Advanced Meta & Google Ads Media Buying Principles (From A Math Ph.D.)

If you are serious about leveraging machine learning, data driven principles, and AI in your meta ads and Google ads account, this is an episode of this show that you're going to like very much because it features two extremely smart guys who really understand the details of that stuff. And that is Dr. Andre Lunev and Russ Garva from Russia coming to us on this call. Dr. Lunev has his PhD in math and physics. He was literally working on supersonic aircrafts before working in meta ads. The guy understands math really, really well and has applied that knowledge to the fixed problem of meta ads ad distribution, media buying, creative, that kind of thing. And that same mentality comes through Russ's business partner who is the Google ads side of their business who's applying that same kind of thinking and actually a developer's mindset as well. So he's building tools on top of Google ads with AI to manage their accounts. This is the kind of stuff that I had this conversation just thought these guys just have more intellectual horsepower than I do to apply to some of these problems at some of the technical levels. It's really helpful to get around them here from them here with their doing and try to figure out how best to use it for myself and my accounts. You're going to like this conversation a lot. Let's jump in with Andre and Russ. Dr. Andre Lunev, I always like to call you a doctor when you do that much work and time for a PhD. You should get to be called doctor in your life. You've got a PhD in math and physics. You're in Russia. Andre say hello to the people. Hello. My name is Andre. Thanks for having me here. Great, great, great to do this. We've had a couple calls now. You and I have interacted on X for a long time. And I think see, see a lot of them very similarly. You're just a lot smarter than I am. And so excited to pick your brain about how that plays itself out. Russ Galba, co-founder with Andre of your as agency, tegr.co link for the agency is in the show notes. If you want to follow up with these guys, including Andre's X account, which is a great follow. There's some other places he can point you to as well, but Russ say hello as well. I know people need to get to know you. Hey, Andre, and thank you for having me on the call. And I'm pretty excited to be here today. Yeah, great. You're both in Russia, right? Yeah, I'm actually just got back like two weeks ago from Brazil. Like we went there with my family. We spent a lot of time in Argentina and Brazil for about two years or something. So now I got back home, you know, we got a kid, you know, in Argentina. So, you know, our parents just want to meet her. So we come back just to spend a little bit more time here. Awesome. Awesome. All right. Andre, let's start with you. This. So, you know, I framed up in the intro. This call is is going to or this this podcast episode is going to be very much about how you're leveraging sort of let's just call them like advanced math and dev type tools in media buying right now. And I think it's a really interesting conversation. Before we hit record, Russ was saying that devs are like much faster on the uptick of AI than marketers. And I certainly feel that I'm not a developer. I'm in fact, extremely terrible. You may have noticed this in conversations with me at thinking in terms of dev stuff. But like Andre, let's just start by talking about like sort of baseline approach to meta ads media buying. You're a big fan of bid caps. I'm a big fan of bid caps. Like before you talk about anything specific, like how are you thinking about the problem of distributing ads to people via meta and how to get the best value out of that tool? Like what attracted you to bid caps? Well, might have taken that is that when I want to add, it's very hard to predict the future performance because we don't have any data points on the specific ad unit that we are currently launching. That's the first point. The second point is that with all the AI tools, the cost of production of the ad, it's just gravitating towards zero. So basically, like it's, it's now there is no difference between producing one creative, like one static image and producing the same image with the hundred of variations. And as a result, as a result, like if we upload ton of those ads, we won't be literally able to test all of them with the classical approach because meta will just waste the money on the majority of the ads because the majority of the ads fail. And the only way to run the ads that is left right now from my perspective is bid caps or like cost controls in general. So that's how you know, automating all of those parts of the processes leads to increased creative volume and much faster account growth when you can produce those ads at scale and launch them at scale. Do you, you were, you were a believer in cost controls before, like the AI explosion happened because I would say, I mean, AI's obviously been on the scene for a little while. But before you and I ever talked about AI, you and I had had some interactions about sort of why we both had embraced cost controls as a way to scale an out account. Is that, is that right? Yeah. Yeah. Correct. So can you make the case for that? Why, why what attracted you to that way of thinking about about meta as and then what have you seen since deploying it for clients of yours? I think it came out of pain. And I would say that like constant performance roller coaster, like that's, that's what pushed me to, uh, to the idea of controlling somehow, controlling the CPA somehow. And, you know, I remember that pivot point. So when I launch an ad and it performs for three days in the role and then all of a sudden, you know, I'm the happy media buyer that tries to scale that. Remember the budget by 20% or by 30% like after three days and all of a sudden performance just tanks. And I like, since that moment, like, I started to dig deep into your Twitter profile, most, uh, most of the time and the YouTube channel. And it leads me to a point to, uh, where I just was thinking, how to, uh, how to run the, how to run the other accounts and my understanding right now is completely different from what I was experiencing. Like, and I can share it. So from my perspective, like we are trying to purchase to buy the impressions, like on, on meta. And there is a different pool of those available impressions that will, uh, lead to conversions with the high probability, right? So and on weekends, as usual, we have more of those auctions that lead to conversions like compare just Wal-Marton Wednesday morning and on Sunday afternoon, right? So the amount of buyers is completely different. And, uh, that Friday night case, so you might take on that. So you launch an ad like in a very favorable period of time, right? So it performs, uh, great on Friday, then on Saturday, then on Sunday, that happy media buyers, buyer things like it was me, you know, that it's time to scale. Like I decide when to spend and where to spend. Then Monday comes, I ramp up the budget. The amount of the options goes down. And you know, that's why CPA just goes through the roof. So in this case, like beatcaps, they would just literally cut the spend instead of making the, uh, making the decisions, you know, like, uh, compared to media buyer. Uh, so in this case, a beatcaps, they just pull the span down, like to the, to the point where the conversions are still remain under the beat. It's so funny. You make that point about, uh, launching an ad in a favorable environment and then trying to scale it. That's something I've never really thought about actually, which is, which is funny because I thought about this a lot, but, um, the idea that you would launch an ad and, and the reason it's performing really well is actually just because you got sort of the right timing relative to the DAU's, right, daily active users. This is something I talk about a lot of manual bids. I just saw somebody tweet this again today. They say, how come my ads always rip on Saturday and Sunday and they do bad on Monday and Tuesday. And the answer, of course, is, is, is it actually what you said, there's more available inventory at your target cost. And so the mechanism for managing that, um, is a manual bid, which is going to scale up and down with the available volume and hold the, um, the outcome, probabilistically constant, right? So the outcome is not actually going to be constant per se, but it's going to probabilistically be relatively constant based on the best information. Now it's not a perfect system. And sometimes people will give me this critique. They'll be like, well, how can you trust the system completely? And it's like, I don't trust the system completely. I just trust it more than I trust me. That's the point. I trust it more than I trust me the most often. And I just more than I trust you. And either of you guys on this call, right? Andre with your PhD in, you know, you were working on, you were telling before your PhD in math and physics, you were working on, um, on the, uh, flight patterns at supersonic speeds of aircraft, uh, before you were a meta media buyer, right? And you, you with that math background are sitting here telling me that you fall prey to all the same stupid mistakes that I did as a media buyer 15 or about 15 years ago when I started, which is like, Add is working, scale it, go nuts, you know, and that's because all of our brains do that all the time. And so it's this real, real challenge. So, okay, so that's your case for manual bids. You also, I want to push into this a little more. You also had a conversation recently with reps from, from meta that sort of confirms some of your inquiries. Is that right? Because this is something we don't always get to hear about a sort of meta side of the story and what they believe I have had some relationship conversations with meta people recently as well. And I'm actually not really at liberty to say much about, um, but they have, I'll just say they have not, um, changed my opinion very much on how to about deploying manual bids. So, um, so, uh, yeah, they've confirmed them if anything. So, uh, But tell me about the conversation you had and sort of some of the behind the curtains, deep or dive into how the auction works and why manual bits are effective. - All right, so like it was an unexpected quote to be honest. They just reached out to me on Meta and invited to talk beatcaps because all of my posts on Meta about the beating strategist. And they basically confirmed the majority of my understanding and of the approach on how to scale their accounts on Meta, how to launch the new ads and basically the auction concept. I can just go. So regarding the beatcaps, so they say if we take the ratio between the price and the expected conversion rate, we get a variable called the ECPA, expected CDA. And with a beatcap of $19, for instance, we will basically sell you every impressions that your ECPA is less than $98. So basically they are trying to sell you the all the impressions available that will probably like with a higher probability lead to this conversion. - Yeah, that's why that's basically the best way to buy the ads on Meta because otherwise, if Meta's prediction model says that you won't be able to get those conversions like there, there's no point in just spending money and they are compared to the lowest cost. - I just mentioned it, but my favorite use of AI tools right now on the creative side is actually not for me to tinker around with anyone tool, but to instead work with my team at behind the scene studio, that has scaled up my design and edit team and pulled them into the process and see how I can help them leverage those tools the best because when behind the scene studio, leverage is the tools best not only do my clients win, but all of their clients win. That's because behind the scene studio is a Philippines-based design and video editing agency with a relentless focus on Meta ads creative. And that means they do a couple things really well. They, first of all, know how to edit and think in terms of advertising first, it's not just a bunch of designers and editors, it's Meta ad specific creative production process. They think in terms of speed and volume, the same way I do because they actually built their process on the back of working with me as a scaled up version of essentially my approach. So if you have liked the way that I talk about creative, you want to apply a volume-based approach to your creative where you maintain a high quality, but you also do that with lots of variations, the right kinds of smart iterations on your ads, and you do that at real scale, you should be looking at behind the scene studio because they're based in the Philippines. They're an affordable solution for design and editing, for design and editing help, whether you're an agency or a brand or freelancer or whatever. You should be getting on the phone with them and seeing if they're right for you. I love them, I trust them. I bring them into as many of my meetings with clients as possible with AI tools as possible to figure out how they can get more of that knowledge to keep building that process. The result is that we output a huge volume of ads for our clients really, really fast. They can do the same for you. Go to behindbtsstudio.co, btsstudio.co, schedule a meeting, see if it's right for you, you absolutely should be exploring it. btsstudio.co. - What they are confirming to you is that as long as your bid actually is at the number that you say, then if their prediction says that the expected CPA, expected CAQ, is at or below that bid, it will deliver to you results at or below that bid. According to that, now there's some attribution questions in there of course and some other issues, but basically that the machine does exactly the thing that you and I and others are saying, which is you set a target price and you say, here's the threshold at which I am willing to pay for a customer and meta says, yes, that's exactly what this tool is built to do. Is that correct? - Yeah, absolutely. I tend to set up the bid a little bit higher again because of the second point that they mentioned. Like we all know that meta-soction is the second price option. It means that for example, if you have a bid cap of $100, and your competitor bids for $80, you don't have to pay $100, you have to pay $81, right? That's why I usually shift that scale a little bit up, just to make this gap closer to my desired cost. So this is basically all the adjustment. - This is a really important point about bid caps in particular that I think a lot of people do not actually understand. You said we all know meta is a second price option. I actually don't think most people know that. The way meta actually works is that when you place a bid, you actually don't get customers at that price. You get them the way the bid works is that you get them, again, according to a probabilistic prediction, right? Because meta can't actually predict exactly what everybody's going to do. You'll get the conversions at the second highest bid price. And so it may be actually lower than your actual threshold. Now, that means you may want to be aggressive. You know, it's interesting. I talked a long time ago to another guy who was a quant type. He also met a H.J. I don't remember. But anyway, he was a quanted open store as an old interview of mine with kind of Andrew Campbell. You can find it if you're on Spotify or Apple still. But he had pointed out this point to me. And he said actually some folks at Cornell had done some studies saying that sort of how to properly and most effectively handle auction bidding and a second price sealed auction is sort of a solved problem from like a game theory perspective. And what they say is that the solved problem, it is a solved problem that you should build your true reserve price, not above it, not below it, whatever. If you actually want that outcome, you should do that. So you should bid at your true reserve. So maybe he would push back a little. Now, of course, every brand's true reserve is a little different. Your brand actually may be willing to say, I'm willing to go five bucks above this price occasionally if it means I get meaningfully more volume or whatever. But that basically, that's the right way to think about things. So it's right around that number. And maybe if you want, you go a little above again. Because almost every brand in my experience has some kind of a preference for more efficiency or more volume based on a certain amount of things. Because they recognize that it's a probabilistic machine. You're going to probably miss on one side or the other. I do this all the time with manual bids. I'll say for a client that is really, really conscious of hitting and number at or under their target CPA, I'll bid my true reserve price or I'll bid a little bit lower even and say like, we're just going to make sure we don't overspend. For customers with extra clients with extremely high LTVs, where it's actually much bigger cost to them to not get the customer, then for them, I'm going to probably bid a little bit more aggressively, et cetera. And now, it may push out the window a few extra days for their LTV to come through or whatever it is, but they're willing to do that at times if it means they get meaningfully more volume and meaningfully more consistent span. So-- OK. And I go ahead. I just wanted to mention the third point that is coming-- that is following this point. So the main mistake, the most common mistake that I see media buyers make when trying to shift to bid caps is trying to force the span towards a particular asset or particular ad by inflating the bid too much. Yeah. So the common statement is like, my bid caps don't span. OK. What to do? Let's try to-- let's try to inflate the bid. And they inflate it, you know, raise up the budget and then up with the inflated CPA. Why? Because we are just moving this limit, this threshold above. And meta is trying to do exactly what we ask it to do, basically, to find the conversions that are below that inflated bid. And that's why they start to get those initial conversions then lower the bid down and they lose the volume. So this is pretty expected behavior, I would say. And usually, they don't pump that much volume into the ad account. And they say that bid caps don't work just based on 5, 10, 8, it's like 20 creators. And again, with all those automation tools, when you can launch 300 creators a day in an hour, it's not a problem when you have the volume. Yeah. I agree. And that's the thing that I think is the killer and all this for like anybody who is not already here, is going to have to get there. And this moves us to AI portions of the conversation a little bit because if mass production of creative-- I mean, even without AI, if people are just thinking this way about creative, they're going to build systems. Now, we were building a lot of ads before we were using much AI. And so AI is only going to accelerate that thing. But as more and more people recognize that you can build a mass volume of ads even cheaper than before, it's going to be crazy. You know, my team, we build a huge volume of ads, and we do that, especially leveraging a team of extremely good designers and video editors in the Philippines. I've talked about them a lot. We had built that system before AI became a part of it. And now what they are doing, which is already like a really cost-effective way to get really high quality talent working on your plan, for a US-based business to outsource that to really good quality people in different countries. And so that's what my team does. That's how we serve our clients. I've got seven or eight people in the Philippines working on four clients. So we can put a huge volume of output against those clients in a way that ends up being really effective for everybody. So now you give those people AI who are already extremely smart and talented people, designers and editors, right? And they're all they're going to do is just increase their output more. And so Andre and Russ, we were on a call with the team lead from that team recently. First time I met you, Russ was because we're all working together going like, how do we make this machine home even more? When you add all of that stuff together, you get to the point. where there is no longer a world where it is possible to behave without manual bids, because there is too much creative to test and to manage the distribution, and therefore you have to have a machine do it for you. You just have to, otherwise, it becomes cost prohibitive to do it. I want to actually jump over to the Google side for a second Russ and just ask you a question, is there in your opinion an equivalence on Google where people are bidding with this same manual bid target row-ass target CPA that is reliable and works at scale? I've always found this to be more of a challenge on Google. I think, first of all, Google is that kind of data-driven machine that is primarily working on top of analyzing the data and using the data correctly, because if we'll go back and I'm in media buying for about 12 years, I think. Initially, the way we were doing the media buying on Google was primarily utilizing manual bids on the search campaigns. The first campaigns, we were running, we were just search campaigns and we weren't using, and we weren't much of the option for respect, and just to use any kind of target row-ass, target CPA kind of automatic bidding. When it came up, it wasn't actually working that well. I think Google just started as the engine for data-driven people to analyze the data and make it more efficient. Now, there are a lot of accounts coming to us, a lot of clients with accounts to just to do the audit, and I see that majority of the accounts actually relying on an automated bidding, which is totally fine. It's so much easier to manage this kind of campaign. You just put target row-ass, target row-ass, target row-ass, spend on your PMAS campaigns, shopping campaigns. Probably, I see more people still using manual bids compared to automatic bidding, even though again, the majority of accounts that I see are shifting from shopping campaigns to the PMAS campaigns. So, a collectile DR is that Google initially is the machine, is it like, you know, demand from you understanding the data and optimizing the data level? Now, this days, when there are LLMs, you know, Claude, ChaGPT, you know, Germany, whatever, like whatever AI LLMs, you know, people prefer to use, now the question is, like, should we manually dive deeper into the data and do the optimizations? Because if we actually go like deep into the manual bidding, then if you're running like the search campaign with exact match, you know, mid-tail to long-tail search keywords, then it's really a lot of work that we need to put to, you know, adjust the bidding and everything. I know that there are, you know, tools like optimizer that are doing this kind of stuff. It's got like more legacy kind of things that people use to use, but I think there's so much better, like, more advanced approach with LLMs. So, that's what we're doing, you know, essentially, now with our Google Ads campaigns, I don't like, I will try to share my screen here and just show it. Okay, so I'll allow and this one. And again, I know that some of the people will be listening to us, so that's why I will try to and not seeing the video. I will try to come in as much as possible. So, what we're doing here is that, okay, so we have like all these different models. We have, you know, Chagy, P/O, P/A, I, we have Germany, we have Claude, we have like all of the other things, you know, dipsec, footed, got sake. And, you know, we can use like all of this, just to analyze our data so we don't have to do it like ourselves. So, the issue then, there is no, like, easy way for us to pull the data automatically and propagate the data automatically for the LLM to analyze. If you want to learn to think the same way that I think about meta-adzi counts, certainly the same way that Andre does and the same way that Rust does about Google ads accounts, which is a machine learning focused data-driven focused profit focused approach to all of this stuff. The best place for you to go do that is through admission from Common Thread Collective. Admission is Common Thread Collective's way to get you all the knowledge that you need concentrated in one place so that you have a foundational knowledge set on which to build your business and your ad accounts. And that is so crucial. If more people would take admission and they would get serious about inhaling the course content from admission, inhaling the webinar content with exclusive webinars of Taylor Holiday, etc. at earlier stages of their business, I would get way less DMs asking me how to implement these things in their ad accounts, how come they're, you know, losing so much money and less DMs saying, "I used to be on auto bids in my ad account and now I switched over to manual bids and now I make a bunch more money." Those DMs would come less often because people would build from the beginning with this machine learning approach in the world of meta-ads in 2025 and beyond. You need to be thinking this way because that's where everything is going. Meta is powering everything they can with more automation and more of the tools that are going to lean on machine learning and AI to distribute your ads effectively. The best place, the best place, the best place to get the information you need at and up to date ways for your ad account and your business is in admission from Common Thread Collective. It's a whole bunch of courses in one place, exclusive webinars and ongoing coaching with media buyers who know how to build these setups in your ad account. If you sign up through my link, which is in the show notes, you can get that first coaching call for free. Go join admission especially if you're earlier seven figures or anywhere below that, especially if you're there, fire your agency, maybe, maybe not. But go consider running your ads yourself with help from the people out of admission. You'll save money. You'll build a more effective ad account and you'll set yourself up for the most success for building a profitable e-commerce business. Go to admission and join today. So it's kind of like what they think that we are trying to achieve here. There are some of these solutions, actually one of the solutions that I will show and we also have the proprietary solutions for this as well. But for the folks just to see themselves, there's this tool called edsvisor.com. So what they're doing, they're just building connectors for Google ads, for matter ads, for Shopify, for Clavio, for Google Analytics, and a bunch of the other things, WooCommerce, etc., which is like not our domain of the expertise and allows to pull all of the data automatically and it's pulling the data, all of the data available. So it's pulling all of the metrics available and all of the breakdowns available. So now there's very interesting situation. Okay, so we're pulling all of this information into the LLM and we know that LLM is very powerful in analyzing the data. And now what we can do, there are two things pretty much. We can either use just a standard chat where this data source is pulled into and we can start asking the questions to this to this chat like, okay, so just pull the data, this data, just analyze the search campaign, just analyze this, whatever PMAX campaign, etc., I can pull everything from Google ads. Or there is another approach is that we can start building the custom Chageept chats. I don't know if people know that, but there's like open AI, Chageept capabilities doesn't end on the just using the chat and asking the questions. So you can actually build your custom Chageept things that wired the way that you wanted to work. You can just put like all of the parameters over there, like what you want to analyze, how you want to analyze. What we also, interesting thing we do is that this is another tool, we analyze Reddit for all of the recent Google ads content that people we know that are very knowledgeable in the niche, putting into and we're pulling all of those, you know, live hacks, optimization tricks and everything into our custom Chageept. So it can analyze all of the new accounts current accounts on the recurring basis and just provide like additional insights, more and more insights. What's working currently? Because Google is changing, Google is, and again, I don't know if I will get in trouble by saying that, like matter in Google businesses, our businesses that generate the money for their shareholders. So sometimes these companies are doing the things that I personally like as media buyer actually see that are created specifically just to get more money out of the advertisers, like for example. And again, just a lot of people just don't know that actually and maybe it's a good time for them to check that. In Google ads, there is automatically created assets. So there was the option on the campaign level where you can just turn off automatically created assets like images or videos or search or site link extensions like all the different stuff. But if you turn off that one, there is another hidden one that is inside of the Google ads account that actually creates this automatically created assets. And I don't know, it's just like, it's silly to say that those automatically created assets that are just getting some spent or they're not performing with the world. So again, just long story short, we're just pulling like all of this very relevant data to what's happening with the Google ads right now from the leaders of opinions for people we trust. And then we put it into this interview. to this Costing Chagy BT bot. And then, again, you can use this as a Wizer tool. It's just pulling all of the available data from the Google Ads account and provide you all of the optimization points or stats or whatever. Again, this is LLM. We can just go ahead and just ask you whatever. So this is like, for example, one of the things that one of the accounts got analyzed in here. So as you can see, we can also compare it to cloud if you want in here as well. So it's just pulling all of the campaigns and provide us like actionable top level insights what can be changed, what can be optimized. And it's pretty thorough. Just going through all of the search campaigns, PMAS campaigns, shopping campaigns, demand generation campaigns, display campaigns, whatever you have on the account. And then what you can do, you can just go ahead and just start to follow up with the additional questions like in regards to every point of it. So that's kind of like what we do and why I was mentioning this approach is that, especially when you work with the manual bidding with multiple search campaigns with the exact, mid to long tail keywords, it's a mass to optimize everything by hand. So that's where it's really good to pull everything in one place, give LLM the opportunity to analyze the data and provide you the actionable insights. So that's pretty cool. - And you're actually, - Russ, you're actually doing that then through the lens of like you're taking, if I understand correctly, you're taking insights from really smart Google ads folks, you know, elsewhere that you believe in an trust and basically having feeding that information to the LLM and then having that analyze your account. Am I understanding that correctly? - Yes, that is correct. So we have like a foundation that we know based on our previous audits that we've done, whatever we learned like so far, we just feed all of this to LLM again and just ask it to create like a list of the parameters for our custom Chage PT bot to work on. And then what we're using is that yeah, we are just parsing all of the data from Reddit. And again, I can show like some of the tools that we could use like to do that and automate that as well to get like all of the relevant. So that's why like all of the relevant information in regards to Google ads. So that makes this system dynamic. So you know what's happening. Your Chage Bot in Chage PT knows what's happening right now on the edge of the Google ads and it just automatically gets it and analyze your accounts based on this information as well. - Russ, do you have any examples of a time where that has been like that? I mean, you probably can't share specific numbers or whatever from clients, but I'm curious to hear if that has like worked, you know, have you found times where that has led to some kind of insight that's been useful to you or Andre, if that's been, if you've done something similar on the meta side or anything like that. - Go ahead, Russ. - Yeah, I mean like, yeah, on the Google ads side of things, like it's just very hard to underestimate like how powerful like just analyzing the data from the search campaigns can be because you can get all of the metrics, like for example, you could have like 300, 200, 300 keywords right now running over there. And to get all of these stats from all of the keywords and analyze them and just find like where you overbid, where you're underbidding it. I'm just talking specifically about many bidding campaigns, for example, if that's the case and someone is using many bidding campaigns and compare all of this data inside of one campaign and across multiple campaigns that you're running, just to find some specific, you know, some keywords that underperform on the different timeframes level, for example, this keyboard was running pretty well for the past three months, but in the past two weeks, for example, it doesn't perform anymore. So these things like are very hard to cross multiple keywords just to find yourself and you need to spend a lot of time on it. When with this, you can actually get this data really fast. Another thing that, you know, we constantly doing for some of our clients is that we analyzing the shopping campaigns and PMAS campaigns on the product level because majority of our clients, they come as businesses and for e-commerce businesses, majority of the spend goes towards shopping placements either through shopping campaigns or PMAS campaigns. So for us, it's really important to analyze the performance of each product, products that we are running. And the tricky part there is that usually we run like different dimensions of the shopping categories or shopping products on the shopping placements. And they are either divided in the asset groups inside of PMAS campaigns or in the separate PMAS campaigns. We are running like the same products. So it's very hard to combine all this data and find underperformers because in one asset group, one product can underperform, but they can perform really well on another one, for example, but it's not statistically significant. And you need to spot that. And across some of the stores where we have 1000 products, for example, it's really hard to do. And that's where LOMs are really great. They can just parse through the data like in the matter of a minute or two and get you all of the information that you need. So it's hard to give like, you know, a specific example is just like whatever account pretty much you feed into this thing, it will show you your winners, your losers across different dimensions, across different campaigns combined for the products, the same for the keywords. So, and-- - So is it a risk for-- - Isn't that what Google is already trying to do? So this is one of the questions I have about this. Like it sounds like you've built a machine on top of Google's machine. And you know, you said earlier, that Google has maybe different incentives than you do. And I think this is one of the funny things is like I have a very high trust in Meta's alignment with my goals to deliver my ads to the best possible customers. Like in my experience, that's been true. And I've seen that play out. I don't have that trust with Google. I find Google often to be something where it seems like money gets blown in ways that are not actually best for me. PMAX I think is like an incrementality mess. And, you know, other people have said the same thing. Is that the reason why you're doing that? Essentially that like, you know, Andre, you're talking about running manual bids, like, you know, bid caps and everything. But Russ, you're like, I'm building a machine on top of Google's machine. So I can check Google's machine. Is that, am I saying that correctly? It's kind of, but neither Meta or Google, as you said, incentivized. Like to some extent, it's incentivized to make the results good. But also its business to get the money from the advertisers. So it's their best interest to give you the result that you are happy, but not to the extent where, you know, it's 100% out of 100%. So it still needs the operator in place just to operate as it should. So for Google ads, I think, you know, if there's some Google ads media bar watching us, like it's very common when in the shopping campaign or PMAX campaign, Google continues to spend a lot of money on the product that is actually not performing. And just forget or give so much less spend to the products that are not performing. It's very common. And if you do not, or you will not go and just fix that on turn off that product that doesn't perform, it will continue doing so. Yes, it's very smart machine. Like there's, and Google is getting so much more, so much smarter and better becoming so much smarter as well. But it's still not there to be fully automatic. Otherwise, like we wouldn't be, and maybe like at some point, we will be relevant as well. Like is the apparatus of these machines that the only thing is that, you know, you just give, like I just need the sales for this amount of money, just give me that, et cetera. And maybe it will be in a way that actually, it creates that the black box where media bar cannot do anything. And this is kind of what's happening with the PMAX campaigns as well. There's some extent of the freedom that you have still, but it's kind of like, you know, black box in a way. So maybe we'll go that direction. But until we can pull like better results for clients until those machines are either become like black boxes or they become so efficient that we are not needed, you know, we would just continue to do that. And I'm not seeing that like in their buy future, even though I can expect that they can happen, like at some point, and I would just vouch for probably, you know, black box forced approach. And not the, you know, for like that, the machine is getting like so smart that the human is not needed in there. I just think these companies will be just closing the levers for us to intervene. You know, there's also like one thing that I, you know, one tool that I want to just throw in, like in regards to this as well. Like if people don't want to mess up with these connectors and everything, there's like very interesting, there's very interesting tool, which is, I don't know, again, and I told, I was telling this was a lot like earlier and before our call is that I think the developers are so much on the edge right now, in terms of using AI, why marketers are falling behind that lead. I think, I think marketers are in a way how we know it without using AI tools, just becoming obsolete. It's very clear for me right now, and I think that's what's going to happen like in the next maybe like year, maybe two, or maybe even faster, we will see, and it also will depend on how many media buyers actually embrace the approach of using AI in the work that they do, right? So that's what I'm talking about. why learning AI, learning how to use these tools, learning how to connect things as well, together, glue them together, which we were doing for a long time, for example, in the past as well through the tools like make or Zapier, for example, which is getting so much more advanced right now. For example, this is the one we were using a lot, but now there are alternatives like Gumloop, for example, just automate everything, but with the sprinkle of the AI, you can just modify the data in the middle, your endpoints like as much as you want. Either it's like form submissions on your lending page that you're running for your, I don't know, like giveaway that you're doing, like whatever you're doing can be augmented with the AI to make it so much better and there are tools for that or just using like an end, like for example, this is a great tool for AI workflow animations, like getting back to what I just said, like in regards to the optimizations on the Google side of things, there's there are two actually, this manus AI, which is developers are embracing like crazy right now. It's still invitation only, which is like makes it a little bit hard to get hands on, but there is another one called cider, which is pretty cool, which is called installing as the extension of the browser and it gets access to your viewport of the browser, being Google ads account, being merchant, merchant center account, Google analytics account, and you can actually, you know, inquire what you want to do, what kind of insights you want to get from whatever, like in your viewport pretty much, it could be like just a YouTube video, it can do that as well, but for the marketers, I think it's important to learn to how to use this stuff to and you can do it like with this one, which is like pretty affordable as well, and it doesn't need any kind of like backhand connections or something like that. You can just get data from your browser, that's pretty much it. So that will be pretty easy. I mean, I think you're right. And the thing I feel as you share all that is just sort of overwhelmed. I'm not a developer, I'm not going to be a developer anytime soon. And the challenge is which of these things do I embrace versus not? I'm going to have a conversation after this call with Alex Cooper, who leads a creative agency, and Alex is a fascinating guy because he's extremely AI forward. He's really actively building these tools in. Yeah, and sorry, rest, you can probably kill your screen share now. But the, and so it's at the creative level, you know, he's extremely AI forward, and we're going to talk about this. And at the same time, it's like, I think, ugly ads are going to work better now than ever before because feeds are about to get absolutely hammered with AI created stuff. And so stuff that feels even more authentic and ugly is maybe, maybe going to be where this ends up going. Now, I don't know exactly what I think of that because at the same time, Alex is generating a huge line of ads for people and sees the value of like thinking about as Taylor Holidays said, think about your ad creative production like a supply chain, right? Where you're trying to make every part of it better, faster and cheaper. That's what you're, that's the goal. The more you can do that, the better your supply chain works. Andre, I'm curious to hear you talk a little bit more about this back on the meta ad side now, sort of like, what are the kind of key things? We've only got a few minutes left. What are the key ways you're actually using AI? If this is the framework, right, leveraging manual bids, leveraging the power of AI to do this, what are some of the like the actual brass tax way that you are using AI for your clients now and how do you expect to do that going forward? Well, people can learn from and replicate. Yeah. So the, the first idea is not even to just use the AI is to understand the processes first that you want to automate and then and then not make them. So for example, like if you just switch your video editing pipeline into a module, modular approach. So for example, imagine you have a long form video or one minute video, like doesn't, doesn't matter. So you can split it into who first like three to five seconds then lead part, which filters the audience and the body part with the product introduction, like with the unique mechanism of solving the problem. So like imagine you just split this approach and you produce 10 hooks, 10 leads and one body part with the explanation. Like it's so much faster for the video editor to come up with 10 hooks and 10 leads and one body part compared to like rendering all this stuff together with them with the AI, you can just mix and match them in a matter of minutes and get like, get like 100 creatives out of 20 assets, right? So you just mix and match all of them and like attach the body part and yeah, you have 100 of assets that are ready to launch and if you just want to double this volume, you just create one extra body part or double the amount of hooks, right? So this is the first point. The second point, again, if you have the winning hooks, you can automatically apply it to every creative in the other account. Like this is basically how like one of our past case studies when we scale the brand from 40,000 dollars of spend on manual bidding to more than 200,000 dollars of spend in a matter of 1.5 months. So we just took all the creatives like five of them on the winning hooks, slapped them in the beginning of each video creative there as we launch it. So like 10XD creative production and the brand grown more than six times in a matter of one month, 1.5 month. Like that's crazy. The second point is there are also tools like we also build those that can apply the text on the image. I'm like it's not that technically, technically hard, but still, if you have this process in place when you just design one template, you have the Google spreadsheet and the tool just applies the text on the specific area. No, I can come up. I've shown you this tool like in a video like which I've sent you. So you understand how it works, but basically multiplying the amount of the creatives like by 100, by 1000 is not an issue right now. So templating the approach and then just applying the tone of variations, text variations, and that. And with these chat GPD tools producing templates became so much easier. And the third one is the creative uploading thing. Like I've built a tool like it will be releasing it very soon with Rostov there like final testing. So many things to test like from the quality standpoints that like to make sure that the process doesn't break. So I literally feed it with thousands of creatives and I just set up the number of ads per ad set that I want to launch. I have the presets there. So the client will offer so automatically pulls the link to the product, the primary text headline, bidding settings, like age settings and so on and so on. So I basically drag and rub the creatives into the tool and it uploads that on the backend and creates the campaigns like in a switched off mode and like the only thing that is left for me is just to switch on the campaign in the other. So I fired the video. Right. You launch them all with manual bids and you make your team a lot faster and cheaper. Now what I think you just said you fired your media buyers. I think there's another element of this, which is that like what we're starting to do is take our most talented people. We haven't fired anybody yet. Like what we're looking at is like what happens when you give really smart and talented people these tools to move faster and put their minds on other things. You know. So yeah, they should. It becomes yeah it becomes. I don't know. And we found it helpful. Yeah. Yeah. Like media backstage should just become creative strategies more of the creative strategies because like the era of test and scale approach is already gone. Yeah, I know like many folks are claiming like I have this like huge spending at account like going to lowest cost. Yes, you can. But if you like go in a breakdowns and try to understand which converts for actually under your bid, right? Like yeah, probably it's not that significant. Yeah, you can ramp up the bid and like end up with the same outcome with the inflated CPA. Yeah. Like if you need volume of course. But I think it's more rational way to do that with infinite creative volume and controls. Yeah. All right guys, thanks. Great conversation. I appreciate so much you guys coming in bringing knowledge of some of these things that you guys are doing that are pretty advanced right now. I'm really curious to see how this gets democratized for those of us who don't have PhDs in math and physics and are or are our developers like us to be able to use this kind of stuff. But yeah, thanks so much for your time guys. All right, I hope you enjoyed that conversation. Don't forget to follow up with Andre and Russ the links for where to do that with their agency are in the show notes. But if you need help with your ad account, they are taking on clients. You can reach out to them and see if they're the right fit for you. You can also follow Andre on X. He does a lot of their content. His content is really great. You should go follow him. I've followed him for a long time. I'm interacting with him a whole bunch. If you want more detailed breakdowns of how things like big caps work, it's some of that Andre at this point, articulates that stuff as well as anybody else out there. So go do that. Don't forget to follow up also behind the scene studio for ad creative production, BTSStudio.co and admission to if this conversation at points is over your head, admission is the place to start. Go go deeper on those things. Again, go to the link in the show notes there or just tell them that I sent you your admission.co and they will get you free coaching calls so that you can get access to really good media buyers. You can coach you in your ad account at a really reasonable cost. which actually starts off as free through my link. So you're gonna wanna follow up with both of those. Thanks so much. I have so many good episodes coming up, including an incredible conversation with Alex Cooper that will release next week, at the time of this episode is released, to get really serious about AI and creative. Alex is probably the best thinker I know of right now at thinking about the intersection of AI and creative from MetaAd specifically, really, really smart guy, and it's a really good conversation. You're not gonna wanna miss that, so make sure to subscribe wherever you are watching or listening. You can see everything else I'm doing at ajf-growth.com and follow me on Ex at Andrea J. Ferris. I'd also love to hear from you, even when we get podcasts at ajf-growth.com. Thanks so much for watching listening. We'll see you next time. (upbeat music)