And so when you look at attributed ROAS in these platforms, it isn't indicative of where you should be looking to scale because it takes into account that only the last 20 % of the customer journey gets attributed and the customer journey is long complicated and almost untracked. So the two fundamentals are number one, understand that the incentive structure of big tech advertising platforms is to attribute as much revenue to them as possible. And number two is understand that customer journeys are long, complicated and almost untrackable. And so when you look at return on ad spend in platforms as an indicator of where to allocate incremental budget increases, it will not typically drive incremental revenue increases, which makes you think as an operator, these platforms don't work for me. These platforms aren't scalable. Of course they are. Right. I almost guaranteed that you have a competitor in your exact niche that's spending five X amount that you currently are on these platforms. Now you could make the assumption that yeah, but they don't know what they're doing and they're just wasting money and spending it inefficiently. Okay. Maybe. But is that a productive ideology to apply to your advertising spend, which is the most scalable component of your business? Probably not. Like probably not. It's probably a fair assumption to say that if. A competitor is spending 5x more than you than they're probably doing it profitably or else they'd be under by now. And if they aren't doing it profitably, but it seems to be profitable from the internal marketing team's perspective, then you have to ask, well, they're getting profit from somewhere to pay for all of this advertising. So where's that profit coming from? And maybe it's on retention. Maybe it's on organic. Maybe it's on word of mouth because they simply have a better product. So there's always something to learn. from a business that's doing more revenue from you, no matter how bad you might look up upon that business, right? And this is something that's actually really common within the actual agency space is that it's very easy to look at very, very large agencies who have scaled from zero to a hundred people in one year, which is obviously going to result in a significant erosion in any kind of core competency within the domain of the service that they're providing. And we can sit here and go, they're a terrible agency. But then we could also sit here and go, well, what are they doing? Well, how have they, how have they achieved that kind of scale that quickly? They would definitely have to have some kind of system in place. They would have to have something here. There's always gold to pull from someone that's higher up than you. And so that's also important to understand. when you get into a bit of a limiting mindset of the fact that you can't increase budgets just because you're in in-house media buyer or your agency couldn't increase budgets. Realistically, they probably put. the increased budget in the wrong spot. This then leads into SOP creation. I want to tag this into this podcast because I think it's an important caveat. Considering that we're talking about Google and meta structures and how to align them with your business goals. And we'll go into exactly some specifics in a moment. But I want to talk about SOPs. There's two principles of creating an SOP in my opinion. Number one is it needs to be data back. And a lot of people, you'd be surprised, create SOPs based on n equals one sample sizes, which just means that they've done one test once on one account. And now they generalize that across the entire of e -commerce or Google ads or Facebook ads. It needs to be data backed. You need to have at least multiple tests that can validate whatever your SOP is starting to create. And then some people think it ends there, but it doesn't. In my opinion, there's also a second... primary principle of SOP creation, which is having a mental model that explains why. And that's as simple as just saying like, have a hypothesis. Okay. So we run dedicated retargeting campaigns on Facebook as we hypothesize or the mental model is that it will have higher bid caps at auction, which will give prioritization of placements on retargeting audiences, which will force cold targeting audiences into actually cold targeting. That's the mental model. But now we have to have the data to back it. So we have to go and actually test that mental model in multiple accounts to validate whether that's actually true or not, to then roll it out as an SOP. A lot of people do one or the other. A lot of people love to just make a mental model and go with it. And I, to be completely transparent here, I used to do that two and a half, three years ago when we had very limited amount of accounts. It was just me running my own e -commerce brands. I had to create mental models and I didn't have... the statistical relevancy of having hundreds of accounts under management to be able to validate anything. So it was just, that happened. Okay. I assume that the mental model here is that. And so then I'm going to do this. And that's a fair way to work. If you're working on a single account siloed in, but that's also the limiter of most internal teams when they're working on one account siloed in without large data sets is that you start to, you can very much so. spin your way down a spiral of mental models that's completely in the wrong direction because you haven't had any kind of statistical validity put in place by testing across multiple accounts. And then a lot of people also don't do the mental model and they just have some data and then they try to pull extrapolations there, which also doesn't make sense because it might not actually align with how the platforms work. I also don't believe that SOPs are important or more importantly that they work for really high competency teams. What I mean by this is a really, you can judge the performance or yeah, the performance within a specific domain of a person based on how long the instruction manual has to be. And what I mean by this is that if you have a new performance marketer or you have a new e -commerce operator and you say, hey, go launch Facebook ads, that's the only instruction, go launch Facebook ads for the brand. Most new performance marketers, most new e-commerce operators would not be able to action anything off the back of that, right? But you could give that instruction to me and I would know immediately, okay, I need to go and create some creatives. I need to figure out what the value proposition is. I need to figure out what landing pages we're driving to. I need to figure out what structure we're going to go for. I need to figure out how much data they have previously is Facebook given the best option. Should we be going for Google? All of these questions naturally are built into that instruction set that I can then go and action straight off the back of it. And so if you wanted to give that to maybe someone with just six months experience, you would have to specify, okay, I want you to launch a Facebook campaign with this structure. I want it to be with these ads. I want us testing five ads a week. I want this, that, the other. And so you have to build in all of these parameters so that someone can actually go and execute on the task. And so when we talk about SOPs, this directly translates into that frame of thinking, which is that if you really restrict everything down to a standard operating procedure, it doesn't allow high competency individuals to go outside and execute rapidly and fast. And so if you had all of these constraints around me and said, Hey, by the way, you can only launch Facebook ads. If you're testing five creatives a week, if you're doing this, if it's in this structure, if it's like this, you have to have this many value propositions. This is how the hooks have to look this, this, this, this, et cetera, et cetera, et cetera. You start to dilute my core competency and being able to go and execute on a very simplified request and then extrapolate based on all of my knowledge that I've built over the last five years. And instead you just restrict someone down into a specific domain and go, Hey, go and apply this. And that works well for low competency teams and low competency individuals that need a very productized script as to how they are delivering a service or a task. But if someone is highly competent, they should not require a ton of SOPs around everything. And every SOP that you add, I believe, restricts the speed of the team. It slows everyone down. Every single SOP is just a putting brakes on a car. And so is that to say you should never have SOPs? Not at all. Like you want brakes, you don't want someone speeding and going off road. But do you want to slow everyone down to a standstill? Probably not. And that's what I think you see in a lot of very, very large organizations that have very, very rigid SOPs around a lot of different things, which is that everyone gets slowed down and you don't stay on the forefront of innovation on. whatever specific domain you're supposedly an expert with. Let's talk about Google. An important understanding to have Google at the moment in June of 2024 is that PMAX goes over very high intent, goes for very high intent users in the existing funnel. We sort of touched on that at the start with a few examples that we pulled from, but fundamentally, if you look at any customer journey map, if you look at third party attribution tools, majority of the time, performance max is going and scooping users up. that are just about to buy. And you might say, well, we have branded negative now. So when we look at the campaign, it's not pulling any branded keywords. So what is Nathan talking about saying that it's driving existing customers back to side or existing high intent users? It's getting cold keywords. We can see that. What I would recommend doing is go into your cold keywords, sort by revenue, and then switch it to conversion rate and have a look at what the conversion rates are of some of these key terms and relate that to. your new customer conversion rate on the backend of site. What you'll end up finding in most instances, not all, but most instances is performance max conversion rates are insanely high on these keywords. And you'll even see statistical relevancy here, right? You'll see like 13 conversions of a keyword that has a 20 % conversion rate. How are we getting a 20 % conversion rate on that key term? The reason we're getting a 20 % conversion rate is because that user has already interacted with the brand. and they've come all the way down, they've searched for something that seemingly looks cold, and then Performance Max is going, gone, this person is super high intent, let's place on them right now, let's bid $4, capture them, because we know they're about to convert, based on all of the psychographic data points that Google has on its end consumers. And so even though Performance Max sometimes seems like it's getting cold customer acquisition, when you look a little bit deeper into the customer journeys, it often isn't. and these users are being generated from elsewhere and then Performance Max ends up claiming it. And it claims it in a way that it looks like it's cold. Now, is that to necessarily say that that's a bad thing? Not really, right? If someone was very, very warm, they'd come through our entire customer journey and then they searched for a cold key term and we didn't bid on them, we might've just lost them to a competitor. So it's good that we came in there. It's good that we captured that keyword. It's good that we placed and spent $4 to get them to site and to get the conversion. But don't misconstrue that with the fact that the campaign is scalable. Don't misconstrue that with the fact that that's cold customer acquisition that's coming through it. Because then when you try to scale it, you don't see incrementality. On Facebook, everything I just said also applies to Advantage+. Advantage plus heavily retargets. Now this is improving a little bit as of late, especially with audience suppression. But Advantage Plus loves to heavy retarget too. Now what we have found, and I alluded to this earlier, was that we have run internal tests of running dedicated retargeting campaigns alongside Advantage Plus with the mental model of the fact that that retargeting campaign will have higher beat caps due to the higher frequency, will therefore get prioritized, prioritization of placements on those individual users, which will prevent Advantage Plus from prioritizing retargeting audiences. Seems to work. We've only tested this on two accounts so far, so we don't have huge statistical relevancy and that's why I'm not shouting it out on my LinkedIn and YouTube, et cetera, but it seems to be pretty valid so far. And so running tests like that consistently within these accounts, well, once again, whether this is TikTok in five years or whether you're running ads right now, being able to identify, okay, the incentive structure of Meta and Facebook and all of these big tab platforms is attribute as much revenue as possible. We know advantage plus is going to try to attribute as much revenue as possible. It's going to do that through heavy retargeting and going after bottom of funnel. Okay. How do we prevent? Cost caps, if we put cost caps on Advantage Plus, will that force it into acquiring new customers? Probably not, logically speaking, because anytime you put a T ROAS or you put a cost cap on Facebook or Google, what ends up happening? It'll just prioritize high -intent audiences even more to try to achieve that cost cap. What if we set a lower cost cap so it's not as aggressive? Okay, that probably could work in new customer acquisition. If we get budgets to a certain level, it can't retarget with...$1 ,000 a day, $3 ,000, $4 ,000, $10 ,000 a day in budget. It's gonna have to go off to new customer acquisition. So that's the solution. Okay. And so you can start to build in ways to structure campaigns, to structure accounts, to approach scaling. Once you have the fundamental understanding of the incentive structure of platforms, customer journeys, and what's actually going on within the campaigns. And so when you look at how do we align Google and Facebook and any advertising platform with our actual financial goals and the the goals of the business. The way to do this fundamentally is to understand what's actually going on in the platforms. What are they optimizing for? What are they trying to achieve? And then how do we structure or delegate split tests within platform to be able to pull apart that structure and actually optimize for new customer acquisition? On Facebook, you also have one day view through attribution, which is quite a big issue because view through attribution will heavily over attribute. particularly if you're a CPG brand that has high returning customer rates, Facebook will just claim almost every conversion that comes back to site through an existing customer, repeat buying or subscription buying, et cetera. And so you have to be really careful there in understanding how to break down attribution windows and get a better understanding for what customer acquisition Met is actually driving. When it comes to trying to correlate performance at a top line level, we've insinuated throughout this podcast quite a bit that return on ad spend doesn't end up being a very good metric of determining scalability of an account. That is not to say that it isn't a highly valuable point of information because it is, right? If you didn't have return on ad spend or attributed revenue at all in these platforms, everything that I've just said would be impossible to even execute on. Because how would you know that performance max an advantage plus heavily retarget? How would you even know that? Well, you would just have to curl line against top line revenue, but you get further insights from in platform ROAS. So it is a helpful metric. It's just understanding the context of how to use it. Outside of in platform ROAS, you really want to be correlating performance with top line revenue as much as possible. There's a few ways to do this. Well, there's a lot of ways to do this, a lot of approaches. Number one is orientating around gross sales or more importantly, new customer sales. And so if you make a change in platform, rather than looking at the return on ad spend change, you want to be looking at your new customer daily change or monthly change, depending on how large you are of a business. And so when you go and double budgets on performance max or an agency, and this is why it's really important that if you're working with a paid ads agency or a performance based agency, that they're looking at your actual revenue figures. your new customer figures, and all of the baseline metrics that associate with the actual financial outcome of the business. Because if they're not, how do they know that when they double budgets on performance max, that it doesn't double back -end revenue? How would they know? And if they're not monitoring that at the time of change and seven days after to have statistical relevancy, how do they know the impact? And how do they know if it aligned with the financial outcome of the business? And this is why, honestly, doesn't compute to me how changes in platform on marketing aren't being made. And then we're just looking at the PNL because I don't know how we're making changes in marketing and then just looking at the platforms and going, okay, let's just keep looking at the platform and use that as an indicator for whether we're seeing success or not. What? But we know that Google and Meta's incentive structure is to attribute as much revenue as possible. So this is just like fake data. And then number two, customer journeys are so complex and long and confusing that this makes no sense. Like what we're looking at is so hard to decipher as to what is actually occurring. Then why don't we look at the actual cash out of the business, right? This is what we do in lead generation businesses, right? We don't generate a bunch of leads, pay$10 ,000 for them and then go, the cost per lead was good. It's like, no, how many closed and what was the cash collected? And then what is the annualized recurring revenue off the back of the $10 ,000 that we just spent on these leads. Like the correlation between marketing and financial operations is absolutely critical if you want to achieve scale at a rapid rate. And so some metrics that you should be looking at new customer sales top line as you're making changes. Secondly is just gross sales. If you don't have a high returning customer rate. Third is. I would be looking at statistical relevancy tests using some kind of incrementality calculator. And so what this means is that if we increase budgets on Google, what actually happens to top line revenue on the store with the correlation and budget changes and the change in top line rev. I would always be looking at incremental returns. except for if you're really small business, because it's not going to be as applicable because of the efficiencies that you gain as you start to achieve scale. But if you're spending an extra thousand dollars, are you getting an extra $5 ,000 directly on top line? And are you making a large enough budget change to actually see that in top line revenue? Because this is a really important caveat that most don't understand, which is the fact that when you up budgets, you need to increase budgets enough to actually see the performance reflected in the baseline metrics on the back end of the store. I've seen a lot of brands who have scaled unbelievably quickly. They've gone from spending 2K a month to 40K a month, 60K a month, 80K a month actually in four months, five months. And you look at those brands and you go, how did they do it? Right? They must've had perfect product market fit. They must've had this, that, the other. And yeah, they definitely did. They have a good product. They have this, they have that. But one of - The core. principles that the operator of that brand was moving with was this. We needed double budgets to see if it actually works. And it seems like an aggressive approach to business. It seems like, why would you go and double budgets? Let's step stone our way up. And we don't have that much cash. So let's do this slowly and not over invest and then waste a bunch of money. However, let's say you're a 100K a month business. What is the... a variance month on month of a business of that size. It depends, but usually five to 10%. Right. And so what I mean by that is that your revenue isn't 100K every month. You're not just 100, 100, 100, 100. It's going to be 95, 105, 92, 107, 102, 98, 94. So you're going to have this little wavering over time, but over a six month period, you average out to 100K. Okay, cool. Now let's assume that you're spending 5K a month. And so you have a lot of organic revenue and repeat revenue in there. And then your assumed MER on incremental budget increases is a three. And so you go from a 5K budget to a 6K budget. Okay. Your agency says, Hey, I think we should increase budgets. And you're okay. Let's go up a thousand from five to six. Okay. So the assumed increase in top line revenue there would be 1000 times a three mer, which is 3000. How are you going to measure a $3 ,000 increase in top line revenue? when you have a five to 10 % variance on 100K rev, you're not gonna see it, right? Because next month you might do, you might drop by 5 % in natural variance, but then you might've got that 3000, which pulled you up to a drop by 3%. Does it look good? You can't tell. You have to wait an entire three to four months before you can see your new revenue baseline average out and you can know whether it worked or not. And a lot of business owners will be super reactive and they won't see an immediate huge uptick in top line and they'll just pull the budget anyway. And so why some of these operators managed to go from one 10 40 60 K ad spend so quickly is because they're confident enough to go, okay, our budgets currently five, let's go straight to 10 for one month, rather than spending one K a month for five months and seeing if it works. Let's just spend the whole five K now. Let's do it next month. We believe we're putting it in the right spot. We've done the analysis. Let's put it in this campaign, see what happens. They put the 5k in and then immediately they see it in top line because 5k to 3 mow is 15k in top line. That's very visible on a 100k a month brand. So they see it come out the other side. And they go, okay, well that worked. Let's do it again. Let's go 10 to 20. They see it reflecting top line. Okay, now that's working. Let's go 20 to 40. Maybe they don't get incremental profit there. And they actually lose some money and they go, okay, let's pull back to 30 and let's restructure. Let's figure out why that didn't work. Did we have enough creative volume? Let's pull that back up. Let's do this. Let's put all the measures in place so that we can do another dumb line. And then they go up to 50 and then they go up to 70. And it's these large budget changes that are actually very measurable at a P and L level because you're not being, you're not being suppressed by variance, natural variance in sales figures. that's not going to show the actual incrementality of adding budget into a business like that. And that is a really over missed concept from a lot of brands. And that's why internally, and if you're a client and you're watching this, I'm gonna give away a little internal SOP here. And if you're a prospect that might be coming on board, I'm gonna give you some transparency, which is that internally, we will always push for the highest budget increase possible so that we anchor clients upwards. If we're confident that we're actually going to drive incremental revenue, and I want to make that very clear, we would never increase budgets if we don't believe that we can drive incremental profit to the business. But if we're very confident, if it's very obvious, if all of the signs make sense, and it's like, we'll obviously drive incremental profit here, there's no doubt about it. I would rather increase budgets from five to 10, then from five to six and let it sit at six for five months. And that's not because of an internal billing structure or anything like that. It's simply because if we go from five to six, we won't be able to see whether it works. for a while. Okay, maybe the in -platform attributed revenue went up, but it's always gonna go up. That will always happen. And that's an important caveat to understand is that if you're a 100K a month business and you're spending five and you go to six, your attributed revenue will go up because Google will just do more retargeting, Facebook will just do more retargeting and they'll just scoop up and attribute more of your top line revenue, even though they might not have driven any new customer acquisition. And so to summarize this podcast up. No matter what time you're watching this, no matter what platform you're advertising on, it's always important to understand the fundamentals that you can then reason up from. And the fundamentals of at the moment, all big tech advertising platforms is they want to attribute as much revenue as possible so that you continue spending and ideally spend more because their job is to increase advertiser revenue. They're a publicly traded company. 90 % of their revenue comes from ads. It is in the direct incentive structure of the board. Number two is customer journeys are incredibly complicated and long and untrackable. And that's why you'll be sold MMM for 10,000, $20 ,000 a month. You'll be sold some AI tracking software that can somehow track people across multiple customer journeys, across multiple devices. None of them work. They do okay. Like they triangulate data a little bit better than the actual platforms do for sure. And they're very useful for someone like me and the, our agency. Where our job is to try to figure these things out and triangulate. Okay. Is performance max at end of customer or is it actually driving new customer revenue? Can we do these incrementality tests? The more data we can get the better, but is it going to really provide incremental value for the price of these people are charging? No, not on a single account on a hundred accounts for sure. But on a single account, absolutely not. SOP creation, it's really important that if you ever roll an SOP out, number one, do you even need to roll it out? Because all you're doing is putting handcuffs on a talented team if you have a talented team. And then if you are going to create that SOP, is it data driven? And is it backed by an actual mental model of an understanding of the fundamentals of these platforms? And then for SOPs of people watching this right now, it's Google PMAX goes after high intent bottom of funnel traffic, structure your campaign, structure your ad accounts so that doesn't happen. If that's how you're going to want to run your account, Facebook advantage plus heavily retargets one day view heavily over attributes structure accounts accordingly, understand that that is occurring and then how can we conceptualize structures that are going to prevent that and drive for new customer acquisition. And then any time that you're trying to correlate performance across these platforms, again, an understanding of how to improve the financial outcome of the business, always do it by correlating to actual financial figures. What is the actual gross sales? What is the actual new customer sales? What is the actual contribution margin on a month on month basis? And then how does that relate to the changes that we're making in platform to then inform whether we're putting budgets in the right position?