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In this episode of In-Ear Insights, Katie and Chris discuss whether or not you need data science skills to be an effective marketer. Find out the answer, plus why machines won’t be taking your job any time soon, as well as some helpful tips for growing your marketing analytics skills.

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Machine-Generated Transcript

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Christopher Penn
In this week’s in your insights, we’re talking about data science, data analytics, and why it’s not necessarily for everyone, nor does it need to be. So, Katie, you want to set the stage about sort of what, how this topic came about?

Katie Robbert
Yeah, absolutely. So, um, I was chatting with a friend yesterday, and we were talking about sort of different career options that she could be taking. And, you know, she’s finishing up her degree, she’s finishing up her bachelor’s degree in business, and she does marketing for her current job. And so I was asking, Well, what does that what does that mean? Because marketing, as we know, is a very broad term. And it means different things to different companies and to different people. You know, to us, it’s more of the technical data collection side, but there is still this part of the industry that still that very traditional, non technical Marketing. And so I started asking, Well, are you familiar with this? Are you familiar with this? And, you know, it really sort of challenged me to think, how do I best help her and give her resources to start with to help get to the answer of what is it that you want to do within marketing, because I don’t want to just send her a bunch of technical things, if that’s not where her head is at and turn her off from it. So that’s where this topic came from is if you’re just getting started in the marketing industry, it’s so broad. How do you start to figure out what sector of the marketing industry you want to be in?

Christopher Penn
It’s interesting because there’s two aspects to that right. There’s a vertical and a horizontal the vertical is what you know, literal industry you want to be and you wanna be an industrial concrete Do you want to be in jewelry, you know, there’s, there’s so much on that front. So there’s that aspect, but then there’s the horizontal two, which is all the different sub disciplines within marketing. I think it’s really important that People will realize there will always be a place for domain expertise for strategy for creative, there will always be a place for those things machines are not going to take over everything. machines will help. But you still need to have that expertise. Over the weekend. I was playing with some stuff from rice lab, which is a company that is a it’s a academic group that is doing a tremendous amount of research on the new Corona virus. And they’re publishing their code. And it’s an honor and I’m reading it, I’m looking at it. Now, I recognize the math they’re using they’re using a remote forecasting with time differencing to do essentially forecasting to see how influenza might be hiding a coronavirus within a population. I understand the math but I don’t understand the science. So I know the what and the how I don’t know the why why they make these decisions, I would need to be a domain expert in epidemiology and infectious diseases to have a full picture. So for someone who is in their career, and is like, Oh my god, you know, does everyone need to become a data scientist? The answer is no. Right? The answer is we still desperately need domain experts, strategists, creatives, we still need people to do a lot of the execution because there’s a lot of tasks that, you know, as we talked about on previous episodes are not worth automating. But to the question of where somebody starts when they just they want to get up to speed on Modern Marketing, I kind of think it’s, it’s kind of like, when you’re in college, like trying to decide what your major is, right? It’s a very similar process. You take a bunch of one on one courses, and you see what you have an aptitude for, what comes naturally to you and what you enjoy, and ideally, you find something. And for us, you know, in the modern world, the equivalent would be reading a bunch of blogs, may pick one or two blogs in each sub sector of marketing. And then Maybe a podcast each one to see what resonates like, Oh, that sounds really cool or that was really boring. Like the way I the way I measured is, especially podcast is if I find my brain wandering if like five minutes into the show, like, yeah, this one’s not for me next episode.

Katie Robbert
Yeah, no, I think that that’s a really good point. It’s you know, it does make me think back, you know, to my own personal college experience, I had no idea what I wanted to major in when I got to college, it was more of the, you know, you must go to college conversation. And so it was sort of a mismatch and I didn’t really figure it out until later on, I think I still on a daily basis and trying to figure out what I want to be when I grow up. And so I think that it’s, you know, it’s it’s hard because there are so many things that interest me and you know, what I enjoy versus what I’m good at, or sometimes two different things. Like I enjoy singing. I cannot sing

Christopher Penn
So let me know TrustInsights.ai holiday special. Yeah,

Katie Robbert
there will be no Well, there might be it will just be really difficult to listen to. But the point being is, you know, I, you’re absolutely right. Like you do need to sort of experiment and test things out and sort of see what sticks, sticks, because you might be surprised. I never thought that I would be in a field that involves so much math, because in high school and college, I hated math, I was terrible at it. I still don’t love math, but the part of the process that I enjoy is like taking the mess and getting it all organized and cleaned up and put together in such a way that makes sense. And then there’s an outcome that you can then do something with. So if I was just to sort of keep my blinders on and say, I don’t like math, I’ve never going to go into this field, I would have missed out on a really great opportunity because you Have to start to think a little bit differently about the way that you’re approaching these things. And so, you know, for the advice that I would give is, you know, Chris, to your point of like, listen to podcasts, read blogs, you really need to step outside of even just like marketing, specific ones or whatever job it is that you want to go into, because you might be surprised. I’m fascinated by. So for instance, you know, you have your recommendation engines that are powered by AI for things like Spotify and Netflix. Personally, I am fascinated by how that all gets put together, because you have to assume that there’s some sort of DNA markers on every single piece of content. And if you get, you know, a couple of these DNA markers in common, then it starts to recommend additional content and it’s constantly myself like I’m fascinated by that. Do I want to learn how to program that? No. But I’m interested in learning how it works. So that I can then do a better job of creating things that have good DNA markers that can be recommended, and that I can teach other people how it all works and be other applications of it. So, you know, I think that it’s, I think, too often, especially when we’re thinking about looking for a job, we stick ourselves into these boxes and say, I can only do this, I am an admin, I can do reporting, I can do this. And I think that it’s such a wonderful opportunity to think outside of that little container that you’ve put yourself in of what other applications does this have in my life, and then you start to pull back from there because that, you know, is where you start to really put yourself into a position that you enjoy versus just slogging away all day for a job.

Christopher Penn
One of the things that I think needs revision is this concept of, you know, the T shaped professional it’s something was very popular the last time You know, be a generalist and loud things but then be a specialist. And one thing I think I like the way you phrase we were just talking about about the your source specialization is sort of three layers. It’s not t shape, it’s three layers. There’s why what and how, right. So, every marketer should have the why of every part of marketing, why is creative important? Why is execution important? Why strategy important? Why is data science important? And the understanding broadly? Then, in the areas where you want functional expertise, you have the what, what techniques do we use this when you look at, you know, creative design, for example, what makes a good design good or bad? Is it white space as a balance as a funnel? Because all these things, when you look at accounting, what kinds of accounting are important? What should we be looking forward to talking to like hiring a new accountant? It’s like, Well, you know, what policies and processes do you have in place something that you’re very, your expert at is That, you know it falls in the umbrella of governance but what makes the thing work and then that third layer is the how and that’s where you’ll may have one or two or three out of you know 40 within the marketing discipline that you’ll be you’ll be specialized in you’ll be exploited I can code in ours that’s one of my my house I can’t code in Python even though it’s part of data science, it’s not my it’s not one of my house. And if we start thinking about you know less about just specializing in one thing and said how far down the rabbit hole Do you want to go in any of these areas, it gives someone a lot more flexibility because to your point you don’t have to be the know the how everything you can’t. The someone who knows creative and strategy and at paid ad execution and data science is probably does not exist. But if you have the how in two or three areas, you can then start to skip around as long as you know the world The why and the why I think it’s the part where someone, whatever point you’re at in your career, you need the why across the board.

Katie Robbert
I agree with that. And, you know, it’s, it’s interesting, you know, sort of to one of the talks that I’m giving upcoming is will a, I take my job? And I think in this conversation we’re demonstrating, no, because there’s so many different things that a human can do and still needs to do. And the more diversified you are in your skill set, the harder you are going to be, the harder it will be for an AI to replace you because AI is really suited for those repetitive tasks. But Chris, you appoint like someone who has the creative side and the strategy and the paid ads, you put those three things together and you’ve got some, you know, killer paid ads out there with a kick ass strategy that an AI may or may not be able to come up with. But you understand is the human the nuance of how other humans think and react? And so it is, it’s an interesting thing to think about, because I think there’s also a little bit of this like, Well, why even bother an AI is going to take my job anyway.

Christopher Penn
Yeah. And here’s the thing about, I’m glad you brought this up, because there’s a really important point. The more risk there is in something, the less you’re going to use AI for it initially. Because AI machines doing math cannot bring domain expertise to the party. They can bring massive data crunching, they can come up with unusual or unique or novel solutions, but they cannot solve in the same way that we do with that level of domain expertise. And I’ll give you a really good example is this is one sort of charlatan who makes the claim to be an AI expert and Scott and a whole bunch of trouble, fraud and stuff. I had to say like, Hey, folks, You can use machine learning and AI to solve how to detect the coronavirus. I’m like I’m sorry, there are people with multiple PhDs and 40 years of experience who have not figured this out. Can a machine learning system take in all the data and come up with some interesting possible solutions? Absolutely. You still want that PhD to look at the solution go, okay. You made this this and this assumptions and they’re wrong. That’s not how an infectious disease works. Or you have a data set that is, in this case, like badly skewed. A lot of data. For example, for the coronavirus coming out, shine is badly skewed. Without that domain expertise to look at it go, Okay, here’s why this is wrong. The machine will solve something and it will make a solution it will not be correct. Now, if that’s a question of, you know, should we spend $5 more per keyword on a pay per click ads? Cool, you can test out and you know, if you’re wrong, you’re wrong, when it’s literally the difference between 70 and 210 million people died You don’t want guessing you don’t want, you know, random machines spinning out answers that may or may not be correct and then doing the trial and error the hard way you want that domain expertise that you cannot right now replace that and I don’t see for the foreseeable future that that being replaced either not for 510 years, you know, maybe if we get quantum computing online, and it can start to broadly, you know, we have the same number of connections that we do in our brains maybe then. But to your other point,

Unknown Speaker
about

Christopher Penn
sort of this domain expertise and and where people are going. machines. Machines also can’t go outside of what data set you give them. So a big part of what you bring to a job or your other life experiences. Now, if you’re 2122 years old, you haven’t had a ton of professional life experience, but you’ve probably had other life experiences that will change how you solve problems. If you had a tough life growing up or you, you were really good at archery or you tried horseback riding all these other things, you’ve at that point in your life had two decades of problem solving. You may or may not be good at some kinds of problems. But you bringing something to the party, that other people don’t have an go off a whole tangent about the importance of diversity and inclusion. But you still bring non marketing solutions to the marketing party. It’s so important people forget, you’re, you’re a human being, you’re bringing a human perspective to its problems.

Katie Robbert
You know, it’s, I feel like there’s a whole bunch of like, rabbit holes that we could go down. But I think, you know, at a high level, what we know about AI is AI cannot make decisions or data it does not have and I think that that’s one of the big concerns about AI taking over in the job field is the AI is only as good as we We make it. And so if you have not given it additional data to make a decision, it’s not going to make a decision on something it doesn’t know. And, Chris, to your point, you as the person, you have that experience, you understand interactions with other people, because ultimately, at the end of the day, it is still a human making the decision of whether or not they’re going to buy your thing, especially if you’re b2c. You want people to buy your stuff. So it’s not necessarily a machine making that decision. Like you still need to have that insight into how people work and think and humans are still very much predictably unpredictable. Because you know, when you think they’re going to say they said, and because the human brain is so complex, chrystia point about, you know, getting the supercomputers hooked up with the same amount of connections. That’s going to be a that’s a long ways off, we still have only, like barely scratched the surface of how The human brain works. And that’s a broad stroke because each individual person has their own set of wirings. And then you factor in things like, you know, medical conditions that change the way that the wiring works, or mental health conditions that also changed the way the wiring works. Like, it’s so complex that I would be shocked if someone thought that a computer could replicate that accurately, in, you know, our lifetime and our generation. And so I guess we’re sort of I don’t want to get us too far off track. But the point being, if we’re thinking about, you know, how do you get started in industry that you might be brand new to let’s say, you’re switching jobs mid career, or you’re just starting out, you know, think about all the different content that’s out there content is king, as they say, and there’s so much content, sign up for a bunch of different blogs, blogs that you wouldn’t necessarily think that you would be interested in becoming There’s so much information out there. And you might be surprised at what actually resonates with you. Do your homework, don’t just jump into something. Because it seems exciting. So an example of that when we were working in a different role, a lot of the staff members wanted to be running social media, because you can’t see me putting air quotes and put up air quotes. Because what they would see it are these large brands like Taco Bell and Oreo, these big consumer brands, who would put out like really funny things or start Twitter wars with other similar brands. And what was not well understood was how much planning and work and skill and expertise and testing went into executing those campaigns. It wasn’t that somebody woke up this morning and said, Hey, I’m Burger King, and I’m going to start a war with McDonald’s. That took months to put together and probably some coordination from the other side so that both parties benefited from it. And there were no legal actions and so really dig into these things because it may not be what you think it is when you get into that job.

Christopher Penn
Yep. And to wrap up but here’s an experiment for you to try that will show you the level of understanding that the machines bring and why I will not be taking your job anytime soon. If you search for right with transformer, you’ll be brought to one of the Google results a experiment by a company called hugging face. And it you can start to type in a sentence and hit tab and and using the most advanced machine learning models for natural language processing. It will help you autocomplete the remainder of your sentence paragraph whatever. Start typing in, in words, a math equation. So I typed in 15 divided by five equals all words. What would we expect the answer to be? Three right? It would it should spit back the word three. Instead it comes back with three nonsensical answers because it does not understand Right, it can recognize patterns and the responses gives our patterns record pattern matches, but it shows the limits of this technology it does not understand. So should you be worried about machines taking your job? No, they’ll take tasks and there’s some tasks you want them to so that you have the opportunity and the time to level up your skills. If you’d like to see a great sampling of some of the blogs and things you might want to be reading, go subscribe to the Trust Insights newsletter every week. There’s 15 links that we share from around the web. Granted very heavily focused on SEO, social media and analytics, but it will give you some good starting points, good starting sources to look at that will help you find more stuff to read more stuff to learn from. And as always, thank you for listening to our show. If you have not subscribed to it on Apple podcasts or Stitcher or Google podcasts or wherever you get show, please go and hit subscribe so that you make sure you hear the show every week and we’ll talk to you next time. Take care


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