So What? Using Exploratory Data Analysis (EDA) to understand your marketing

Well, hi there, happy Thursday. Welcome to So What, the Marketing Analytics at Insights live show. Joined by Chris and John. This week we are talking about using exploratory data analysis or EDA to understand your marketing. Um so Chris, you and I have been talking about this quite a bit with for ourselves and also with our clients about is the issue with when things are when things aren't working, is it the audience, the offer, or the creative?

And I think what we often see is that people immediately try to start tweaking the website or you know, tweaking the copy in the ads or on the pages, but we want to be sure that we know what the exact issue is before we start making changes. Because you could inadvertently be fixing the thing that's not broken. That's absolutely true. And so with exploratory data analysis, it you're really talking about looking in the information you have um with an eye towards answering a very specific question. And so I I thought it would be useful to define uh a few things here as we get started.

Um exploratory data analysis, if you read the Wikipedia definition, is uh an approach of analyzing data sets to summarize their main characteristics, often using graphics and and data visualization. Uh and the purpose of exploratory data analysis, the formal purpose is to figure out it does the data you have answer the questions you have, right? If you if you are fundamentally looking at data that answers the wrong question, you're gonna have a bad time. So there's a couple of things that I think are important to say what it's not. Um so exploratory data analysis is is different from but encompasses what's called initial data analysis.

Initial data analysis is simply kind of like uh uh you know checking the invoice on the box, right? So is what's in the box what you bought kind of thing. Um and that's what initial data analysis does. It really is just verification of basic data requirements. And that's part of what EDA is.

Yeah, EDA is also not machine learning and AI. It is a precursor step to it, but it's not uh that. And the last thing, and probably the most important thing is it's not data dredging. So this is a practice that costs marketers hours and hours every month and every year. And data dredging is simply saying, okay, I'm gonna mess around with this data or this spreadsheet until I find something interesting, but you don't really have a purpose.

You're just kind of saying, uh, I'm just gonna have to muck around here and see if I find anything interesting, which is fun, but not necessarily impactful. John, you know what else it's not? It's not a quick answer to your question. I know it's it's not a silver bullet or uh any other the hack need sales measures for yeah, instant answer. It is.

And the other thing that the reason why we're we're talking a lot more about this now uh is twofold. One, because um, as we saw in the CMO survey recently, uh something like 59% of companies are starting to use artificial intelligence, not have fully deployed it in production stuff. That's still about 11%. But are saying, yeah, we're gonna put some time and effort into this. 29%, I think said that they have have got something, at least something in production for measurement and attribution.

But the big thing is because of Google Analytics 4, which you knew we were going to come back to, in GA4, there's this entirely new section called explore. And when you look at it, and when you you know start it up, you're like, what am I supposed to do with this? It is an exploratory data analysis environment that Google has provided to you. The challenge being that without knowing how to do exploratory data analysis, not understanding the process, it's kind of like someone just hands you a bunch of appliances you don't know how to use. Well, keep them away from me if any of those appliances have sharp edges.

Because if I start monkeying around with that, it's an instant trick to the ER. So uh I think that probably the first place to start in terms of our discussion would be to talk about the process itself. So I'm gonna go ahead and just share screen here. And this is exploratory data analysis in a nutshell. The first part, the most important part, your goals and your strategies.

Why are you doing this? Like what's the point of doing exploratory data analysis? If you're not sure, probably don't do it. Um that would be uh part one. Part two would be uh the data collection.

Uh you have to get the stuff. You have to find where your data is that you want to do the analysis on. Um, develop some requirements about it so that you know what it is that you're after, and ultimately um, you know, see see what condition it's in. The third step is attribute classification. So what's in the box?

What kinds of data are involved? You know, uh what dimensions, what metrics, what data types. Even in Google Analytics 4, as an example, there are something like 210 different dimensions and metrics. So understanding what's in the box and what you have to work with. The fourth step is doing some initial analysis, just looking at one variable at a time, two variables at a time, um, see if your data is any good, if it's in in good condition or not, uh, see if there's anything weird about it.

Step five is sort of a final validation, like yes, we've we've got the ability to answer the question that we're we're trying to answer. We we've gone through some initial exploration, we've looked in the box. Um, you know, it's like in cooking. If you were to look in the fridge, and yes, you have all the ingredients you need to cook the recipe you set out to cook. Uh you're not like, oh, look, there's no milk in here.

Uh whoops. I guess we're not making uh uh fed of Gini Alfredo. Uh six is data preparation, any kind of of modifying the data that you need to do to make it work, uh, which in a tool like Google Analytics, there's not going to be very much of that because you're using Google's existing tools. If you export the data to your own software, there might be more of that. The seventh would be feature engineering, which is where you're um maybe adding new features that are there.

Again, not something you're gonna do in built in software a lot, but for your own stuff. Yeah, and even in tools like Excel, you'll do some feature engineering. And then the last step in the process is your modeling and insights, trying to figure out is there a there there? The questions you've asked, do you can you can you come up with some answers for it? Um we have a comment uh that it really just reinforces why we're talking about this.

Um, from Chris on LinkedIn. EDA is seriously exciting for me. We're starting to get ideas for the types of modeling that may be effective. It also unearths new insights about the business and how operations work. What I like about Chris's comment is that it reiterates, Chris, sort of at the top of your process, you can't get around, you can't skip the step of stating your intent, having a reason for doing the thing.

So as much as we all just kind of like like to look at the data and monkey around, if you don't have a reason, then you're not really gonna find it. You don't know what you're gonna find. Like it's not a it's not a you know, Indiana Jones expedition of like, let's just go digging in the dirt and try to figure out if anything is there. Like you have to have a point. Exactly.

So when we talk about things like you know, audience and offer and creative, we're really talking about the stuff we're make we make and who we're making it for. Uh and that's a a really kind of a big open-ended question. Like, are are we making the right stuff? Are we are we doing the right things with our marketing? So why don't we take a very quick um I guess uh walkthrough of how would you go about answering this question?

So, Katie, I'm gonna start with you and ask how would you know that we're making the right stuff for the audience, the offer, and the creative? Right. What would be your first instinct? My first instinct would just start with would to be to start with the audience, because without an audience, the offer and the creative really don't matter. So that's where I would start.

And you know, in terms of how to get to that information, you know, I would look to my network, my community, my peers, the people who I assume would be my customers and say, what do you want? How what problems do you have that the things that I do can solve? So I would start there with the audience, because you can create a website, you can create products, but if there's no one to buy the thing, then you're just kind of streaming into the void. Exactly. I think that's a really uh good place to start.

And one of the things that we can figure out and you know very easily from the from the beginning is looking in existing systems and data that we have, like for example, Google Search Console, which uh absolutely positively shameless plug. If you'd like to uh dig deep into it, you can take our free course or not free course at trusteinsights.ai slash search console. But one of the first things we would ask is are we attracting the right kinds of people with the content we have? So maybe for this for today, we'll we'll think about this from an organic search perspective. But again, this can apply to uh email marketing, to social media marketing, any any of the marketing you do.

So in Search Console, let's take a look at our search results. Let's specifically look at our queries. And are we being found for the things that we care about? Um, Google Search Console, yay, that's good. The click's not so good, but at least that's there.

Marketing analysts consulting, okay, really showing up. And so this initially tells me that we're doing a good job from Google's point of view, right? Because we're we're being shown a search results. We're just not doing a great job of being shown being getting people to click on our listings when they show up, right? Because we got a bunch of impressions, which means yay, we're in search, but we're not getting clicks.

People like, I don't I don't see how this is relevant to me. So based on this, what would be your next step? Probably to look at my Google Analytics. I'm guessing here. Um Chris did not feed me the answers ahead of time.

So this is purely special I think asking me to see if I still know what I'm talking about. Um, so if I'm seeing that we're showing up in search, but we're not getting the clicks, then I would probably look at two things. I would probably look at my Google Analytics data to see, you know, are people going to the pages that I have set out, you know, to create content around these topics. And then I would have probably also look at some uh SEO tools to see like what's the you to do my keyword analysis to see what people are actually looking for. I don't know, John, what what do you think?

Yeah, well that's a that's one right there is keyword analysis, right? Because so we're showing up on these impressions, but somebody else is getting all that traffic. So to look at those keywords and see who is taking that traffic, you know, and is getting it. That's one place to look. Um, and then the other one would be on our site, you know, if the language obviously something in the site description is not catching the eye of all those impressions.

So yeah, that's you know, this is the first hole in the funnel for us. I think that's a really good way of thinking about it. So let's go ahead and actually go straight into the Google Analytics Reports first to look at our uh our content. So I'm gonna go to uh pages and screens here. This is Google Analytics 4.

Uh fun fact um you can rearrange the menus in Google Analytics 4 to make it look exactly like Google Analytics 3, which is what we've done. Um stay tuned, there will be a Trust Insights course for that as well. Uh anyway, so when we look real straightforward here at what's getting, you know what this I I despise the scatterplot chart, it's just useless. Um yeah, I feel like in this context, it really doesn't add any value at all. No, it doesn't.

All right. There we go. Go back. Okay. So we've got for for top content.

We've got some pages not found, which probably that's that's a different issue. Uh our Google Search Console stuff. We've got our uh landing page for one of our downloads. We've got our poll stuff stuff, we've got some GA4 stuff. But sort of again, we're not really seeing a ton of blog content would be the thing.

We've got some newsletters showing up, which is good, but but raw blog content itself not really showing up. So I think um we're John, we're we're you're on to something. So we're not even getting the traffic. So we can't really use Google Analytics to solve this problem because you can't you can't extract useful information from data that you don't have. And because the people aren't even making it to our website, it's not gonna show up in Google Analytics.

So the next logical question is okay, well then who is getting the traffic, right? So to Katie's point, looking at a tool like an SEO tool. So here is uh Hrefs. I put in marketing analytics as our search term. And even without doing any like super super fancy stuff, we can scroll through and look, okay, what's you know, what's happening, you know, who's who is getting the stuff.

Let's sort this by page traffic. Let's see who's getting all the eyeballs. Come on, Google. This is Hrefs. Oh, this is HRS.

That's I can't blame Google. So you could see this there's some stuff in here that's a little odd, right? So marketing analytics is our search term, but you have a whole bunch of content that actually doesn't seem to have anything to do with it. Um let's put that in quotes. Um, see if we can clean this up a little just a little bit.

Okay, that's a little bit better. So Google design, uh make a QR code in seven easy steps, software, things like that. What are the methods of data collection? So Marquette, low tame. So a lot of these are either publications or companies um that are competing and doing well on this term.

So the question we would go ahead, Katie. I was gonna say it seems a little unfocused, quite honestly. Um, you know, and I think that that, you know, sort of as as I'm watching you do this, my thinking is do we need to get more specific with the keywords, like the I guess what people consider the long tail keywords rather than just marketing analytics, because it's a very broad term, it could mean a lot of different things, and that could be why when someone's searching for marketing analytics, they see R stuff and like, no, that's not what I want. What I really meant was TikTok. What I really meant was QR codes.

That's a really good question. And it's a question that you're not necessarily going to be able to answer within the SEO tool itself, largely because the SEO tools that are out there don't have any kind of data analysis facilities, right? They they really are very quick visualizations and uh you know the ability to export the data for your own use, but they're not going to provide any kind of exploration hub where you can you know mix and match and make pivot tables and things. It's it's just here's a bar chart. So I think what I heard you say was we we probably need to dig a little bit deeper to see you know what's in here.

So let's do some actual formal uh exploratory data analysis. And for that, we're gonna switch over to our friend R. Uh if, and I know because this is a question Katie has every single episode that we do this. If you don't have access to a tool like R, you can do some of this in tools like uh Tableau, you can do some of it in Excel. Um, it's just that tools like R make things a lot uh faster to do uh and have more rigorous methods.

You can also use um boxed products like IBM Watson Studio or uh SPSS modeler. Uh those would be also good choices. So I exported a bunch of those results. We have a table now of 18,000 different uh results. The first thing we want to do is if you look it just very quickly, we see that there's uh a uh result number that's not super helpful.

And let's go ahead and just into the do into the table here and sort by URL. And I already see we have some duplicate URLs in here too, which is also not helpful. So let's do a little bit of cleaning. So this is the data that you exported from hrefs uh of the pages that are getting traffic for the keyword that we were keyword marketing analytics. Yes, that's right.

Got it. Okay. So with a little bit of cleaning, we found out there's actually only 14,000 total URLs. Um, you know, there was about 4,000 duplicates, which is just some good basic cleaning uh to get uh a sense of what's in here. From here, we're gonna create a report, and we just want to get a sense of what is in the box.

Um any tools like R and Python stuff like that have the ability to do um some automated analysis, although it looks like in this case it's really unhappy with some of the data that's in here. Well, and what I find interesting, uh, and John, you know, I definitely want your perspective on this. It's interesting to me that you know the software systems like hrefs and other um you know social listening tools, they don't have any kind of like true reporting or visualization built in. It's just here's the data. Good luck.

Is that is that what you find as well, John, when you're looking at these tools? Yeah, because it's you know, nobody's kind of been able to the technology is moving so fast that nobody can do end-to-end, so it pretty much just becomes a hey, get your export file of choice, you know, and it's just assumed that they're gonna dump it into something, and the user will have a better idea how to clean that up. You know, there's there's no easy profitable thing for the vendors to kind of come up with a standard file format. Well, and I think that you know, I think that that's a really good point. And that's why I'm glad we're talking about exploratory data analysis, because in my personal experience working in-house net agencies, that's one of the deficits of you know, the teams and these tools is they can have this whole Martex stack of tools.

You know, they have their SEO tool, they have their social listening tool, they have their website traffic tool. But then that's where it ends because there's an assumption like, well, I have the tool, I should be able to get the answer, right? That's like every marketing automation solution. It's like we bought the tool, we we should be golden, right? And uh well, no, no, somebody eventually will do some work somewhere.

Well, and that if you think about it, it's a lot like you know, buying all the ingredients for Thanksgiving dinner uh and having a stove and kind of saying, okay, we've got the tool, we've got the ingredients. When's dinner? And and being really disappointed when the oven doesn't cook dinner for you. Man, that's me. Well, you know.

Um, so one of the things that again, a lot of these tools can do is some basic profiling of your data. This is part of this is parts three and four of exploratory data analysis where you're looking at the data and saying, is it in good condition? Is it is it workable, right? So for the most part, we've got uh almost 100% coverage, so which means we don't have major chunks of missing data, right? That's a really good sign that um uh our our data is not uh punched full of holes.

We can see the only things where we have some a few things missing, and these are relatively small, 0.01%, our website traffic value and website traffic. Otherwise, we have uh no missing data at all, and then some basic histograms, right? So what's in the box? What kinds of distributions? Uh, this is very helpful here.

This is domain rating, right? So this is the rating of domains. We see here um a very, very um strong right-leaning bell curve, right? It's like the bell curve is squashed uh one side to the right. Even without digging any further today, the one things we see is that for marketing analytics, when we're looking at pages that get any traffic at all, um, they tend to have fairly high domain ratings, right?

So that's a useful indicator. We also see um, you know, not uh not a ton of uh other useful stuff here, uh, mostly you know, single variables. When we look at distributions, we see uh there are a number of what were called power law curves, where uh it's sort of a big head and a long tail for all the data, uh, which again, if you're looking, this is especially useful if you're looking at data where you might have a bias in the data set that that is on a protected class. Like if we were looking at customer records, for example, and we were looking, say, at gender, and we saw uh a distribution that looked like this where it was wildly in one direction, we go, okay, there's a bias in our data set, and and that's an immediate red flag that something's gone wrong. Um going on further, we could start you could start seeing just basic correlation analysis.

What are the things that have a relationship to others? So this is very interesting here. Traffic, which is a variable we care about, um, has a pretty strong relationship to referring domains, obviously to traffic value, um, to Pinterest, interestingly enough, and then you know, uh, some other things that are missing throughout here. And finally, doing um principal component analysis, trying to figure out uh what are the features that if you had to reduce this down to maybe two variables, what are the features that you would pay the most attention to? So that's the a formal EDA report.

Now, the challenge for the average marketer is that there's a really, really big four foot high so what on this, right? You have all this data, and you're like, I don't know what do I do with this. Well, yeah, I'm looking at this, and obviously I understand, you know, if I if I'm acting as you know, a principal at Trust Insights, I understand why you're doing this, but if I'm putting on my, you know, I'm a marketer like everybody else. I'm looking at this, I'm like, so what? Like, you still haven't told me what the analysis, you haven't actually done any analysis, to be quite honest.

You're just looking at the quality of the data that's available. Like, is that a step that I can skip? Can I just sort of like chance it? You can. There are consequences.

I mean, it's like this this step in the process is essentially a health check, right? This is a clean bill of health on this data. If you were running a restaurant, right, uh, do you have somebody inspect the produce as it comes in that morning, or do you say, ah, you know, whatever's in the truck, just throw it in the in the in the fridge and we'll worry about it later. Um it depends. It depends on on who your produce supplier is.

If you have a super reputable local supplier, you may say, you know what, I trust you. I trust you to not give me uh, you know, a two crates of rotten lettuce. If you don't have that level of trust, then say, Yeah, you know what, I'm gonna look in each crate before you load it in the fridge, and I'm not gonna sign off and pay for this before uh before I check it over. The same thing was true of your data. Do you need to do a health check on all your data?

Depends on how reputable the vendor is and and how trustworthy the the data sources. So, like if for example, if you were doing this Google Analytics data, Google itself is very trustworthy when it comes to you know exporting data, but is your analytics install set up correctly? Right? If you didn't do your tagging properly, then your data that you're manufacturing is not trustworthy. So it sounds like if you do this, you don't necessarily have to do this health check every single time you're exporting from the same vendor that you've exported from numerous times before because you've already determined yes, this is reputable.

I know I can trust this data. Is that an accurate statement? I would say you do you should once you validated a data source. Yeah, you can you can do it infrequently. It still doesn't hurt to spot check from time to time, just to see, especially if um you don't have total control over the systems.

Like for us with with Trust Insights data. Um, we did the spot check once, and we know who's working on our Google Analytics account, right? It it's us. So if something's gone wrong, it's our fault and we should have fixed it. On the other hand, when we work with some of our clients, for example, um, I will sometimes spot check monthly to because there are other people, other agencies in those accounts and stuff, and I may trust us, I may trust our client, I may not trust some of the other vendors.

Um I find this to be a a really big problem actually with with other SEO agencies. Gotcha. All right. Well, John, I expect all these data profiling reports on my desk first thing in the morning for every single vendor that we use, okay? Right.

We run these continually regardless of whether we believe them. How about do you ever have a point where you've run further reports down the line and then so you come back and do this analysis to see if the problem was with the data set? Like does that happen? Um that does happen. It shouldn't happen.

Right. It um that's actually kind of working in reverse, which is it means that you didn't do your dil due diligence up front. Maybe you didn't define your requirements up front and as a result um you kind of are kind of finding yourself going backwards going, well, so what happened there? And that's that's really not a good position to be in. Yeah but I could see if you had a data set that you built yourself that was you know dependent on like three or four scripts gathering data from other places.

And after you've checked that like eight months in a row you're like okay I don't need to check this every month until of course something breaks. Yeah exactly those are cases where um yeah you you don't need to do it as much. So the next step here is to start dig, you know we still have that question of well why we we not doing well for marketing analytics, right? That's kind of our our our premise the thing we're investigating. So when we look at all this other stuff that has um that is doing well for that um we want to look at some of the correlations.

Now we saw an initial correlation plot in the health check report but it was wrong. It was wrong because the test it used in that report makes some assumptions about the data that are not true. Marketing data, especially, tends to to very rarely be at what's called a normal distribution, a bell curve, right? Where there's a lot of stuff that that's in the middle and then not much stuff on the ends. As we saw from that first health check, marketing data tends to be more power law.

Like there's one end of the curve that's really high, and then the rest is kind of flat. And so that tells you that a standard correlation check isn't going to work. It's going to give you bad answers. So in this case, we uh ran this, we ran with the exact same data set, but a different correlation check to see what has a relationship to traffic. What are the things that have some sort of mathematical relationship?

And the the obviously traffic value is kind of a covariance, we can ignore that one. It's referring domains. Referring domains seems to have the highest relationship. Pinterest is actually not statistically significant, neither is anything else. Even the number of words in an article, not significant.

So it's not, you know, it from our SEO data that we're looking at, the immediate guidance is don't you don't need to have you know war and peace for every single uh blog post you write. It's not it's not a good indicator. What is a good indicator is those referring domains. So where in here, because I know we looked at Google Analytics data to start with, and we said this isn't going to tell us anything, but where, like let's say it wasn't us, and let's say we were looking at a client's website for the first time, how would we know if they even have the content that that supports the keyword that they want to be ranking for? Um, you know, we know with SEO, it's not enough to just like put it on the homepage of your website and assume like if you build it, they will come.

Like you have to keep creating the content. So, how do we find out if we even have enough of the right content to be ranking for marketing analytics? The place to do that would actually be in Google Search Console itself. Search Console would be the thing that would tell us like, yes, you have content and it's being found for these terms, or no, you don't have the content to be found for these terms. And as a result, you know, you are not going to be found for it because you you just simply nothing there.

Um of the easiest ways to do that is to actually export the data from Search Console. And you know, here's the page, uh, here's the query for that page, and then you know that your clicks and your impressions. And if you were to go through this list, um, if you know that you have a specific keyword like marketing analytics, look for it in the keyword list. Um, and say, okay, do we are there any pages that are getting any impressions for this term whatsoever? Or or is it really, you know, what Google thinks you're about, which is the impressions number, um you're you're really not about those things.

And so if I resort this really quickly here, uh marketing analytics consulting actually does show up there. Um and we actually have a decent number of impressions about it. So Google at least thinks that's okay. We got two clicks for it. So that you know, the users don't think so, but um it's in there.

And so my inclination would be okay, let's let's dig into some of these terms. And I would put I would repeat this process with any term that we think we would want to have uh and attract the right audience for, right? Because search in particular is all about attracting audience. If you can get people to your site, you know, be found in organic search, by its very nature, search has exhibits slightly more intent than somebody who comes to your site, you know, purely by accident. Um therefore, if you're if you're being found for the right things like digital customer journey, then that audience should at least be inherently slightly more qualified.

Because again, you don't go on Google and search for digital customer journey for fun, right? You search for like you know what's new on Netflix this week. Um, you search for digital customer journey because you have a need of some kind. And so is it and I I don't want to derail this too too much, but just to clarify, so these so when you get the query, those are the pages that Google has found the relevant content on, not necessarily the focus keyword that you have placed in your you know, WordPress, your that kind of a thing. And that is actually a really, really uh important step that we didn't get to here because we're still on the audience part.

Um but what you've what you've tapped into is the offer part, right? So audience offer creative. If you put together if you extract your focus keyword list and the URLs in your site, and you match that up to what Google thinks, you can see whether the offer is out of alignment, right? If what you think the page is about is not what Google thinks the page is about, then you've got a mismatch. And at that point, you kind of have to do what Google says.

I mean, you can try and fight it, but if Google says you are you are you're important for competitive benchmarking, and you're like, no, no, no. Like this is a good one. Uh, here we did an episode on so what Google Analytics convention uh competitive benchmarking, right? And and and google says, hey, that we think this page is about competitive benchmarking. Is it was that episode what that was really about um maybe uh google thinks so it's shown at page 5200 times users don't think so it no one's clicked on so again you've got a a a shear you got a a mismatch between what Google thinks something is about versus what the audience thinks something is about now we've talked in past episodes uh about our utter lack of follow through on a lot of our content in that these that no I'm pointing the finger at myself here oh I was gonna say like I wouldn't call it lack of follow through because I think that we do have follow through I think it's more lack of resources.

Lack of follow through makes it sound like we can't complete anything we chose not to do it. Yeah we just we just did a brand reputation episode so let's not shoot ourselves in the foot it's true um with that understanding though um if I pull up that page in in WordPress right competitive benchmarking and I scroll down here I scroll and I scroll and I scroll because we sure do like our transcripts this is what somebody sees in the preview right if you were searching for competitive benchmarking that's not a super helpful thing right now we're in creative right we're in creative now so we've we figured out that competitive benchmarking in this example would be a good probably a good thing, right? We don't we're not gonna repeat the whole process of going through HRFs and extracting a lot of stuff out. We figured out that's probably a good thing. Um we figured out that the the offer is relevant in that Google is showing us our uh suppressions, right?

So we're we're okay there. But this could probably be it's a reasonable guess, and it is still a guess, but it's a reasonable guess that this is not the most compelling search result to look at, right? This is probably not attractive enough that uh you would want to uh to click on that result. On the other hand, it doesn't tell people enough information for them to go, yes, that answers my question. Exactly.

So if I were to take the preview statement that we actually use when we post this thing on social media and replace the built in, that's a lot better to look at, right? If you were to see this, focus on competitive benchmarking using Google Analytics data, oh okay, that's a lot better. A piece of creative, right? Your search result preview is a pay-per-click ad that you're not paying for. I you know, I can't say that any more clearly.

The preview snippet is a pay-per-click ad that you're not paying for. So if you're not spending the same amount of time, which we're not like you said, Kate, it's a resource issue. But if your if your ad sucks, right? If your creative sucks, then yeah, you might have gotten the right audience. But the the offer and the creative don't look like it.

I I will admit when you said it's a pay-per-click ad that you're not paying for, the wheels in my head just went. Wait, what? And they had to re it took about a second, it had to recalibrate. But I understand what you're saying because you know, it's that old, I don't know how old it is, but it's the old saying of you know, anything past page one on a Google on a search result, you're like, you might as well just not even bother because people will keep refining their search versus digging into the web pages to see does the content answer my question. It's let me refine my question until I find something that almost exactly matches what I want.

And here's the funny thing. And again, this is one of those things that I know we just don't have a good process for in place yet. Every episode of this show that we do, we have a social post that says, in this episode, you'll learn and three bullet points. And yet we don't put that in here in the search result. Even though that would be exactly what somebody would be looking for and what they would see when they typed in competitive benchmarking.

They could see this result going, oh, we walked through how it works, okay, and you know, it's using Google Analytics in. And so in some ways, we're not making use of the resources we already have because we already wrote the social post. Um, we just didn't reuse it in the content. Um, so fun fact is that we are we have been doing that moving forward since we did our own SEO reporting, but now what you're looking at is an older post, and so now where we have a project that's ongoing of to clean up these older posts with that information. So we have you know, a small handful of posts that are now structured the correct way with the snippet that actually gives better information.

But again, back to the you know, just the admitting like we have a resource constraint, it's just not getting done as quickly as we would like it to be because it's not as high of a priority as a bunch of other things. And I think that that's what a lot of other companies are going to run into is we you know, because Chris, you have a lot of expertise in exploratory data analysis and doing this kind of process, within a matter of 40 minutes or less, we were able to start to, you know, winnow down the is it the audience, the offer, or the creative. Whereas I think a lot of other companies that don't necessarily have your exploratory data analysis skill set, they won't be able to figure out that well, it's actually a really simple fix of editing the snippet in your post so that you're giving people the right information, you know, to click on your website to click on your search results. That to me, that's the disconnect is yes, people want to be able to do this type of analysis to figure out what's not working, but it's a lack of time and it's a lack of skill set in order to do that. So it's more of the well, let's just target a different audience or let's just change the creative, or let's just change the offer without really knowing if that's the issue.

Exactly. I'm gonna do one other thing here. It's gonna take me a couple of minutes to to do this, but I want to understand if it's possible that the article titles themselves might be important. We don't know for sure if that's the case, but I think it is a possibility that um that there might be some um some useful uh words and phrases that lead to a positive outcome. So what I've done is I've split the data set into four pieces into quartiles by traffic, and I'm only taking the top 25%.

Those are the only things that I want to look at. I want to uh leave out the rest. And what you end up with obviously is a large table of uh those two word phrases. From there, let's go ahead and turn this into a corpus, which is nothing more than a big collection of words. Uh what did I forget to do?

I did something wrong here. Ah I need to. When you're planning out episodes for marketing over coffee, so obviously the podcast has a very strong, following, very strong audience. Do you look at any of this information, sort of take it into consideration of like this is the audience that I have, therefore, this is the content that I should be serving up to this audience to keep them coming back. Yeah, and it's funny.

So what we've gone through, um, I I'm in the middle of digging into trying to figure out because I found that the show notes uh we have an H2 heading that is that summary. And so if I would just update the SEO plugin to grab that instead of the other random crap that is grabbing, like I wouldn't have to go on every episode and tweak that copy. So I'm in the middle of doing that. Um and yeah, we do some of that analysis for um marketing over coffee, but the big thing with marking over coffee is just there's this endless churn of tech news, you know. It's like every week there's some kind of disaster or something changing.

So that feeds a lot of the uh uh of the you know, just incoming content. Um, but the other thing that I do have unfortunately, there is like some bad news that to drop in that you don't think that when we say is it offer audience or creative, that it's just one of those. It can be all three. You can be totally, you know, completely screwed on every single front. So yeah, you're not gonna know until you play with each of those three if you're actually close to the mark.

I think that's a really important point uh to be making, John, because we have been talking about it as if they are each individual pieces, but it could be a combination uh of things, absolutely. Um, and then you know, building your test plans. I feel like that's a whole different uh episode because if you're testing too much at once, then you don't know what's working and what's changing. Exactly. So what we've done is we've we've gone through and digested down all of the titles of all these different uh pieces of content, and you can see there's actually a fair amount of stuff in here in in seven different areas that are not super relevant.

So this tells us, John, to what you were saying much earlier before, this tells us that even though numerically the data set was healthy, from a relevance perspective, this data set is not healthy because there's a lot of stuff that's showing up in here that like Olympic results has nothing to do with marketing analytics, right? So this tells us that from a relevance perspective, a topical perspective, the keywords, the you know what what's surfaced from our SEO tool is not yet reliable, right? It is not yet reliable for figuring out what we should be doing, right? So our next step, you know, we're not gonna do it right now because we're starting to get close on time, but our next step would be actually be to use some of this data to say, okay, let's refine even just the article topics and say, like there, there's certain words or phrases that probably should be there, like marketing or social media or analytics or data that would indicate that the article at least has some relevance to what we're talking about and not Olympic results. Right.

We know why that's in there because um during the Olympics, there was a lot of coverage this year about predictive analytics, right? Um, and so from a purely language perspective, yes, predictive analytics is also something that's talked about a lot with marketing analytics. But in this case, it was picking up something that's that's totally unrelated. Well it's interesting the TikTok ones too pull in Chinese, you know, topics. I feel like when you start to see this and you start to question, it's an opportunity to go back and start to do some of that.

Um, I think you call it the adjacent keyword research, Chris, of like, okay, so we know marketing analytics is something we want to rank for, but what else is relevant to marketing analytics that we should be co capturing in our titles and our descriptions and our content. And we've done episodes on that. And if you want to see those, you can find them on our YouTube channel. We have all of our so what episodes uh in the so what playlist. If you go to trust insights or AI slash YouTube, you can find that there.

We actually did, I think a few weeks on uh the different phases of SEO, including a pretty in-depth walkthrough of getting those adjacent keywords so that you're not just trying to hammer marketing analytics over and over and over again and continuing to lose that battle. Exactly. Let's do one more time and just see if this looks any better uh the second time around. What did you do? Did you dump Olympics or uh I specified it the title should have like marketing or analytics or data or you know something relevant there.

There we go. And so uh clearly something broke along the way so I'll have to figure that out uh after this episode is over. But again that sort of goes back to the term marketing and the term analytics can be so broad that we as the company who are trying to rank for it need to narrow it down and get more specific with the content that we're sharing so that we are attracting the right audience. So you know to John's point there's there's a few different pieces that we need to address not just one or the other. Exactly yeah it looks like I totally hose the format for the regular expression.

I'll worry about that later. So in summary when we think about exploratory data analysis, right? We are talking about an eight-step process which you know what question you're trying to answer, what are you trying to prove, getting the data and making sure it's okay, going through its classification, doing some initial analysis, validating your requirements, preparing it, doing some feature engineering as we were doing with the words testing and then coming up with insights and and models that can help us make more out of the data. When we're you know as we walk through what we did for audience offer and creative for content that same process applies to social media to email marketing you name it. If you've got data about it, you can do exploratory data analysis on it.

Yeah, I think that it's such it's such an important process. It's something that companies should be investing time in. Very similar to investing time in research and development. This could fall under your RD time because you're really doing that research to try and understand what is it that's working and what is it that's not working, so that when you're spending money to make changes, you're spending money in the right places versus just kind of guessing like, uh, I wanna I want to get into a new, you know, age demographic. Let me just go ahead and start, you know, giving them the same thing we've always given to the other demographics.

So you should probably invest in the exploratory data analysis. It is just as important as every other kind of analysis that you're doing. Exactly. If you would if you would inspect something to make sure it's okay before you using it, um, in any other part of your business, make sure that you inspect your data and make sure it is okay for using it as well. All right, so any final words before you hop on out this week?

Good luck. Don't light yourself on fire. That's a lot of data there. All right, folks, we'll talk to you next week. Bye.

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