{PODCAST} In-Ear Insights: Math Skills for Marketing Analytics

In this week’s In-Ear Insights, Katie and Chris discuss math. Specifically, what are the math skills that marketers of all stripes, but especially those who have an interest in marketing analytics, marketing data science, and machine learning/AI applied to marketing need to know. Do you need a Ph.D.? Do you need to spend a year in a data science bootcamp? The answers might surprise you.

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Machine-Generated Transcript

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Christopher Penn: In this week’s In-Ear Insights, we’re talking about required math skills in our Analytics for Marketers Slack, which by the way, if you’re not a member of please come on over to TrustInsights.ai/analyticsformarketers.

When we spoke on Social Media Marketing World, Bill asked a rather pointed question: What if I’m bad at math? I responded that you may not have had the best teachers because math is a language. If you can learn other languages, you can learn math. And he had a follow-up question: What mathematical skills do you believe are required to work with marketing analytics effectively?

So Katie, are you bad at math? And also, which math skills do you think are necessary to be good at analytics?

Katie Robbert: I’ll tackle the first part of it. I am self-admittedly bad at math. I always have been. I understand the concepts of it, but I definitely transpose numbers. But I was always really good at geometry and proofs and those kinds of things. So there’s a side of math that is very logical to me. I think I had decent math teachers. Although I have not been able to learn more than one language. I at best speak broken English on a good day. Now, I don’t know if that saying about having bad teachers is necessarily true. Some people are just not wired to be good at math, and I happen to be one of those people. That said, I don’t think you have to have a Ph.D. in math in order to be good at analytics. I think that you need to understand the fundamental concepts—basic addition, subtraction, division, multiplication—to get to where you need to go. Because ultimately what you’re doing is trying to answer a question. Maybe you rely on someone like Chris Penn to help you get to that answer. But first, you need to understand what question you’re trying to answer, because just looking at a spreadsheet of numbers is going to make your eyes go crossed and blurry, and it’s going to be really difficult to do.

So to answer the first question, you don’t necessarily have to be a wizard in math in order to be able to do analytics and reporting. The second question is, “What math skills should I have?” Well, again, you should have the fundamentals. You don’t have to be able to necessarily master things like regression analysis to understand the concepts of how numbers go together, but understanding what it is is going to be really helpful.

Chris, you’re going to feel a bit differently than I do about this.

CP: Yes and no. It may amuse you to find out that I was a straight C student in math for most of my life, except for Statistics and Probability which I failed. (Laughs). So I have no shortage of understanding the frustration people have. And my comment about bad teachers is exactly due to the experiences I had. I didn’t have the best teachers, I had people who were extremely good practitioners of the field but were not good communicators and explainers of what it was that they were teaching. You’re absolutely right that the basics are essential.

You also have to develop the mindset. And I think this is what you’re getting at as well with the mindset of “How do you think about the numbers when you have a row of them in a spreadsheet?” A lot of people see the numbers, and they try to look at them and somebody is trying to do the math in their heads. And that’s a really bad idea because your brain is not equipped for that. There are very few people who can do complex mathematics in their heads without any assistance. But when you start thinking, Okay, what do I want to know? The number one thing that people should focus on in analytics is change. What has changed when you look at your website traffic, your email opens, etc. A number by itself means nothing, right?

The open rate for this week’s Trust Insights newsletter was 21%. Is that good? Is that bad? Did it change from the previous week, from the previous month? How does it compare to other industry publications? Knowing that gives context to the number And that’s what I think a lot of marketers are actually looking for. We learn what it means so that we can decide what we should do. And to your point, that doesn’t require much more than basic division or some very simple formulas.

The other area that I think is essential, and an interesting thing to talk about is statistics and probability. Our friend and colleague Tom Webster over at Edison Research has a very interesting perspective on this. He says that statistics and probability are not mathematics. They are a separate discipline entirely. The fact that they both use numbers is true but it’s like the difference between writing a white paper and writing poetry. They’re not the same thing. They both use letters and words, but they’re completely different in their intents and outcomes. And there is a lot to statistics and probability that I believe is essential to mathematics and analytics. The first one is that even just knowing statistical validity is something incredibly significant. We see this a lot, especially in email marketing. “Our AB test here says that this email got 540 opens and this one got 539. Version A is the winner.” Nope. You failed statistical significance. There’s no way you can attribute that with such a small sample size; to say that A won and B did not if the likelihood of it being random chance is as high or higher than that outcome. So being able to do that type of math—to understand the validity of what your data is telling you—is an essential skill. And again, if it’s not valid, you have no business making decisions on it. If you do, you’re going to end up shooting yourself in the foot.

KR: So, would you say that most people know to start with “Is my pile of data statistically valid?”

CP: That’s one of the starting points. Yes, I think understanding—

KR: You think that yes, they do know to start there?

CP: I say that’s where you should start. Do people know that? No, absolutely not.

KR: Yeah, that was my question, if people even knew that’s where they should start. So for example, let’s say I’m looking at the open rate of my newsletter and I’m seeing a .01% change week over week. In my brain I go, Okay, it’s going up, therefore it’s good. Do I need, then, to worry about the statistical validity of that .01 %? Or do I just care that it’s going up?

CP: It depends. The key question is if that is happening because of something you’re doing. Or is it happening because of a random chance of noise? If it’s.01 % it’s almost certainly going to be noise, so the thing to do there might be to look at a different time frame. Week over week, month over month, are you seeing greater aggregated change there? And again, you can run a chi-squared test or a t-test or a two-tailed test, or whatever the stats thing you want to do to assess that. But ultimately, you want to know if an action you are taking has an actual impact or if what you’re seeing is data noise.

This is brain-bending for a lot of people. If you look in your Google Analytics, it says, “Your users were down 1% for last week.” It’s got that red arrow down, and you think bad things have happened. But are you sure? Or is that noise? Do you know whether that is an important enough distinction? If your addressable audience is the entire United States, for example, and your traffic is down 1%, that may not mean anything. On the other hand, if you have 10 users—well, you can’t have 1% of 10 users—(laughs). If you have 100 users and you’re down one user, is that statistically significant? It might be, depending on your population. So there’s a lot of things you have to unpack to figure out if this is important. Is this worth paying attention to? Is this worth worrying about?

There are other cases where you get into bigger effects like we have a client that over the last six weeks lost 70% of the traffic. Guess what? That’s statistically valid. Something’s clearly gone off the rails there. So you should know the difference between those two different scenarios. You may not necessarily have to run a formal chi-squared test to determine that, but you can clearly tell there’s a big enough effect like, yes, something’s up.

KR: I think that’s an important distinction. Because I would probably take a while to figure out where to start with running a chi-squared test, or even know that a chi-squared test is a thing that I should be running. But what I’m hearing you say is, it’s less about math and more about common sense. And I mean it as a very blanket statement, but what you’re saying is, if your usual audience is only about 100 people, and you lose one person and you see the red arrow go down, it’s probably not a big deal. But if your audience is usually 100 people and you lose 90 people, it’s sort of that proportional, common sense thing of, “I lost 10%.” But if my audience is a million people, what is 10% of a million? And what else was going on?

So it’s about having the awareness and understanding of a variety of different factors that may have been happening at the same time. It’s the numbers and all of the other situations around them that contributed to why the number is. So a lot of it is digging around and understanding the environment and understanding the time and place, versus just running some mathematical formulas. It’s really about using your brain to say, “Well, I lost 10% of my users on a Sunday, but Sunday was Easter and nobody was doing anything.”

CP: It is absolutely common sense as well as understanding the context in which your numbers exist, and both are hard for a lot of people. When you think about it, we look at things like national polls: ‘So and so politicians’ ratings are this’, and you look at the underlying numbers that say, ‘We did a telephone survey of 1500 people’ and you think, how can 1500 people represent a nation of 330 million? It’s because the scale and the sampling of the way you do the sampling, as long as you’re getting it evenly mixed, is good enough that you’re going to get a representation of the population as a whole.

On the other hand, if you’re trying to survey the Top 50 CMOs, you’re going to have to survey like 25 of them. You know, you can’t do 1/10th or 1%, or cast one guy and extrapolate his opinions because there’s too much of a chance for variation in that small sample. When you’re looking at your marketing analytics, you have to ask yourself the same question. If you are a gigantic company and you have a database of 100 million people, you can do sampling on a very small scale and be okay. If you’re like us, and your client list is not even in three zeros yet, then you have to ask pretty much all of them to get a sense of where our client base is.

It’s that understanding when you look at any of your analytics, that will help you decide if it is less or more important. Our email marketing list when we started the company had about 500 people on it. When we sent out a survey, we’d have to get a lot of responses back. As that list grows and grows and grows—now we’re in the thousands and soon to be tens of thousands—we can sample a much smaller set because as long as it’s representative we’re going to be able to get better results from the sample as a whole. All of this is why Tom says statistics is a totally separate discipline from regular math.

The challenge for marketers is that you kind of need both because you can’t have one without the other, to get a full understanding of your data and a full string of what it is you should be doing to make changes. So to answer Bill’s question, you need common sense, you need some of the skills, and then you need to do something with the information. Because if you don’t, you’ve just wasted your time.

KR: I think one of the things that any marketer should have in their toolkit is a really good working understanding of an Excel spreadsheet or a Google Sheet. I have this really bad habit of taking notes in my notebook, manually writing down numbers, and then trying to do the math out by hand. I still operate that way, which is not the best way to operate. So I have to get more disciplined about putting numbers into an Excel spreadsheet because Excel will do the math for you. If you are someone who is bad at math, start to learn how these tools operate. You don’t have to do the underlying math, but understanding the formulas and how to apply them to a series of numbers is really going to help you shortcut. You don’t have to be a mathematician in order to use Excel, you just need to have some understanding of how the functions work; the average function, the sum function; there are more advanced things that you can do, but that’s a really great place to start if you’re not comfortable doing the math yourself.

CP: Totally, totally. Modern spreadsheets are super powerful. There’s a tremendous amount you can do with them before you have to graduate to a bigger tool. There are some limitations and some of those can bite you fairly early on, but you’ll discover them quickly when you look at a result and it makes absolutely no sense. The bigger problem that I think people run into with analytics is not having a plan of action for when the numbers tell you something. So this goes back to what we were talking about a few weeks ago with scenario planning: the worst-case scenario, the best-case scenario, the do-nothing scenario. If you haven’t written those out for your analytics, then you’re going to do the computation and have no idea what to do next.

KR: I agree with that. It’s about what you’re going to do, but also what is the question you’re trying to answer? Why are you looking at the numbers in the first place? It’s great that I can look at my website visits week over week and see if it goes up or down. But have I done anything about it? No. So why do I continue to look at this number, and then close the tab on my computer and walk away and go make a cup of coffee? It’s a very real thing that happens every week. I look at our Google Analytics and I think, Huh, okay, and then I move on with my life. Why am I looking at these numbers if I’m not going to do anything? So I need to figure out for myself which numbers I care about. And those numbers are the conversion numbers. People filling out a form on our website, taking action, raising their hand, saying, “I’m interested enough in you that I’m willing to give you my contact information.” That’s the number that I really care about. So why am I looking at the number of people coming to the website?

CP: This is where the KPI mapping exercise really comes in handy. If you care about conversions, what’s the number that directly influences conversions? It is actually users to the website. Then you have the three ‘What’s’: What happened? So what? Now what? Where you’re stuck at is ‘What happened.’ You look at the number, what happened? But we don’t dig into the ‘So what?’ Why did this happen? Why is that number up or down or sideways? Or entirely made of sixes? Why? Do we do more with email? Did we say something clever on Twitter? And then, what are we going to do about it? That middle piece is the gap. If you are at ‘So what?’ and you’re struggling to get to ‘Now what?’ It’s because you didn’t do the why in the middle: Why is this number changing? And if you care about why this number is changing.

Again, this goes back to Bill’s original question. Do you have to be good at math to do this? No. You can actually do this on Excel really easily, but you need to know that’s the sequence; the recipe in order. Math skills are like cooking skills, right? If you can cook and fry and bake and all this, that’s great. But if you don’t have a recipe or, to your point, an idea of what it is you’re trying to make, all those skills don’t mean anything. You can make a lot of really weird stuff but at the end of the day, if you’re hungry, it’s not going to fix the problem. So I think the meta-skill that lays on top of all the math is context and purpose. Like why are we doing this?

KR: That’s a great question, Chris. Why are we doing this? (Laughter). But you’re absolutely right. You need to start with, why am I even looking at this information? Why am I going to break my brain to get the math to add up if I don’t even know what I’m looking at? I think that I can fall on the sword myself. I do it. It’s a bad habit that we all have. And when you’re limited on time, limited on resources, limited on budget, having the discipline or even the awareness to start focusing only on the numbers that matter is going to help you save your sanity.

It’s not a matter of being good at math. It’s a matter of having the awareness to say, “I don’t need to focus so hard on the number of visits to my website or what the bounce rate was, I need to worry about how many people took action,” and start there. That’s going to help me refocus.

So I think Bill raised an interesting question. Because I think there is too much of this focus on, “I can’t do it because I’m not good at math,” whereas it should be, “I’m not doing it because I’m super unfocused and don’t even know where to start.”
CP: Yep. We’ll close on a great quote from Seth Godin, who said about all analytics: “If you’re not going to change what you eat, or how often you exercise, don’t get on the scale.” Right? Because if you’re not going to change what you do in your marketing; not going to change how you approach your business; not going to focus on the impact of the actions you take, don’t bother measuring. It’s a waste of time. It’s a waste of effort. It will just frustrate you. Now obviously, if your boss is telling you you gotta do the thing, you gotta do the thing. But think about what Katie was saying. If you are thinking about numbers and you feel stuck it’s probably because you haven’t figured out the why. You’re trying to jump ahead to the ‘What should I do?’ and you just get frustrated. Understand what happened, know why it happened. That will give you insight as to whether you can do something about it or not.

If you’ve got a question like Bill’s that you want to ask us, head over to the Trust Insights website. Go to trustinsights.ai, you’ll find Analytics for Marketers, our free, slack community, our newsletter, this podcast and so much more. We look forward to seeing you and hope that you are staying safe and well. Talk to you next time.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.