The future of marketing analytics & attribution

Posted on 06 Mar 2022
Data and AnalyticsGrowth Marketing

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About this episode

In this podcast, Daniel Johnson and Michael Taylor have a wide-ranging conversation looking at the future of marketing analytics and attribution. We learn more about how the industry is evolving, particularly after iOS 14.5 and the skills you need to know.

What they talked about:

  • The effect iOS 14.5 had on the industry.
  • These are the things you need to know as the industry evolves.
  • When How important is university to a career in analytics.


Daniel Johnson: Cool. Well, thank you very much, Michael, for joining the first, or one of the first interviews that GrowthMentor will be doing. I’m very excited to be hosting this and to be asking you a bunch of questions all around the world of startup marketing and analytics. This is pretty raw. So I’m going to keep it. Lights, we’ve got some questions, but we’re gonna organically go through and see if there’s any areas that seem particularly interesting. For our audience. How does that sound?

Michael Taylor: Yeah, sounds great. Yeah, happy to be here. I’ve got my Christmas cap on, obviously.

Daniel Johnson: And why don’t you give a brief description of what you do, your experience and what you’re looking to do in the future?

Michael Taylor: Sure. Yeah. So I describe myself now as like a recovering agency owner. So I co- founded a company called Ladder, which we grew to 50 people, and actually, like a bunch of the growth mentor mentors, or Ladder employees or ex-Ladder employees as well, which is pretty cool. But, but, you know, kind of got burned out from that game, you know, the agency game was tough, like, you know, this, as well. You know, I was managing managers who manage other managers and, you know, wanted to kind of get more technical again. So, so left, just at the height of COVID, and started working on a new business called Vexpower, which is solving one of the big problems that we always faced it Ladder, which is training. So the way we do it is with simulator-based courses, and it’s very focused on data and analytics can teach you how to do marketing mix modeling, or, you know, cohort analysis, or pricing optimization, all these things that you might not actually know how to do in your organization, you might not be able to get on the job training for so you watch these videos, and you go through the simulator, and you kind of get that on the job training from from me or from someone else who’s produced the course.

Daniel Johnson: Cool, and you bring all your sort of experience from Ladder into this very practical course. And for those who haven’t heard of Michael before, I’m very excited to be on the other side of this interview, because I’ve been following Michael, since he was writing blog articles at Ladder. And now I’m a member at Vexpower. And I’ve been a big fan of all your work. I’ve personally, it’s been very valuable to me. So I’m very excited to be able to ask you questions and to dig into your insight.

Michael Taylor: Yeah, that’s great. I mean, it’s, it’s kind of nuts, like we, you know, we were just like a, you know, 10-12 person agency when we started writing content. And I actually put my salary on the line to hire our first content writer. It was like, I wouldn’t be able to pay myself if he didn’t get a certain number of leads within the first three months. So, so I’m glad it worked out. But like, we ended up getting 60% of our leads directly from content. And, you know, still to this day people, you know, joined Vexpower, because like, Oh, I remember you writing a lot of good stuff at Ladder. So yeah, it’s, I think content marketing has been a real, real benefit a real trajectory changer for our career.

Daniel Johnson: I think the fact that, you know, content marketing in this field is such a highly competitive space, there is already so many good people. So the fact that you’re generating leads means that the quality of your content must be at the top of its game, which I can attest to. So the theme for today’s conversation is around iOS 14.5 GDPR. And all the new privacy regulations, and everything that’s changing, what does that mean for me and my career? Do you want to expand on that a little bit?

Michael Taylor: Yeah, sure. So I thought this was a topic that’s at the top of everyone’s minds, because we’ve seen a seismic shift now in the way that your tracking is done, like tracking, as we traditionally have, have done, it is broken now. Because Apple has given users the right to choose whether they want to be tracked. And obviously, when the prompt comes up on the app, it’s worded in such a way that makes it sound very scary. You know, it’s like, nobody really wants, like companies to track them around the web. But obviously, you know, there’s all sorts of implications, right? Like there are a lot of small businesses are really hurting now, because Facebook ads aren’t working as well as these two, you know, there’s, you know, people thinking about what the career is going to be now, like, should I actually go into analytics? Should I be more data driven? Or should I just get back to doing brand advertising? So it’s, I think it’s it’s a really interesting topic and one that I’ve been swimming in a lot because, you know, a lot of work at Ladder was trying to prove that we were doing a good job, and like find out what’s working, what’s not working, as well as You know, with max power, a lot of what I’m teaching is attribution and marketing attribution. And actually, we even have a course that is like, help my iOS 14 is broken. My tracking, what do I do? So? So yeah, it’s a top of mind all the time over the past couple of years.

Daniel Johnson: Yeah, so I think, you know, I think analytics and attribution is a concept that has been increasingly brought to light. And it’s not exclusively for developers as it used to be, the more and more people are paying attention and trying to understand it. And I really think that, from my perspective, having worked with startups, that everyone in the company should have a basic understanding of how that process works, even if they’re not directly involved, because I think it’s so integral, just being able to really understand where your customers are coming from, and how they’re interacting. I know that with the changes from Apple, and I imagine future EU GDPR related changes with Google and storing data in the EU, there’s going to be lots of changes there. We’re going to go into this in a much more deeper phase a bit later. But I wanted to get your perspective, as someone who’s been doing this for a few years, what is your opinion on the changes as a broad sense? And where do you think it’s gonna go? And how does this affect both current marketers? And then we’ll talk about if this is, you know, the going into it as a career as we move on?

Michael Taylor: Yeah, I think it’s, it’s an arms race. It always has been, you know, like, marketers ruin everything. Eventually, you know, like, in the golden age of advertising, it was, you know, magazines and coupons. And, and, you know, marketers were AV testing what, what, you know, what copy drove for most coupon redemptions. But then eventually, you know, now, when I get a magazine at the door, I just throw it away, because it’s like, probably spam and full of advertising. Like it’s not, you know, it doesn’t have like useful content in it anymore. And I get all of my kind of information from from Google. And then Google advertising has, you know, remarkably, not really broken Google, like, they’ve been pretty smart, historically, about inserting, you know, the right number of ads and kind of making them more useful kind of incentivizing advertising advertisers to do good behavior. But But I think, you know, it’s always been an arms race, like, nobody really wants to be tracked, but everyone wants to content for free. And what happens is, a new channel will come up, like Tik Tok, and and like everyone moved from Facebook to Tik Tok because Facebook has ads, it’s not cool anymore. And your grandpa grandparents are on Facebook. But then, you know, now, Tik Tok is this new, cool thing. Obviously, they’re, you know, monetizing. Everyone has to monetize, eventually, you can’t just, you know, provide social networking for free or, you know, news, you know, news for free, like, all the things are supported by ads. And on the flip side, you know, small businesses need a way to, like, get known in the world, you know, go from small to big. And obviously, big business doesn’t really get hurt when these shifts happen, because they already have a lot of data. And they already have a lot of kind of financial muscle like they, if they have a big audience, they can, they can usually track it much better than somebody who’s boring someone else’s audience. So, so I think this has always happened, like advertisers have always tried to innovate to close the gap on tracking, but I don’t think it will ever happen. You know, I don’t think we’ll ever have perfect tracking. And when we do, then it’ll be almost too effective. And, you know, there’ll be a backlash, right. So I think this is always a game that we need to learn how to play. But I think there’s a lot that you can still do to, you know, to directionally understand what is working and what’s not in, in your advertising.

Daniel Johnson: So I guess this leads me into my next question, which is, how would you see the skill set of a sort of digital marketer/analytics expert, go from what it was say? I was about to say five years ago, but the industry has completely changed from five years ago. What it is now to what it’s moving into? How is it becoming less technical, more technical, more? I don’t know, mathematical, more creative. How would you see that process involving?

Michael Taylor: Yeah, I think it’s, it’s bifurcating, like it’s splitting into two, two groups. On one hand, you know, one way to tell if something is working is to like, cause a really big impact. And I think that the only way you can really do that nowadays is by doing a lot of really good creative testing. So I think, you know, a lot of people kind of moving more into, you know, let’s generate really interesting creative, let’s do like a big ad or you know, piece of content that gets talked about, and then you don’t need Statistics, if it works really well, like, there’s a famous quote, I can’t remember who said it off to look it up. But it was, if you need to calculate statistical significance, you probably need to run a better experiment. Like that. It’s like, if if you do something big like Coinbase, you know, with a QR code ad in the Superbowl, like, you don’t really need attribution to see that, that causes a huge spike in, you know, searches for Coinbase, registrations, etc. And it’s probably not going to that spike is not gonna be explained by anything else. The same time, you have people who kind of get more technical and more statistical, because, you know, there are businesses that do still have a lot of data. And, you know, like, if you work for a subscription company like Netflix, that’s all powered by data, you know, you need to build recommendation engines to decide, you know, what things to show, you know, you need to kind of understand, like, when you invest in a new piece of content, is it going to work? Or is it not, you need to be able to categorize video at scale, and say, like, these are all the horror movies, these are all the comedy movies, these are the, you know, period pieces based on real life events, from the 1960s, or whatever, you know, like, it’s, like, you need all these tags. And I think like, that’s not something that can be done, the human scale, like it needs to be automated needs to be kind of done analytically. A lot of a lot of people are using things like marketing mix modeling, now for attribution, because it has the benefit of like not actually needing user level data. So it’s robust to these privacy changes. But that is a much harder thing to do than just looking at Google Analytics, like it’s a project that might take, you know, anything from a couple of days to a couple of weeks. And, you know, to get it working always on requires a really, really in depth knowledge of statistics, and engineering kind of data pipelines. So kind of seeing people go into two groups.

Daniel Johnson: Interesting. Yeah, it’s, from my perspective, I never really liked math at school, but it’s something that I really have spent a lot of time learning now.

Michael Taylor: I failed math in school actually.

Daniel Johnson: I’m glad to hear that because it makes me feel slightly better. I was in the bottom set.

Michael Taylor: Yeah, I to give it up a level because I didn’t pass AS. And then actually my I did study business university, but dropped all of the all of the marketing requirements, and also didn’t do anything, any maths with it. So it was just kind of luck that I ended up learning how to do pivot tables in Excel. And then and then and then you got like an early kind of Google Ads job and then started to see the power of math that way.

Daniel Johnson: hmm. Yeah, I think when you’re in the industry, you know, I did a presentation not too long ago about, about growth, marketing side of marketing. And really, you know, when I talked about data, I didn’t talk about data and marketing almost being a separate activity, but was completely integrated, because they’re so important to each other. Yeah. Which, and thinking about that a bit? So do you think I actually be a role for people in a post? I was 14.5 world? Or do you think that these skills will be taken on by existing marketers? Or maybe other people in the company?

Michael Taylor: Yeah, good question. The way I see it is like, you know, when you’re selling a business, you want to talk to customers, right? And, like, that’s sales, right? Like one one to one kind of talking of customers, or, you know, or that kind of like, customer service, kind of understanding what your existing customers want. For me, marketing is just doing that at scale. So obviously, you can’t do like, you know, 1000 interviews. So you have to look at Google Analytics and see what 1000 people are doing, kind of observe their behavior instead. So I think certain types of businesses will always need, you know, to do that at scale. So for me, marketing is just that, you know, kind of built up, and regardless of what, you know, technologies available, regardless of what rules are out that that limit kind of certain practices, you’re still gonna need to do that, right. Actually, a lot of the analysis, the thing that really struck me a lot, I was like, most of the analysis we did was actually really simple. You know, it was like this ad performed better than this ad, you know, or like, you know, we’re spending this much and, and then the, the ROI is dropped off a cliff, or the ROI is, like, increased a lot like what happened. And it’s just kind of basic comparison. It’s like, oh, well, this campaign went up and this campaign went down. And, you know, it wasn’t rocket science. Like we did do a lot of data science type stuff. But it was it was pretty niche and like, only really applicable to very, very big clients. I think just understanding like, the fundamentals of statistics and the fundamentals of like how to do a pivot table and Excel is really like 80-90% of the job. And what you really need is like people who think analytically, or think about kind of technical kind of systems, rather than necessarily like, you know, someone who’s like a PhD in statistics, because those people are actually like, pretty cheap to hire like that. And you don’t need a lot of their time. Because it’s very niche, you know, what they can do for your business? Unless you’re at massive scale?

Daniel Johnson: Do you think people are going to need these skills moving forwards with the introduction of no code? And simplified systems? You know, even GA4 has a slightly more simple, easier, user friendly interface, shall we say? Do people need to learn the technical side?

Michael Taylor: Yeah, actually, I’m not a big fan of GA4. Maybe I’ve just, you know, just just really wedded to the old version. And I don’t think anyone is, yeah, I think find it as user friendly. But, um, but yeah, no, I think no code is an interesting one. Because, you know, I know how to code now I, you know, I was coding for five years at Ladder, like, in evenings and weekends. You know, just like, you know, it wasn’t much, but it was just like learning how to add tracking to the website, you know, learning how to automate some stuff. And, and then when I left flutter, I was coding 20 hours a week, and actually, more recently, what, like, 40 hours a week to build Vexpower. So I actually know how to code but, but like, I use no code tools all the time. Like, there’s no, there’s no reason why I would, you know, automate something. Like, there’s no reason why I would like spend a lot of time building a connector into type form for surveys, when I could just like hook up Zapier. And then whenever someone fills in the survey, then it just sends a sends it on to the sheet that I’m storing them all in, you know, like, I use notion a lot for, you know, a lot of the backend stuff, and kind of use that as a database as well as kind of No, no, no keeping apps. It’s like, it’s no code really, to me. It’s it’s not like, it’s not actually easier than coding, like coding is learning a language. It’s like learning Spanish, right? Like, yeah, it’s like slightly more difficult, because computers are very precise in how they talk. But you also need to be very precise in how you think when you build a no code system. Like it’s systems thinking that’s important, not necessarily the language. So language is what looks scary, just like when you’re learning Spanish and grammar sounds really hard. But like, you know, it’s not, it’s not like learning to speak Spanish, it’s like, you know, learning to be interesting, Spanish is the hard part, you know, learning to have a conversation in a business meeting in Spanish, just the hard part, you know, not just memorizing words. And that’s the same with coding, you know, like, no code does make it easier and more accessible. But you still need to think like a programmer, it’s still like, I think it’s, you know, just as valid. You know, like, I don’t see a difference between a no code engineer and an engineer. Like, if you get the job done, it doesn’t really matter who wrote the code. But like getting the job done by understanding how to automate something is really is like actually the hard part.

Daniel Johnson: So do you think people are going to people need a computer science degree in order to get into the industry?

Michael Taylor: I hope not like, I don’t have one. I mean, a lot of, you know, a lot of the people I know who are coding full time, don’t have one. I think it’s helpful, obviously, like, if you’re in a situation where you know, you studied hard and you went to the right schools, and you ended up with like, a CS degree from Stanford, then yeah, you’re gonna probably gonna do a lot better like Facebook or hire you for 120-150 grand a year plus stock options, straight out of the class. So, so you know, why not? Why not do that, like if you can do that. And I think that you’ll probably work on more challenging problems, and really good money. But like, vast, vast majority of people who write code every day, don’t actually, you know, don’t actually have a CS degree. And I think you can learn CS afterwards. Like everything that you learned in MIT, or Stanford, like it’s available online for free. You can self learn that stuff, if you’re motivated enough. So. So that’s, you know, kind of what I’ve been doing. So yeah, I don’t think it’s really that important to have a CS degree. I think it can help but it’s just like one potential path.

Daniel Johnson: So actually, there’s a good question, what if someone wants to get into the field apart from signing up to Vexpower? What else? Yes, they do, too. You know, how would you start? Do you need to learn the fundamentals of statistics? First, you need to understand how businesses operate. What kind of what would say the first like six months of learning look like?

Michael Taylor: Yeah, good question. So I think it doesn’t work to just, just like understand the problem side, like just understand business. Because if you don’t know what’s possible with automation, you’re just never automate. And in fact, if you go too far in your career, without learning statistics, or without learning, coding, or no code, then you just will develop the mindset that it like, you’ll never think of, like you will be a native programmer, right? Like, you will think of business solutions and human solutions first, and that is still a great career, but it’s not, it’s gonna be very, very hard to unlearn that. And the further you go, I think that I was quite lucky in that in my first job, everyone there used to excel really aggressively. So for me, that was like, my introduction, like Excel is the biggest note code tool in history, right. And, you know, you can even code in Excel as well. So I think that, like, if you are doing technical stuff like that, and you kind of solving problems with cell templates, you know, you’re, you’re kind of learning how to do macros. And then you are learning how to code. And I think that you’re kind of on that path. If you know how to write macros in Excel, and you understand like formulas, and pivot tables, like your, you can basically apply the same principles to coding as well. It’s just, you know, more admin, essentially. So, so yeah, I think I think, I think you’re in a good position, if you’re doing that. The, the way that I learned I did a course called One Month Rails by a guy called Mattan Griffel, he was he actually founded the first growth hacking agency in New York, which is how I heard about him. But, but that was really good, because it showed me, it was kind of like, just do what I say. And then, and then like, by the end of the course, it’s like you have a website live. And I think that you should always kind of look for those types of courses first, like, you know, people who learn a foreign language, like we talked about Spanish earlier, they learn it because they have a girlfriend or boyfriend who speaks that language, you know, because they live in that country. And it’s useful for them every day. And I think that, like, you know, you don’t really learn a language by going grammar first, it’s really boring, too, too tedious, you know. And, like, stupidly, everyone tries to learn code by going grammar first, you actually want to learn how to solve problems. And then kind of think about the systems for solving problems. And then and then eventually, kind of learn how to code it. So I think that kind of reverse view is, is really important. Like if you can tend to take specific problems and go like, how would I automate this? Like, if I needed to do this 1000 times, 10,000 times? Like, how would I change the task of learning how to formalize problems first? Then then kind of learn how to automate it afterwards. I think like that’s, that’s really the best best path.

Daniel Johnson: I find there’s so many great resources online, I’m taking creed code Free Code Camp. And I used to be signed up to some paid programs and actually preferred the content on this. Yeah, yeah. And every time I have a question, I’ve got my teacher, YouTube. So I find that the resources are always out there. And there is actually really high quality free information, if, you know, if you want it if a person can look for it, but what resources would you recommend people go to if they want to get and let’s talk about sort of three kind of levels? complete novice, someone who has a basic understanding, and maybe someone who wants to progress to a more advanced level. So starting with the basic one, yeah, what kind of resources would you point them to?

Michael Taylor: Yeah, so actually, like, I agree with you that the free resources are usually the best. Especially because, you know, there’s, there’s always like some random YouTube video that you can find or some Stack Overflow comment that, you know, that just saves your day when you’re running into a problem. So, so that’s, that’s pretty common. And that’s why engineers always like, even though engineers and a lot of money, they’re also very wary of paying for anything, because the quality of the free content in this domain is amazing. But no, I think you can sometimes take shortcuts by paying. Like, if you’re a complete beginner, I think it is helpful to have a coach, you know, someone to kind of walk you through it, and kind of make sure you are held accountable. Like I think about it, like fitness, right? Like if you, if you if you’re completely new to the gym, you’re not going to have a good time, you wouldn’t know how to use any of the machines. You know, you might be like exercising the wrong way and cause loads of problems. hurt your back or something. Right. So, so like, you know, spending some time with a fitness trainer is useful. So I think doing a coding boot camp is a valid approach. Don’t expect like when you come out of the coding boot camp, that you’ll actually be able to get a software development job. I think it’s still like another year after that before You’re ready. But, but like, that is a good shortcut and will keep you motivated. I just kind of hacked it myself, like I, I like looked at the cost of Lamda School, which is like, you know, 20-30 grand, and just thought, what could I? How many people could I hire? For for 20 grand, you know, like how much time with with a developer one on one can I get so I just went in, like, paid a bunch of people. You know, I didn’t, I didn’t even cost me 20 grand, I think it was like five, and I just paid as I as I ran into problems, I would just like pay different people to teach for them. And, and that worked out really well like give it’s like 60 bucks an hour, say. So you can get a lot of one on one tuition that way.

Daniel Johnson: I do something very similar. I have a regular call with a student who I pay. And he’s a PhD student. And every week, I’ll assemble all the questions that I have. And I’ll be like, okay, cool. I’ll ask you a bunch of really dumb questions. But I find that a really fun way. And I tried to sort of build learn by building. And I’ve got a bunch of side projects.

Michael Taylor: That, yeah, that’s absolutely, that’s what I would say you need to do as like, you know, to get into the intermediate stage, you need to actually apply it, like you can do tutorials forever and never make any progress. You know, you can’t really build anything after doing Code Academy, right? Like, it’s, I think, but they by trying to, like you do need a base level of knowledge. But once you have that, like actually trying to build something like I was trying to build just before it started back power was trying to build an analytics tool that attract virality, very topical. But, but it was, you know, like, that ultimately didn’t succeed. But, but it was really, really valuable. Like it taught me a lot about, you know, when you’re publishing stuff to production, and you have bugs, like it causes lots of problems on people’s websites, and, you know, you need to you need to have systems in place to fix that, you know, kind of like, learn by doing is really powerful. So yeah, I think intermediate stage, you really want to get into actually solving a real problem trying to build something, a side project or even going full time on on something. And then for advanced, the way you learn is usually by mentorship. So like actually talking to people who are further along than you are and kind of understanding what they find important. Because they can kind of you don’t need to be too far advanced, but like close enough to your range, like a year ahead of you, then, you know, there’ll be really good to learn from, and I found, I found it been really useful to do things like pair programming. So, you know, you you jump on at the same time, like one of you is driving and the other other one is watching and talking. And you’re it might seem like really inefficient to be paying two developers to be, you know, working on the same thing, but but, you know, it just catches a lot of bugs, you know, like, make sure you build things the right way, you cannot keep each other accountable. So I think that that’s been really key as well.

Daniel Johnson: Is any websites or communities that you’d like to give a shout out to?

Michael Taylor: Yeah, so obviously, you know, like, we, you know, we have, you know, we have a problem first approach. So, it’s kind of, like every, like I said before, like, actually solving problems is one of the ways that you get better at this. And so, you know, we don’t really teach any theory, you know, we didn’t really teach and it’s not like lecture style videos, it’s like, how do you specifically solve this problem. So that’s kind of the niche we’re trying to occupy. But in terms of resources, I found helpful Freako camp was really good. I specifically enjoyed data quests, I think was really, really good for learning the data science side of things, which is really valuable for marketing. That’s how I really learned Python. And it was only really when I started learning Python that it clicked for me, and I kind of got more into coding, because JavaScript is useful. But it’s also there’s a lot going on. And like it’s used for a lot of different things. Whereas the Python community is, you know, very, very data kind of manipulation oriented. So if you kind of want to get better at data analysis and getting into that ecosystem, as you saw, and then eventually I kind of circled back around. And, you know, once I wanted to put a front end on some of the stuff I was building, that’s when I started to learn react, and like, next year is a platform that I use, then, and that’s all JavaScript. So kind of understanding how those interplays is really useful. Elite data science is another one that I found useful. And then actually the most useful is I did a couple of the MIT courses. And I can’t say like I understood everything. It definitely made me feel dumb, but also kind of made me feel smart at the same time, because I’m like, wow, I can, like do an MIT class and like, pass the test. It’s kind of nuts. So I think I think like that, you know, that kind of pushed me a lot further than I would have gotten just doing basic tutorials.

Daniel Johnson: Cool. Yeah, that sounds really useful. I’m definitely excited to check that out. What What kind of languages would you say are important to know, in this kind of failed?

Michael Taylor: Yeah, so I think languages aren’t as important, as everyone thinks. It’s like most programmers, like as they get better will just use different languages. Because different languages are good for different things. They have different libraries, different ecosystems. But I would say you can’t really go wrong with JavaScript and Python, like that’s, you know, what I primarily use, JavaScript is really good for front end. So if if the thing that you really care about is like setting up tracking, or building landing pages, building websites, you really need to know JavaScript, there’s no substitute. And if you only ever learned JavaScript, then you’re going to be in a good position. So I would say like, that’s probably the most useful, it can also be used on the backend as well. So you can really do pretty much anything you want with JavaScript. That said, if you are, you know, going to be more of the data science side, like if you don’t really care about, you know, the presentation, and like building a tool, building a website, but much you care about the insights and kind of looking at analysis, looking at customer data, then Python, is is much better for that. And a lot of the best, like machine learning libraries are in Python as well, which makes it quite interesting, exciting. And that stuff is actually way more accessible than you’d realize. For specific statistics, a lot of people use R, which is, I find it a bit old school kind of reminds me of like, when I was in university, and we did like a couple of like, old school kind of statistics, packaging, it kind of reminds me of that. But it’s very, very good for things like marketing mix modeling, like a lot of experts in that use are, like Facebook’s marketing mix modeling tools is written in our, for example. And then yeah, I think I think like, SQL is universally useful. Like, if you’re graduating, if you’re doing a lot of Excel work, and you want to graduate to database work, then then SQL is like, you know, the language that you use for that. And everyone, every programmer also needs to know a little bit of SQL, because, you know, programming is almost always about, like getting data from a database, doing something with it, and then sending it somewhere. So SQL is, you know, this stands for Structured Query Language, it’s like the language of the database. So that’s, that’s very, very useful as well.

Daniel Johnson: What about Regex?

Michael Taylor: Yeah, Regex I don’t know anyone who likes regex. But it’s, it’s very, very useful. But like, you know, even today, like I, you know, I’ve been doing regex, I probably done regex for longer than any other type of coding thing. Because it’s usually useful in, in like, analysis, right? Like, if you want to extract if your campaigns name something like us, slash like, look alike slash, you know, like, blah, blah, blah, you know, you can kind of extract different parts of the campaign and do better analysis that way. Kind of look at all the US campaigns or the look likes and kind of see how they perform. So it’s useful for that, but I still don’t really 100% know how it works. And I still have to use like, one of these regex testing websites. And it’s kind of voodoo. And it’s, it’s a really weird language. But But yeah, I think like, a regex is similar to SQL in that, like, every developer uses at some point, but, but like, nobody really like not that many people, like just just do that, if that makes sense.

Daniel Johnson: Cool. And, I mean, talking about sort of your evolution in the field, based on what you know, now, if you could tell yourself something two years ago, with that in with what you know, now, what would that be something to guide you something to focus your learnings?

Michael Taylor: Yeah, good question. Yeah, I think it’s hard to even if I told myself this, like, I wouldn’t have listened. Maybe people did, didn’t even tell me this. But like, division of labor is important. You know, one of the things we always fought against it Ladder was we wanted to be generalists. Like we always found it was really good to you know, be able to like do some email CRM and like, you know, do like some landing page testing and do like tracking and Facebook ads, Google ads, everything. So like, we did growth, like whatever helps you grow, but like, then, you know, what are you also going to do finance as well because, you know, raising that also helps you grow and like you’re going to do HR because hiring people better makes you grow. So so like we really hit a wall once we get got past like 20-30 people, and really had to reinvent the agency around specialism. We actually lost a lot of our best people because they wanted to be generalists. And we just realized we couldn’t afford to be a company of generalists anymore. I think that happens with every company, but I kind of learned the power specialism because I you know, the, the thing that made a lot of sense to me, once I figured it out was like, when I was building the training plans, Filata and I’m like, Okay, well, there’s like, you know, I mapped out, like, all these skills that you need to be a great marketer at Ladder and, and it was, like, 50-50 things, right? And, and like, when I looked at it, I was like, Okay, well, if I like, you know, in month one, if we teach you these five things about like PPC, and then like, month two, we teach you these five things about like design copywriting. And then like month three, we teach you these five things about landing pages, like you’re going to go through and like by month, six, you’ll have like a small amount of knowledge in each channel. But like, you know, if you just started month one, and you learn five things about PPC, and then month, two, five more things about PPC and 135. things, like you’ll get like, you will have like six times the PPC knowledge of, you know, someone who’s been a generalist, and it’s so like, it’s inescapable the economics of it, right? Like, you just, you’re gonna be so much better at PPC if that’s all you did. And maybe a little bit of dabbling in other things, wherever it brushed up against PPC. But like, the other thing that’s interesting about that is, is you’re going to be like so much more confident in a meeting, like if you’ve only if you just learned a little bit of PPC last month, and then you go to meeting about PPC with the client, they’re gonna run circles around you. And like, we have clients like Munzo, bank, and, who are like world class at this stuff themselves. So like, we couldn’t afford to be, you know, the least knowledgeable people in the room. So like, when we switched the specialism we went from, you know, we’re always either like, making 5% margin or losing 5% margin. And then when we when we switched, the specialism and kind of had like PPC team, you know, design team, copy team conversion team tracking team, then then we went, we went from, like that losing 5% of money to, you know, making 20% plus margin. It was it was night and day. And, and the weird thing about it is that, like, we’re not very good judges of our own productivity, like everyone in the company thought that productivity had gone down. But the numbers showed the opposite. So like, I think generalism is one of those things where you feel productive, because you’re doing lots of different things. And novelty is something you get that gets noticed by your brain, but you’re actually not being more productive, like you, it’s much more productive to do the boring stuff that like you already know how to do and just like do it 1000 times, and kind of become the best person in the world at that thing. Because almost all the profits come from being the best. Like if you’re, you know, if you’re just if you’re doing SEO for a client, and like they’re in position for on Google, and then you like spend some time doing Facebook ads, instead of trying to rank position three, position two, you’re doing a massive disservice, because you’re going to get like two or three times the volume position two, or three, and like maybe even 10 times the volume of position one. So there’s an exponential return for being slightly better in that one channel. And if you’re gonna then go do another channel, you’re going to be like, positioned for it everything right? And you’re never going to kind of get into the the money positions. So So I think that’s what really changed my thinking. And, again, it’s, I think it’s something you have to kind of go through the pain of and like, internalize, because we’re hardwired to be to chase novelty. And and it’s very, very hard to be more mature in showing like, No, I’m just going to focus on this one thing that I’m really good at. And once you kind of do that you realize you’re making a lot more money, you’re having a much bigger impact. You’re working for more more interesting companies as well.

Daniel Johnson: I think one of the most common pieces of advice that I share with my clients is to focus their value proposition on either like a niche segment of customer or, you know, segment of the problem that basically like focus. And in fact, for my own agency, I would say that’s probably the single biggest fault that I have been too much journalist ever. And I know it, I see it, and yet, it’s it’s very, very difficult. I think if you’re starting off and you’re going to the industry, it’s a good you know, you start with a clean slate and it’s a good time to go in from a specific, focused perspective and analytics and attribution is definitely a fantastic opportunity for career wise. There’s so much demand for good people. So much interesting technology. It’s such a interesting field. Really, it’s evolving very quick.

Michael Taylor: Yeah, I mean, one thing that I keep getting surprised by by shouldn’t be at this point is just how deep every field goes, right? Like, I remember a lot of where I was very proud of the fact that we had run 7000 experiments, like when we, when we look back at like, all of our experiment logs, like, for every client, it was like 7000 experiments, like 30% of them were successful, it’s really cool to see that we wrote a blog post about it. And then I like, the next day, I read a post about how Ubers team run like 80,000 experiments a year. And I think he like we’ve run like 7000 experiments in six years, you know, with a team of 50 people. And Uber is just doing like, just conversion optimization. And like retention optimization, like for one specific app, like if we had Uber as a client, we’d be doing six years worth of work in one year. And, you know, and like, what would how good would we have to be a conversion optimization to land Uber as a client, you know, and then can we see that and like, Uber’s, just one company, there’s like 100, or 200 of these companies of a similar size, as you could have, like, five of them as clients would be enormous, it’d be like 10, or 100 times bigger than the Ladder is. And that kind of really struck home because, like, you’re never going to land over as a client, on conversion optimization, if, like, that’s not the only thing you’ve you’re doing, right? So, so, so like, the amazing depth like, and, you know, I went into the niche of marketing mix modeling last year, when I was consulting, and again, I saw it, like, you know, I was doing marketing mix models for like, small companies, I was doing Excel and doing it, like, you know, like, two day project, you charge a couple grand, it was good money, and pretty happy with it. But then, but then, like, I talked to someone who does like the McSteamy solo for Disney. And it’s like, six people full time, they do two models a year. And this is just for Disney parks, right. Like, they have different visions. And like, think of all like the complexity that goes into that, you know, and this is, this is tiring these six people full time, just to improve the model. You know, and it’s, you know, that’s just like one company, right? Like this, the fortune 500 out there, every single one of them has a marketing mix model. You know, so, so like, there’s so much depth in even like, what you would think is a small niche that maybe like most most marketers haven’t even heard of, mmm, you know, but like that, that in itself is enormous niche, stone. I think that like, the just trust that like, you could honestly roll the dice and pick a specialism at random as like a junior person. And this and like most people never, you know, because they’re doing generalism. Like most people never even spend six months on one thing, right? So if you just spend six months on one thing, you’re suddenly already much better than the most of the people with 510 years experience on you. So like it’s very accessible. And don’t worry too much about what specialism you choose, because you can always kind of leverage that to get into something different.

Daniel Johnson: So onto a slightly different topic. With the introduction of GDPR, California’s regulation, you know, the increased awareness about GA and its data storage in the EU? Do people need to know, let me rephrase that question. To what extent do people need to understand policy and government regulation? With regards to data and tracking?

Michael Taylor: Yeah, good question. You know, and I’m not a lawyer, so this isn’t legal advice. But by enlarge, the, this is like a political power play, right? Like this is, you know, the, the, you know, the there are people making laws, like whether it’s Apple making changing the laws of privacy for their app store. Or if it’s, you know, the EU changing privacy laws, it’s not necessarily actually to prevent consumer harm. Because it’s very hard to argue that, you know, showing someone more relevant ads for things that they’re more likely to buy is a bad thing, right. But it gets worded that way, but it’s really, I think, like a power grab, and who are they trying to grab power from? Well, they’re trying to grab power from Facebook from Google. And they’re the ones that are gonna hit be hit by the fines. You know, and they’re the ones that these laws are kind of targeted at. I think if you’re running a small business, as long as you’re not doing anything really dodgy, you’re probably going to, you know, go under the radar. I think, you know, if you’re, if you’re not really looking at your data, like if you’re not, you know, analyzing if you’re not logging into Google Analytics every week, you probably don’t even need it by controversially. You know, like, if, you know, like, what, like, I mean, it’s nice to have but, but like, if most of your business comes from like, leads and like you just tracking how many leads you got, you know, like, you’re not gonna learn a lot like if you If you’re small business, you’re not going to learn a lot by having J on your site. If you really want that data and you care about it, then you can use more privacy friendly solution like plausible analytics, or simple analytics. Like they they do charge like $30 a month, I think. But I bet like that’s that’s like, it’s, it’s kind of done in a way that it doesn’t use cookies like Google does. So I think there are options out there. But But usually, you know, none of that data is being used. And if data is not being used, you shouldn’t collect it in the first place. And that’s actually one of the principles of GDPR. So I think, I think in general, just just think, like, be honest with yourself about whether you’re actually going to use the data before you collect it? And do you really need like a single customer view, which is actually impossible to achieve anyway, when you could, you know, just kind of go the other way. Like, if you’re a small, you know, you’re a B test results are not going to be statistically significant. Like, if you’re small, like, you know, you’re going to get like 10 visits from Twitter this week, and like, zero visits next week, like, what does that really tell you? You know, like, you will see, like, when you posted a tweet, there gets a lot of traction, you’ll see like, a bunch of people will tell you, Hey, I came from Twitter, right? Like, I kind of like it, I think Analytics is a game for the big dogs, you know, like, if unless you’re actually going to be using that data, you have a team of people whose job is to use that data, you probably don’t need to collect it in the first place.

Daniel Johnson: Cool. Yeah, I think that’s, I think that’s a very valid point. If you if there is no need for that data, save yourself, stress and, you know, be more adherent to policy regulation by just not collecting in the first place. Interesting to hear your recommendation, I guess it makes sense, though, if you’re just looking at it, and you’re not actually using that information for anything valuable, more out of just pure curiosity, then there is a case to be made for, for not having GA at all.

Michael Taylor: Yeah, I mean, you don’t need data to think logically about your strategy, necessarily, right? Like, you know, you, like data will tell you about what you have been doing and whether that has worked. But it won’t really help you, like, figure out what to do next. And that’s a big mistake, a lot of people make, like to figure out what’s gonna, what’s gonna work, you actually need, like, third party data, you need like, benchmarks, you know, you need to talk to other people and find out what’s working for them. Right. That’s the data you need. And, and you know, that doesn’t come from GA, like, you want to kind of understand, like, from Ahrefs, like, what, you know, what keywords are underserved, right, like, that’s the data you need. But like, looking at your own keywords, like is only really useful if you’re already ranking for a lot of stuff, if that makes sense. So, especially if you’re very small, you basically can just ignore data, until you get big enough, you know, and like, just go off, you know, like, what’s causing a spike, you know, and like, what customers are telling you directly. It’s only really, when you get to scale, that you need to, you know, scale the collection of data as well. And I think there’s different levels of data that are appropriate at different stages of the business. And obviously, once you get to Uber’s level, then you need to track everything, but but then you also have the resources to hire lawyers to tell you exactly what you’re allowed to track and what you’re not allowed to track. So like if you can’t afford a lawyer, you probably can’t afford data collection.

Daniel Johnson: So I’m thinking about the tools and platforms that we use to collect data? To what extent do they vary by industry? So if you’re a B2C company versus B2B, and then also, you know, thinking about mobile applications versus retail stores, very different user journeys type of customers? How does that vary? Can you develop a generalist understanding then specialize or should you go industry specific? What are your thoughts?

Michael Taylor: Yeah, good question. Coz I, you know, grew up on the web, open web, mostly. And then, and then we started to get mobile app clients. And I was like, Oh, God, this is so different. Like, like, on the web, you can use url parameters to track in that comes as standard, right? Like every analytics tool can pick up on these, you can even write your own code to pick up on them. That’s just not possible in mobile, right? Like, you have to use something like Appsflyer or Adjust, you know, like one of these kind of link attribution tools branch was another one that we used a lot. And, and they need to have some kind of really fancy way of telling that the person who, who like clicked on the ad was the same person who then installed later, that connection is broken by Apple. And it actually always was before iOS 14, like Apple never shared, you know, where this user came from, when they open the app. So So that’s, that’s been the biggest difference in terms of analytics. The biggest challenge, I think, at large scale analytics is all the same. It kind of converges. You eventually you know, Like all of your data is in the database, you use sequel to access the database. And that’s what, like every big company uses. And you might use a layer on top of the sequel, like, you might use a BI tool. Like, Tableau, for example, oh, Google Data Studio, and that sits on top and kind of makes it no code. But like, the data lives in the database, and it’s just about joining data together. So eventually, it converges to that, but at the smaller level, there are differences in tools and, and like, usually comes down to user experience, like, you know, some people prefer the old Google Analytics, some people prefer the new one, the new one is much more flexible in terms of its like, targeted that it doesn’t assume that there’s a page view, right, like, it’s more about events. So I think there are smaller differences. But, you know, I learned how to use Google Analytics first. And then when Mixpanel came out, like it was pretty quick, for me to pick it up is slightly different architecture, it’s more about funnels stuff. But then, you know, once I knew Mixpanel, I also pretty easily learned amplitude. So so once you understand how data is structured, and kind of understand from first principles, what’s happening, then it’s usually pretty quick to jump to another tool, as I think people worry about the tool much more than they should, you know, like, if you’re not tracking data, you know, if you’re not doing analysis, then like using any tool is better than no tool, right? You know, like, I got, you know, we had a client that was using Omniture, Adobe product, which is really old school, and not as easy to use as some of the more modern ones. But But like, we still managed to do the analysis, right? Like, the data is still there. They, they all kind of converge on the same charts. And, you know, the same ways of looking at the data, because, fundamentally, that’s what like the customers are asking them to provide. And if it exists in one analytics tool, then, you know, the next analytics tool that comes along is gonna copy it. So yeah, I wouldn’t worry too much about which tool you learn.

Daniel Johnson: Cool. Yeah, there is there’s so many tools on so many platforms out there. If someone’s getting into the industry, what, what, what aspects? Should they be thinking about? As they’re learning? Should they be thinking about B2B versus B2C? Whether they want to focus on apps or not? Like, what is the criteria that they should start to ask themselves at the beginning of that journey?

Michael Taylor: Yeah, I think I touched on this a little bit at the beginning, but, but the business model makes a massive difference to how much data you’re going to need, and how much data analytics is going to be valuable in the company. Like, if you’re a coffee shop, like you probably need zero data analytics, right? Like you post on Instagram, and like people come in, and then they might come into the store, like when they walk past. But like, if you’re Uber, you’re gonna need to, you know, 1000s of data engineers and analysts, you know, so I think, you know, if you’re in the app ecosystem, usually the scale is very large, because even a small app that you release by an indie developer, if you if you release a game gaming app, that becomes popular, it can very easily scale to like, millions of users. Like, I remember, I don’t know, if you remember, like Flappy Bird. It’s like, this applicant, I was just like one guy in Vietnam that made it. And then it was like, suddenly printing like 60 grand a day in revenue because it went viral. So like, I think you need like, very large, like, a lot of analytics when it comes to the mobile app ecosystem. I think for DTC like E commerce, it’s like a nice healthy mix, like you need to understand brand. And like the business and like what, like, you don’t have enough data to make every decision with data, but you have enough data to kind of make some decisions. So I think, like, DTC is probably like a nice entry ramp onto analytics. And like DTC companies, usually pretty easy to understand with analytics so much easier than being in the gaming industry. Or, you know, FinTech, something like that. But yeah, no, I think I think like publishing is also really interesting industry because you have like a lot of readers and, and most of them aren’t necessarily locked in. If you work for a subscription business usually need a lot more analytics than then if you just work for an advertising back business. Because like when you have the subscription, you have a customer ID and that customer ID ties all the different platforms together. So you need to understand like, do they go from mobile to desktop and vice versa? So So yeah, I think about that. You know, it’s about the scale the business and also like whether, you know, analytics is the reason they succeed versus just like one of the things they do and and, you know, you can choose your own comfort level. Like you might not be happy in a company that uses a lot of analytics. If you don’t want to get to PhD level. You know, like it you know, DC is a great business degree, a type of business to work if that’s if that’s what you want, if you want to, you know, be with some of the DTC businesses I’ve worked with, like, just knowing how to do pivot tables and knowing how to export data from GA, make to like, the smart data guy in the company. And like, that’s a really fun position to be in. Because like, everyone comes to you with questions like the CEO, asks, you know, for your input and stuff. So like that you actually hold a lot of power with like it, but it’s like an accessible power. It’s not something that like takes a long time to learn. So like that, that’s probably like the sweet spot, I think. Cool.

Daniel Johnson: No roughing up and coming near to the end, got to change topic ever so slightly. With blockchain and the world of crypto and web three, how do you see that in terms of analytics and tracking? Is it different? Is it the same? Do you have to think about different aspects of it? What’s your thoughts?

Michael Taylor: Yeah, it’s interesting, like, I, I haven’t been along for the ride on this wave as much as the previous crypto waves. You know, I bought Bitcoin back when it was like, you know, $300, and then sold it when it was 400. So like, I am both a visionary and an idiot. But, you know, we also participate in the ICO craze, like, you know, we ran like six ICO campaigns for different companies about actually one of them did succeed, and all the rest of it. So like, I was a little bit hesitant to go into like this, this wave of crypto and, and NFT’s, but it does feel like there’s something there now, you know, like, it feels like, there’s still a lot of scamming going on. But, but it is like, you know, it feels like they are kind of starting to build infrastructure, kind of, I liken it to, you know, web 1.0, like burst, you know, when that bubble bursts, like, you know, all the kind of crap companies got found out and looks like the bubbles bursting now. So I think like, the next wave of crypto is the right one to be on. Because that’s, like, you know, web 2.0 was the right, right time to enter the market. Right. Like, if you became an early Facebook employee or early Google employee, like, yeah, he made back, you know, so. So yeah, I think I think like, it’s a good, it will, like, during a down, run, like, it’s a really good time to get into an industry, because then you’re there, and you have the experience and the knowledge like for when it starts to peak. But I crypto is really interesting from an analytics point of view, because it’s like, very technical, and it’s very open, like the database is public. You know, like Ubers, databases and public, you have to work at Uber, you know, to do analytics there. But like, you know, if there’s a crypto Uber out there, and they did, this is public, because it’s on the blockchain, you can do some really crazy analysis, even without any permission from that company. So I think that does change the game. And you’re gonna see a lot of really exciting stuff happening in analytics there. Just because, you know, you can, you know, it is accessible to know, people who maybe wouldn’t, like pass a traditional job interview, maybe like that, that company doesn’t hire people in Botswana. Right. But like, if you’re a talented data scientists in Botswana, and you’ve kind of, you know, really struggled to, like, learn how to do this stuff, and you’ve been doing Freako camp, and, you know, you kind of get there and, and then and then you like, do an amazing piece of analysis, you might actually then get, you know, hired by that company, because they’re like, Wow, this guy really understands our chain, you know. So, I think, I think that’s like, that’s a really exciting thing. I think it opens the entry point for like, more junior people to really just like, share their work, kind of show that they know how to solve problems. And I practice on these open datasets. So I think like, that’s the biggest difference.

Daniel Johnson: Cool, and slightly unrelated, but you’ve worked with hundreds of companies over the years, what would you say is the most common issues that you see?

Michael Taylor: Okay, yeah, most common issue, so yeah, like, we had Omen Relax we had, like 120-130 companies who worked with a Ladder. And most of them were small startups, I would say, probably the biggest problem was that they just didn’t have like, any grounding in the like, forecasts. Right, like, I mean, obviously, like with startups, you have to be ambitious, but you have to kind of say, like, Oh, we’re gonna, you know, we’re gonna grow to this level. But they’re not like very scientific with these forecasts. Like they’ll say, you know, they’ll put together like a five year P&L And it’d be like, you know, we’re gonna double this year and then double in this year and like when you look at the formulas in the Excel it’s like, they literally just like, went like equals last year times, you know, to like, they didn’t do any analysis to figure it out. Like they didn’t even look at any benchmarks like I I helped a client launch it launch, like a pretty prominent startup in a new market. And they were like a really successful company that obviously, like, uses data a lot. But like, the forecasts they gave me were like, they were gonna get app installs for $2. And, and 40% of people were going to convert, you know, and this is, like, you know, this was like a banking app. So it was like, it was a really long conversion funnel. And I was just like, it’s just not gonna, like, that’s not realistic, like, you could build the best banking app in the world, and you’re not going to get like 40% conversion from from like, install to, like, actually having a card delivered. Like, it’s just not realistic. So so like, you know, like, like, I could tell, I did tell them that that was gonna fail, you know, straight up and said, like, you know, you need to have some more realistic assumptions. And all, like, we need to do some testing to kind of establish what those assumptions could be. But also from, from their perspective, they know that like, if, like, they’re raising money based on these assumptions, and if their investors, you know, if they don’t sell the story, then then like, potentially, they’re not even going to get the investment in the first place. So we need to get a chance to try so kind of understand it. But like, that’s the biggest problem I see is like, half to kind of, I think they’re really good businesses. Like, they, they like, they don’t assume they’re gonna do really, really well. They and then and then launch and then and then they fail, like fall fall short of that think they’re really good businesses are the ones where they do something, and they go, Wow, that works better than I expected. That’s not, I think, like, that’s a subtle shift. So it’s like, you know, I obviously can’t share what a monster banks conversion rate was, but it was like very high. And like, they didn’t like launch the business, I’m sure saying, Hey, we’re gonna have a really high conversion rate. But what they did is they built a really good product, and then the conversion rate was ridiculously high. So then they’re like, great, it’s very, very high. So we can like afford to spend on paid ads, with this conversion rate, like we can afford to do TV with this conversion rate. So So I think like, that’s the subtle difference is like, if they don’t like, expect magic to happen, and then and then get disappointed. Instead, they look for magic happening, and then build on that.

Daniel Johnson: Yeah, yeah, I definitely see that. I think having a good understanding of the benefits of analytics and attribution. It’s just, I find myself educating startups and like talking about how that process works, and just basically being like, get better inside you can understand. And people are like, Oh, that’s cool. Okay. Yeah, sure. But previously, I’ve, you know, gone into companies and being like, they may have GA installed, but they’re not looking at the data or something. So, there’s, there’s a lot of issues around that field. Yeah. So we’re coming up to an hour now, or just over an hour? Is there anything that you’d like to share with the audience?

Michael Taylor: Yeah, I mean, for for us right now, like, we’re pretty early on at Vexpower, like, you know, we built the platform myself, as I was learning how to code. And, you know, we’ve been producing the content in house, I think we’re still like, trying to figure out the model and how to scale it. Like, you know, we’re still like, we think magic is happening. You know, but, but like, we’re still looking for ways to double down on it. So I think like, if anyone wants to give any feedback, like we have a, you know, open discord, chat, like, similar to Slack, where anyone could just join and kind of give feedback, or ask for help on on the courses. So, yeah, definitely, like, you know, we’re in the stage where we’re just like, looking for feedback, like we’re trying to, you know, figure out ways to hustle and grow. You know, so so like, any always happy to have those conversations, if anyone does have a strong opinion on it. And, you know, I think I generally think the courses are good, like, I’m trying to design them to be like, the things that I wish I had when I was a Ladder, you know, that I’m really channeling our actual experiences, like a lot of the problems and questions and characters are based off for clients. Obviously, I wouldn’t tell them which ones. But, but yeah, like, that’s, you know, that’s, that’s what I’m focused on right now. And, you know, and I, like, also happy to help point anyone in the right direction if they are early in their career, kind of figuring out like, should I be spending my time on this, like attribution stuff or, you know, creative or like, what channel should I attach my name to, like, what why should my specialism be? You know, that’s, I think that’s, that’s like an endlessly fascinating topic. I’m always just interested in hearing what you know, people are considering now because it keeps me fresh and kind of helps me understand like, what channels are starting to become popular and, you know, like, what, like, where’s interesting moving. So, so yeah, always happy to have those conversations.

Daniel Johnson: Cool. Well, yeah, thank you very much for taking the time to chat with me. It’s been really educational and fun. And hopefully it will be for the rest of the Growth Mentor community and anyone else who happens to watch this.

Great. Yeah, thanks for thanks for the interview. It’s been. It’s been I mean, like, you know, at Growth Mentor and, you know, the communities are just crazy. Like, it’s like too good. Like it shouldn’t exist. So yeah, we’re happy to do some.

Daniel Johnson: Cool. sure Michael.

Michael Taylor: Alright, take care.

In this episode

Daniel Johnson Growth Consultant for Ambitious Tech Startups | Fractional CMO | Mentor @ Google

I focus on creating scalable, predictable and repeatable growth machines for startups using digital marketing and data-driven experimentation.

Michael Taylor Co-Founder @ Vexpower | Marketing Memeticist | Ex-Founder @ Ladder

Data-driven, technical marketer with 11 years experience, 8,000 experiments run, and $50m optimized across all 4 major growth channels. Author of Marketing Memetics, Co-Founder at Vexpower, Ex-Founder at Ladder.

A talk by Michael Taylor
Co-Founder @ Vexpower | Marketing Memeticist | Ex-Founder @ Ladder
Hosted by
Daniel Johnson Growth Consultant for Ambitious Tech Startups | Fractional CMO | Mentor @ Google

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