In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris explore how generative AI for SEO and PPC is transforming search marketing forever. Traditional keyword lists no longer guarantee your content reaches your target audience, but semantic analysis can align your website with the way modern AI models actually process information. The secret? Leveraging industry jargon to boost your content’s relevance in AI search summaries, and applying simple, non-technical methods to optimize your existing pages for this new era of search.
Whether you’re a seasoned marketer or just getting started with generative engine optimization, this episode gives you practical tactics you can implement today.
00:00 – Introduction
01:15 – Why keywords are no longer the anchor of SEO
04:30 – Defining Generative Engine Optimization (GEO)
08:20 – How to perform semantic analysis without coding
13:10 – Using jargon to boost AI relevance
17:45 – The future of the SEO agency
20:00 – Call to action
Watch this episode now to stop wasting time on outdated SEO tactics and start ranking in the age of generative AI.
#SEO #GenerativeAI #MarketingStrategy #TrustInsights #DigitalMarketing
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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 S. Penn: In this week’s In-Ear Insights. Hello from Charlotte, North Carolina and the Grand Bohemian Hotel. Katie was asking what’s up with this background? This is not a virtual background. This is the actual dresser in this room. There’s a whole bunch of unusual art in this room, including a painting of an eyeless woman that’s hanging over the bed, which is a delight to wake up to.
Katie Robbert: I mean, naturescapes. Nothing wrong with naturescapes. Pictures of a forest or the ocean, sailboats, sunrises. Anywho, Chris, what are we talking about today?
Christopher S. Penn: Today we’re going to talk about generative AI for SEO and PPC. We did a workshop recently about this, a full-day workshop for SEO and PPC marketers. As a result of this workshop, one of the things we’ve done is turned it into a book which we’re going to have on the Trust Insights website. You’ll be able to find it in our bookstore.
One of the things I thought was so interesting is a lot of the feedback and questions we got in the workshop was, how much of this stuff is even relevant anymore given GEO and things like that? Of course, we have our GEO 101 course as well. There are so many things under this umbrella. Let’s start off by talking about the biggest single mistake and misconception in this entire thing, and that is keywords.
Keywords as we’ve done for 25 years are really no longer relevant for two reasons. One, all AI and LLMs work on semantics, so the ability for a topic to be the focus of something. Two, especially for Google, and what Google does is googling for you when Gemini is writing its own search queries. Your keyword, even if it’s a short thing like “best consulting firm for AI,” gets rewritten by Google itself. So Katie, what is it about? Is it the comfort of having keyword lists that makes marketers stick with this thing, which is really outmoded?
Katie Robbert: I think so. I think it was drilled into our brains for so long that single keywords, long-tail keywords, and keyword clusters were the heart and soul of any good SEO program that you were running, because a keyword would basically follow you along all three initiatives: on-site, off-site, and technical. On-site is your content, what is the keyword? Off-site, what are the things people are searching for? That’s still your keyword. And technical, what are you anchoring your webpage or website around so that when something like a Google search algorithm comes looking for content to answer a question, it can easily find you by a keyword?
I can understand why people are so committed to keeping keywords as the anchor because it’s what we’ve always known. When you look in tools like Ahrefs and Semrush or any other SEO tool, there’s a big section on keywords. If you look inside your website editor for your blogs or your pages, it’s “what’s your keyword?” It’s literally everywhere. The technology that we use every single day has not caught up to how GEO is changing the rules.
It’s 100% understandable why marketers are struggling with this notion that keywords aren’t as important anymore. We’ve seen comments on our social media pages like “Trust Insights says keywords don’t matter anymore. What do you think?” We’re trying to explain why that’s true, and we don’t control all of these other pieces of software that are really slow to keep up. So, long story short, I totally understand where marketers are coming from because the software that they’re using is still keyword-forward.
Christopher S. Penn: So here’s the question—and I have an answer, but I want to hear your answer—what should we be doing instead?
Katie Robbert: Aside from writing high-quality content that people actually want to read? We should be putting our stuff everywhere. To quote Christopher S. Penn, it’s like Nutella. You want to just put it on everything. Although I think we had this conversation and I think we said we’re gonna change the stand-in to hot honey, which I don’t personally care for. I’d prefer Nutella.
But instead of if we’re saying keywords aren’t the thing, to your question, what do we do instead? We need to look at how people are consuming the information, and how are they getting answers to the questions? We know that a lot of people are turning to Google’s Gemini or OpenAI’s ChatGPT or Anthropic’s Claude to ask a question as opposed to a Google search. If they are using a Google search, which my understanding from you, Chris, is that it still monopolizes 95% of search. That’s where I would start. I’m probably not too worried about that 5%.
What is Google doing? It has Google search, YouTube, Gemini, and all of its different properties in the Google marketing platform ecosystem. How do I make sure my stuff is tight there and then worry about everything else later? That’s sort of at a high level. I know there are specific tactics that you would do, but that’s what I would start thinking about as a marketer. If keywords aren’t the thing, what is the thing? Well, Google is still the thing. I still need my stuff to show up in Google. So let me make sure my content is good, let me make sure I have it in all the right places in the Google ecosystem, and then I can worry about the rest. Those are two basics.
Christopher S. Penn: One of the things—and I want to come back to Rand Fishkin in a minute because he had a very interesting insight last week—is we can know the embedding space in a model. The embedding space is the big statistical database that makes up an AI model. You can see, if you take a keyword and work with the embedding model, which is what takes your search queries and turns them into numbers, what is related to it.
This is a technical thing to do. You need to dust off your Python skills or dust off your Claude Code Python skills to make this happen. It involves a lot of math. But it has been proven in multiple research papers that you can reverse-engineer an embedding database and see what the related concepts are that are most likely to trigger an LLM to respond. If you are trying to figure out what is related to “AI consulting firm,” you can ascertain that. But you have to do it in the right place in the model.
In every system like ChatGPT, Gemini, or Claude, there is a very small embedding model that translates words into numbers and vice versa, and then there’s the big knowledge model which translates everything else. The embedding model is the one that we would need to focus on. So if you are a technically savvy marketer who can work with those databases directly, you can get more insights than your average non-technical marketer. There’s a hidden competitive advantage there. If you don’t know how to do that, feel free to ask Trust Insights for help.
Katie Robbert: I was going to say, you just casually say, “dust off your Python and Claude Code skills,” as if I had them in the first place. They’re not collecting dust; they don’t exist. But I want to be clear, there are a lot of marketers who do have those technical skills because the way that software has evolved over the past 15 years or so, there was someone on your team who had to have some level of technical skills to code against an API to get the data that you were looking for, or they needed to understand the R programming language or Python in order to do more advanced analysis and calculations.
There’s someone on your team who needed to know that, so marketers have developed those skills. When I say we don’t know what that means, I don’t mean everybody. There are a lot of marketers who 100% have those skills. There are a lot of marketers, however, who don’t have those skills and have been doing that very niche part of marketing, like just content or just social or just this or that. Or they have been doing SEO and PPC, but it didn’t involve any kind of coding because they had a wide array of tools at their disposal.
So let’s say in that scenario, I’m an SEO marketer, I’ve been doing SEO the same way for the past decade with keywords as the anchor. Now you’re telling me a keyword doesn’t matter. Should I panic?
Christopher S. Penn: Well, and this is something that you always push me for, Katie, which is what is the non-technical marketer supposed to do? Here’s the simplest solution that will get you 85% of the way there: Take your top 10 keywords off your keyword list, go to Google, type them in, and then copy and paste the text of the top 10 results into a text document, Word document, Google Sheet, or Google Doc. Then go to your AI of choice like ChatGPT or Gemini and say, “Do a semantic analysis of this document. What are the top themes throughout it and what language do they all have in common?”
That will tell the model, “Hey, here’s what shows up well in actual live Google for these particular terms after it’s been washed through Gemini,” because you’re getting it from things like AI Overviews. So a very non-technical way is just copy and paste those 10 pages. Go click through each page, copy and paste the text on the page, make sure you’re in incognito mode, and what you’ll get is a semantic analysis of those themes.
Then you can say, “Great, based on these themes that you’ve identified, here’s the page that I want to show up in these results. Help me align this page with these known themes that Google says matter.” That is laborious; that is a lot of copy and paste. You are at level one AI where you are copy-pasting everything. But if you don’t have the technical capabilities, that will get you 85% of the way there without needing any extra tools.
Katie Robbert: I want to go back and clarify something. You said something along the lines of, “as you’re doing the initial upfront analysis, the language it aligns with.” Can you explain a little more about what that means?
Christopher S. Penn: The word “language” in this case is literally referring to semantics—how we use words. There are some words that are very information-dense. In linguistics, this is called holophrasis; in regular people’s language, this is called jargon. Holophrasis is important because AI knows what that means, and it’s a very specific term and a great term to use in prompts. When you look at the top results and you ask a tool for a semantic or linguistic analysis, you are looking for things like jargon. What is the jargon that a customer or a prospect you’re trying to attract is familiar with?
For a very long time, marketers have been told, “Don’t use jargon because it makes your content inaccessible.” That is true for humans, but the opposite is true for AI. When you’re using LLMs of any kind, jargon is your friend because it helps the model triangulate very quickly on a topic. If I say “paying for college,” that could mean a whole bunch of things in a whole bunch of different places. If I say “FAFSA,” the Free Application for Federal Student Aid, that immediately refers to federal student aid for United States colleges. Just that one tiny word is packed with information density. If as a marketer you can figure out what is the jargon that will trigger an LLM, it will have a halo effect around the rest of your content because it will immediately tell the model what this is exactly about.
Katie Robbert: A good human way to gut-check your content for jargon is if you read it and your eye twitches and you get secondhand embarrassment, you probably have enough jargon in it. If you don’t, then you should probably start again. It’s a really clear example; paying for college could mean a bunch of different things, whereas FAFSA, we all kind of shudder a little bit because we’ve had experiences with it, but we know exactly what it refers to. There is no other interpretation. It’s a very clear word, but it’s not something that we use in everyday conversation. You don’t start a conversation with “So, FAFSA,” but you get it.
So I would say for marketers, you’re absolutely right. We were told forever that keywords matter and don’t use jargon. Now we’re saying keywords don’t matter as much and do use jargon. It’s understandable why marketers are frustrated. The thing I think we’ve been trying to encourage is: don’t get rid of your traditional SEO skills. Those still matter, but they don’t matter for GEO as much. So SEO is not dead. Nothing is dead. Everything is still very much alive, but GEO is a different beast altogether. Don’t keep trying to think that it’s replacing SEO; it’s supplementing it. It’s adjacent to SEO. It’s the fourth pillar of SEO if you want to go that route, but think of GEO as different. It’s SEO and GEO; they’re two different things.
Christopher S. Penn: I would argue that SEO is now a subset of GEO. And of course, you can find this in our GEO course. We have the three phases of it: presence, appearance, and relevance. Presence is: does the model even know that you’re there? Appearance is: how do you appear in searches, which is SEO? And then relevance, which is: when the model gets its results back from a search, is it relevant?
If you were to give an AI tool a prompt, something like “Summarize this page, maximizing lexical density, lexical compression, and holophrases in 400 words or less,” you’ll get a pretty beefy paragraph that is very information-dense. If that is the TL;DR of your page, when a tool like ChatGPT pulls back that result, it will read that and go, “Oh, I know what that page is about, and this is relevant to the user.” If, on the other hand, your page is like CNN and the first 400 words are “What do you think about this ad?” you’re toast.
There’s an opportunity to use the models themselves to create those lexically dense summaries that will help in the relevance phase of GEO. If that lexically dense summary is a “nothingburger,” it means one of two things: either the page doesn’t have enough useful content, or it’s out of sync with what Google is actually returning. Spend some time googling what the focus of that page is. That’s where the keyword still comes in handy. You can say, “Here’s what this page is supposed to be about, here’s the top 10 results, copy-pasted, and here’s the instructions for how to do this analysis. Help me improve this page.”
Katie Robbert: That’s important because it’s not that we’re saying don’t use keywords at all; it’s that the context of how you use a keyword has changed. You’re not necessarily optimizing for a single keyword anymore. You can still use the keyword to plan out your content—call it a theme, whatever you want—but it’s still good to have that focus. Google is still going to disregard content it can’t figure out. It’s going to say, “This was not helpful,” and it’s not going to show that.
A keyword is still important, it just has a different kind of importance. It’s not the traditional “here are the five keywords I’m optimizing for and here’s what Rank Math says.” That’s going to help you with humans; that is not going to help you with a large language model. If you want to do a quick free analysis of any given page, you can go to TrustInsights.ai/view and get a snapshot of how well your page is doing as a large language model is reviewing it.
Christopher S. Penn: Exactly. What AI sees in that relevance phase of GEO, it doesn’t talk about the presence or appearance phases. Woe to those marketers who fired their SEO agency because guess what, you’re going to hire them back as a GEO agency and add a zero to the retainer. So perhaps don’t do that.
Katie Robbert: Companies are way too quick to give certain roles and responsibilities the boot without long-term thinking. Just to be clear, SEO is still a thing. GEO is another layer that sits above it or next to it. Regardless, you have to understand how GEO fits into your overall strategy. If you don’t care if you show up as an answer in a large language model, GEO is not for you. If you do, go through the 5P Framework: Start with your purpose, people, process, platform, and performance.
We cover that in the GEO 101 course. We have so many banners and I’m not great at multitasking, but there’s a lot to cover.
Christopher S. Penn: I feel like there’s so much more we could talk about, but we will have more of it in the new ebook that is going to be called “Generative AI for SEO and PPC Marketers.” It’ll be available this coming week on the Trust Insights website. We’ll be sending an email; don’t you worry. That’s going to do it for this episode. If you’ve got thoughts about SEO, PPC, and generative AI, pop by our free Slack group at TrustInsights.ai/analytics-for-marketers, where over 4,600 marketers are asking and answering questions every single day. Wherever you watch or listen to the show, if there’s a channel you’d rather have it on, go to TrustInsights.ai/TIpodcast. Thanks for tuning in; talk to you on the next one.
(The following section is a description of the firm provided by the content creator)
Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.
Trust Insights specializes in helping businesses leverage data, AI, and machine learning to drive measurable marketing ROI. Our services span from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. We also offer expert guidance on social media analytics, marketing technology selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic’s Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama.
Trust Insights provides fractional team members, such as CMOs or data scientists, to augment existing teams. We actively contribute to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the “So What?” livestream webinars, and keynote speaking. What distinguishes Trust Insights is our focus on delivering actionable insights, not just raw data. We are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet we excel at explaining complex concepts clearly through compelling narratives and data storytelling. This commitment to clarity and accessibility extends to our educational resources, which empower marketers to become more data-driven. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information.
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Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
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