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In this week’s In-Ear Insights, Katie and Chris discuss marketing analytics skills and techniques. Why don’t more people use their data? Why are analytics and data science skills so siloed and so under-utilized in organizations? From examples using Chinese food menus and restaurants to dealing with confused C-Suite people, you’ll learn how to start breaking down those barriers and using the analytics techniques you already know.

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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 when we’re talking about marketing analytics, an awful lot of the time, we’re talking about a combination of tools and tactics and outcomes. But one of the challenges I see a lot of marketers having is that they don’t actually have the right tools. And by that, I mean, individual analytical techniques. They don’t necessarily have the right tactics, which is how do you do the thing, and there isn’t necessarily an outcome in mind as to what the thing is supposed to do. So let’s take an example. For example, the, the very, very, very simple technique of a moving average, which is when you take any given period of time, like seven days, and the average that time together, and then when the next day happens, you drop the first day off and you move that window of time. So that’s still seven days, but the average changes as time goes on. This is a very useful technique for smoothing out wild variations, particularly if you’re a b2b business, you have weekends, which never Good new analytics. So why don’t more people use this technique? why don’t more people adopt it? why don’t more people used to build insights? Okay, when you look at an organization and you look at people who are doing their their work, and these techniques are not new, like moving averages are centuries old. Why do you not see people adopting these techniques to make their lives easier and to make their insights more fruitful?

Katie Robbert
I think it really comes down to a lack of understanding. Or, you know, when if you think about, you know, marketing as a discipline and as a practice and what they teach you in school about marketing, they’re teaching you sort of the front end of marketing, they’re teaching you, you know, know your audience. Porter’s five forces SWOT analysis, and the term analysis is, you know, in quotations because it’s not like a true it’s more of like a, these are my competitors. So I need to make sure I do better than them. It’s a data analysis is a different skill set. And my, from what I’ve seen, and the larger broad stroke assumption is that marketers are not knowing that they need to take some sort of a statistics course, in order to analyze their data beyond. Let me put my numbers in a spreadsheet. And so a moving average is a step above doing the actual average. And so to your point about weekends, what I’ve seen happen is, well, the numbers suck on the weekends. Let me just pull that data out altogether. So I’m looking at the Monday through Friday data only. And therefore, Saturday and Sunday is never accounted for in the data, because it does drop it down a bunch. And so I’m seeing more of that behavior than I am seeing a moving average behavior because I would guarantee if you said to a marketer who primarily focuses on executing campaigns Well do you use a moving average, they would give you a blank stare. And it’s not because marketers aren’t smart and capable, it’s because they’re not aware, or they haven’t been given the education in statistics in order to do the type of analysis that we tend to talk about.

Christopher Penn
So I’m thinking about, you know, in a lot of our client work, we have one client who you know, there’s there’s one analytics person who literally lives in like a 400,000 page spreadsheet, we have 48 tabs, and you know, a gazillion things and this, that and the other thing. And even in the case there, I do see use of I do see the use of more techniques. But it’s still isn’t impactful, it still isn’t creating any usable insights. And one of the things I think might be a problem is that any given technique is going to give you some kind of outcome but people forget that the that a number in isolation doesn’t mean anything, right. You have to be comparing it to something you have to be comparing To the previous week’s number, if you compare it to competitors number a year over year number, there has to be some kind of change because a number only has meaning when you can see the change from some other kind of number. And that’s not something that’s a mathematical technique, right? That’s not something that requires any statistics. That’s that would seem to me to be common sense that you know, a number without context is unhelpful. But why do we then consistently see, you know, reports and spreadsheets and dashboards that as Avinash Kaushik says, you’ll have data puke all over there, here’s all these numbers, but there’s no meaning to them. what’s what’s our What’s our mental blockage in marketing analytics, that’s preventing us from saying, This is the context around this number.

Katie Robbert
I think it’s knowing what to do when you see the number. And so, you know, we oftentimes prepare ourselves for okay the number went down or the campaign is Doing anything at all. So the number is zero. I, I feel like as marketers, and I’ve been there myself, we really only think about the scenario of what do we do if nothing happens, what do we do if the number is zero? And so when we see, you know, let’s say our goal is five, and we see the numbers three, then the number is four, and then the number is three. Okay, we’re getting closer to our goal. So we’re just going to wait it out. There’s no real plan of action for what we do if the number is three, versus the number is five. And I think that that’s where, you know, it always, it always comes back to the planning phases of things. Where, what do you do in different scenarios? When the number is x the number is this the number is this, okay, we’ve met our goal. How do we make sure we continue to meet our goal and then exceed our goal? And so I think there’s very loose conversation around that. But if you’re looking at the numbers day to day or, you know, Chris, to your point, you’re talking About a totally separate person from the marketing team, a data analyst. Does that person have the General Instruction of let me know when the number hits this because I want to do X. And I think it’s just a matter of having that conversation and making those plans for all of those different scenarios. And so back to your original question about the different, you know, analysis techniques. If you have a separate data analyst, that person probably knows, but they don’t have the context to apply the technique to get to the right thing. And then if you have a marketing analyst, my assumption and what I’ve seen is that the marketing analyst has lighter weight analytical skill sets, so they can pull the average just the straight average, but not a moving average or account for weekends and anomalies and those types of things. So they don’t necessarily have the time or the skill set to do something more advanced.

Christopher Penn
So in This scenario the marketer is the customer. And the analyst is the chef. Is it a case where the market is saying I’m hungry, and the chef is going, so what do you want? And the marketer just says, I’m hungry. And the chef’s like Fine, I’ll cook something, but I have no idea what you actually want,

Katie Robbert
essentially.

Christopher Penn
So how do I get the customer to ask? I want an arbor, you know, I want sushi or I want, I want a pizza. How do we get to that point? Is it incumbent upon the chef to press for more details isn’t coming upon the marketer to have a sense of what they want? How do you get them to come to that vision? Because like in a regular restaurant, it’s pretty easy. You go to a pizza joint if you want pizza, right? You don’t go to a pizza joint, say I want sushi expect a rational response.

Katie Robbert
Yes, the answer is yes to all of that. So it is it. It’s the responsibility of both parties. And so if you have someone doing the analysis of your data, they should have a general set of questions requirements, one might call them Try to figure out what it is that you’re after, what is the question you’re trying to answer? What do you hope to see in the outcome? So it’s not necessarily going to say, I want the outcome to be x. So make that data say that it’s what are you hoping to understand? You know, so that way I can apply the proper type of analysis technique. And then if the customer the marketer says, I don’t know, well, then that’s, you know, you have to continue the conversation. And so you know, if you know, I mean, Chris, you have kids and so how many times have you said to them, what do you want for dinner? And they say, I don’t know I’m hungry. So you say great, you’re going to have peanut butter and jelly for dinner and they say, I don’t want that. You say that’s what we have. So it’s like it’s very much that similar conversation of, well, this is what I have to offer you. Do you want something else if you want something else you need to tell me but unless you tell me this is what you’re going to get.

Christopher Penn
What if they build I love that analogy, because In a lot of cases, the marketer is beholden then to a CMO or VP or somebody else in the company, right? And they’re like, kid who’s like, I don’t know. And you know, the Joker on our houses, when you say, I don’t know, you’re getting a salad, which usually immediately some spots, spot some kind of response. But in that case, where, you know, the boss is saying, I want I want better results, and the marketing will What do you want? And I was like, I don’t know. And then you have the data analyst who is the person actually doing the cooking now your cut your the market is kind of caught in the middle between the, you know, the noisy kid and the and the restaurant cook. That’s an even worse situation, because then it’s not incumbent upon the marketer in a lot of ways to to have the answer. it’s incumbent upon the marketer to be this like trapped in the crossfire intermediary. And to your point early if the marketer doesn’t know what’s possible like this, if there’s a Michelin star chef Kitchen, they can cook pretty much damn near anything, right? They could use a lot a lot of pizza with sushi on it, they’ll figure a way to make it work and not be avid be something disgusting. But if the marketer doesn’t know that that’s possible, how is the market of the bottleneck?

Katie Robbert
In some ways they are. You know it that is a really interesting analogy as well because, you know, I would say I don’t like if you keep the analogy of food, I don’t even really know what’s possible. I only know what I’ve been exposed to, you know, and living in, you know, the suburbs of Massachusetts. It’s still pretty limited. Like we have our fair share of chain restaurants. Our Italian food isn’t really Italian food, you have a lot of Irish bars that don’t actually serve Irish food. So, you know, you are behold into what you have you have had exposure to so yes, in some ways, the marketer is the bottleneck. But then if you have your data analyst slash Michelin five star chef, it is that uncommon upon them to start to educate Well, did you know that I could make you fall grow? Oh, what squirrel grew up? And then you start to have the conversation of well, it’s this, why don’t you explain to me some of the flavors that you like, why don’t you explain to me some of the textures that you like. And so there’s a way to have the conversation, so that whoever’s on the receiving end of the data can start to pull out the information. And so we’re now sort of stepping out of the conversation of analytical techniques, and more into, you know, how to manage up. And so, you know, so Chris, and your analogy, you had said the CEO, the CMO says, I want better results, but what does that mean? I don’t know. Okay, does the marketer then just go away and say, Well, he said he didn’t know. So I’m just going to go about my merry way and try to figure it out. Or it Is there a way for that marketer to continue to ask questions, intelligent questions, to try to get more information or even just say, I don’t have enough information to execute what you’re asking me to do. And then if that’s the case, if they say, go away and busy, well, then they can start to document. Okay, the CMO is wholly unhelpful, and I can’t do what they’re asking me to. So when my ass is on the line, I have at least documented that I tried. But it’s the actual trying to get the more information that I think sort of like doesn’t happen. It’s an interesting, it depends on the kind of company culture that you’re in. But and I know we’ve talked about this asking questions, tends to be frowned upon. It tends to or there’s a perception that if I ask too many questions, it makes me look like I’m unintelligent, or I don’t know what I’m doing or, you know, whatever the thing is, whereas asking questions, when you don’t know is the best thing you can do and it’s okay to say, I don’t know the answer. And so we’ve sort of gone off The path of where we started, but I think it all kind of connects together.

Christopher Penn
Going back to the analogy, though, I think that for the data analyst person, there is the equivalent of, yeah, it’s something that’s done a lot in like Asian restaurants, not so much in other restaurants, although some chains do it. But the rest of the food menus at Asian restaurants, especially Chinese restaurants, have tons of photos like, Hey, you could get dish, it looks like this could get additional click this. And you know, there’s pages and pages and pages of these things. And you got to go to TGI Fridays, and there’s photos of all the foods, I think, Oh, that looks good. And you look at that and go, Okay, this gives me a sense of what but the restaurant serves what the chef can cook and things. To your point about education. For the folks who are analytically inclined. Is there a version of that Chinese food menu is it like a gallery of dashboards and reports they could creative techniques. that illustrate, Hey, does this help? Do you like this kind of thing? Did you know we can do this? Like, for example, if you were to think about, you know, stats, you have things like Interquartile ranges and box whisker plots and, and stuff. And it’s obviously hard to visualize that in a pot in a podcast. But if you had a, an example of like, here’s what our goal completions in Google Analytics look like with box whisker plots, where here’s the high, here’s the low, here’s the the average. You could see that over time that you go ha, that’s a neat way of looking at that,

you know, maybe I want one from column A to from column B.

Katie Robbert
I would say maybe, okay. Um, you know, I think in this instance, the analogy of pictures of food does not necessarily translate to pictures of charts and graphs. The only reason I say that is and this is something that we talk about a lot for our company is, you can’t then understand the underlying technique of How you got to the answer? And so I could look at, you know, a bar chart. And I could have two bar charts side by side and one is done with moving averages and one is done with a regression analysis. Am I going to know the difference between the 2am? I going to understand, you know, that this one gives me better results. All I know is that the numbers sort of look the same. And so I don’t know the answer to that question. Other than, you know, it is definitely incumbent upon the person doing the analysis to educate the person asking the question, and, you know, ask more questions, try to help them understand. I could do it this way. I could do it this way. If you do it this way, you get x result, you know, lunch and learns are a great thing.

It’s it’s a big question.

Christopher Penn
Right? No, that makes sense because over the weekend, I was playing around with Some financial analysis techniques, and this one programming package that I use in our past something like 70 some odd techniques that you can use to analyze mostly stock data, or any financial data, but about 70% of it has direct application to marketing analytics. The challenge is a you’ve got to know what the technique is, be you got to know what the output is. And see you got to be able to explain how the output is useful to someone who is not looking at the analysis. And so from that perspective of you know, the chef, yes, the chef is constantly skilling up, which is what you want to have happen. But the restaurant audience may not be ready to eat some of it yet you put a exotic food people like oh, what the heck is right, how do we get why is have spikes?

Katie Robbert
Well, you know, and I think it always goes back to what is the question you’re trying to answer with this data? What are you trying to understand? With this one particular data point or this set of data, and then the person doing the analysis can say, okay, it sounds like you’re trying to understand which day of the week we get the most foot traffic. Okay, well, we’re closed on Saturday and Sunday. So it’s like having that context of like, okay, Saturday and Sunday, we’re closed. So do I do a moving average? Or do I do something else to make sure that I’m accounting for the zeros that are going to be happening on certain times? And so it’s also the marketers responsibility to educate the person doing the analysis. Here’s the context,

Christopher Penn
right. So I think that’s a good place to wrap up in for the marketer who’s not doing the analysis. be crystal clear about what you want, what you need and what you’re going to use it for. Because and a lot of cases if you’re talking to a really experienced data analyst, they have a huge palette of tools available to them, but they need Know what specifically you’re trying to get at because what you say initially may not be what you actually need. The data analyst side be ready to, to have the full scope and scale of the tools you have available to you. But don’t just throw them all out there. Listen carefully and try to use the the feedback you’re getting from the the customer, the business, business professional as to what techniques you will eventually SELECT FROM because if you don’t have that you will just be puking data. And if you are in a situation where you have none of this, feel free to give us a ring. We’re happy to have these conversations with your organization and and try and figure out what should be on the menu and who’s eating at the restaurant. As always, please leave comments on this podcast episode over at Trust insights.ai and subscribe to the newsletter and our YouTube channel or podcast again over at Trust insights.ai Talk to you soon.


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