In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss generative analytics, a new way to use AI to get more from your data. Discover why simply collecting data isn’t enough and how generative analytics helps you uncover actionable insights. Learn practical ways to apply this approach to your marketing efforts and avoid common analysis pitfalls. Finally, understand the importance of good data governance for getting accurate results from any AI-powered tool.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn – 00:00
In this week’s In Ear Insights, let’s talk about a term you probably haven’t heard yet: generative analytics. Now, before I dig into what that means, Katie, what does that mean to you when you hear that term?
Katie Robbert – 00:19
It sounds akin to predictive analytics, where there’s some kind of machine learning involved. It sounds like perhaps we’re going to be using generative AI to make up data, which is probably incorrect. I don’t know. But yeah, that’s what it sounds like. We’re going to make some stuff up.
Christopher Penn – 00:50
I mean, when it comes to analytics, generally making stuff up is not the first choice in the context of what we do at Trust Insights and what we’d like to see more people do. Generative analytics is the use of generative AI to process analytics data and answer the question “so what?” There is so much data. We are drowning in data. We’ve got Google Analytics and your marketing automation and your CRM and your customer service.
There’s so much data, and we don’t really look at it. Or if we do look at it, we put it in PowerPoint slides and binders and look at it once. We go, “Huh, that looks good.” And then we never do anything with it. It just goes on a shelf.
Christopher Penn – 01:37
Generative analytics—at least in the way I’m defining it in today’s show—is the use of generative AI to answer the question “so what?” And then answer the question “now what?” “Hey, the website went up last week.” “Okay, so what?” “Well, it was probably because of this. What are we going to do about it? Do more of this?” That’s kind of the idea behind it.
Katie Robbert – 02:03
I think the term analysis paralysis is what comes to mind. I was talking with someone recently, and she quoted to me, she’s like, “Back when there was big data and everybody was collecting all of their data.” And I just kind of had to laugh a little bit. I’m like, “That’s just a fancy marketing term for ‘let’s just try to get our hands on everything we can and then do nothing with it because we’re all greedy.'”
I feel like if there’s something—some tool, some technique, some product, some service—that can help us, all of us, get to the bottom of the pile where all the good stuff is, then I think that’s a gold mine, because it’s true.
We as marketers, we as business people, we as humans like to collect things because it makes us feel safe and comfy. Chris, you’re a self-proclaimed data hoarder. You have lots and lots of data that you may not have looked at in maybe a decade, if not longer, but you have it for the “just in case.” And I feel like a lot of companies fall into the “just in case” category, but what ends up happening is you have to pay for storage. You have to have skill sets on your team of people who can access it. If not, you have to hire somebody last second.
That gets really expensive when you’re like, “Oh, crap, that data from five years ago, we’re suddenly being audited, and we need someone who can get to that data and actually do something with it.”
Wouldn’t it be great if in a world, you could collect that data and have it running through a system automatically and giving you actions and insights every single day or every week or every month? Even once a year is more often than a lot of people look at some of this data. So I’m here for it. I’m excited. I want to know, as you like to tell people, my favorite question is “so what?” And it is because, like most other people, I want to know what’s in it for me. What are we doing? Why am I looking at this?
Katie Robbert – 04:31
They’re normal, natural questions. And all too often, analysis can’t answer the basics. It’ll tell you what happened. Hard stop. That’s it. But then it’s too late to do something about it because it’s already happened. It’s in the past. It’s in the rearview mirror, as you like to say. So, yeah, tell me more about this magical quest that we’re going on with generative analytics. I’m all in. I’m excited.
Christopher Penn – 05:02
It’s not magic, but you’re right. We called this—we have called this since the start of the company—the three “what’s.” What happened, which is traditional analytics. So what, which is insights. Like, why did this thing happen? And then now what? What are we going to do about it?
We’ve got, we know website conversions are up or down. Email opens were up or down. What do we do with that information? Part of the power of generative AI is that these models, particularly the big foundation models, have so much knowledge about everything, including marketing, that with things like, for example, the PAIR framework that we have, you can have these models build out best practices and say, “Here is what you should do in this situation. When website traffic is up from these channels, do more of this.
“When website traffic is down from these channels, do more of this instead.”
Last week on our livestream—and I’ll find the YouTube link here—you took some time to build out what was effectively a piece of software. You built some branching logic. That same logic can be applied to marketing data that’s analytics in nature. You can pass that table of data to a system and say, “Okay, well, if you see this, then here’s what this should mean. If you see this, here’s what this should mean, and here’s what you should do about it.”
This week, one of the things that we do internally is every Monday this tiny little Python script runs on our Google Analytics. Actually, no, this is an R script, runs on Google Analytics data and says, “Hey, here’s what happened.”
This is at your request. When you look at this, Katie, this thing that you had us build, what does it tell you?
Katie Robbert – 06:57
The “so what” from this tells me if our marketing efforts are working. So these are our four main conversions that I care about. I actually only care about two of them, but we have four of them in there just for fun. And it tells me that we are reaching the right people with the right things.
And if not—if the numbers start to drop drastically—so you can see on line two, it looks like a negative 33% change. Well, because it dropped by one number. And so when you have low numbers, obviously you’re going to see the percent change look really dramatic. But I know from historic data, from historic analysis, that those numbers are right on par, especially for this time of year.
So what I’m looking to see is, are there spikes or are there anomalies or is everything sort of staying pretty steady? I’m looking for it to stay pretty steady. And then when I see, like the first and fourth line, when those spike up, I have to think back to, “Well, what were we doing? What did we change? Does this make sense? Or do we need to be aware of some sort of spam bot or something like that?”
So my “so what” is, are the trains running? Are they running on time?
Christopher Penn – 08:28
Right. And I think that’s a fantastic process. It’s good insights into what this data can mean, but it’s still a lot of cognitive load on you. You still have to go, “Well, what happened last week? What did we do last week? What’s changed? What does this mean?”
Katie Robbert – 08:44
Yeah.
Christopher Penn – 08:45
If you were to apply generative analytics to this—and maybe this is a project that we should take on just as a proof of concept—you would take this data on the backend, out of sight, process it and say, “Okay, well, for example, website visits. What pages went up in visits last week compared to the week before?” That would get fed to a model like Google Gemini, and Google Gemini could then say, “Well, hey, it looks like your YouTube page went up substantially in views, or your podcast homepage went up substantially in views from these sources and mediums. It looks like you did good stuff on LinkedIn last week. Maybe you should do more of that.”
So it takes that boring analysis part of, like, “What happened? Where do I find the data?” Puts it all into a really big old table, and then comes up with some conclusions that you could either agree with and say, “Okay, that makes sense,” or you could obviously question, say, “Hey, I think you missed the point here.”
Katie Robbert – 09:46
Well, what’s—so here’s what’s interesting, and I want to sort of get the counterpoint here—so at least I can say for us, for Trust Insights, when we do analysis for a client or for ourselves, we include those “so what” and “what’s next.”
So I’m interested to know—excuse me—how using generative AI is going to be any different in the sense that we’ve always put, “When you see this number go up, do more of this.” That information has always been there.
So I have two questions for you. First question, why is using generative AI suddenly going to get people to pay attention to the insights and actions that we’ve always been giving them?
And number two, is there a world where we can tie this data directly to the ideal customer profile so that when you say, “Do more of this,” you will attract more customers because you’re attracting your ideal customer profile? Finally, after 20 years of being in…
Christopher Penn – 10:59
Business with Google Analytics data. I’ll answer the second question first. With Google Analytics data, the answer is no, because there’s no personally identifiable information in there. With CRM data, yes, you could say, “Here’s the number of contacts and who they were, who came in last week. How well does this align with what’s in the ICP?” And you absolutely could have a script, extract the data from our HubSpot instance, compare it with the ICP, and then say—score of zero to ten—here’s how, here’s how on target your audience was last week.
That is eminently doable. It’s a lot of work, but it is doable, and it requires more than just generative AI. That’s why we’re calling it general generative analytics, because you still need all the infrastructure and all the plumbing to make, to grab the data out of HubSpot or your CRM and process it and then pull in your ICP and then hand that to the AI and say, “Do the comparison.”
Katie Robbert – 11:54
Yes, I have a follow-up question on that. Before you get to is this viable or not, so personally identifiable—oh my goodness—PII aside, in the ICP that we build for clients, we talk about things like, “Your ICB cares about this topic, cares about these things.” Wouldn’t that then map to your Google Analytics because you’re looking at specific pages to say—or your YouTube data to say—”Hey, you just did a video on making pasta. Your ICP really cares about how to make pasta. Do more of that.”
Like, isn’t that how you would use it?
Christopher Penn – 12:40
It is if you have the data. So, for example, if your ICP really cares about B2B lead generation, and you don’t have any content about lead generation, then that’s kind of a blind spot. Your work with generative AI to build your ICP and to build your marketing strategy should have told you to build that content, but you might not have it. And therefore, because you don’t have a page on your website for that, it won’t show up in your Google Analytics, and therefore, you won’t know whether you’re aligned to that or not.
Katie Robbert – 13:11
Well, sure, but what I’m saying is if you happen to build something about B2B lead gen, and your ICP cares about that, isn’t that what you’re talking about here, where you’re using generative analytics, and it says, “Great, you’ve had a spike in traffic. You created a page about B2B lead gen. Your ICP cares about that. Now you’re hitting the mark.”
Christopher Penn – 13:33
Yes, absolutely. On a topic basis from your content, you could do that in aggregate, and you could even break it down by source. So, for example, if you know that your ICP lives on LinkedIn, you could say, “And the traffic—this page predominantly came from LinkedIn. So good job.”
Katie Robbert – 13:49
Okay, so why is this, why are people suddenly going to pay attention to their data when we’ve been giving them insights and actions all along? Like, why is this different?
Christopher Penn – 14:05
So this is going to be different for the vast majority of people, not our clients, because we do this already for our clients, but a lot of marketers don’t. And so this would be the first time that they could get a catered version of, “Here’s what you should do. And here’s what this means. Here’s what you should do.”
If you look at a lot of the reporting that other marketers put together, it’s just reams of data. “Here’s a pile of spreadsheets, here’s this dashboard.” You look at it, you go, “And so what?”
You think of your average corporate executive. He says, “I’ve got 16 seconds. Tell me what I need to know.” And you’re like, “But I’ve got 40 spreadsheets, and this and this.” And he’s like, “16 seconds up. Time’s up, gotta go.” As opposed to using these generative tools to kind of boil it down and say, “You need to invest $50,000 more in LinkedIn and give me a raise.”
Katie Robbert – 15:01
And I would say, “But you haven’t told me why your time is up.”
Christopher Penn – 15:06
Exactly. But it’s a step closer than “here’s a pile of spreadsheets that no one’s ever going to look at.”
Katie Robbert – 15:14
I guess it brings up a different question that I don’t know that you can answer. But why do marketing analysts still operate that way? Like, we know that getting to the action plan is what people need. So why are marketing analysts still handing over big piles of spreadsheets and PowerPoint decks when we all know—it’s not a secret—that nobody does anything with them? Like, why do we still operate that way when the world globally knows that is just the worst possible delivery of the information to sit at the bottom of a pile or go unread in an email.
Christopher Penn – 15:57
Think of it like any cooked dish. If you don’t have the skills or the appliances or the ingredients, then you can’t make the cooked dish. So if the analyst knows insights and actions are what’s needed, but they don’t have the skills, or they don’t have the right tools, or their data is crap, then what they do know is, “Well, here’s what I’ve got. Like, here’s a pile of flour. I know you asked for pasta, but all I can give you is a big pile of flour, because that’s all I got. I have no pots. I have no utensils. My stove is broken. So here’s your pile of flour.” That’s, that’s kind of what happens.
Katie Robbert – 16:29
But then why isn’t the company as a whole saying, “We need to do something, we need to know what to do? Let me find someone who can help us figure out what to do based on our data?” Or are people just saying, “Screw it, I’m going to wing it and just do stuff?”
Christopher Penn – 16:47
Bingo. They’ll say that, or they’ll say that most dreaded of phrases: “Well, this is the way we’ve always done it.”
Katie Robbert – 16:55
I like to tell people that I’m really chill, but I am the opposite of chill. I’m like zero chill. And so I need as much data as possible before I can make a decision. Like, if you look up “wing it” in the dictionary, you’re never going to find a picture of me, ever. And so, and so obviously, like, that’s why I’m well suited for this role in this company, because I’m someone who lives and breathes the information before I can make any kind of decision. Gut instinct, not really going to fly around here. We really need to know what we’re talking about.
And it’ll be interesting to see if other marketers, other business people start to feel like, “Okay, now I have the data in a usable form, now I can use it to make decisions.” However, the other side of that is, if the data is bad, they’re still not going to see what they want, and they’re still going to ignore it. So how do we overcome, for lack of a better term, the data blindness or the willful ignorance?
Christopher Penn – 18:13
Well, here’s the thing. As Katie Robbert often says, “New technology doesn’t solve old problems.”
Katie Robbert – 18:21
It sure doesn’t.
Christopher Penn – 18:24
And so all that data governance and data cleansing and proper implementation, that still matters. In fact, it matters more because one of the things that tends to happen is people tend to blindly trust the machine, which means that if the data going into the machine is bad, the conclusions it draws would be bad. And if you inherently trust the machine, that it’s “clearly generative AI said it, so it must be right,” then you’re not going to have a good time.
Katie Robbert – 18:49
Well, I mean, replace “generative AI” with Facebook, and you have the same problem.
Christopher Penn – 18:54
Exactly. So the infrastructure, the plumbing, the pipes and all that stuff needs to be ship shape. It needs to be as clean as it can be so that what you feed into a generative model and what it recommends is correct.
If you have a big blind spot in your data, and you forgot to put tracking codes on your Facebook ads, so you have a bunch of direct traffic to the Facebook traffic. When you feed that to a model, the model’s going to say, “Hey, you’ve done a good job building your brand. Maybe, you know what? Maybe do some more press releases or other things that build brand,” not knowing that it’s actually misattributed data.
So everything that we’ve said for six years now as a company, as Trust Insights, about having good data matters more than ever in the era of generative analytics. However, if your data is in good condition, or good enough condition, then you can go past the “here’s the pile of spreadsheets” to “here’s some insights and things we can do next.”
Katie Robbert – 19:59
Do you think there’s a risk now—so talking about how people are just trusting the machines—do you think there’s a risk of marketers putting their data into a generative AI system, taking those insights and running with them, not realizing that there’s a whole, process around it, or they don’t have any data governance, or their data is bad? Like, what is the likelihood that the machines are going to suddenly lead us astray because we’re giving them bad data, and people aren’t paying close attention? They just think it’s 100% okay. The likelihood. But how do we help them not do that?
Christopher Penn – 20:44
With—now, this is much more advanced generative analytics—part of what you can use these tools for is auditing things like your infrastructure. So, for example, with Google Tag Manager, you can export your Tag Manager configuration as a JSON file. Google Gemini, Chat GPT, and the cloud all speak JSON. And you can say—you can go through both the RACE framework and the PAIR framework to build out a strong set of system instructions for how to audit Tag Manager—load in your configuration and say, “So what am I doing wrong? How does this deviate from those best practices?” Now, your system instructions are probably going to be five or six pages long, because Tag Manager is not a straightforward piece of software anymore. But generative analytics also means fixing up that infrastructure so that you can use it for actually getting those insights.
So to your point, there is that risk, but you can use the tools to mitigate that risk if you think to ask the question.
Katie Robbert – 21:49
Well, therein lies the challenge: thinking to ask the question, knowing what to ask the machines so that you’re getting the right information. I think a lot of us have already made the assumption, “Well, it’s generative AI, it must have the right answer.” And, how do we find out before we give them all of our data? How do we find out whether or not it even has the right information, the right knowledge, so that we can use it to do that insightful analysis?
Christopher Penn – 22:27
Maybe we should do that for part two of this episode. If you’ve got some ideas about how you should be using generative AI with your analytics data, and you want to share them, pop on over to our free Slack group. Go to trustinsights.ai/analyticsformarketers, where you and over 3,500 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to trustinsights.ai/tipodcast, where you can find us in most places podcasts are served.
Thanks for tuning in, and we’ll talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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