So What? Measuring Competitive Relevance in GEO

Oh Lord. Uh this week we're talking about measuring competitive relevance in GEO. So we've been going through, and you can find on our uh live stream playlist on our YouTube channel, go to trust insights.ai slash YouTube. We've talked about the basics of GEO. What is it?

What do you need to know? How do you implement it? How do you measure it? You can also get a lot of that through our GEO course, which you can get at TrustInsights.ai slash GEO101. But now the next natural question that people are asking is okay, cool.

So I've implemented it for myself. What's the industry standard? How do I stack up against my competitors? What are they doing? Am I showing up as often as they are?

Um, you know, and so now we're dipping our toes basically into GEO 201, measuring competitive relevance in GEO. And so before we get into um, you know, all of the, you know, theory and implementation, John, I want to ask you, because you're talking with a lot of people who are asking about help and support with GEO. What kinds of questions are people asking as it comes to GEO in general? Yeah, there's, I mean, everything from, you know, the prospects that come in that the CEO has told them they need to do something and they haven't done anything and they don't know anything. Like that's actually really common having to educate right from square one.

Um yeah, there's really nobody we've never had any prospects come in that have said, like, hey, we have these programs already going. The only thing that does help once in a while is clients who are already doing some SEO, you know, and at least have a process for how to handle their website and make changes, and they're doing some outside um, you know, some uh offsite authority stuff to generate traffic. But yeah, for the most part, people kind of have no idea what's going on or where even to start because that's why our AI view page scanner has been such a hit right because it gives people at least a place to start but for most clients it's just they go into their favorite four tools and see if their name comes up and just you know take that as as what it is and I think we'll kick into that today but you know there are a lot of vendors that are saying that they can do that and most of the academic stuff we've seen and stuff that Chris has said is that's all those are all just straight out vendor lies. And um but the the one that's key for us is you know in with the absence of all the data and where it's going we do know that it brings us business. We have closed deal data that says hey yeah this one they found us on Gemini like they never knew about us and they went to Gemini and it said we can do the training for them and we did it and it got done and it's a closed win.

So you know it is one of those things where the outside world is difficult or maybe impossible to measure but you can't um you can't deny the dollar figures when they hit the budget. And we actually wrote a case study about that. So if you want to check it out you can find it on our website which is at TrustInsights.ai we have a whole section on case studies and that is one of them because we have been doing the work that we are recommending to our clients to do everything again that's covered in our GEO 101 course we do it for ourselves and to John's point we're actually seeing the proof of if you do it it works. But today we're taking it to that next step. And so Chris, when it comes to measuring competitive relevance in GEO, what are we talking about?

What what do people want to do? So we should probably go back to uh what we mean, because we're using relevance in a very specific way here. So this is from our GEO webinar, uh, which is still available on the Trusted Trust Insights website. However, uh, it's also covered in much greater detail in the GEO 101, of course. The three phases of GEO are presence, awareness, and relevance.

Presence means is our data part of the model's training data set, uh, meaning that the model is aware of us. Because when you go and you talk to any model, uh, it has a knowledge cutoff. There's a period of time after which it has no new information. And when you look specifically at Google, which according to Gum Shoe and SparkToro as of January of this year, still 93 to 95% of all search. The if when Google Googles itself, uh, part of what it does is it rewrites your query into several dozen queries based on its knowledge.

So if we said, hey, who's the best AI consulting firm uh in the Boston area that can help you with AI implementation? If we've done a good job with our own GEO, in like the 20th query, we might be included in a roundup. We might be in there with you know McKinsey, Bain, you know, BCG, Trust Insights, et cetera, because the model knows about us. If the model doesn't know about us, Google, when it googles itself, will not know that. And as a result, it will not uh even invoke us in the consideration set.

Think of this almost like sales in a lot of ways. There's awareness, consideration, evaluation, close, you know, purchase. That's kind of what the sales phone has been. Oh, in this context, that the model, even knowing that we exist, is vital. If the model has no idea we exist, we will not even make it to the short list.

Because phase two is the shortlist when Google does a Googling. And when we get to relevance, which is phase three, and the results come back, that's when what's on our site really matters. This is especially important because uh, and I was posting about this on LinkedIn earlier today, and it's and you, if you want more details, it's in the analytics for marketers lack group. At SEO Week, which was earlier this week, it was a conference in New York City. Garrett Sussman did a fantastic and fascinating session about Google personalization impacting phases one and two.

And what he was able to demonstrate over a year with hundreds of queries, dummy accounts set up for testing and all this this crazy well very well thought out uh evidence is that there is no single AI overview anymore. Google has turned on personalization and is vacuuming up all of our data that it has about us, and that is influencing how Google Googles itself. So Google, when I was in Charlotte this week speaking, if Google had my flight plans, which it does, because all my uh flight invoices go to my Gmail and it knows what hotel I'm at, which is again because confirmations go to my Gmail. When I'm searching, it inside when Google Googles itself is silently injecting my personal context into how it googles. So it knows, even if I don't tell it that you know, this week I was in Charlotte, and it and so our ability to predict anything in phase one is zero.

Zero. Our ability to predict anything in phase two is close to zero because of personalization. So all we have left that we can compare on is phase three, the relevance. And to what John was saying, that's where AI View plays a role. If we can see what an AI engine like ChatGPT or Gemini or whoever sees when they go to our page and what they see when they go to a competitor's page, we can compare and say, okay, well, which one is going to be better from an AI's perspective?

Which one is more relevant? So, what does that have to do with measuring our uh ourselves against competitors? Because that's really what we're talking about today. Like this, the overview is helpful, but people want to know where do I stack up against my competitors? How does that work?

And that's where exactly where you would use AI View. So the tool that we came up with, uh, if you were to put in your a page that you cared about, like maybe it's your services page, um, and run AI View on that page, get the report, and then you were to put in a competitor's page because it's not restricted to any domain, and put in their equivalent services page, and then get the report and take it, and you know, you take those two documents that it produces. So we email it to you. If you were to do that, you could then say, well, given this topic, who's gonna fare better when when the results come back in phase three, when the results come back to the tool of your choice, the page that is scores higher is more likely to be seen as relevant by the AI model, and the results will be incorporated, it will be visible in what the user is told. Because what we tell people all the time is you can make it through the first two phases.

If your site is a hot mess and returns garbage like CNN's does, even if you come back in a result, the AI model is gonna look at that and go, that's not relevant. I don't see how this doesn't have to do, and therefore you don't make any of the result, you don't get the nod. So I mean, so okay, so that's one way to handle it. So people can keep running, you know, individual pages through AI View, you know, and then or maybe sign up for AI View Pro. Uh, but that seems like a really inefficient way to approach it.

So when we're talking about uh, you know, competitive insights, uh, you know, against our competitors, like let's dig a little bit deeper, other than just telling people to go to our AI view free thing one page at a time, because it's a good starting place. But I think what people are really trying to understand is what is that next phase of GEO? Um, one of the things, Chris, that you and I have been working on in the background is putting together material for a GEO 201 course, including a competitive scorecard. Mm-hmm. And for that, because it in the GEO 101 course, we teach you the different metrics, the ways to measure, and one of the things for phase one is how prominent are you in all the places that models look in terms of news and social media and things like that, building essentially a digital footprint.

Uh in phase two, it's all your traditional SEO stuff. How well do you show up at search results? And in phase three, that's where the relevance part comes in for GEOs. So that's how the three pieces relate. And when we're if you're if you want to benchmark how you stand up versus others, you need data from all three pieces.

Right. You look like you were going to pull something up. We're gonna pull something up. No, I was just making sure that we were staying on track because I want to make sure that we're for the purposes of the live stream, what we're demonstrating. Because, you know, again, I understand that we can show people to go to the AI view one page at a time or sign up, but people are looking, you know, when we when I think about a competitive analysis, I think about how do I stack up against, you know, uh the guy down the street.

And we've used the example of, you know, a McKinsey and a BCG and a Deloitte. Like we know we don't really stack up against them, um, but we're not picking on them in terms of showing any weaknesses. And so, you know, we try to be respectful of when we're giving the examples, but you know, what we're trying to do is come up with some sort of a way to truly do a deep dive into measuring the competitive relevance. And so, you know, it's not ready for prime time, but we're working on that scoring in the background. So, I guess what I'm wondering is, you know, how much of that can we give away today?

Like, what are we going to demo today? So, what I was suggesting that we demo is actually looking at two of the different pages in AI View. So, let's okay. Let's do that because I think that's sort of the easiest way to do this. So, here's the Trust Insights web page.

Put in Trust Insights, put in my email address so I get all of our emails. And we have those four pillars. We have the meta information, which is is the metadata in the page correct. We have the content structure, right? So is the linearized content relevant and clear, or is it filled with crap?

Like we always see with uh we always use CNN as the example. Um is the structured data, your schema and your JSON LD, is that correct? And then from an alignment perspective, is what's in your metadata and your structured data reflective of the content that is in the content structure part of the page. Those are the the four buckets that we look at. And we here for this is for the home page, show up with about an 85%.

We're we're in pretty good shape across the board. If I bring up, I use BCG as the example. Because again, like like you said, K, this is aspirational, right? Uh BCG's uh budget is their budget for cream cheese is like our an annual annual revenue. Um if we look at their site, there's some issues.

They score an F. Um, their meta information is okay, their content structure is linearized content structure is kind of messy, and you can see this when you scroll down into the individual sections as to what it sees, right? So there's there's a lot of uh messiness there in terms of you know the struct the page structure itself, the headings are not logical, etc. It's absent any structured data, no JSON LD, no schema, uh, which means that when a language model looks at the returned data, it's gonna be like, uh, what is this about? Right?

There's nothing telling it from a structural perspective. This is about this. One of the things we've said forever and ever with AI is when it reads data, anything that is in structure, JSON LD, YAML, JSON itself, XML, it knows what to do with this. Oh, this is structured data. I can find my way around.

When you don't have that, you just have free form text, AI has to be like, well, I'm gonna try to interpret this. And we know from years of painful experience now that when AI has to wing it, it doesn't go so well for us. And then the fourth one is because there's no structured data, the alignment's all over the place, right? Because you from a machine's perspective, it doesn't know what this page is about. And so if I'm a marketing analyst or I'm a product marketing manager, and my boss says, I need a competitive analysis, is this going to make sense to my you know, VP if I say, well, we have better meta information, but they have terrible content structure, so we win?

Like, I guess what I'm trying to understand, you know, from when we talk about, you know, measuring competitive relevance, especially when it comes to GEO, you know, we know that GEO is, you know, for lack of a better term, your visibility in a large language model. That's what people are striving for. How do I put this information together in a way that my VP might understand? So I can say, well, the four things that matter in GEO are meta information, content structure, structured data, and content alignment. I understand what that means, but is that something that you can easily communicate to like your VP to say, you know, we have better content alignment than them?

You can. And so each of these four categories has a letter grade. And of course, there's the overall letter grade, right? Which is to the ability to say, uh, hey, I in you know, our we scored a B, they scored an F. Like we're doing a better job than them on this thing.

Um, I know you hate this, Katie. I apologize. I'm waiting. You're not selling it. In the in the staging prototype version of AI View, there's now the ability to compare two or URLs.

So instead of us manually having to do it one at a time, we can now look at the two side by side. And this is what the boss wants to see. The boss wants to see how am I doing? How's my competitor doing for this in this given page? What are the differences?

And look, the score differences, we're doing great, boss. Don't fire me. So for those who don't know, for those new to this, I hate surprises. Um, but anyway, so I guess so. This says that we're doing better overall for those four things.

But if I'm channeling my, you know, 10,000 view seagull VP persona, you know, I'm like, well, how many more times do we show up in the results than them? Right. And that's unknowable. That is completely unknowable. Um, because back to going back to what we were started the episode with, which is now because AI personalization is so it is so probabilistic now with Google injecting context into everything, there is absolutely no way to know that.

Um we can't tell, and worse, any tool that says that you can know it is just as John said, lying, outright lying, because you cannot know that you can't even benchmark that anymore. So, one of the things that uh Garrett showed in his talk, which I thought was fascinating was how much this is very clever, how much you inject yourself into other parts of a user's Google ecosystem influences what they see in AI overviews. So, people, for example, who receive the inbox insights, the Trust Insights newsletter, and subscribe to In Ear Insights, the Trust Insights podcast and stuff, and have all this in their Google history influences what they see in AI overviews. So, someone who is subscribed to all of our stuff is more probably going to see us as a recommendation in AI overviews than someone who is not subscribed to our stuff. Even though our email newsletter has nothing to do with GEO, it now does because Google is vacuuming up all this information.

Garrett was showing in the in the slides in his talk. He set up synthetic dummy Gmail accounts and you know, and some and in one of the Gmail accounts, subscribe to you know his company's newsletters and YouTube and all this stuff, and then did the same in a second account for a competitor and was able to show that the two accounts in using Google search got different recommendations that were pulling from the person's Gmail history. Right. Think about that for a second about what a mind bender that is in terms of how do you ever measure what an AI is saying? You can't.

There's no way to know anymore. But what we have control over is what we do in AIView, which is that third phase of when the model pulls our data back, what is it that we are sending? So if I can maybe oversimplify what I'm hearing you say, in a way, it sounds like the way that we in uh you know advertising in digital ads use things like cookies to retarget people. So, you know, I was shopping on a home improvement site yesterday, and now my social media feeds are filled with these home improvement ads that I was like, I looked at one thing, or if a friend of mine sends me a link to a product she's using that I don't really care about, I suddenly have to look at all of these ads. It feels like we never, in theory, I'm not saying it's the same, but in theory, we never got rid of this idea of a cookie.

So we talked for eons about the cookie less future. What are we gonna do about with when we don't have cookies and the first party cookies are gone? How are we gonna personalize? And then large language models came about, and they're like, hmm, I can fix that. And I feel like the theory of these cookies never went away.

We are just calling them something different now. You are not only right, you are also highlighting something that is deeply problematic. When cookies were the thing, and we could retarget on anything, right? That was open to all advertisers. That was open to everybody.

Yeah. And well, yes, it was abused, and yes, it caused massive privacy violations. In this new world, only Google has that. No one else. Only Google couldn't do that retargeting.

Only Google has access to that data. So yes, they can say, yep, no more cookies. And that reinforces their monopoly. It makes them stronger at the expense of everyone else. I think I saw without getting too off topic, I think I saw something the other day that Google was investing in anthropic in order for anthropic to buy more Google software.

And it was very like wait, isn't that how monopolies work? Yep, exactly. And I think the other thing with you know, and again, it wasn't a perfect system, but to your point, it was available to all advertisers. Is that with cookies and you still get these pop-ups, at least the user felt like, and I'm not saying they did or didn't, but they felt like they had the opportunity to opt in or opt out. Now, because you get those pops up of only use, you know, required ones or accept all or reject all.

Now, as a skeptic of all of this, I'm saying it doesn't matter what you choose. As soon as you hit that website, all of the scrapers are immediately activated and getting all of your information. And I feel like, you know, we've gotten very vague notices from Google about your Gmail accounts and this. And it's like you can opt out, but oh, but if you opt out, I can't offer you all this cool stuff. So you should probably just opt back in.

And so it's not be it's not disclosing in reality what kind of information it's capturing, it's trying to position it as oh, but man, if you opt out, you're gonna miss out on all this other cool stuff, and what a bummer. So just you know, it's cool. Just opt back in. And I feel like we're just not getting those disclosures like we used to. And so when I look at something like this question of measuring competitive relevance, like I'm almost I'm almost I'm almost talking myself into a depression of why bother.

And that is why we we're focusing on okay, what do we as marketers have control over? What can we do? So in phase one, and we say this in the GEO one-on-one course, there's a bunch of things you can do to increase the likelihood that a model will detect you. And to your point, Katie, you've made this point a number of times. You can also be monitoring the things, the those activities with things like media monitoring software to see from a competitive perspective you know, is BCG just blowing us out of the water on this topic or that type of what are they getting coverage for?

Whose podcasts are they showing up on, etc., etc. And we can do the exact same thing on phase two, traditional SEO stuff. You have those tools. If you have Href, SEMrush, Moz, SpyFoo, blah blah blah, any of those tools, you have benchmarks for that from a competitive perspective. Phase three relevance, and that's why with this episode is titled competitive relevance, because phase three, when you use AI View, particularly the newly enhanced version, uh, that we're gonna roll out later today once we do a final bug check.

This is why it's on the staging site and not live. Uh, because I have not run QA on it yet. Um that will tell you that third part, and that's why it's so important because we have control over what we send back to an AI agent when it comes to our site. We have control over how orderly our site is, how we write our content. We have control over the inverse pyramid of putting the obvious stuff.

This page is about this up front, so that if we make it through phases one and two, we stand a chance of being seen in phase three. And it's and it's a crying shame when you get through those first two gates and you just run face first into phase three. And you're like, oh, our site was terrible. And so even though we did the hard work of being everywhere and doing good technical SEO, we still didn't get the nod. So let's and I hear you all of this.

We'll call them Devon. Yes. So let's say Devin asks the question, and I've already asked you this question, so I'm gonna ask you to repeat it and reinforce it so that we can all be saying the same thing. So Devin keeps asking, but how many times am I showing up? And how many times are my competitors showing up?

And you say, Devin, that's an impossible question to answer. And Devin says, I don't care. I want an answer to that question. I need to know because the board's breathing down my neck. I need to know how we show up against our competitors.

Because when we look at traditional SEO, and we look at things like number like we show up for a number of keywords, or you know, McKinsey is winning this keyword and we're not winning this keyword. So we have to optimize for it. And maybe next month we're winning this keyword and they're not, or they have a higher domain rating than us. Like those are metrics that people can wrap their heads around. And so Devin is asking, I want to know how often I'm showing up more than my competitors, because that's my goal.

That's all I care about. And you say, Devin, that's the wrong thing to care about. And Kevin says, I don't care. You're wrong. That's what I care about.

Like, how do we address that? Because that I feel like is the, you know, I don't want to pick on elephants. That is, you know, the tick in the room. Nobody likes ticks. That's the tick in the room that, you know, is, you know, unhelpful, unuseful, but it's like not going away.

And it's everywhere. Um I would look to you as to how to message this, but fundamentally, that data is not available in a truthful form. That is that is the bottom line. Yes, there are tools that will give you false information. And if you are comfortable handing false information to your boss and staking your reputation on it, use any of the tools that claim that they can do this.

But the reality is all of it is false information. All of it is likely harmful to your business, uh, and it should never be used for any form of decision making. Now, Katie, you and I have both had a lot of experience with stakeholders like Devin. I'm usually not allowed to give them this kind of message. And that's totally fair.

Um, you know, so given that, I think the way that I would handle it is, you know, and this is sort of just like back of the envelope, but there was that there would definitely be more steps to this. So this isn't oversimplification of how to handle this kind of situation. You know, it's saying something like, Devin, I hear what you're saying at this time and probably never that that data is proprietary to the large language models. That's what they make their money on. That's their secret sauce.

So they're never going to give us that information. And so Devin's gonna say, but this tool over here that I saw that's about $20,000 a month said that I could get it. Yes, I hear you, but that's a waste of $20,000. And here's why, you know, and so there's like, you know, probably academic papers and research that Devin doesn't care about. And so it's really just helping Devin understand that yes, you can get an answer, but no, it's not an answer that you can stake your reputation on.

And so it, Devin, if you are comfortable giving that information to the board and standing behind it, and they're able to disprove that information. Are you okay with that? Because it's not going to make you look good if you're giving the board information that you cannot then back up. Because if you run, I forget, Chris, you said that there was like um two major SEO providers who ran that like side by side test of changing like one word. So you had your control and experimental group saying the amount of times we ran this query and until we got the same results was you know, X number, and it was a really large number.

So what I'm saying is you can tell Devin, we can try to get to a result. It's gonna be a waste of time, it's gonna be a waste of resources. The tools themselves are not reliable to give you that information. But if you really need an answer, we can get one that's not going to be factual. Yes, exactly.

And I would point again towards things like this to say this is what you can get that you can stand behind. And you can see, you know, Devin, there's your URL, there's your competitor's URL, and it could be, you know, whatever page you want it to be. But all those green numbers, Devin, you can you can hand this to the board and say, look, I'm doing a great job. Please don't fire me. And I think that, you know, and I think this makes sense, but I think we would probably need to add a little more context as to what that actually means.

So, you know, when you think about a large firm like BCG and they have a larger budget and they have more resources, we'd probably want to deep dive into what does it mean if they're getting an F in something? What page were we looking at? You know, uh, we're a small firm, we have more control over all of the things. So, what is that like in reality actually mean in plain language? Mm-hmm.

Yep. But uh you also get, you know, there's the the deltas in each area. So Devin can say, we're 85 points ahead of our competitor, like, yay, us. Again, thinking about that that stakeholder, particularly the a stakeholder that is fact resistant, um, this gives them something they can put on a slide. Mm-hmm.

What we can't do, and I think that the, you know, and I keep coming back to this because I always think through, you know, it's a little bit for a little bit of information is kind of dangerous in the wrong hands. And so I feel like this could potentially open that floodgate of well, we're a B and BCG is an F, so why are we not winning more clients than BCG? Why are we not more profitable than a BCG? Well, that's not what this says. That's not what this is telling us.

And I feel like there's a lot of training and coaching and conversation around this kind of analysis before it gets to Devon. Exactly. And that's again, that goes back to this. AI view only covers phase three. Phases one and two are not something that any tool can accurately dig super deep in, right?

Particularly phase one in the in the model itself. We can't know. And if you want to start getting super nerdy about things like, you know, it's not just the model, it's also the harness that's in and how Google's search harness is different than you know, the Gemini harness, et cetera, et cetera. There's no way to know what's going on inside that box. That is a complete black box.

There's some things that we're testing right now at Trust Insights, but are so far away from even being able to show I can't even show it to Katie yet. It's not like it doesn't even work yet. Um but there's some experimental things where we're trying to see if there's a there. But for right now, what you can do is you can say, here's how much we're creating that is publicly visible, again, with your media management tool, media monitoring tools, your coverage monitoring tools, your social media monitoring tools, that you can do, and that is that is something you can stand behind as factual today. Like we're we're showing up in 12 more podcasts this month than we did last month.

We're showing up in these, you know, 18 more articles. Yes, it was the East P or evening news, but from an LLM perspective, who cares? Um, in phase two, you could say, yes, our technical SEO is better than this company. Our lighthouse numbers are better, our Chrome UX numbers are better, and so on and so forth. And every good SEO marketer already has that data.

And if you don't, that's your homework. And then phase three is AI view. Well, and I think that it reinforces the point that SEO is in fact not dead. Um, that those metrics still matter, if not even more so now. And I'm wondering if, you know, it would be as simple as.

So let's say, you know, Devin's like, I just want to know, you know, how many times we show up. And so, you know, could you have Devin run a simple experiment? Give them a prompt to say, tell me the best, you know, AI and management agencies in Boston, and have him go through and paste that into open AI, paste that into Gemini, paste that into anthropic cloud, and just show you got three different sets of results. How are we supposed to measure that? You could do that.

My caution there would be that knowing Devin as well as we do, um, he would interpret that as fact and then hold you to it. And it's it would actually work at the opposite of the point you're trying to prove, which is look, there's no reliability in these results. Devin, like, oh look, I show up number one in this, I better show up number one all the time now. And the next month he's like, Why don't I show up number one this week? You know, when I paste the same query, and you're like, oh, for God's sakes, that's not what that's the opposite of what we're trying to say.

I got you. Yeah, no, and that, and again, that goes back to really understanding your audience, and perhaps, you know, doing something silly, like uh, I don't know, going through the 5P framework to figure out why you care about this in the first place. So, purpose, what is the question you're trying to answer? People who's involved, your stakeholders, your analysts, you know, your engineers, process. How are we gathering this data in a consistent, reliable way?

Platforms, what tools are you using, Chris? You talked about standard SEO tools, hrefs, um, you know, all of those good things. Uh platform, not only uh the tools, but also how you delivering the information. You know, is it an you know, quick HTML webpage? Is it a report?

Like all that good stuff, and then performance. How do you know? Not the performance of you against your competitors, but how do you know if you answered the question being asked? So I want to know how I stand against my competitors. Is the question that you want to be able to say, yes, we answered it, not what is the actual metric.

Yep. Exactly. That's that's how that's how to answer the question in a way that won't get you fired, but also will satisfy Devin to to as good a degree as possible. And you know, one of the things, Katie, you talk about often is trying to see it from the that stakeholders' point of view. Like, yeah, Devin's gonna be sitting in front of the board, and the board's gonna say, give me a number that you know shows shows some progress to justify your salary.

Uh you know, whether or not we like Devin, that's a hard, that's a hard ask to to make. So we can have some empathy, like, yeah, that that sucks as an ask, but we also, I mean, it's one of our core values as a company. We we don't want to be lying, we don't want to be showing up with fabricated data or data that makes no sense because that always comes back to bite you. Uh-10 out of 10 times, it always comes back to bite you. And I just want to put a big fat asterisk on what you just said.

We can't guarantee that you won't get fired. We cannot make that guarantee. We can support and make sure that you're giving really good factual data, but if it's not the data they want, we have no control over that. Yeah. If we could do that, we'd be trillionaires.

Yes. So, John, I want to ask you, um, based on all of this information and your dealings with you know the Devons of the world. And if your name is Devin, I apologize. We're not picking on you. We had to pick a name.

Um, and so, John, what would you tell the prospects who are saying, I want to know how I stack up against my competitors? Like, based on all of this, like, how does this change your conversation? Yeah, that this is kind of one of those classic sales things that you have to, you know, talk to the prospect enough and get what the real story is because ultimately you're gonna have to ask the questions about okay, can Devin be convinced or not? And that will define, you know, define the way you're gonna go. And it really is gonna go just one of two directions.

You're either going to have to buy some kind of reporting tool, and you can at least couch it in well, we're using this tool, which is the market best, and then that way, if you know, two years down the line the scandal breaks about that vendor lying on everything. You at least said, Well, that was you know, that's the whole um nobody gets fired using IBM, you know, like you can do that, or it's the other angle of, you know, yes, Devin and or his team can be trained, and let's do a whole walkthrough, explain how this works and get them to understand the pieces to the puzzle and how they fit together and what you can do and what you can measure. And yeah, it's it's basically it's unfortunately it's like a devil's bargain, right? You can either commit the crime and your life might be a whole lot easier, but you, you know, in the long run, everyone gets fired. Or you can, you know, do it the right way and put in the extra work.

And I don't know, the one benefit of that that I try to play up a lot is just that you can build a quality team. You know, you can have a bunch of people that are all doing it the right way. And then you can stick with those people going forward. Even when you move beyond the org you're at, you've got a network of people who actually understand what the truth is and aren't gonna you know bend and break and collapse to hit the quarterly numbers. Unless of course your quarterly number you know if you're gonna get a 20 million dollar bonus this quarter then just go ahead and lie your ass off.

And if you want to know the basics of GEO check out our course at TrustInsights.ai slash GEO 101. If you want to take a look at the tool that we were showing today, we do have a free version you can do one web page at a time. Allegedly we're going to be able to do a comparison this is news to me. Go to trustinsites.ai slash AI view and if you have more questions about this that we have not been able to get to you can join our free Slack community at TrustInsights.ai slash analytics for marketers. Let me check I think we have oh goodness uh we're closing in on almost 5,000 members and it's a very active community and it's a great place to ask all of your GEO questions because everyone's in the same place.

Everyone's trying to answer those questions. So it's a great support system for those of you who are trying to figure it out for yourselves. All right. That is going to do it for this week's show. Thanks for tuning in, folks, and we will see you all on the next one.

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 trustinsights.ai slash TI podcast and a weekly email newsletter at TrustInsights.ai slash newsletter. Got questions about what you saw in today's episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai slash analytics for marketers.

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