How LLMs Are Rewriting the B2B Discovery Journey

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Here's today's host of the Voices of Search Podcast, Jordan Cooney.

Nearly 9 in 10 B2B buyers have adopted generative AI, and many now use it across the buying process.

That's not experimental, that's behavior, and it's changing the discovery journey. We're moving

from search and discover to ask an answer, and when buyers ask the model, the model becomes the

front door. This creates a huge truth for SEO teams. Your first audience isn't always a human anymore.

It's a bot, so the question is simple. Is your website readable by the machines doing the research?

I'm Jordan Cooney, and joining me today is Jeff Reign, co-founder at Everything Machines.

Jeff spent two decades in enterprise marketing and platform strategy, and now he's building

everything cash, a brand-side cash that makes websites readable for LLM crawlers without rebuilding

the human site. Today, Jeff breaks down what's changing in B2B discovery, why measurement alone isn't

enough, and what AI first infrastructure looks like when your buyers stop clicking. Jeff,

welcome to the Voices Search Podcast. Thank you so much for having me. It's a pleasure to be here.

Yeah, I'm super pumped about this. This is obviously very new territory. Although many of these

concepts are things that the SEO community, the industry, has seen for many years, concepts like

site maps and caching and accessibility aren't necessarily new concepts, but the way that they're

changing is very different now, and a lot of new expectations are out there for not just SEOs,

but marketers to ensure that they're being found and seen in these LLM models.

In our intro here, we brought up this concept of search and discover to ask and answer.

What's this mean? How does this change impact buyers and users?

Yeah, look, this is the fundamental change, and so it's really important to spend a second

just really grappling with what it is and what it means. In some of the obvious and also some

of the non-obvious ways, I think we all know how we search the web yesterday. I won't say today,

because today is already different, but if we go back in time, if we had our Michael J. Fox,

Delorean, go back in time, how would we search? Google, there's no two ways about it. God bless

Microsoft and God bless others who are trying to capture the old traditional search. We would Google

something. What does that mean? We all know we get those blue links and we go spelunking around

the internet ourselves. It's a DIY project. You give it a couple of words and you go spelunking.

Now, unless it's in an industry that Google has decided that they want to control the first

third of the screen, but we'll let Jeremy Staupleman and others worry about that.

Fight that fight. Yep. Yep.

Let them worry about that. But you did it yourself. You spelunked around the internet and you looked

at the pages that they delivered and otherwise potentially infinite list. And you

intrepid researcher, we're in charge of figuring it out. So that's that's traditional search.

You search and then you discovered the answer. But now it's it's asking answer. Now you prompt

an A in an AI search or an LLM, whatever the parlance you want to use. And you get an answer.

It's not the answer, but it's an answer. And it looks pretty discrete. It ends. It doesn't have

an arrow that says there's more of these links over here. There's not an alternative unless you're

in some test cohort from from open AI or somebody. So you get an answer. And we're humans. We're

trained. We've been trained for tens of years now at this point that when someone texts you,

you respond. You're now in a conversation. So so one thing that's changed is instead of giving

you raw materials to search for from Google, you are getting an answer. And that answer is presented

to you like a chat and open AI stumbled on this with chat GPT. But it's really important. It's

presented to you like a chat. So it feels like not only an answer, but in some cases the answer.

Yeah. So that's too big. Yeah. So there's a couple more, but we can pause and talk about this one

or we can connect. Yeah, let's dive into this answer concept for just a second because I think

the answers is where most of us are spending a lot of our time. And I have to ask you because I

think this is all going to come back when we talk about accessibility and ensuring that these

LLMs can can see what your your websites about. We got to talk about hallucinations, right? We got

to talk about when these answers aren't always exactly as we thought that they would come out,

right? It is a machine at the end of the day, which is where like I think a lot of the traditional

SEOs might go, Hey, the rankings are great because you just allow a human, although they are

spelunking as you said. And I love that the internet. You still get to choose your adventure,

right? Yeah. And so, but here you don't necessarily get to choose your adventure because to your

point, you're getting an answer. So let's talk briefly about hallucinations and what the implications

of that is in an ask and answer world and how that fundamentally, I think, is maybe changing the

way brands are thinking about setting up their content and websites. Yeah. Look, and I we can talk

about hallucination or we can talk about it. And that's that's that means something to people.

It means I'm seeing something that's not real. That's not really there. And so there's a question

is it not really there? Is it wrong? Or is it not what the brand or the business wants to be

portraying? These are all different shades of what is essentially the same problem, which is

that's that's not what I really want to say. Yeah. Because think about it, the the answer engines,

Chachypt, Claude, et cetera, are now meta influencers, mega influencers. We tell them things

and then we don't decide. We don't control what they then say to their followers, their

prompters. We have to rely on them to interpret it. And so sometimes you would call it a hallucination,

but maybe it's just it's just a little bit wrong. It's just a little bit off. What it means,

though, is as a brand, what you have to control, which you can control, you have to put forward

content on your control properties that is as specific as possible and as transparent as possible

and as positive a light as you're able to muster so that the the AI search bots can retrieve that

information and present it back. Otherwise, you are really relying on the information coming from

elsewhere. And you're not in control. And so, you know, one person's hallucination might be

another person's disinformation. Right. So as a brand, you have to try to control the narrative

as best you're able. Yeah. I think I think that's one of the the unique differentiators here

between search and, you know, the the new AI discovery is this realization that there's there's

a lot more control than we actually believe there is as marketers around the narrative.

All right. And we dictate that whether it's on our websites, whether it's with our partnerships,

whether it's in these other ecosystems, but there's a there's a tremendous amount of control that

we do have around the narrative and what we expect these models to respond with.

On this ask and answer like like journey that we're on, right? Like does this differ, though? Like

does the does the expectation of a user differ based on who the user is? Is a buyer

have different expectations than someone who's just perusing the internet? And, you know, like what

what what are your thoughts on on the the differentiation that is is out there for the journeys

that different users have? Yeah. This is actually a really really good topic, the journey that they're

on and how the journey is starting now versus how it used to start, right? And whether you said it

before, it's a little choose your own adventure. We used to be on a choose your own adventure,

you'd scroll Google and you'd find the right spot to go to. Right. Of course, you know, that

information was presented to us in a specific way, but we'll leave that aside for now.

Now you start your journey in this conversation and the party that you're having the conversation

with, whether it's Claude or perplexity has some memory, has some knowledge of you.

And we actually think at everything machines that this is the beginning of true one-to-one marketing.

And that the journeys now, the journeys that will be taking place within the confines of a

chat window are in effect the completion of the one-to-one marketing journey we've been on for

25 years. We've been getting closer, but we've never really gotten there and we're starting to

rub up against privacy. But now we get to imbue our personality and our knowledge and the things

that we care about into our little chat assistant. And we let it go search and bring back just what's

right for me. So if I'm a buyer in a corporate environment, you know, and I'm looking for HR software

for my specific niche, from the company that I work for in the location I work for with the type

of employees I have, my assistant can know that. It's going to know that, you know, from all the things

I've done with it before. And when it's going to look out and fetch the information about this new

HR system that I want, it can do so in a very, very personalized way. That's extremely different

than any level of personalization that we could get to on the open web as a casual browser.

And again, this can be true in a personal way. If you're looking for a mountain bike,

perhaps you and your chat bot have already had conversations about what it means for you to have

a bicycle and what the kind of trails you like. So we think this journey is going to get ever more

personal. And that ironically, it took a third party. It takes a third party to really do the

personalization instead of just buyer and seller. It's now there's this intermediary who could help

truly personalize it. That's right. And that that personalization aspect I think is where this gets

really tricky. And I think that's where a lot of SEOs spend most of their time trying to kind of

decode what's happening, right? Because the intimacy of a prompt is so much different from the

Vanilla aspect of a keyword, right? Yeah. Look, I mean, as much as you can get out of one,

would used to be one keyword, then we inched up to whatever 1.1 average keywords. I don't know what

it is now. Yeah, the slow growth of keyword. But we were measuring it in intensive a keyword,

right? And I don't know about you, but I've never really typed in a tenth of a keyword.

Which means the vast majority of people were typing one word, you know, brand searches. I used

to work at Coca-Cola. Like, come on, it was usually Coke. Yeah, exactly. But at the end of the day,

we're moving now from that being the prompt to this one word and Google having some knowledge of who

we are to sometimes a paragraph to paragraphs to if then statements being given to these bots

and them having memory and knowledge of us. And because of that, we just are able to dive right

in to the buyer's journey at a level that which we dreamed of, we've dreamed of for 20 years.

And the best practitioners of a company's marketing are approaching it, but this is a much wider,

much broader move. And we think it changes the way brands and businesses need to think about what

they're publishing and how they're publishing it and how deep they're publishing so that the

answers can be as as customers as the user really wants it to be. Absolutely. One of the things that

this kind of brings us to is the reality that in order for us to truly understand where things are

going in this ask and answer world, we likely need to change the way we measure what's happening.

And there's a lot of news around this, just even yesterday being just announced, Microsoft

announced that they're going to have an AI performance section. We're recording here just on the day

after this announcement, obviously this will be shared publicly and I'm sure people are going to

start digging in and researching and unpacking all of the data that's behind that. But the concept

of measurement is under extreme change in this moment. And most folks are rushing to this concept

of AI visibility. But candidly, as we talk about how everything machines steps into this equation,

it's very different. You guys are looking at the space very different. You're looking at this from

an infrastructure and accessibility perspective versus a visibility perspective. Can you tell us

more about that and why you're looking at that direction? Yeah, it's a great question. And

we think that there's really a few parts of this stack and this is going to evolve. Heck,

this is so fast moving and evolved before the end of this podcast, right? Yesterday,

Microsoft makes an announcement right now that something is happening. But we think there's

several layers that you need in your answer engine optimization, AEO or otherwise called GEO

toolbox. You need to monitor and you need to be constantly and consistently monitoring.

There are many ways to do that. We work with a company called Gumshoe. We work with another one

called Otterly, but there's profound and there's scrunch and there's there's dozens really

and different methodologies because you need to know how you exist right now in the eye and the

mind of these LLMs. Are you showing up and you need to do this regularly because this is a bit

of a quantum product, quantum problem instead of a physical problem. There's probabilities

involved here. The probability of you showing up even under the same prompt are not always the same.

So you need to keep measuring. But that's not enough. You need to understand how your content

is being published to the world. How is it look to an LLM search bot? And that's where we come in.

We decided to focus here to have as much impact as we could have as broadly as possible

in the industry. And so no matter how you want to measure whichever tool you want to measure

your visibility for whatever fits your business, you will always need a better way to communicate

your content to the bots. And that's where we've built the everything cache.

This is a product that is meant to really be a thin knowledge layer in between what you would

normally and what you've been telling humans for these many years publishing a website and what

you need to now tell these bots. And then we think and just to complete the stack at the very

least there is a third layer, which is content enrichment, which is publishing more content,

richer, deeper content, more specific content, than perhaps you would ever imagine doing

on a website aimed at humans who would never get into the kind of specific content that you will

happily publish for a bot, which has infinite appetite for your content. So these three layers

are distinct. Some people, some providers are going to combine them. Some will pull

aspects of each. We've decided to focus our capability and our expertise on this publishing

aspect of it building the cache for our brand partners. One of the things that I find remarkable

about this and it's one of the complicated changes that's taking place in the entire LLM space.

And that is that historically, Google and search engines basically created a standardization

around the process for this. You'll build a site map, you should have follow some of these general

rules. And you'll have the ability to allow a search engine to access your content. And SEOs

and marketers were forever trying to measure this bench mark of like this progression of,

oh, did we get crawled? Did those pages get indexed? Are we getting any rankings? Like you kind of

followed the sequence of work. But what I find really remarkable about the way you're thinking

about this transition is that it's not just about the sequence of work. Did this model get access to

what we have? But you're also thinking about the organization of it. So going back to this whole

measurement concept, you're thinking about like what is what are the assets that actually matter

within this ecosystem of a website? And how do I point this machine to the right places on the site?

Will this become standardized? Will this become like organized to a to a to a fault maybe? I'm

curious to get your future thinking on that because I fundamentally think that right now we're

just scratching the surface on this. But like I also want to stress tests like how far does it go?

Yeah, it's a great question. And I go back to first principles when we don't know something,

we can't know the future. We can try, we can predict, you know, you can bet on anything,

it gosh, but unfortunately these days you can, yeah. I know, and it's really frightening to

me. It's a great, perfectly belonged to me. But we go to first principles. Why did SEO get

organized in the way it did? I believe one of the reasons it's Google won. Google won. Google won

90% of all search volume. And so their rules, the way to win there was the definition of winning.

Now it didn't start. I used altivista. I used to excite back in the day. And there's any young

people listening to this go check out dumb pile. You know, these were, there was a competition.

But Google won and they did it. The good old fashioned where they built a better mouse trap.

I don't think we at everything machines don't think this is going to be a one horse race.

We think this is going to be a multi party party. We think there are lots of people who are coming

to this party by definition. It's a little bit too big to sit out. And we'll see how it happens,

right? You know, has Apple decided they're going to sit it out and just take the money from Google

and run like they did with search on the browser. Maybe, maybe not. They have an entire app store

that they're going to have to figure out how to protect. So how are those things going to interact?

Meta, they're not going to sit this one out. They're building glasses and they want you to ask

Meta with a swipe on your temple, right? Amazon. Amazon's not going to sit this out. Now,

what's going to happen to have Thropic and perplexity and open AI? Well, when the bubble pops,

the dominoes start falling. We'll find out what happens with the pieces. But we think this is

at least a three party system. Yeah. This is at least going to look like a European Parliament,

much more so than it will look like an American Congress. Right. Or I won't, I won't get particular.

What does that look like, Jeff?

Like a single party system. We don't think it's going to be those because it's too important.

It's because these front doors, these new LLM searches seek to be everything machines

hence the name of the company. They want to go all the way down to the purchase. They want to

control you through that or at least guide you through that. And you can't afford to lose that

battle for the front door. If you're Meta, if you're Amazon, if you're Google, you just can't

afford to lose that. So that's an important first point. SEO evolved in one way because Google

won. If we don't think one party wins, if we think this is three, let's call it 33%, you know,

shared companies, who's to say what the standard becomes? Does doing one set of rules optimize you

on one system? Does doing another set of rules optimize you on the others? Right now we're seeing

a little bit of that. We're seeing, I think it's fair to say, Google attempting to use

its current leadership role or it starts with Ma ends with Opily in search to try to set the rules

for how answer engines will be optimized. Oh, no, no, no. Do what you're doing. Keep doing SEO.

It's working perfectly for you and it's working for Gemini.

You know, that's their self-interest. So we'll say it's a wonderful question. I mean, I'd be

interested to hear your thoughts. You've been doing this longer, frankly, than I have.

Well, I think there's a couple components that you brought up there. Notably, the one that I

think we're going to go into in our next topic here is the fact that there's an expectation of

a conversion event. And so if the goal in this new ecosystem is that there's an event that

takes place and maybe conversion was an overly specific aspect of that. But if you want a user to

do something and you need them to engage and do something, then the byproduct by which you engage

them with this, this ask an answer ecosystem is solving for a very different approach than the

subalunking that once was just search results. Right. And so, so I think, I think you brought us

right to the place that many marketers, many SEOs are thinking, which is, hey, the ask an answer

world, an LLM response gets us to a conclusion. If I'm having a conversation with an LLM model,

I am by by nature refining my way to a specific outcome. And that specific outcome is

satisfying me as a person. It is giving me what I want. It is found the running shoes that I need.

It has helped me solve my health care problem. It is defining my specific personal finance issue.

It is doing the thing that I need done. And that is a very clear outcome based experience.

And in that, we're measuring a very different aspect. It's probably not rankings. It's definitely not

visibility. And it's really, it's really some form of user satisfaction. But what I found really

interesting from the everything machines perspective is that you all are looking at the layers

that are happening before the user engages. That there is so much happening behind this machine

to get us to that satisfied answer. That the LLM is not only processing our content, but it is

looking at third party sources like Reddit and other places. It needs to categorize and organize

our data and information about our brand. And it's doing so much energy and work to get to the

satisfaction of that specific user. And you're probably one of the first companies that's really

trying to unpack that. So tell me more about this cache. Tell me why the cache is so valuable

in how it gets to that conclusion event of a satisfied user. Yeah.

So if we talk, if we think about a previous statement, it was about one-to-one marketing.

We are getting ever closer to one-to-one marketing and a cache is a publishing

canvas for getting towards one-to-one marketing. If we are a sophisticated business these days,

we might be looking at our 10% or our 5% and that's how we're doing personalization. But when

you realize that those 5 or 10% are asking these 5 to 10 key topics or the asking about these

questions they tend to do. If we look at that grid of those 10 topics and those 10 personas and

that grid of 100 representing some large percentage of prompts and conversations that we show up in,

we then realize we could expand that from 10 to 100 from 100 to 1000. We can subdivide each of those

boxes on the checkerboard. And each one of them has its own nuance. So each combination of a given

persona and a given topic has its own nuance and it's that language, it's that nuance in that

language that is going to allow a brand to provide better raw materials to the AI search bot

to improve their likelihood of showing up. If I know that a given demographic in a given area with

a given use case is talking about their problem with certain language, I as a brand can and should

attempt to mirror that. Talking about my opportunity, my products, my services and how it satisfies

for that use case in that very specific way. Previously, we would never imagine doing this.

We would be thrilled to present this information to an influencer or to an agency and have them

concoct some creative around that and present it once in the ephemeral stream of content. But we

would never think of publishing it in an evergreen way. That's the idea of a fact frequently

asked questions. We are really moving away from that idea of a fact to specifically prompted

content, you know, the SPAC, I guess, but that would that mean something else. Yes, it's specifically

asked questions. And that's a bad, you know, that's a bad one sack, you know, sacks of content.

But that's what it becomes. If you can constantly and iteratively put your brand and products

into the perfect context for prompts that are on their way, you are giving the raw material to

the LLM to do its job better. And it's going to reward you for that by coming back and visiting more.

And if it rewards you and is coming to you as the authority for this information, it's putting

less weight on your competitor site. It's putting less weight, perhaps on those blogs.

Maybe it's putting less weight even on going to Reddit because it's getting high authority,

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That's interesting. I mean, one of the things that I have found fascinating looking at the data,

of LLM traffic, or even notably, like we were just discussing earlier, Bing's AI performance

dashboard that they released yesterday, regardless of either of those two data sources,

which arguably are likely the most factual sets of data we can look at about what's happening

inside of an LLM, because none of these companies have exposed a reporting engine to us.

They haven't given us a Google Analytics. They haven't given us a Google search console.

I mean, Bing is not arguably giving us a taste of this. And the thing that fascinates me the most

is that when you look at the pages for large established brands that receive the most traffic,

or now in some cases are most cited, like in the Bing AI Performance Report,

the pages that often show up are the ones that I would least expect. They are support pages

that explain a very specific use case for a user. They're learning center sections that help

a user walk through an entire process. They're like some of the most functional pages that I'm sure

received visits in the past, but by no stretch of the imagination that I anticipate these being

the top 10 on a report. That's funny. It's crazy. Yeah, when you said you weren't expect to get,

those are exactly the pages that I expect to see. Those are precisely the pages I expect to see.

And those are precisely the pages that need to have a Cambrian explosion of creation that we imagine

machines will create a lot of it. They'll be human in the loop. They'll be different flavors

for different, but even further beyond that, publishing chat logs with anonymized chat logs with

actual customers to really get into the heart of it and publishing motivations for the production

of that product. Why did you produce this product in the first place? What niche were you hoping to

satisfy in the market? Tell the LLMs everything. Again, looking the name of the, this is why we call

the company everything machine. These bots seek to become everything machines for their

prompters and you need to feed it everything. There is going to be some declining marginal return

for that next marginal page or that next marginal answer we think is even more important. Forget

pages. What we're trying to do is produce questions and answers in conversation that are going to

help the LLMs learn. And it's exactly the support page. It's exactly the very, very specific case

study you published with that customer in that particular niche that is going to get cited.

That's what we want and how do we do that infinitely and not overwhelm human visitors?

Our answer is that you don't. You create a parallel structure. The internet is splitting

into and for a good long time lots and lots of humans are going to come visit web pages.

But more and more traffic is going to be consumed by search bots by these AI driven search bots.

The cache is a solution for this challenge. Let's dive into that. Let's talk more about the cache.

Let's talk more about, I really want you to give our listeners just a brief overview of how

everything cache works. Why you've built this first version or aspect? And then I'm going to ask you

if you follow up questions about the whole discussion here that we've been having, which is why

LLMs are accessing it and all that. But just give us the lay of the land of what this thing is.

Yeah, look, it is a true and faithful mirror at the beginning of your existing content.

So you have a website, you have pages, you need to, we would argue you need to replicate that

and mirror it so that the bots can easily access it because they're not going to fight through

your JavaScript because they can't. Yeah. So all of these beautiful dynamic things that you

designed on your website for humans, a puppy with big eyes looking at you, beckoning you to buy

insurance. It doesn't matter to Claude, it just, it doesn't matter. The lovely colors and the logo

and how it melds down, you know, beautifully into the navigation window, it doesn't matter.

What matters is clean, fast, mark down, wrapped in JSON, LD, that is lightning fast, that is

pre-rendered, just get it in, get it out. And so it's just completely different experience.

And so, you know, the world right now is struggling. It's like, do we, do we compromise, do we build

a camel, if you will, and optimize for everything, but everyone who's ever tried to, you know,

satisfy multiple stakeholders knows that you fail in, in some regard. You can't have it fast,

good and cheap. You, you, you can optimize for people and for Google search index to send more

people to it, or you can optimize for these AI search bots and, and these prompting environments.

So we say split into, and we're SEO neutral. So we, we, we start, we scrape your site,

we produce this cache, and we do it in, it's designed to be SEO neutral. That is a very, very

important first step. It does not compete with the original content. There's no index protection,

all pages include no index, no follow meta tags for Google Bing and everybody else, canonical links,

every page that we create points to the original source URL as canonical, there's clear

attribution, and there's cache identification. Pages are clearly marked as cached archive content.

So that's the first important part is what it's not. It's not competition for your search

optimized pages and your human pages. So we're not going to get cited, we don't get cited.

We are there to communicate to wave our hands in the air, almost wave them like we just don't care,

and bring, bring those search bots to us. So then what do we do? We, we are there to reduce noise,

we're there to enhance the semantic structure, put it in structure data, and then as I said before,

to be SEO neutral. I mean, it starts and a lot of it is done by just stripping out JavaScript,

CSS and tracking, tracking scripts. You know, that first and foremost, it does, does a lot of the

work. Get rid of the ads. The, the LLM doesn't care that there's an avenue page. Right. Right.

Getting the navigation and the boilerplate content separated, getting forms and the interact

developments to be documented, but not preserved, and then semantic enhancement. Those are,

these are, these are the biggest aspects of it, really using HTML5 semantic structure,

having really clear heading hierarchy and validity is validated and correct, and then there's

a checklist. Right. But these are, these are some of the key things, having the structure data

semantically enhanced with as little noise as possible to serve it up.

So one of my first key questions on this is, if you think about this, this cache, this ecosystem

that we're serving up for, for these models, is the intention here to be

to have the, the model leverage this for training or inference or both, right. So the training

aspect is, hey, this is deeply ingrained into the model. And now they kind of use this as a source

over and over again to, to, to define or respond and answers. Infraints is like, hey, we're

trying to figure this out and understand this, right. Concepts like grounding are part of the

inference process that an LLM uses. So I'm trying to understand like, where, where does this cache

sit and is it, is it one, the other, both? We believe it's both in all, right, for an inference

for web search, prexcating that subtext, some task of a web search, and then over time for training.

We can't claim that right now. You know, we don't have nearly the volume and we haven't

existed through the training cycles. Sure. Yeah. It is reasonable to believe that if we are able

to put language in front of LLM bots in their training processes that perhaps there, there

are concepts that can be trained. And I've had these conversations with brands before who,

I say, you're their experts in their, in their field. You're a hospitality brand. You're an expert

in hoteling. Why aren't you teaching the LLM's what it means to be a family-friendly hotel in

Paris for a family of three from, you know, Atlanta? So it's interesting. Is there training

value? We believe there could be. We don't know yet. At the moment, inference and really doing

these web searches is where we're going to see the most benefit and where we're seeing benefit

that we can measure. So we are live with clients. We are watching their visibility.

We are watching the number of times they are cited. Their content is cited because it's citations

that are indicating, hey, you know, we care about this content and then how often you're mentioned

by name in these responses. And we're seeing good results. How will measure how it's impacting

training? We haven't given a thought to that yet. Fair enough. Fair enough. I am curious on this

concept of organization because when I've seen these caches and we talked about this UNI,

I found it really remarkable that you guys are organizing this content for, or this, the structure

of this cache, excuse me, for the brands that you're working with. Why is that? And how have you

been thinking about the future of that organization of the cache? Why is that? I mean, our,

our goal is to help that business and its products, its brands be represented as they would like

as they hope to have them represented inside these responses. That's the goal.

You know, if we worked for an LLM, our goal might be different if one of them wants to

call us and reorient our work or our phone's will ring. But our goal and we believe what is

extremely valuable for marketers is to ensure that they can communicate to this new audience.

So that's our intent is to organize it by brand by the economic actors who are interested first

and foremost in its showing up correctly as they believe it should. Is that different from the truth

or from reality? We'll leave that to the model owners and the search prompt owners themselves.

Fair enough, fair enough. You brought up a concept earlier and I want to dive into this last

main topic that I think is super relevant to our audience and that's this notion of one-to-one

marketing that we're now in this era of one-to-one marketing and that what we build and the experiences

we have on our websites are genuinely there to serve that one human being behind them.

You know, this goes in many ways in contradiction to much of the

foundational practices that many search marketers and performance marketers have been taught,

right? We've been taught to go after search volume. We've been taught to go after massive trends.

We've been taught to seek out, you know, audiences in our content or personas in our content.

Persona's audiences, massive search volume, none of those is one-to-one marketing.

So tell us why the importance of one-to-one marketing is the future of how to best serve these models.

Gosh, I might argue with the premise. I think it's rooted in persona.

It's just it's a huge unlock and so our frame of reference just has to change and we always,

you know, and this is different marketers with different schools of thought and different practices.

We're going to differ on the tactics but you know, we are, we are all, we all are trying to get

as close to one-to-one as possible. We just know the limitations of the system that we're operating

in and so we internalize those limitations and we say, well, we can't run a million campaigns

for this candy bar for this spring but we can run a hundred. That's doable.

We can manage that and then meta changes something, Google changes something and I was like, oh,

maybe we can try 500 campaigns or maybe we can do dynamic

content optimization. Okay, let's try that. All of a sudden we expand our view and what we thought

was a constraint is no longer a constraint. And I think that's what we're seeing here.

The thing that was previously a constraint in order to drive volume,

I can come up with certain personas and I can deliver content against those personas,

but if I try to parse it any further, I won't be able to deliver the volume at the top.

But that constraint is now different because the context for those individual queries prompts

is now baked in. It's baked into the prompt. It's always there or it's increasingly frequently there.

Now, there's still an argument on the other side of what is the marginal rate of value?

Well, we'll figure it out, but it's a lot. It's going to be a lot. And it's also just different

structures. But, you know, I've been in all sorts of marketing roles and we almost start

think about this as how would you build infinite landing pages? If there's that perfect,

perfect landing page for every user out there to land on, how would you go about building that?

And that's sort of how we think about everything, Cashy. It's the tool to build these infinite

or nearly infinite landing pages, which again, in some gets you back to the audience.

Now, I will say we're perfectly rational humans. We know not everyone is moving to AI,

you know, prompted search immediately tomorrow. Right. But it's happening. It's taking on more volume

and it has to be looked at very, very carefully in the overall mix. It's not the same as running a

television campaign. It's not the same as running a display campaign. It's not the same as doing

your search campaign. It's its own channel. And it's going to have slightly different practices.

I love that. I think it's remarkable to have that perspective. I think a lot of people

are still looking at this channel as a byproduct of traditional search. And I think there are

some similarities. But to your point, there are some very unique differences here that we all

have to grapple with to ensure that our campaigns are working effectively.

Yeah, here, look, Jordan, I'm not a search guy. I haven't been doing SEO and SEM

for the past umpteen years. I've done all sorts of things and I've interacted very closely

with the search world. But I have a little bit of a beginner's mindset here. And my

colleagues are a little bit more steeped in this industry than I am. But my beginner's mindset

goes back to first principles. And that's how we're trying to orient everything machines and

the everything cash is if we go back to first principles and we think this is going to be a

multi-party system. If we think personalization is sort of baked into the idea of having

assisted, if we think that the learning mechanism is content and asking questions and giving answers,

then how do we reorient our publishing mechanism from here? If we know they can't read JS,

if we know they're hungry for more content, okay, well, what do we build? So it's a little bit of

um the beginner's mindset because frankly, I'm a little bit of a beginner. And it's been

really fun. It's been really exciting these these last six months as we've been on this journey.

And you know, this is going to be another decade that I think we're in. So there's no rest for

the wicked, they say. That's right. That's right. And I love the beginner's mindset. I think many of

us that have been in this space for a long time need to re-evaluate. And that re-evaluation can

can pay off in a big way in terms of how we we implement, execute and leverage the technology we

have around around this no longer monopolistic environment that we've been in for the better part

of 20 years, right? It's a multi-pronged approach now when when you look at these LLM models.

Yeah. And when we think just this is and some of this gets a little philosophical, but when you

think about the context of receiving this answer, it comes to you as a chat. We have been trained,

I used to work at Skype many many moons ago. And but for years now, we have gotten used to what the

text messaging format looks like and how we behave in a text messaging format. When you prompt

your expecting an answer, you receive that answer, you consume it, not looking for anything else.

You're no longer your mind. And I'm sure someone smarter than me knows this. Your mind is in a

receptive mode. I'm reading what I was what was just written to me. And I just take it as it is.

And so there might be more and there might be a follow-up or clarification, etc. But I'm in this

linear mode. That is fundamentally different mind space than querying Google with a keyword.

And then you being the active agent, the entity that is now spolumpking.

Okay, Google has returned me a list of stuff. Let me go figure it out versus, oh, this chat

creature just delivered me an answer. Let me accept it, integrate it, and then figure out what to do

next. I don't know if this is, you know, we'll have to talk to some professionals here. But it's

a fundamentally different way of finding new information. And I think we're just businesses,

especially B2B right now, but eventually and ever more increasingly B2C, we're just going to have

to do things differently. No question. So this is a great moment for us to move into our

lightning round. I'm going to ask you five questions from our discussion today. And you give me

like a 30 second or so response. I want to just get kind of that, that, that, that real clean,

direct, and, uh, Jordan, you've been, you've been on this long enough with me to know I don't give

30 seconds or so. I know. I will try. I will try. All right. Love it. Love it. Okay. Let's dive in, Jeff.

What's one thing B2B marketers still measure that matters less every month?

Oh, they're search traffic. We're for us. Yeah. Yeah. Where should they be going then?

Where, I mean, it's such a, it's that, that has been such a well of, of, of knowledge for,

for markers for a long time. Where do they go now? We wait until someone builds the tools to

actually figure out how many people started their journey somewhere else. Yeah. This is the hardest

thing. There's not a clean replacement. I mean, look, being, being just threw something out into

the world yesterday, talked about it. More tools are coming. We, we are looking very carefully at how

we measure the traffic from bots and give that back to our brands, our clients, so that they

understand how frequently they're being interacted with from another, from this other entity.

If that's where the volume is going, if it's leaving google.com and it's moving to these four places,

how do we get that representation? Because people aren't just clicking through on citations

and mentions. It's just not happening. Yeah, not happening. It's like one to a thousand.

But we don't, we don't, we don't have an answer yet, but it's a really, it's a very good

love it, love it for a whole other episode. All right, next question. What's the biggest mistake

brands make when they treat LLM optimization like SEO? Well, I think the biggest mistake is they

assume the bots all behave the same way and that they assume they behave like Googlebot and they don't.

Yep, yeah, 100%. I think that that is a very clear shift that the market needs to make, so

love that. And they have economic incentives not to work in the same way. Of course, it's not,

like it's not a coincidence. There's, there's reason and there's market rationale for them to work

differently. Now that might converge, but that might not, I get you further. All right, if you could

change one thing on every enterprise website tomorrow, what would it be? You mean besides the page

Jeff button. We think every enterprise website should have a cache. And now that's maybe a

little bit of a self serving answer, but we think it's true. They are not, they are currently not

communicating as well as they can to an extremely important other constituency. That is the search

bots. And if anyone's a nerd, you know, out there in your listener audience who's watched Star Trek

before you're you're communicating to all the humans. So Captain Kirk's here in your message,

but you're not communicating to the Vulcans. So Spock's getting half of it because he's half an

half, but the Vulcans aren't getting any, they're not getting the message clearly. Why aren't you

talking to the Vulcans? Yep, love it, love it. All right, what's the clearest sign a company's

content is invisible to an LLM bot? Oh, readability. We actually have a readability score

that we run. We we have an audit, which because we're really original. We call the everything audit.

That that will go through the site and will break it down in a deterministic way, looking at your

universal signals, Raj TML signals, discovery, and rendered signals. This is becoming more of a

science. And so the higher your readability score, the more visible it is to the bots themselves.

And that's the start. And it's it's grown. We're learning every day. We're on our third version

of it and we're getting better every day. I love it. You guys are always on brand with everything.

So maybe two on. All right, last last lightning round question here.

In two years, what will B2B teams wish they would have never ignored in the current LLM shift?

Since I don't think they will ignore readability and building caches because I think they will

good. We'll see those things as required and necessary. But I think they will wish they had

ignored is the increasing truthfulness and transparency that will be rewarded by LLM's

on forever now forward. That it is now ever more difficult and will not be rewarded attempting to

persuade but rather to present the truthful and right information. And you know persuasion is

one thing and that's a neutral term, but you can't trick anybody. We're moving away from

being able to pull a fast one on the buyer because they will have an extremely rational agent

as their partner in crime in figuring out what's right for them. And if there's lots of

reasons why there might be kind of non-rational or irrational use cases and I just like that brand

better and that color looks better on me and I just love Nike and that's just what I love.

But it won't be because of fooling anyone. All right, and I don't know that that's particularly

obvious right now. We try to repeat it and to make it clear because we think that's the future.

The future is in this perfect one-to-one match which is this agent, this intelligent agent finding

the right things for my, my human, my person. And it's not going to be fooled.

Love it and that's a great place for us to wrap up this episode of The Voices Search Podcast.

Thanks to Jeff Reign, co-founder at Everything Machines for joining us. If you'd like to contact

Jeff, you can find a link to his LinkedIn profile on our show notes or on VoicesOfSearch.com.

Or you can visit his company website, Everything Machines.com. If you haven't subscribed yet

and we're like a daily stream of SEO and content marketing knowledge in your podcast feed,

hit the subscribe button in your podcast app or on YouTube and we'll be in your feed every week.

Okay, that's all for today, but until next time, remember, the answers are always in the data.