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Hey, what is up?
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Welcome to this episode of the Wantrepreneur to Entrepreneur podcast.
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As always, I'm your host, brian LoFermento, and if there's one thing that we can all unite on that we do not like it is wasted dollars.
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None of us like to waste money when it comes to ad campaigns marketing.
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None of us want that.
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We want our ad dollars to make us money, not cost us money, and that's why I'm so excited to introduce you to today's guest and fellow entrepreneur, talgat Musin.
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Talgat is a marketing scientist with over a decade of experience in ad tech.
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It feels like we're getting a little behind the scenes glimpse into this world today, because he has had roles at Google, amazon and TikTok.
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He's helped leading tech companies optimize over $300 million in marketing spend and has a deep understanding of incrementality testing and causal inference.
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Talga is now applying his expertise as an entrepreneur, guiding marketers to achieve clarity and confidence in their marketing investments through Incrementality Insider, which he puts out an incredible newsletter every single week.
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I love the work that he does.
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He's also putting out great content on LinkedIn.
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There's so much that we're gonna learn from him, but what I really appreciate about his mission in business and in his life is he wants to rescue one billion with a B one billion dollars in wasted ad spend.
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So this is something that we'll all get to learn from, because it's important as our businesses grow.
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So I'm excited about this one.
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I'm not going to say anything else.
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Let's dive straight into my interview with Talgat Musin.
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All right, talgat, I am so very excited that you're here with us today.
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First things first.
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Welcome to the show, thank you, thank you.
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I'm excited as well.
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Heck.
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Yes, we are going to learn a lot of very important things from you today, but before we get there, take us beyond the bio.
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Who's Talga?
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How did you start doing all these cool things?
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I'll start from the very beginning that I have an economist education.
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I spent some time in marketing and back in 2015, I immigrated to the US and by some random chance event, I landed at Google as a contractor, worked there as a year and a half and then converted to a full-time role.
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So I didn't know that I liked it until I tried.
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So I landed to the role of the marketing experiments and I was helping internally to develop program experimentation and do the GTM strategies, and over time, I was involved in a client-facing role where I was helping advertisers understand the causality of the ads so understanding what truly works and what's actually driving impact and that leads to the whole career of the causal measurement and incrementality.
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That's pretty much it and within the, let's say, after working at big tech companies across Google, amazon and TikTok, I finally came to the point where I actually like to do it on my own and then just last year, in in may, I started my own thing and it's still.
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It's still in the process of development, but I see the feedback from the market.
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There's high demand for this knowledge, there's opportunity to create value by minimizing the spend, and there's so much that can be done in that area.
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That's why I'm very excited to spread the knowledge and also educate interested marketeers.
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Yeah, I love that overview, Talgat.
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It's such a cool background, because what really stands out to me is the fact that as entrepreneurs, as business owners, we all understand the concept of spending ad dollars and putting together marketing campaigns, but the verb that you use is experiment.
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You talk so much about ad experimentation and all of the more scientific approach to advertising.
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Talk to us about that, because probably most people don't view it as experimentation.
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Yeah, depending on the view it.
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So experimentation we all experiment in our life, we do some stuff.
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We all experiment in our life.
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We do some stuff, we observe and we try new things and we see how it's changing the outcome or not.
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So in case of the digital advertising, there's like long history.
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My personal take on this there was been relatively on par speed between academia and business back in the days when it started with radio, then television, the print and different media formats where people get exposed to ads and then, as a result, buying product and services or being aware of the brand or the different objective for the marketing campaigns of the brand or the different objective for the marketing campaigns.
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Then what happens?
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With the rapid acceleration of technology, specifically the cell phones?
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The adoption was so fast that academia, I think, lagged behind in terms of the understanding and applying the scientific approach, meaning that in today's world, in the multi-device, multi-dimensional, multi-network where we live, it's really hard to understand what's actually driving impact.
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And the only way to do it nowadays with this complex world, is to run experiment and understand the causal factor.
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Because there is a phrase I don't really like that one, but I think everybody is saying over and over that correlation doesn't mean causation.
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So, yes, that's the reality, a lot of things correlated, and the problem also that advertising platforms trying to, they're doing the really great job, driving the business and creating the value, but in the same time, there is a limited credit, performance credit which all advertising platforms claiming to drive.
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So and there's a simple reality that if you take all those dashboards across all the media platforms you have, for example, let's say like you have seven media channels, and then they all claim some level of attribution, if you take all those numbers and combine them together, it's not going to match to your sales, it's going to exceed your sales.
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So there is a problem of double counting, there's a problem of inflating metrics, there's like many issues so and sophisticated advertisers nowadays they aware and know that.
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That's why they they are running this uh causal experiments to understand what actually driving impact.
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And also there is a kind of next layer of complexity that uh that can be primary cause, there can be a fractional cause, some, some channels can be amplifiers, some, some channels can be uh just cannibalizing on the other.
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So that's that's very complex uh web of causes and and synergies and and then uh negative effects across channels yeah, tal, hearing you talk about these things, it's probably the first time that most listeners are being told about these and it really feels like it's from behind the scenes, because we only see that end result.
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We only ever see the Facebook ads platform or the Google ads platform, and we hope to make sense of all the buttons that we can click, but there's so much data analysis happening in real time behind the scenes that we don't understand.
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So take us into some of those complexities and, of course, hopefully make it a little bit simpler for us, because a lot of people probably have never heard that term of incrementality testing.
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You've given us a little bit of an intro into causal inference, but walk us through.
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What are these mechanisms?
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Why do you pay attention to these when it comes to helping to rescue those wasted ad dollars?
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I think the magnitude of problem depends on the complexity of the media mix and also spend levels.
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And if you're a small business owner and you're implementing some spend across Google, maybe Meta, it's not going to be a big deal for you.
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The wasted portion can be very tiny.
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But over time I think when you grow the brand and you start leveraging more and more channels, the complexity and the potential waste proportion exponentially increase.
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So it's more applicable for the bigger businesses.
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But also I would say that certain medium-sized businesses can benefit from knowing this, particularly those who are doing well in organic meaning that they do their social game really well.
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They do, uh storytelling, there is some uh good brand dna and and they drive organic traffic.
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And on top of that, you know, to amplify the use paid media.
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This is where uh things get wonky, uh, where uh advertising it's a good ad, it's a good mechanism for business.
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Don't get me wrong, I love advertising, I love industry, I see it as an oil to move economy machine faster, but everything within the limits of understanding what really works.
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And then, when things get bigger, budget get bigger and then spend get higher, things get really complex.
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So I think that's a very complex topic.
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If to try to simplify it, but I'll try.
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I'll try to make it some simple analogies.
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One of the analogies I think I used several times with a client at Help that if you take a sport team professional sport team and the attribution problem is similar to how coach evaluate the players, if you only reward the player who actually scoring the points, then that's like what actually happening across the board.
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It's called last click attribution, where the last interaction with the ad, with the last channel who interacted with the user before the conversion sale, getting all the credit.
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Same if coach only crediting the, let's say, quarterback and then ignore all other contribution of other players, like defense and then some combination of passes, then there will be the problem.
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So I think similar logic can apply here and one of the let's say what will be the incrementality testing of this particular team and the impact of certain, let's say, defense players are not visible.
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But let's do the incrementality tests In the next game.
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Let's remove the biggest contributor to defense and see how it goes.
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So it doesn't matter how many it's going to change the whole game because it's all relative to each other and there are so many uh combinations of the game and how many uh points you get and how many uh you lose.
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So, uh, that's actually answering the question like, oh, uh, actually, uh, it's not only about the, the one who lost the touch, the ball and scored the goal, or got the guy the point, but it's all it teamwork, and there's a combination of factors.
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Yeah, this is one of the way I can talk more, but this is one of the metaphor I can use.
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It's such a good analogy, talga, I'll tell you, and especially as a sports fan, a lot of sports analysts want to say, oh, if you remove the best player from this team, they're going to miss out on those 20 goals that he scores every season.
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But to your point, no, there's so many different variables and factors at play that other things will fill in that missing gap, and so it might not be as straightforward.
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Of course it's not.
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It's such a complex world.
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I want to ask you about this, though, because a lot of people will be hearing you talk about experimentation and I'm sure you hear this with companies and leaders, business leaders that you advise with which is Talgat.
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I don't have the budget to experiment.
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It sounds like I'm going to have to use ad dollars in order to get these answers, because I have to run these ads in order to get the data back.
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How do you navigate them through that?
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It sounds to me like you have a very experimentation mindset and you understand that that's what's going to bring the positive ROI.
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How much of our budget should we just view as experimentation?
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I would say that in the case of advertising is not always like this particular example you described.
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That's for the new channel, when you're trying to understand how it works and there's a new spend like allocating new dollars towards this channel.
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But that also comes into assumption that you already know how.
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Let's say you have already like three, four main channels Google, meta, some others you already know exactly how much they're contributing.
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So if you're already advertising, you already have the budget per se for the experiment, because there is a way, there's different way to do it.
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You can turn off the ads and see what all the impact.
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There's different experiment designs, but idea that you need to understand the incremental contribution of each channel relative to the mix.
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So, and if you have a big spend across, let's say if it's more than two channels, then you probably need to understand what's happening there.
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Then, after you answer those questions, you have okay, now I know true contribution of my Google channel and meta channel.
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And there is one technique when you can actually, after you done the study, you can operationalize that learning to the business to make it actually actionable.
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You, let's say, you use multipliers, so you see that Google reporting dashboard claiming this number of conversion and then meta this number and if you're able to test each of them and you know what true incremental contribution of each channel, you can come up with multi-privacy.
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Most of the times it's a fraction, let's say, out of hundreds of reported conversions.
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Through scientific experiments I proved that it's only 60 are incremental to this channel.
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So attribution most of the times is inflated, not always some channels under-attributing, but mostly they're over-attributing.
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And for the next channel you also within relative to the mix.
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Again, that's a problem that you cannot view them in a silos Because the way how they work together it's like relative to each other and you don't live in silos.
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You treat the user across different ad platforms.
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It's usually same user.
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You just bombard them with the messages, if you're able to reach them.
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Then you get those multipliers assigning to your channels and then, okay, now I see my incremental contribution of each channel, this and like, let's say, this multiplier, and then you get a sense okay, now if I spend more on this channel, I should expect, uh, true contribution of this much.
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Then you solve so your baseline, how all this works.
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Now you explore new channels.
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Then, if you uh, for example, you're able to understand what's true contribution of these two channels and then you're able to understand what's true contribution of these two channels.
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And then you're able to optimize budgets, meaning that reducing the spend to a certain degree didn't hurt your sales at all because it's not incremental.
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So now you're freed up and additional budgets you can allocate to a different new channel which you never used before.
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Then, of course, you're starting slowly with experimenting and seeing how it works, and that's iterative process.
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The more you channels utilize, the more you spend.
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You continuously should be experimenting and understanding what was a true impact.
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Yeah, what I really appreciate about that overview, talgad, is that a lot of people, when it comes to advertising, they obsess about those advertising analytics.
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But it sounds to me like you bring that comprehensive business approach to it of saying, hey, let's look across your entire marketing mix, of course, let's look at your business analytics and let's figure out what's working and what we should ramp up.
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I really appreciate that approach that you're sharing with us here today.
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What I want to ask is just thinking about a business owner who has advertised on many of these platforms that we're talking about.
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I always thought and hoped and believed that these ad platforms, they also want me to succeed.
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Of course, they have their own engines and algorithms that are happening behind the scenes in real time in order to maximize my return on investment as well, of course, as best as they can, because you've already talked about a lot of the attribution stuff that happens behind the scenes.
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But is that the case?
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Can we, as business owners, count on the fact that these algorithms are also helping us in getting our message and our campaigns in front of the right audience?
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Or should we still retain that accountability and ownership and self-responsibility on our own side and say, hey, it's us that has to drive these changes, the iterative processes that you've talked about.
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Yeah, good question, so very nuanced one.
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I'll say this In the capitalist world, every organization have one objective maximize shareholder value.
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So, knowing that objective and motivation, you should act and then make a decision.
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Knowing that.
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So ad platforms like they, of course want the business to succeed, but in the same time they need to provide the return on investment and every quarter when they report the wall street, which is affect the share price.
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So, and that leads to the point where objective is to keeping the balance between helping businesses grow but also getting the return.
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So, knowing that, I wouldn't count on the ad platform that it will help certain business, particularly the ad platforms, optimize for the maximizing revenue and that distribution can be different depending on how the different segments of businesses look like on the ad platform.
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And logic that apply to that is the use internal metric called ARPU, which is average revenue per RPA sorry, average revenue per advertiser, and for that metric, everything optimized towards that.
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So of course there is also different variables regarding algorithm, the changes to algorithm and and overall advertising is very beneficial for business, but I I would say always experiment, always keep accountable platforms and representative platforms, because anything in life, everything moderation.
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So if you lose control, then uh things like can get uh south not in your, in your favor yeah, it's true.
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I love the way that you talk about that because it is so important.
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There are so many things that we can look at, that we can control, that we can tweak inside all of our ad campaigns, which is why I do.
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I want to put you on the spot and ask you about a few of the metrics that you pay attention to, because I'll out myself, I'm a data, a marketing scientist, like you, talgat, and so typically I keep it a little bit simple and I just look at click through rate, cost per click.
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I pay attention to, to those two metrics, among others, of course, and I'm always looking at conversion rate and percentage on the backend.
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But where does your mind go when you look at an ad campaign?
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What are some of those KPIs that you look at?
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If I myself run the campaign, I will not be too obsessed with the I'll call it surface level metrics, those CTRs and other things.
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There's many variables impacting it.
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There's internal auction.
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There is also seasonality.
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There's your category you're playing with, like how tough is the competition?
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Because most of the ad platform auction-based and, depending on the bids, there is a certain like battles happening on different level of the bits.
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So I will say that platform metrics are directional.
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I think the most important thing to pay attention to is actual sales, like not the reported sales, but your CRM data, your actual sales and understanding that there is a lack effect happening when it comes to advertising Like sometimes it's not realized this fast.
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And also the trend nowadays advertising platform become very sophisticated, platform become very sophisticated and it takes some learning periods to optimize, to know what, what your ideal customer profile and for you as a business or agency, who helping you to target specific audience profile.
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So over time, algorithm learn and I think important step to do if there is a possibility to report back your conversion, to upload it or have a pixel in place so that way algorithm will know better when the conversion happening, it will optimize for it and then perform better.
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But from the reporting side, that's like a service level reporting From the business side.
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Business side again, keeping in mind that this is all attributed, it's not caused, so you can imply causality, but uh, that's require experimentation.
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So there is no simple answer to this.
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It's depending on platform.
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I would say that, uh, just uh, take it easy on the surface level metrics and look holistically and see how your sales and conversion fluctuates and also, of course, check carefully what's being reported and what actually happened.
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Yeah, I love that realistic and pragmatic view to it, because you call those out as surface level metrics and I think it's important for all of us, as business owners, to really come back to the fact and it's obviously a core part of your mission is we are spending ad dollars to make sales and make that revenue back.
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So the fact that to you Talga you went straight there when I asked you the question that is ultimately the most important metric.
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So I really appreciate that I wanna.
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That is ultimately the most important metric, so I really appreciate that I want to ask you this question because it, of course, is coming up in all of our conversations this year and you and I are talking in the first half of 2025.
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And AI everybody's using it, Everybody's wanting to get better at it.
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How's AI factoring into all of this?
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I would imagine that it helps when it comes to making sense of the data.
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It can help with regards to some of the experiments that we're running.
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But of the data, it can help with regards to some of the experiments that we're running, but what's your view of where we are with AI and how it can best suit us where we are right now, and what's that view for the future?
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In which context?
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As a business owner or as an advertising platform?
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I would say for the business owners.
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Okay, business owners, I think there's a huge unlock of productivity.
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I use AI every day.
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You can 10x your learning speed using AI.
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I think you can view it as your always-on advisor.
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And when it comes to advertising, there have been some bottlenecks in terms of the production, creating the visuals, creating the creative assets.
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That's also speeding up the process.
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But when it comes to measurement, I don't see, at least in the short and midterm, ai helping to make it better.
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Make it better, maybe in some cases it's, it's making it worse because mostly it's a correlation driven and uh, it can.
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It can inflate those already inflated metric even further.
00:25:18.125 --> 00:25:33.853
And uh, and also another thing that, with the agents and other things happening, that there'll be a rise of the bots, uh, which affecting the advertising dollars, because it's already big portion of the bots, which affecting the advertising dollars because it's already a big portion of the activity.
00:25:33.853 --> 00:25:40.646
It's not the humans, it's the bots and they're able to click your ads and waste your dollars.
00:25:40.646 --> 00:25:52.558
So that's, I think, also important to know and understand when it comes to budget optimization, important to know and understand when it comes to budget optimization.
00:25:52.558 --> 00:25:56.167
But, again, from the productivity standpoint and for the business owners, it's a huge, huge productivity booster.
00:25:56.167 --> 00:25:57.817
It helped to scale.
00:25:57.817 --> 00:26:02.006
It helped to scale globally because of the like.
00:26:02.027 --> 00:26:04.376
Let's say, using AI selling your product.
00:26:04.376 --> 00:26:07.425
Services across the board if there's no logistical issues.
00:26:07.425 --> 00:26:14.361
Services across the board if there's no logistical issues globally across different languages.
00:26:14.361 --> 00:26:19.949
So I see that AI it's an amplifier of the humans' activity as electricity.
00:26:19.949 --> 00:26:34.503
Back in the days Not my words, it's already been used, but I think back in the day, every big innovation, back in the day, every big innovation there have been early adoptions and there have also been opponents to it.
00:26:34.503 --> 00:26:40.729
But I think it's easier to connect dots backwards.
00:26:40.729 --> 00:26:46.073
So, of course, electricity makes sense, but back in the day, there has been also a hard time to adopt it.
00:26:46.073 --> 00:26:54.277
Same with AI, hard time to adopt it.
00:26:54.277 --> 00:26:55.118
Same with ai.
00:26:55.118 --> 00:26:55.799
I think it's just like a.
00:26:55.799 --> 00:26:58.744
There is no threat to uh businesses from ai itself.
00:26:58.744 --> 00:27:02.155
It's just like business who, amplifying ai, do better than than those who don't.
00:27:02.616 --> 00:27:04.903
That's my theory really well said, talgan.
00:27:04.903 --> 00:27:16.604
I love hearing the way that you talk about this stuff, because there's so much noise out there about AI and how we can be using it, how we should be using it, but I love that practical approach to it that you bring in real life considerations.
00:27:16.604 --> 00:27:25.940
Like you've said a few times in our conversation today, it's a complex world and, even thinking about AI agents, we've all seen the power of agents going to websites, doing research.
00:27:25.940 --> 00:27:32.230
I didn't even think about the fact that, of course, those agents are clicking ads in order to gain that data and that research.
00:27:32.230 --> 00:27:34.576
So really useful to hear your thoughts there.
00:27:34.576 --> 00:27:49.268
I want to ask you about Incrementality Insider, the newsletter that you announced last year and that you've been running, and that's a core part of your mission in order to teach people and really expose people to the fact that, hey, this is how you can reduce your wasted ad dollars.
00:27:54.961 --> 00:27:58.335
Talk to us about Incrementality Insider and what people look forward to seeing in there.
00:27:58.335 --> 00:28:20.132
So since I started, there was one very, let's say, high level vision and after some time I spend most of the time nowadays with the actual working on the front lines consulting, helping advertisers to elevate the measurement on the capacity as a consultant.
00:28:20.132 --> 00:28:26.202
So in overtime I was able to hone and clarify what I'm trying to do.
00:28:26.202 --> 00:28:47.840
Of course, missions stay the same, but now I get the clarity how I actually can help advertisers and marketeers or whoever interested in this topic, that I'm going to make a focus on education and education how does things work, how it can help, how businesses can benefit from knowing this.
00:28:47.840 --> 00:28:52.637
And uh, there's the two issues I observed like.
00:28:52.637 --> 00:29:21.626
First one, that uh, there's a not a lot, but uh, I'll say like a some uh critical mass of professionals who who know how to do this, who have this specific subject matter knowledge, but most of them, like 99%, working for big tech, very attractive salaries and, let's say, like golden handcuffs, and that knowledge is gated inside.
00:29:21.626 --> 00:29:23.234
It's also benefiting the platforms.
00:29:23.234 --> 00:29:25.621
Of course, there's a certain level of advertisers.
00:29:25.621 --> 00:29:42.483
Where they reach certain levels spent, they get access to this knowledge and expertise, where I was a part of it, helping advertisers to succeed, but on open market there is a very limited thought, leadership and sharing the knowledge.
00:29:42.483 --> 00:30:15.369
And second one, that area by itself it's relatively new and still developing because I think, as I mentioned previously, the lack of academia behind it, so a lot of professionals learning on the fly and this knowledge only now become to getting synthesized and then coming to some sort of broader industry bodies were being explored.
00:30:15.369 --> 00:30:26.141
So it's a very exciting topic overall, but there are so many gaps that exist and my mission is to address those gaps, helping businesses succeed.
00:30:26.470 --> 00:30:30.020
And also, I don't claim that I know all the answers.
00:30:30.020 --> 00:30:32.598
I'm learning all the fly, every day something new.
00:30:32.598 --> 00:30:43.349
That's why it's important to stay on the front line and actually do the work before trying to help others.
00:30:43.349 --> 00:30:49.173
I think that's important to be on the front lines, learn it, front lines, learn it.
00:30:49.173 --> 00:30:58.980
And I can share more perspective regarding, like, why is important and why is relevant to human being as a users.
00:30:58.980 --> 00:31:02.460
But like, that can be probably already long answer for this.
00:31:02.460 --> 00:31:03.873
But yeah, long story short.
00:31:03.873 --> 00:31:18.555
I refined the vision, I refined my POV and perspective and I'm gonna uh, let's say uh, accelerate the content production for my newsletter.
00:31:18.555 --> 00:31:19.738
And uh, it is.
00:31:19.738 --> 00:31:21.020
It is free, by the way.
00:31:21.020 --> 00:31:27.316
There will be some uh paid premium content moving forward, but for now it's free.
00:31:27.415 --> 00:31:49.680
Anyone can subscribe, read and and I also uh developing the whole uh strategy across different channels to to share the ideas through visuals, infographics and many other things yes, I love the way you talk about that and really position it in the marketplace, talga, it is much needed education that, as you said, most people have never had access to this.
00:31:49.680 --> 00:31:52.835
So, talga, that's why you are looking at your latest subscriber.
00:31:52.835 --> 00:31:55.770
I'm so excited to continue getting your genius and listeners.
00:31:55.770 --> 00:32:02.096
We're going to drop that link in just a minute, but before we do, talgat, I have to ask you for your number one best piece of advice.
00:32:02.096 --> 00:32:14.016
Knowing that we're being listened to by both entrepreneurs and entrepreneurs at all different stages of their own growth journeys, and knowing that you are not only a subject matter yourself and the things we've talked about today, but you're also one of us.
00:32:14.016 --> 00:32:15.901
You're also a fellow entrepreneur.
00:32:15.901 --> 00:32:20.501
So, with that hat on, what is that one piece of advice that you want to leave our listeners with today?
00:32:22.691 --> 00:32:24.173
If it comes to entrepreneurs.
00:32:24.173 --> 00:32:28.061
I would say it took me some time to understand this.
00:32:28.061 --> 00:32:39.704
It's not related to advertising, just overall, overall that I spent a lot of time researching, analyzing, analysis, paralysis and then strategizing.
