So What? Marketing Analytics and Insights Live
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In this week’s episode of So What? we focus on Exploratory Data Analysis (EDA). We walk through the stages of EDA and how to make the analysis actionable. Catch the replay here:
In this episode you’ll learn:
- the stages of EDA
- using EDA to analyze the audience, offer, and creative
- how to make the analysis actionable
Upcoming Episodes:
- TBD
Have a question or topic you’d like to see us cover? Reach out here: https://www.trustinsights.ai/resources/so-what-the-marketing-analytics-and-insights-show/
AI-Generated Transcript:
Unknown Speaker 0:25
Well, hi there Happy Thursday. Welcome to SWOT the marketing analytics insights live show, joined by Chris and John. This week, we are talking about using exploratory data analysis or EDA to understand your marketing. 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.
Unknown Speaker 1:15
That’s absolutely true. And so with exploratory data analysis, it, you’re really talking about looking in the information you have, with an eye towards answering a very specific question. And so I thought it really be useful to define a few things here. As we get started. exploratory data analysis, if you read the Wikipedia definition, is an approach of analyzing datasets to summarize their main characteristics, often using graphics and data visualization. And the purpose of exploratory data analysis, the formal purpose is to figure out
Unknown Speaker 1:55
does the data you have answered 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. There’s just no two ways around it. So there’s a couple of things that I think are important to say what it’s not. So exploratory data analysis is different from what encompasses what’s called initial data analysis. Initial data analysis is simply kind of like,
Unknown Speaker 2:23
you know, checking the invoice on the box, right? So is what’s in the box, what you bought kind of thing. And that’s what initial data analysis says, it really is just verification of basic data requirements. And that’s part of what EDA is. EDA is also not machine learning and AI, it is a precursor step to it. But it’s not 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,
Unknown Speaker 2:49
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, I’m just gonna have to muck around here and see if I find anything interesting. Which is fun, but not necessarily impactful.
Unknown Speaker 3:10
John, yeah, what else? It’s not? Not a quick answer to your question. I know. It’s not a silver bullet or any other the hackneyed sales measures for Yeah, instant answer.
Unknown Speaker 3:25
It is. And the other thing that the reason why we’re talking a lot more about this now, is twofold. One, because as we saw on the CMO survey recently, something like 59% of companies are starting to use artificial intelligence not have fully deployed it in production. So 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 for measurement and attribution. But the big thing is, because Google Analytics 4, which you knew we were gonna come back to, in, GA for this is an entirely new section called Explore. And when you look at it, and when you 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, now they’re saying the process, it’s kind of like someone just hands you a bunch of appliances you don’t know how to use
Unknown Speaker 4:30
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 trip to the ER.
Unknown Speaker 4:39
So I think the 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
Unknown Speaker 5:00
point of doing exploratory data analysis if you’re not sure, probably don’t do it.
Unknown Speaker 5:06
That would be part one. Part two would be the data collection.
Unknown Speaker 5:13
You have to get the stuff, you have to find where your data is that you want to do the analysis on.
Unknown Speaker 5:21
Develop some requirements about it so that you know what it is that you’re after. And ultimately, 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, what dimensions what metrics, what data types, even in Google Analytics, 4, as an example, there are something a 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. See if your data is any good, if it’s in good condition or not. 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’ve gone through some initial exploration, we’ve looked in the box.
Unknown Speaker 6:17
You know, it’s like in cooking, if you were to look in their fridge, and yes, you have all the ingredients you need to cook the recipe you set out to cook. You’re not like, oh, look, there’s no milk in here. Oops, I guess we’re not making fettuccine alfredo.
Unknown Speaker 6:31
Sick six is data preparation, any kind of of modifying the data that you need do to make it work.
Unknown Speaker 6:38
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 baby adding new features that aren’t there. Again, not something you’re going to do in built in software a lot. But for your own stuff. 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?
Unknown Speaker 7:14
We have a comment, that it really just reinforces why we’re talking about this. From Chris on LinkedIn, EDA, seriously exciting for me, we’re starting to get ideas for the types of modeling that may be affected it also on Earth to 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 going to find it, you don’t know what you’re going to 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.
Unknown Speaker 8:11
Exactly. So when we talk about things, you know, audience and offer and creative, we’re really talking about
Unknown Speaker 8:18
the stuff we make we make and who we’re making it for. And that’s a really kind of a big, open ended question like, are we making the right stuff? Are we already doing the right things with our marketing? So why don’t we take a very quick, I guess, walkthrough of how would you go about answering this question. So Katie, I’m going to 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 to start with would to be to start with the audience. Because without an audience, the offer and the creative really don’t matter.
Unknown Speaker 9:01
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 help? What problems do you have that the things that I do consult, 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 screaming into the void.
Unknown Speaker 9:31
Exactly. That’s a really 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 absolutely positively shameless plug if you’d like to dig deep into it, you can take our free course I have not free course are at trust insights.ai/search console. But one of the first things we ask is are we
Unknown Speaker 10:00
be 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 email marketing, 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? Google Search Console, yay, that’s good. Clicks. Not so good. But at least that’s their
Unknown Speaker 10:31
marketing analytics consulting, it really is showing up. And so this initially tells me that
Unknown Speaker 10:38
we’re doing a good job. from Google’s point of view, right? Because we’re being shown in 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?
Unknown Speaker 11:08
Probably to look at
Unknown Speaker 11:11
my Google Analytics, I’m guessing here.
Unknown Speaker 11:16
Because, Chris, to be clear, Chris did not feed me the answers ahead of time. So this is purely
Unknown Speaker 11:24
to see if I still know what I’m talking about?
Unknown Speaker 11:27
No, 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 probably also look at some SEO tools to see like, what’s the
Unknown Speaker 11:51
need 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, 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. And then the other one would be on our site, you know, if the language, obviously, something in that site description is not catching the eye of all those impressions. So that’s, you know, this is the first hole in the funnel for us.
Unknown Speaker 12:23
I think that’s a really good way of thinking about it. So let’s go ahead and actually go straight into Google Analytics reports. First, to look at our, our content. So I’m gonna go to pages and screens here. This is Google Analytics 4. Fun facts. You can rearrange the menus and Google Analytics 4 to make it look exactly like Google Analytics 3, which is what we’ve done.
Unknown Speaker 12:45
Stay tuned, there will be a Trust Insights course for that as well.
Unknown Speaker 12:49
So
Unknown Speaker 12:50
when we look real straightforward here at what’s getting this, I despise the scatterplot chart. It’s just useless. Yeah, I feel like in this context, it really doesn’t add any value at all. No, it doesn’t. Alright.
Unknown Speaker 13:08
There we go.
Unknown Speaker 13:09
Go back. Okay.
Unknown Speaker 13:12
So we’ve got for top content, got some pages not found, which that’s, that’s a different issue. Our Google Search Console stuff, we’ve got our landing page for one of our downloads, we’ve got our pollster stuff, we’ve got some GA for stuff. But again, we’re not really seeing a ton of
Unknown Speaker 13:31
blog content would be the thing. We got some newsletters showing up, which is good. But But raw blog content itself, not really showing up. So I think
Unknown Speaker 13:42
we’re John, we’re, you’re onto 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 going to show up in Google Analytics. So the next logical question is okay, well, then, who is getting the traffic, right?
Unknown Speaker 14:06
So
Unknown Speaker 14:07
to Katie’s point, looking at a tool like an SEO tool, so here’s H refs. I put in marketing analytics as our search term. And even without doing any like super, super fancy stuff. We could 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
Unknown Speaker 14:37
Come on, Google. Says HS. O. This is HSS. I can’t blame Google.
Unknown Speaker 14:45
So you could just see this. There’s some stuff in here that is 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. So let’s put that in quotes. See if we can clean this up just a little bit.
Unknown Speaker 15:00
Okay, that’s a little bit better. So Google Design, make a QR code and seven easy steps, software, things like that.
Unknown Speaker 15:09
What are the methods of data collection? So Marketo Lotame. So there’s a lot of these are either publications or companies that are competing and doing well on this term.
Unknown Speaker 15:21
So the question we go ahead, okay. I was gonna say it seems a little unfocused, quite honestly.
Unknown Speaker 15:29
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 longtail 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 our suffer like no, that’s not what I want. What I really meant was Tiktok. What I really meant was QR codes?
Unknown Speaker 15:58
That’s a really good question. And it’s a question that you’re not necessarily going to be able to answer within
Unknown Speaker 16:05
the SEO tool itself, largely because the SEO tools that are out there, don’t have any kind of data analysis facilities, right. They really are very quick visualizations, and
Unknown Speaker 16:20
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 mix and match and make pivot tables and things. It’s just use a bar chart. So I think what I heard you say was, we probably need to dig a little bit deeper to see what’s in here. So let’s do some actual formal exploratory data analysis. And for that, we’re going to switch over to our friend our
Unknown Speaker 16:47
if, and I don’t, because this is a question Katie has every single episode, we do this, if you don’t have access to a tool like ours, you can do some of this in tools like Tableau, you can do some of it in Excel. It’s just that tools like our make things a lot faster to do, and have more rigorous methods. You can also use Box products like IBM Watson Studio, or SPSS Modeler. Those be also good choices. So I exported a bunch of those results, we’ve have a table now of 18,000 different results.
Unknown Speaker 17:26
The first thing we want to do is if you look it, just very quickly, we see that this result number that’s not super helpful. And let’s go ahead and just into the, 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.
Unknown Speaker 17:49
So this is the data that you exported from H refs,
Unknown Speaker 17:54
of the pages that are getting traffic for the keywords that we were
Unknown Speaker 18:00
keyword marketing analytics. Yeah, that’s right. Got it. Okay. So, with a little bit of cleaning, we found out there’s actually only 14,000 Total URLs, there was about 4000, duplicates, which is it’s just good basically, to get a sense of what’s in here. From here, we’re going to create a report. Now we’re just want to get a sense of what is in the box.
Unknown Speaker 18:26
Many tools like R and Python, stuff like that have the ability to do
Unknown Speaker 18:33
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, and John, you know, and definitely want your perspective on this. It’s interesting to me that, you know, the software systems like H refs, and other 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 has 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 hate get your export file of choice, you know, and it’s just assumed that they’re going to 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.
Unknown Speaker 19:28
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 and at agencies, that’s one of the deficits of, you know, the teams and these tools is they can have this whole martech 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
Unknown Speaker 20:00
That’s it, right?
Unknown Speaker 20:03
That’s like every marketing automation solution. We bought the tool, we should be golden, right? And well, no, no, somebody eventually will do some work somewhere.
Unknown Speaker 20:13
Well, and if you think about it, it’s a lot like, you know, buying all the ingredients for Thanksgiving dinner, and having a stove and kind of using, okay, we’ve got the tool, we’ve got the ingredients, when’s dinner, and be really disappointed when the oven doesn’t cook dinner for you. Man, that’s me.
Unknown Speaker 20:32
You know.
Unknown Speaker 20:33
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 part three and four of exploratory data analysis where you’re looking at the data and saying,
Unknown Speaker 20:44
Is it in good condition? Is it is it workable, right, so for the most part, we’ve got almost 100% coverage. So which means we don’t have major chunks of missing data, right, that’s a really good sign that
Unknown Speaker 20:57
our data is not punched full of holes, we can see the only things where we have some a few things missing. And these are relatively small point zero 1%, our website traffic value and website traffic, otherwise, we have no missing data at all. And then some basic histograms. Right, so what’s in the box, what kinds of distributions, this is very helpful. Here’s domain rating, right. So this is the rating of domains.
Unknown Speaker 21:22
We see here, a very, very strong right leaning bell curve, right, it’s like the bell curve is squashed one side to the right.
Unknown Speaker 21:32
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, they tend to have fairly high domain ratings. So that’s a useful indicator, we also see, you know, not not a ton of other useful stuff here, mostly single variables. When we look at distributions we see, there are a number of what are called power law curves, where it’s sort of a big head and a long tail for all the data, 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 a distribution near that look like this way was wildly in one direction we go, okay, there’s a bias in our data set. And that’s an immediate red flag that something’s gone wrong.
Unknown Speaker 22:35
Going on further, we could start, you can start seeing just basic correlation analysis, what are the things that have a relationship to others? So this is very interesting here, traffic, it, which is a variable we care about, has a pretty strong relationship to referring domains, obviously, to traffic value to Pinterest. Interesting enough. And then, you know, some other things that are missing throughout here. And finally, doing principal component analysis trying to figure out what are the features that if you had to reduce this down to maybe two variables, what are the features you would pay the most attention to? So that’s the 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? Yeah, well, state Id like, what do I do with this? Well, yeah, I’m looking at this. And obviously, I understand, you know, if 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?
Unknown Speaker 23:56
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.
Unknown Speaker 24:09
If you were running a restaurant, right? Do you have somebody inspect the produce as it comes in that morning? Or do you say yeah, you know, whatever’s in the truck, just throw it in the in the in the fridge and we’ll worry about it later.
Unknown Speaker 24:22
It depends. It depends on on who your produce supplier is. If you have a super reputable local supplier, you may just say, you know, I trust you. I trust you to not give me two crates of rotten lettuce. If you don’t have that level of trust, then say yeah, you know what, I’m going to look at each crate before you load in the fridge. I’m not going to sign off too late and pay for this before. Before I check it over. The same thing is true 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 data sources. So like if for example, if you were doing this Google Analytics data.
Unknown Speaker 24:58
Google itself is very true.
Unknown Speaker 25:00
Roseworthy when it comes to, you know, exporting data, but is your analytics install setup correctly?
Unknown Speaker 25:06
If you didn’t do your tagging properly, then your data that you’re manufacturing is not trustworthy.
Unknown Speaker 25:12
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?
Unknown Speaker 25:33
I would say you should, once you validated a data source. Yeah, you can, you can do infrequently, it still doesn’t hurt to spot check from time to time, just to see, especially if
Unknown Speaker 25:47
you don’t have total control over the systems like for us with with Trust Insights data. We did the spot check once and we know who’s working on a Google Analytics account, right? It’s so
Unknown Speaker 25:59
if something’s gone wrong, it’s our fault. And we should have fixed it.
Unknown Speaker 26:03
On the other hand, when we work with some of our clients, for example,
Unknown Speaker 26:08
I will sometimes spot check monthly to because there are other people, other agencies and those accounts and stuff. And I am a trust us, I may trust our client, I may not trust some of the other vendors.
Unknown Speaker 26:21
I find this to be 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?
Unknown Speaker 26:50
That does happen. It shouldn’t happen. Right?
Unknown Speaker 26:54
That’s actually kind of working in reverse, which is, it means that you didn’t do your due diligence upfront, maybe you didn’t define your requirements upfront. And as a result, you kind of kind of find 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 dataset 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, nothing breaks. Yeah, exactly. Those are cases where
Unknown Speaker 27:30
you don’t need to do it as much. So the next step here, is to start digging, we still have that question of, well, why are we not doing well, for marketing analytics, right? That’s kind of our premise, the thing we’re investigating. So we look at all this other stuff that has that is doing well for that, we want to look at some of the correlation. So we saw an initial correlation plot in the health check report. But it was wrong. It was wrong, because the test used in that report, make some assumptions about the data that are not true. marketing data, especially tends to very rarely be 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 ran this reran with the exact same dataset, but a different correlation check to see what has relationship to traffic, what are the things that have some sort of mathematical relationship? And the obviously traffic value is kind of a covariance, we can ignore that one is 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 in our SEO data that we’re looking at.
Unknown Speaker 29:04
The immediate guidance is don’t You don’t need to have you know, War and Peace for every single blog post you write. It’s not it’s not a good indicator, what is a good indicator? Is those referring domains.
Unknown Speaker 29:17
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 were
Unknown Speaker 29:28
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 supports the key word that they want to be ranking for?
Unknown Speaker 29:42
You know, we know with SEO, it’s not enough to just like, put it on the homepage, 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?
Unknown Speaker 30:01
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 are not going to be found for because you just simply nothing there. One of the easiest ways to do that is to actually export the data from Search Console. And, you know, here’s the page, here’s the query for that page, and then you know that your clicks and impressions. And if you were to go through this list,
Unknown Speaker 30:37
if you know that you have a specific keyword like marketing analytics, look for it, and the keyword list and say, Okay, do we have 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,
Unknown Speaker 30:55
you’re really not about those things. And so if I resort this really quickly here,
Unknown Speaker 31:02
marketing analytics consulting actually does show up there, 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
Unknown Speaker 31:15
it’s in there. And so my inclination would be okay, let’s, let’s dig into some of these terms. And I would, I would repeat this process with any term that we think we would want to have 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 organic search. By its very nature, search has exhibit slightly more intent. And somebody who comes to your site, you know, purely by accident.
Unknown Speaker 31:49
And 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, what’s new on Netflix this week,
Unknown Speaker 32:06
you search for deals the customer journey, because you have a need of some kind. And so is it. And 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 thing. And that is actually a really, really
Unknown Speaker 32:39
important step that we didn’t get to here, because we’re still on the audience part.
Unknown Speaker 32:45
But what you’ve what you’ve tapped into is the offer part, right? So audience offer creative pay, if you put together if you extract the 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’re you’re important for competitive benchmarking, you’re like, No, no, like, this is a good one here. We did an episode on so what Google Analytics content competitive benchmarking, right. And Google says, Hey, we think this page is about competitive benchmarking.
Unknown Speaker 33:29
Is it was that episode what that was really about? Maybe a Google thing. So it’s shown at page 5200 times users don’t think so no one.
Unknown Speaker 33:41
So again, you’ve got a shear, you got a mismatch between what Google thinks something is about versus what the audience thinks something is about. Now, we’ve talked in past episodes, about our utter lack of follow through on a lot of our content, in that these that I pointed the finger at myself here, so 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.
Unknown Speaker 34:18
We just did a brand reputation episode, so let’s not shoot ourselves in the foot.
Unknown Speaker 34:26
It’s true.
Unknown Speaker 34:28
With that understanding, though,
Unknown Speaker 34:31
if I pull up that page, in, in WordPress, right, competitive benchmarking, and I scroll down here, I scroll I scroll and scroll because we should do like our transcripts.
Unknown Speaker 34:45
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.
Unknown Speaker 35:00
So we’ve we figured out that competitive benchmarking in this example would be a good probably a good thing. We’re not going to repeat the whole process going through H refs extracting a lot stuff out, we feel that that’s probably a good thing.
Unknown Speaker 35:12
We figured out that the the offer is relevant in that Google is showing us our impressions, right? So we’re okay there.
Unknown Speaker 35:22
But this could probably be, it’s a reasonable guess. And it is silly gasp is a reasonable guess that this is not the most compelling search results to look at. Right? This is probably not attractive enough that you would want to click on that result, on the other hand, help people enough information for them to go. Yes, that answers my question. Exactly. So if I were to take the previous statement that we actually use when we post this thing on social media, and replace the built in,
Unknown Speaker 35:55
that’s a lot better to look at. Right? If you were to see this focus on competitive benchmarking using Google Analytics data, 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.
Unknown Speaker 36:12
I can’t say that any more clearly.
Unknown Speaker 36:15
The previous 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, we like you said, Kate, it’s a resource issue. But if you’re, if your ad sucks, if you’re creative sucks, then yeah, you might have gotten the right audience. But the the offer in the creative don’t look like it.
Unknown Speaker 36:37
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?
Unknown Speaker 36:43
Wait, what? And they had to read, it took about a second it had to recalibrate. But I understand 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 on a search result, like you might as well just not even bother because
Unknown Speaker 37:03
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.
Unknown Speaker 37:17
And here’s the funny thing. And again, this is one of those things that, oh, we just don’t
Unknown Speaker 37:25
have a good process for in place yet.
Unknown Speaker 37:29
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.
Unknown Speaker 37:37
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 was, oh, we walked through how it works, okay, and you know, was using Google Analytics it and so
Unknown Speaker 37:56
in some ways, we’re not making use of the resources we already have, because we already wrote the social post.
Unknown Speaker 38:03
We just didn’t reuse it in the content.
Unknown Speaker 38:07
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, have 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 constrained, 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.
Unknown Speaker 38:49
And I think that that’s what a lot of other
Unknown Speaker 38:52
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 to the is that the audience the offer or to creative,
Unknown Speaker 39:14
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 posts 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 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 and let’s just change the creative or let’s just change the offer without really knowing if that’s the issue.
Unknown Speaker 39:59
Exact
Unknown Speaker 40:00
Click, I’m gonna do one other thing here. That’s going to take me a couple of minutes 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 there might be some, some useful
Unknown Speaker 40:26
words and phrases that
Unknown Speaker 40:29
lead to a positive outcome. So what I’ve done is I’ve split the dataset into four pieces into quartiles by traffic, and we’re only taking the top 25%, those are the only things that I want to look at, I want to leave out the rest. And what you end up with, obviously, is a large table of those two word phrases. From there, let’s go ahead and turn this into a, a corpus, which is nothing more than a big collection of words.
Unknown Speaker 40:59
What did I forget to get something wrong here?
Unknown Speaker 41:04
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 taken 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 that so what we’ve gone through, I’m in the middle of digging into trying to figure out, because I found that the shownotes, 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. And yeah, we do some of that analysis for marketing over coffee. But the big thing with marketing 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 of the, you know, just incoming content. But the other thing that I do have, unfortunately, there is like some bad news that to drop in that. 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 going to 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 to be making, John, because we have been talking about it as if they are each individual pieces, but it could be a combination of things. Absolutely. And then, you know, building your test plans, I feel like that’s a whole different episode, because if you’re testing too much at once, then you don’t know what’s working and what’s changing.
Unknown Speaker 42:50
Exactly. So what we’ve done is we’ve we’ve gone through and digested down all the titles of all these different pieces of content. And you can see, there’s actually a fair amount of stuff in here in seven different errors that are not super relevant. So this tells us, John, to what you were saying much earlier before. This tells us that
Unknown Speaker 43:09
even though numerically, the data set was healthy. From a relevance perspective, this dataset is not healthy, because there’s a lot of stuff that’s showing up in here that like Olympic results.
Unknown Speaker 43:23
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’s surfaced from our SEO tool is not yet reliable, right is not yet reliable for figuring out
Unknown Speaker 43:42
what we should be doing. Right. So our next step, you know, we’re not going to do it right now, because we’re starting to get close on time. But our next step would actually be to use some of this data to say, Okay, let’s refine even just the article topics and say like, 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 is has some relevance to what we’re talking about and not Olympic results. Right. We know why that’s in there. Because during the Olympics, there was a lot of coverage this year about predictive analytics, right. And so from a purely language perspective, yes, predictive analytics assaults are something that’s talked about a lot with marketing analytics, but
Unknown Speaker 44:29
in this case, it was picking up something that’s that’s totally unrelated.
Unknown Speaker 44:34
Well, and I think the Tiktok ones to pull in Chinese 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. 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
Unknown Speaker 45:00
count capturing in our titles in our descriptions in our content. And we’ve done episodes on that if you want to see those, you can find them on our YouTube channel, we have all of our so what episodes in the sowhat 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 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.
Unknown Speaker 45:36
Exactly. Let’s do
Unknown Speaker 45:39
one more time and just see if this looks any better. The second time around.
Unknown Speaker 45:46
What did you do? Did you dump the Olympics, I specified it the title should have, like marketing, or analytics or data or something, right?
Unknown Speaker 45:56
Here we go. And so
Unknown Speaker 45:59
clearly, something broke long way. So I have to figure that out 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 we’re 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.
Unknown Speaker 46:28
Exactly.
Unknown Speaker 46:30
Yeah, it looks like a totally hosed the format for the regular expression. I’ll worry about that later. So
Unknown Speaker 46:37
in summary, when we think about exploratory data analysis, right, we’re talking about an eight step process, you know, what question you trying to answer? What are you trying to prove, getting the data, making sure it’s okay, going through its classification, doing some initial analysis, validating your requirements, repairing it, doing some feature engineering, as we were doing with the words testing, and then coming up with insights and models that can help us make more out of the data? When we’re, you know, as we walked through what we did for audience offering creative for content, that same process applies to social media, to email marketing, you name it, if you’ve got data about it.
Unknown Speaker 47:18
You can do exploratory data analysis on it?
Unknown Speaker 47:22
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 r&d 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, oh, I want to 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.
Unknown Speaker 48:07
It is just as important as every other kind of analysis that you’re doing. Exactly. If you wouldn’t, if you would inspect something to make sure it’s okay before using it. 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 we hop on out this week?
Unknown Speaker 48:26
Good luck. Don’t let yourself on fire. That’s a lot of data there.
Unknown Speaker 48:32
All right. Thanks. I’ll talk to you next week.
Unknown Speaker 48:38
Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources. And to learn more, check out the Trust Insights podcast at trust insights.ai/t AI podcast, and a weekly email newsletter at trust insights.ai/newsletter Got questions about what you saw in today’s episode. Join our free analytics for markers slack group at trust insights.ai/analytics for marketers, see you next time.
Transcribed by https://otter.ai
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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.
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