Hi Ed here. Just a little note to let you know that this is a compilation of all 12 episodes from our entity series. Now a few people asked me if I could just stick it all together in one episode, and yeah, it made good sense. So here it is. This is every single one of the 12 all together in one go, especially for your listening pleasure. Hello and welcome to SEO is not that hard. I'm your host, Ed Dawson, the founder of the SEO intelligence platform KeywordspeopleEaser.com, where we help you discover the questions people ask online and then how to optimise your content with traffic and authority. I've been in SEO from online marketing for over 20 years, and I'm here to share the wealth of knowledge, hints and tips I've amassed over that time. Hello and welcome back to SEO Is Not That Hard. It's me here at Dawson as usual. And today I'm going to be talking about something that I've probably quite neglected over all the podcast episodes I've done so far. It's a theme that I have mentioned in some podcasts, but I've never done a dedicated podcast to it. And in fact, there's so much about this theme that I'm actually going to do quite a few podcasts on this. It's going to be like a little mini-season on this theme. And that is entities. Now, if you've been in marketing or SEO world for any length of time, most people end up being trained to think in just one language, and that's the language of keywords. And people obsess over them, they research them, they track them, they stuff them in their content. And for a very long time, keywords were the fundamental building blocks as the internet as we knew. But the ground has shifted over time. And the web evolves, search engines evolve, and the way they map things have evolved into something far more sophisticated. They no longer just read words, they understand concepts. And that's why today we're going to talk about what? Someone might you might call it the new language of SEO, but we're going to talk about entities. So let's start with the most important question. What on earth is an entity? The simplest way to remember this is that an entity is a thing, not a string. You know, a keyword is a string of text. It's like a literal sequence of letters that you type into a search bar and it has no inherent meaning on its own. It's just characters. An entity, on the other hand, is a real-world concept. It's the actual thing that the string of text is trying to represent. So the classic example that you may have heard elsewhere if you've come across entities is the the keyword Apple. You know, it's a string of five letters. It's completely ambiguous. If you type Apple into a search bar, what do you actually mean? Are you looking for the fruit, the Apple? Are you looking for the multi-trillion dollar technology company, Apple? Are you looking for Apple Records, the label that if you're a Beatles fan, you know, that's the label that Beatles founded to put their music out on? Or are you looking for Gwyneth Peltra's daughter, Apple? An old school search engine, one that only thinks in keywords, will get confused. Try to solve the puzzle by matching the string Apple to web pages that contain the string Apple many times, you know, how they used to do, and the results would be a pretty mixed bag. The search engine would only be guessing at your intent. But a modern search engine like Google thinks in entities. It understands there is a well defined, unique entity called Apple Inc. And this entity is a thing, and it has specific attributes and even more importantly, relationships to other things. It's an organization, its founder is the entity Steve Jobs, a key product of theirs is the entity iPhone, and a major competitor of theirs is the entity Google. And there is also a completely separate and distinct entity, Apple, the fruit, which has its own set of attributes and relationships. So it's a type of food, it grows on trees, it's related to other entities like pie and cider. An entity resolves the ambiguity that keywords create. It gives you a contextual layer that machines need to deliver truly relevant and really accurate results. And it's not just limited to people, places or companies. An entity can be almost anything that is a single sort of unique and distinguishable concept. So it could be a product like an iPhone 15, or a historical event like World War II, or even a kind of abstract concept like climate change or happiness. As long as it's a well-defined concept, a machine can understand it as an entity. So the question that comes up is why did this shift happen? Why did search engines move away from keyword models that worked for so long? The answer is that the technology behind them got just smarter. This wasn't an overnight change, but a gradual evolution driven by all the advancements in AI. And these are and that's not just like the AI of LLMs. AI goes back, people have been researching AI a long way back. I mean, I remember back when I was studying computer science back in the late 90s, AI was something that we talked about at university then. It was an area of study. So Google and the other search engines have been putting a lot of AI and machine learning into their algorithms over the past few years before the whole new AI revolution that OpenII and ChatGPT ushered in. And this gradual evolution included things, a field called natural language processing, otherwise known as NLP for short. And NLP is what allows computers to comprehend and understand and interact with human language. There's a few landmarks in this evolution, and you might have even heard of some of them. The first was a Google algorithm update back in 2013 called Hummingbird. Now, Hummingbird was revolutionary because it was designed to understand the meaning behind an entire query, not just individual words. And it was a big change at the time. It was the first major step towards understanding user intent. Then the next probably big update that related to it was BERT in 2019. Now, BERT was a massive leap forward and it allowed Google to understand the nuance and the context of words in a sentence in a way that they couldn't be formed. It could figure out how prepositions like four and two could completely change the meaning of a query. And these sort of technological leaps made the old way of doing SEO more and more obsolete. The tactic of keyword stuff and you're just repeating your target keyword over and over again on a page suddenly became useless. In fact, it becomes a negative signal because it starts to look unnatural. And search engines are they're no longer indexing just keywords, they're trying to understand the world. And this represents a complete philosophical change in how machines view information. The old view was the web was a collection of documents, and the search engine's job was to rank those documents. The new entity-based view sees the web differently. The primary sort of unit of information is no longer the document itself, but the real thing that the document is about. Now this has implications for us as website owners and SEOs. Your website is no longer just a collection of pages to be ranked for keywords. It's now like an information hub that provides data points about the entities that it represents. So your organization, your people, your products, your expertise, the subject matter that you talk about. And the role evolves from being like page optimizer to becoming more of a data curator. So your job is to provide the clearest, most authoritative, and most consistent data about your specific corner of the world so that machines, the search engine LLMs can understand it without any confusion. So thinking about it practically, imagine you run a website dedicated to speciality coffee, say. And in the old keyword world, you would create a page, optimise for the keyword pour over coffee. You'd make that sure that phrase appeared in your title, your headings, and throw out the text. But in this new entity-based world, you think bigger. You want to create a comprehensive resource about the entity of pour-over coffee. You don't just mention the keyword, you need to explain its relationship to other entities. You might talk about different products that produce coffees, like the Harryo V60 or the ChemX, the Bloom, which is a concept entity, the gooseneck kettle, another product entity, and maybe even a link to an article about the entity. James Hoffman, who's a person who's famous in the coffee world. By doing this, you're demonstrating to Google you you're not just demonstrating them that you know about a keyword, you're proving to Google that you have a deep authoritative understanding of the entire topic and the universe of concepts around it. Now, if this is sounding familiar, yes, this is the topical authority concept that I've been speaking about years and years now on the podcast. And explaining about how you need to answer questions and cover a subject in depth. And the covering the entities of a subject are a real key part of this. To wrap up this episode, this brings me back to the core concept, the core idea of the difference of what entities compared to keywords, and that is things not strings. And this is a defining concept and the foundation for everything I'm going to talk about in the rest of this series on entities. We're going to talk about how it's key to unlocking real those sort of powerful knowledge panels you'll see in search results to appearing in AI-generated answers and to how to build durable long-term competitive advantage that can't easily be copied. So before you listen to the next episode, I want you to think about these questions and what the what's important to your business, what th what things are important to your business in terms of what is your main organization entity, your main website entity? What are your main products or your services or your topics? Who are the key people entities in your business or in your website or in your topical sphere? And what are the core concept entities in your industry that you solve problems for? Make a list of these things, even if it's just in your head. Think about what the entities are that you are involved in. If you're if you have a site about music, if it's about guitars, you might be starting to think about the entities within that are important to you. So there's going to be types of guitars, models of guitars, there's going to be guitarists, there's going to be bands, there's going to be songs, all these entities that are really important. So think about this between now and the next episode. Because if you start thinking about what entities are important in your world, then it's going to help you understand the future episodes going forwards. So I hope that's interesting for you. And until next time, remember keep optimising, stay curious. And remember, SEO is not that hard when you understand the basics. Hello, welcome back to SEO's Not That Hard. It's me here, Ed Dawson, as usual. And this is part two in our mini-series on and in the last episode, I introduced the single most important new concept in modern SEO, and that's the entity. And we learned that an entity is a thing, not a string. It's a real-world concept like a person, a product, or a place, not just an ambiguous keyword that we type into a search bar. And we use the example of Apple to see how thinking in entities helps search engines resolve that ambiguity and truly understand what we're looking for. That naturally leads to the next big question. So if the web is just a massive, chaotic library of trillions of pages of text, how does a machine like Google or an AI like ChatGPT actually read a sentence on your website and figure out what real-world things that you're talking about? Now, it's actually a very logical process and it's called information extraction. And today we're going to look at how this works and the three-step pipeline that machines use to learn how to read and understand what is on a page. So the first thing to understand is that most of the internet is made up of completely unstructured text. If you think about it, there's blog posts, news articles, product descriptions, forum comments. It's all just free-form flowing language. And for a machine to make sense of it, it has to convert that unstructured mess into structured interconnected data. And the analogy that's often used is you think about it, a detective arriving at a very complex crime scene. They don't just look at the room all in one go at once. They have a process. They are looking to identify the potential clues. They will then want to analyse the context around those clues to figure out what they mean, and then they look to link them together to solve the case. Machines do something very similar, and their process has three main steps. So step one is called named entity recognition or NER. You might also hear it called entity identification or sometimes entity chunking. And this is what is essentially their first suite of the crime scene if we consider our document to be a crime scene. But let's say we're looking at sweeping, having that first overall look at the document and the text that's contained within it. The machine will scan sentences on the website with imagine having a digital highlighter, and its only job at this stage is to mark any word or phrase that might be a distinct entity. And it highlights them. It puts them into broad, predefined categories like person, organization, location, product or date. For example, if it reads the sentence, Michael Jordan, who played for the Chicago Bulls, later became the owner of the Charlotte Hornets, the named entity recognition system would highlight Michael Jordan as a person, Chicago Bulls as an organisation, and Charlotte Hornets as an organisation. And this process, it's like it's really versatile, it can be applied to any kind of text, whether it's a corporate blog post, a dense academic research paper, or just even a simple tweet. It's the machine's kind of first step in finding the core concepts in any document. Okay, so in our case, the machine has highlighted its clues, but this leads to the most critical and the most challenging part of the investigation, and that's step two, which is entity disambiguation. So the machine has highlighted the name Jordan. Now it has to figure out which specific real-world Jordan we're talking about, and this is a big challenge. To solve it, the system first generates a list of possible candidates from its vast knowledge base. So with the word Jordan, we've got Jordan the country, we've got Jordan the River Jordan, we've got Michael Jordan, the person, or Jordan, the night brand. So how does it choose the right one? It does what any good detective would do. It looks for context, it analyses the surrounding words and other entities in the text to resolve that ambiguity. So in our example sentence, the context includes the entities Chicago Bulls and Charlotte Hornets. And the machine knows that these are basketball teams. So it's identified them, it's not there is much less ambiguity and Chicago Bulls and Charlotte Hornets. They are that's clearly they are the basketball team. There's nothing else named that. So this surrounding context of basketball means that the overwhelmingly is probable that Jordan refers to Michael Jordan, the person. If we got a different sentence and say it said, the ancient city of Petra is the crown jewel of Jordan, a country in the Middle East, the context of Petra and Middle East would allow the machine to confidently identify the entity as a country. So this contextual analysis is the core of how machines achieve an almost human-like understanding of the text. They're not just seeing the words, they're not just seeing keywords here. What they're seeing is a web of relationships, and they're using those relationships to figure out what you truly mean. So we're now on to step three. This is where once the machine is confident it's identified the correct entity, it moves to the final step of the process. That's our step three, entity linking. And this is where cases closed and filed away. We've solved, we've solved the puzzle. We've now sorted out the ambiguity, we've worked out that Michael Jordan is the person, and we've connected them or linked them to its unique identifier in a massive centralized knowledge base. Now, think of this knowledge base as like a universal library, and every unique entity in the world has its own library card with a unique ID number. Entity linking is the act of stamping the mention on your website web page with the exact library card number. So this action transforms a simple string of text into a rich structured data point that we is now officially part of a larger network of global knowledge. And this is where it can now get really interesting to us as website owners. What are these universal libraries that search engines and AI systems use for their source of truth? For the most part, they're these massive public humanly created knowledge bases. The two most important ones are Wikipedia and its sister project, Wikidata. And in the field of AI, the task of linking mentions to their corresponding Wikipedia pages is so common that it's got its own name, Wikification. And this shows us there's a deep symbiotic relationship here. AI systems like Google rely heavily on these structured verified information in places like Wikipedia to build their own models of the world. And this essentially creates a really powerful feedback loop that you need to be aware of. So if your organization, your founder, or your products are accurately and authoritatively represented on these public knowledge bases like Wikidata, you're providing clean, high-quality data that feeds directly into systems like Google Knowledge Graph. And in turn, when Google has high confidence in your entity, it might grant you a knowledge panel in the search results, which reinforces your notability. And notability is a key criteria for being included and maintained in Wikipedia in the first place. It's not easy to get in Wikipedia. So if you're working on smaller sites or smaller clients, don't get hung up on trying to get in Wikipedia or these data sets, it can be really hard. But it's still really important to understand these data sets because, as we'll see later on, making sure that if you're talking about an entity that includes those other related entities in your content is really important to boost the authority of your content. Yeah, please don't get hung up on trying to get in Wikipedia. Don't think I'm saying that everybody has to get in Wikipedia. But if you're working with larger clients or you are a larger client yourself, then if you have got into Wikipedia already or you're capable of getting into it, then it's important that you get there and it's important that you make sure that what is there is correct. And what this means is if you're going to have a truly comprehensive entity strategy, it has to extend beyond your own website. You need to actively ensure that you're accurate representative in these places, in these foundational knowledge bases. And yeah, it's it's quite simply one of the most direct ways of injecting authoritative data into the very core of the ecosystem that both the search engines and the LLMs depend upon. Wrapping up for today, what we've covered and what we've learned is that machines read our content using a three-step process. And that is step one, named entity recognition, where they highlight the potential entities. Step two, entity entity disamb disambiguation, not easy to say, where they use context to figure out exactly which entity you mean. And thirdly, entity linking, which is where they connect that entity to a universal knowledge base like Wikipedia or Wiki. So that brings us to you know what I think would be great if you could do, and that is to go to Wikipedia or Wikitata and search for your brand, your founder, your main product. Does a page exist for them? If it does, is the information 100% accurate and well sourced? The answer to that question will tell you a lot about how well the AI world currently understands you and your business, and it'll show whether you've got a strong foundational entry in that universal library if there is work to be done. Next time, we're going to take a close look at the place where Google stores all of this linked information, its gigantic digital brain, the knowledge graph. We'll explore how it's built and how it directly impacts what users see in their search results. So until next time, keep optimizing, stay curious, and remember SEO is not that hard when you understand the basics. Hello, welcome back to SEO is not that hard. It's me here, Head Dawson, as usual, and this is part three of our entity series, and today we are going to be looking at the knowledge graph. So just looking back at our last episode, there we looked at how machines learn to read, we walked through that three-step process of information extraction. First, identifying potential entities on a page, secondly, using context to disambiguate them, and thirdly, linking them to a universal library of knowledge like Wikipedia. And this brings us to the next part of the question. Once Google's done all that work, once it's read your content, identified your entities, and linked them to a global understanding of the world, where does all that information go? And the answer to that is it goes into probably one of the most powerful and inf influential databases on the planet. If you are looking at things from an SEO perspective, that's it into, I think you might call it a digital brain. We're going to explore the knowledge graph. So what is the knowledge graph? The simplest way to think of it is Google's massive, interconnected encyclopedia of the world. But instead of pages, it's made up of facts, billions and billions of facts about people, places, things, and most importantly, the relationships that connect them. And its purpose is to help Google move beyond just matching keywords to a genuine understanding of real-world concepts. This is what allows Google to answer factual questions directly in search results. So when you ask it like how tall is the Eiffel Tower, or where were the 2016 Summer Olympics held, the answer that pops up instantly comes directly from the knowledge graph. But how is this incredible knowledge graph actually built? And it's not from a single source. The knowledge graph is a dynamic system that constantly aggregates information from a huge variety of inputs. First of all, there's the public web. This is where we're interested. This is our websites. This is Google's crawlers constantly processing unstructured information from billions of web pages, just like we discussed in part two, and crawling through all that data and pulling out the information and understanding the information within it. Secondly, Google licenses data for really timely, highly structured information, so things like stock prices, sports results, weather forecasts. Google pays specialized providers for this clean, reliable, authoritative data. Third, and this is a crucial point, it relies on human edited knowledge bases. And then we touched on them last time. These are the authoritative sources like Wikipedia and Wikidata. These are foundational pillars of the knowledge graph. And they provide a structured, verified set of facts about notable entities that Google deeply trusts. And fourthly, finally, Google gets information through direct feeds from content owners. So that's you. That's businesses and individuals who provide factual information directly by using structured data markup on their websites or by claiming the knowledge panel and suggesting edits. Now here we come to like the most important concept to understand about how the knowledge graph works. Google doesn't just blindly accept information from these sources, it performs a constant process of authority validation. So if you think of the knowledge graph less like a static encyclopedia and more like a dynamic kind of trust ledger, every fact about an entity has a confidence score. So when numerous trusted sources consistently report the same fact, Google's confidence in that fact increases. It's like a credit to the ledger. The opposite, when sources report conflicting information that acts like a debit and Google's confidence goes down. So if you think of an example, so say Google wants to know who the CEO of your company is, and you want Google to know that the CEO of a company is called, say, Jane Doe. If your website's about page says the CEO is Jane Doe, that's one signal. If your company's official LinkedIn profile also lists Jane Doe as CEO, that's another strong signal. If a major industry publication, say like Forbes, writes an article and mentions Jane Doe, CEO of your company, that's a very powerful third-party signal. And if your company has a Wikidata entry that lists Jane Doe as CEO, that's another foundational signal. And when all those trusted sources align, Google's confidence score for that fact becomes extremely high and it'll consider that like to be the verified truth. But imagine a different scenario where your website says Jane Doe is the CEO, but your LinkedIn profile is outdated and still lists the old CEO, John Smith. That creates a conflict. Google's seeing two different sets of facts from two sources it considers relatively authoritative, and its confidence will drop because it becomes unsure of the truth. It might display the incorrect information or no information at all. And this has big implications. It means you can't simply declare facts about your business on your website and expect them to be accepted as the canonical truth. You must cultivate a consistent and verifiable paper trail across the entire sort of digital world. And this elevates the role of digital PR and online reputation management to core components of technical SEO, ensuring the consistency of your entities' core attributes, like your name, address, your phone number, your key personnel across all digital places where it might be found. It's no longer just good practice, it's critical to build high trust, authoritative entry about you, about your entities in that global knowledge graph. So, how does your website sit into this ecosystem? What's its specific role? Your website serves as the primary and highly influential data source for the entities you directly represent, your company, your products, your people. The most direct way you can communicate with the knowledge graph is by implementing structured data using a vocabulary called schema.org. Now I have spoken about this in other podcasts previously, but this should show you how it fits into the big picture. And I'm going to do a whole episode specifically on structured data and entities later in this series. But for now, you think of it as like a set of special tags. You can add to your website's code that explicitly define your entities in a machine readable format. It's like adding a label to your content that says, hey Google, just to be clear, this page is about an organization, its legal name is this, its logo is this, and it dramatically reduces ambiguity for the search engines. And also by linking from your own content out to external or authoritative databases like Wikipedia, you can help Google further disambiguate your entities and firmly connect them to sort of this broader global graph. So this brings me to what I'd like you to go away and do after this episode, and that's to go and look at the knowledge panel. The inner workings of the knowledge graph are a black box, but its outputs are publicly visible on every search results page. And the most direct view of this that we have is the knowledge panel. So the knowledge panel is that box of information that typically appears on the right hand side of a desktop search. When you search for a specific unambiguous entity, it'll always display a curated summary of the facts, images, and related information pulled directly from the knowledge graph. So go to Google, search for your own brand name. If your brand name doesn't trigger a knowledge panel, then look for a well-known brand name to get an example of how it works. And you'll see how it shows a whole bunch of information about your brand. Now, if you don't have one, that's a sign that Google doesn't yet have enough consistent authoritative information about you to be confident in who you are. And if you do have one, I want you to look at it. Is every single piece of information 100% accurate? Is it the current logo? Is the founding date correct? Is your CEO listed correctly? All those kind of things. Any inaccuracy, no matter how small it is, that's a red flag. It's a signal that somewhere out there on the web, Google is finding conflicting information about your brand. And that panel is like your diagnostic tool. It gives you your first clue about where you need to go and start cleaning up data about your entities to make sure that you've got a consistent, trustworthy entity. Now, next time, we'll explore how a strong and accurate entity profile doesn't just get you a knowledge panel, it also makes you eligible for a wide array of other high visibility features that can help you really dominate the search result pages. So, until next time, remember keep optimizing, stay curious, and remember SEO is not that hard when you understand the basics. Hello, welcome back to SEO Is Not That Hard. It's me, Ed Dawson, as usual, and today we're on part four of our series on entities. In the last episode, we went inside Google's knowledge graph. We learned that it's a massive dynamic trust ledger, and it's constantly cross-referencing facts from across the web to build a confident understanding of real-world entities. We also learned that what the key thing is consistency. You need to ensure the information about your brand is accurate everywhere it appears online. It's critical for building trust with Google. So imagine you've done the work, you've cleaned up your online presence, you're providing clear signals, and Google is starting to build a strong, confident profile of your core entities in its knowledge graph. So what's the payoff? Why is this worth doing? And if you think the ultimate prize is just a number one ranking on Google, then you know that's Really, not it. There is the game is so much bigger now. And this is just on Google, okay. A strong entity profile doesn't just help you rank, it makes you eligible to get pieces of prime real estate on the search results page that you're not going to get access to otherwise. In ways that go far beyond the traditional list of links. That's why today we're talking about what is beyond those 10 blue links. So I want you to think about last time you searched for something on Google, did the results page look like a simple cleanness of 10 blue links? It's probably not. For years that was the standard formatting. But the modern search engine results page or SERP, it's dynamic, it's feature-rich environment. In fact, according to some data from August 2024, only just under one and a half percent of Google's first page results were just plain blue links, something else. The other 98.5% were filled with what we call SERP features. And these features like info boxes, image carousels, and direct answers are the most direct and visible way that you will see the knowledge graph at work. They Google's attempt to provide users with rich contextual information and immediate answers. And they're all powered by entities. Getting your brand to appear in these features is how you're truly going to get the most out of your SEO. So let's break down the most important ones. First up, the one we've touched on in the episode, the last episode, was the knowledge panel. And this is like the flagship, it's like the crown jewel of entity-driven SERP features. It's that large box of information that typically appears on the right-hand side of the desktop search built. Sometimes it'll be on the top. And it's almost like an official business card for your entity. And it's going to display a really curated summary of the facts, images, and other related information pulled directly from the knowledge graph. It's not pulling from a single web page, it's pulling that and synthesizing information from a huge variety of trusted sources like Wikipedia, your official website, social media profiles, and all sorts of places. And the appearance of a knowledge panel is an automated process. It's Google's way of saying that we've gathered enough consistent information about the SNC and we're really confident in its identity and attributes. The next one to look at is rich snippets, which you might also hear some people call rich results. And these are visual enhancements to a standard organic search result that display extra helpful information. And you'll see these all the time, even if you don't know what they're called. So things like star ratings under a product, that's a rich snippet. If you search for recipes and your cooking times, calories and a picture before you even click, that's a rich snippet. If you see a price and an in-stock availability for an e-commerce item, then that's a rich snippet. And yeah, seeing those little expandable QA sections under a result, that's also an FAQ rich snippet. And these snippets are really valuable because they really make your results pop. You will stand out compared to others. And they take up more space, they provide immediate value, and it can really improve your click-through rate compared to just all those plain blue links around them. And the best part is you if you have direct control over these, rich snippets are generated when you add specific structured data using the scheme.org vocabulary to your website's code. A topic we'll go into in a future episode. Now let's move over to the most coveted area of real estate on the entire page. And that's the very top. This is where people are always going to pay the most attention. This is where you'll find featured snippets and the newer AI overviews. A featured snippet, sometimes people call it position zero. That's a direct excerpt of text that Google pulls from a single web page that its algorithm has determined is the best answer for the user's question. It might be a paragraph, a bulleted list, or a table. And for many years this was what people really aim to go for. But now the new player, the new kid on the block, AI overviews. Now these are the AI generated summaries that are becoming more and more common at the top of the cert. The key difference is that an AI overview is synthesized from information across multiple top-ranking sources, not just one. Now, eligibility for both of these top-tier features is really heavily influenced by having a strong entity profile. When Google recognises you as a trustworthy and authoritative source on a topic, it increases the likelihood that it will select your content for these really highly visible positions. So having well-structured entity-rich content that answers questions clearly and concisely is the way to get to the top of the page. So we have knowledge panels, rich snippets, and top of page answers. It's clear that a strong entity profile is going to have huge benefits for you. And here's the big kind of takeaway from today's episode: these SERP features are not just prizes to be won, they're your most direct public facing, almost like an API for understanding how Google perceives your industry, your topic area. So while the inner workings of the knowledge graph and all the rest of Google are a bit of a black box, its outputs are there to see every time you do a search. So if you're a strategist, this is a kind of a reverse-engineered map of the knowledge profile and the Google algorithm that you need to see and understand. So you can use it to diagnose real critical questions. Which of my competitors does Google deem authoritative enough to grant a knowledge panel? What attributes and related entities does Google consider most important for my main topic? Look at the people also search for section for this. And most importantly, and one you're not going to be surprised when we're talking about, the specific questions that appear in the people also ask boxes. These are not guesses. This is Google telling you the exact questions and information needs of people and the subtopics people have around the query that you are searching for and your topic area. The answers to these questions, they will provide you proper data-driven roadmap for your content strategy, your schema implementation, and your entire entity optimization plan. So this brings me to what your takeaway, what you should go and do next from this is. Go to Google and search for your main product, your service, topic area, whatever it is that is important to you and your site. But this time, ignore the rankings. Instead, look at the features on the page. Who has rich snippets? What kinds are they? Is there a feature snippet on an AI overview and what sources are they pulling from? What are the top three, four questions in the people also ask box? And obviously you can use keywords people to use if you want to delve down further levels of that. And write them down. You know, that isn't just a list of questions. This is your next content plan. It's handed to you on a silver platter by Google itself. Now, next time we're going to change up a bit. We're focused on how Google understands the world, but there is, do you know need me to tell you, those are revolution happening with large language models. So we're next time we're going to look at how large language models like ChatGPT think and how you can optimise for the AI-driven future of search. So until next time, keep optimising, stay curious, and remember SEO is not that hard when you understand the basics. And today we're on to the next episode in special series about entities, and that is part five, and that is how ChatGPT really thinks about your brand. So far, we focused on understanding Google when it comes to entities. And we started by defining entities as what real-world things, not strings, and how the search engines now prioritize those. And we looked at how Google reads our websites to identify those entities and then files that information away in its gigantic database, the knowledge graph. We learned that the knowledge graph is like a really well-organised library with a like catalogue cards that detail the exact relationship between billions of facts. It knows Apple Inc. is an organization and Steve Jobs is a person and it knows the relationship between them is founder. But in the last couple of years, there's a new player on the scene, and it's really changed the conversation in SEO. And I'm talking, obviously, of course, about the large language models and the technology behind tools like ChatGPT, Claude, Gemini, Perplexity. And those AI systems, they're not just another feature of Google, they represent what is really a fundamentally different way of processing and understanding information. And this brings up the really important question. If an LLM doesn't have a neat labelled knowledge graph like Google does, then how does an AI that only understands maths essentially actually represent a complex entity like your brand? So today we're going to look at that question. So we're leaving the organized library of the knowledge graph behind to venture into the sort of fascinating but very different mathematical mind of an LLM. So the first most important thing to understand is that an LLM is not a database, and this is a really crucial distinction. Google's Knowledge Graph is designed to be a database. It stores discrete, verifiable facts. The capital of France is Paris. Steve Jobs was the founder of Apple. When you ask Google a factual question like that and it goes into the knowledge graph, it's finding the right sort of book, the right library book, the right part of the database, and it's bringing you back a and reading you a fact from that book that it is convinced of. And LLM doesn't work that way, okay? It's not retrieving stored facts. Well, fundamentally, an LLM is a giant, really incredibly sophisticated statistical prediction engine. It's been trained on a colossal amount of text on the internet, from books, from articles, from all over, and its primary function is to do one thing, and that is predict the next most probable word in any given sequence. So when you ask ChatGPT what is the capital of France, it doesn't know the answer in the way that a database does. Instead, what it's done is it's analysed countless documents where that question appeared, and it has learned that the most statistically probable word to follow that sequence, the capital of France, is going to be the word Paris. It's completing a pattern, it's not retrieving a fact. And it might sound like a small difference, but this really is fundamental to the whole difference between LLMs and everything that came before. It's what gives the LLMs their most incredible power, but it's also what gives them its most dangerous flaw. So to get our heads around this, we really need to understand how an LLM represents meaning itself. So, how does a machine that only understands numbers learn the meaning of our entities? But it does this by creating a universal map of meaning, and this is where we connect the dots together. So the process starts by breaking our language down into a format the machine can use. And it takes our text and it splits it into smaller units called tokens. Then each token, whether it's a word, a phrase, or a whole entity, is converted into a long list of numbers. This numerical representation is called a vector embedding. And this is the answer to the question. Okay, the vector embedding is the LLM's internal representation of an entity. It's the semantic core, the backbone that transforms a real-world concept into a string of numbers that the model can actually process. So think of it like this: so imagine you wanted to create a map of every concept in the world. To place a concept on that map, you need a set of coordinates. And a vector embedding is essentially a set of highly complex coordinates for an entity. But instead of obviously in a normal graph, we just have two coordinates, you know, the X and the Y, or like the longitude and latitude on a map. These vectors is said can have hundreds or even thousands of coordinates, and these are the dimensions that it is represented in. And each represents a different feature or attribute of the entity's meaning. So by that we mean once every entity has its coordinates, the LLM can then place it as a point on a vast multi-dimensional map. And this map is called a latent space or a vector space. And the way this map is organised is what allows the AI to understand our world. So the model's training process organises this space so that entities with similar meanings are entities that frequently appear in similar contexts to position close to one another. So the point on a map for the entity king will be always very close to the point for queen. The point for dog will be near puppy and canine. And crucially, for sort of disambiguation of similarly named entities, if you have the entity for the point of a bank in a financial context of a bank where you know you you can get cash, open a bank account, that will be in a neighborhood with the words the entities loan and account. The point for the entity bank, when it we're talking about a river bank, will be completely different region of the map, and it will be close to terms like stream and shore. So the model by doing this learns to create different vector embeddings for the same word based on the other entities surrounding it. That provides it with that semantic context. But it gets even more incredible. The model doesn't just learn the proximity of terms, it learns relationships. So the distance and direction, the literal, it's like literal vector math. So that distance and direction between king and queen will be remarkably similar to the mathematical relationship between man and woman. So it's learned the concept of gender as a direction on its map. Likewise, the vector relationship between entity France and the entity Paris will be very similar to that between Japan and Tokyo, or the UK and London. What it's done there is it's learned the concept of a capital city. And this geometric arrangement of entities is how the model captures deep context and nuance and the semantic relationships without needing a formal human-built knowledge graph. It's built its own map of meaning based purely on those statistical patterns and the data it was trained on. So if an LLM is a prediction engine using a giant map of meaning, how does it reason? Its reasoning process is completely different from the logical deduction of a traditional computer. It's more like reasoning by analogy and probability. So when you give an LLM a prompt, it converts your words and the entities within it into a series of vectors to find its starting location on its map. And then based on all the patterns it's learned, it calculates the most probable path to take from that point. So it predicts the most likely next vector, which it then translates back into a word, then the next, and then the next, and it generates the response token by token. Now, this is what allows the model to make incredible, really creative leaps that a human might not. It can find abstract mathematical similarities between the vector clusters for seemingly unrelated topics. For example, it might find a structural pattern in its map that is shared between protein folding algorithms and urban traffic flow. And this allow will allow it to generate like a novel insight as about how one field could inform the other. It's not performing any kind of logic, it's just identifying and extending a deep, really deep mathematical pattern. Now, this is the source of the LLM's greatest strength, but it's also, as we mentioned earlier, is its most significant weakness because the model is always just completing a pattern rather than retrieving verified facts. So it can generate highly plausible but entirely false information with absolute confidence. You know, what we know as hallucinations. So to address this, researchers have developed and are continuing to develop techniques to guide this process, this probabilistic, to try and get you better reasonings. And you might have heard of chain of thought prompting or COT. And this is a technique where you explicitly instruct the model to think step by step before giving a final answer. And this is what most of the LLMs now do out of the box. And what it does is it forces the model to break complex problems down into sort of smaller, intermediate steps, much in the way that a human might process and work out how to perform a task that requires any kind of logic. And this is a way of putting guardrails onto that pattern-matching LLM brain to steer it towards hopefully more logical and more accurate conclusions. So bringing all that together, today what we've covered is how LLMs like ChatGPT have a completely different architecture for knowledge than Google's knowledge graph. They're not databases of facts, they're probabilistic prediction engines. They don't understand entities directly, they convert them into numerical coordinates called vector embeddings. They organise these coordinates on a giant multidimensional map of meaning that's called a latent space where proximity and direction represent semantic relationships. And they reason by navigating this map and finding the most likely path forward, which allows for incredible creativity but also opens up the door for those factual errors, those hallucinations. So what's your takeaway from this today? Your goal always is to provide the clearest, most interconnected and factually accurate information you can about your niche, your topic. And the reason why is because every piece of high quality content you create helps those LLMs build a better, more accurate map of your corner of the world. So when your website continuingly provides clear definitions, logical structures, verifiable facts, what you're doing is you're helping the AI place the entities related to your brand in the right neighbourhood on its map and with the right connections to other authoritative concepts and ideas. But this problemistic nature, this tendency to complete a pattern even when it doesn't have the facts, does lead to that single biggest problem with this whole LLM technology, those AI hallucinations. So in our next episode, we're going to tackle that problem head on. We'll look at why hallucinations happen, and more importantly, we'll discuss the new groundbreaking strategies that allows you to position your website as the source of truth that helps ground those AIs in reality. So that's it for today. So until next time, remember keep optimising, stay curious, and remember SEO is not that hard when you understand the basics. So in the last episode, we looked into how a large language model works, and we learned that an LLM like ChatGPT or Perplexity or Claude or Gemini, they're not a database of facts, but instead a giant statistical prediction engines. And they represent entities and concepts as coordinates on a vast multidimensional map of meaning, and it reasons by finding the most probable path from one point to another depending on their input prompt. Now, this ability to find and extend patterns is what gives LLMs their the really incredible power for creativity and insight, but it also leads directly to the single biggest, most dangerous flaw, a problem that has resulted in legal sanctions, reputational damage, and some truly bizarre and incorrect answers being presented with perfect confidence. So today we're going to talk about these AI hallucinations and we'll explore why they happen. And more importantly, we'll discuss what the strategy is that allows you to position your website as a source of truth that helps ground those AIs in reality to help reduce and prevent those hallucinations. So what exactly is an AI hallucination? An hallucination is when an LLM generates information that sounds completely plausible and it's delivered with a really confident tone as a factual statement, but it's partially or entirely completely made up. And it's crucial to understand why this happens. So remember, an LLM's core function is to predict the next most likely word. It's a pattern completed, it's not a fact checker. So when the model is asked a question and it encounters a gap in its training data, a topic it doesn't have like really good, robust information on. It doesn't just stop and say, I don't know. Its very nature compels it to fill that gap by generating the most statistically probable sequence of words regardless of their connections to factual reality. And it's not just a quirky little book, it's real serious real-world consequences. There's a few cases, so like in a now quite famous case, a chatbot for Air Canada completely invented a bereavement fare policy when a customer asked about it. So the customer booked a flight based on this information, and then when the airline refused to honour this non-existent policy, the customer sued. And a court later forced Air Canada to honour the policy that its chatbot had fabricated. And then there's another high-profile incident where two lawyers in New York City faced legal sanctions after they submitted a legal brief that cited several entirely fake court cases. Where did they get these cases? They were generated completely but convincingly with convincing sounding legal citations by ChatGPT. So these examples they highlight this the core vulnerability of the technology. Without a connection to a verifiable source of truth, an LLM's output can be dangerously unreliable. So how do we fight this? How do the LLMs fight this? And how do we harness the power of the LLMs while mitigating this risk of misinformation? The primary solution is a strategy called grounding. And so grounding is a word that you might hear come up more and more now in the realm of LLMs. And the idea is to ground the LLM's response in an external, verifiable source of truth. So rather than allowing it to rely solely on its eternal and its potentially flawed memory and this way does a prediction and making connections, this grounding helps give it something to verify the information that it's talking about. And the most important and widely adopted technique for this is called retrieval augmented generation or rag. And the best way to understand RAG is to think of it, it's like giving the AI an open book exam. So a standard LLM prompt is like a closed book exam. You ask it a question and it has to answer based only on what it knows from its training. And this is where the hallucinations can happen. A rag system is different, it works in two phases, retrieval and then generation. First is the retrieval phase. So when you submit a query, the system doesn't immediately send it to the LLM. Instead, it first uses a query to search through a trusted up-to-date knowledge base. Now, this could be a company's internal documentation, it could be specifics of academic papers or the content of an entire website. It could be a Google search, you know, where it goes to Google and does some search, gets some information from the websites that are ranking well in Google for terms around the topic that you're asking them about. And it retrieves the most relevant snippets of information related to your question from these sources. And then second is the generation phase. And this is where the system takes those retrieved factual snippets and it augments the original prompt you give it. And then it essentially says to the LLM, here's the user's question. Here are some relevant facts I found from trusted sources. Now using these facts, please generate an answer. So this simple, powerful process really does massively improve things. It forces the AI to base its answers on the provided verifiable content, dramatically reducing the chances of hallucinations and ensuring that information is current. And this is where everything we've been talking about in the series comes together for you as a website owner. So in a world powered by reg systems, what is the most valuable asset? It's the book that the AI uses for its open book exam. It's the trusted external knowledge base. And for your industry, your website has the potential to be that definitive source of truth. And this is why factual accuracy and unambiguous language are becoming really much more important on the web. Vague marketing claims, they're not only unhelpful to users, they're poison for AI systems. An LLM can't reliably pass and understand the statement that says our product offers unparalleled performance. It's subjective, it's nonsense. It contains no verifiable information. But a statement like our product processes data at 10,000 records per second, a 20% improvement on the previous version, now that's much, much better. It's clear, it's factual, and it's an unambiguous piece of data that a rag system can retrieve and use to answer the user's questions with much more confidence. And this is where entities play a really critical role. So when a user asks a question, the rag system uses entity linking to identify the specific concepts that need to be looked up in the knowledge base, ensuring that retrieved information is highly relevant. So a well-structured website which is rich with clearly defined entities makes this retrieval process faster and more accurate. Now there are other more complex strategies that the LLMs use, like post-processing systems that will they'll extract all the factual assertions that the LLMs made from its output and it'll then compare that against ground truth sources. So in effect, doing the same thing in reverse. But the principle's the same. It's connecting the AI to verifiable sources of facts. So let's come out and look at the bigger picture. This isn't just about optimized optimising for public chatbots like ChatGPT. The widespread challenge of large language model unreliability is creating what some are calling a new authority economy. And in this new economy, the most valuable digital assets are no longer just high-traffic websites, clean, well-structured and verifiable databases of entity-based knowledge. So businesses everywhere are building their own internal and external rag systems to power their customer service bots, their internal search tools, and things like data analysis pipelines. And when they build the systems, they will point their data sort of ingestion pipelines towards sources that they deem authoritative and trustworthy. So a website that really meticulously structures its content, ensures factual accuracy and clearly defines its entities, is in effect, it's going to transform itself from a simple marketing channel into a really premium machine readable data source for its specific niche. So by optimising for entities and accuracy right now, you're not just improving your visibility in current search engines, but you're really setting yourself up for your brand to become a really foundational in an AI-driven economy. So the brands that are most consistently and authoritative cited by these systems will become the default go-to places for answers. So this brings us to what I want you to really think about between now and the next episodes. I want you to look at your most important pages on your website with your kind of AI facts checker hat on. So it could be your homepage, it could be your about us page, or it could be your key product or service pages. And just read through it and see if you can find one or two sort of vague subjective marketing claims. And your task really is to look at those claims and rewrite them into something that's more verifiable and data backed. So instead of saying we're a leading provider, try saying things like we served over 5,000 customers in our sector since 2015. Or instead of saying our software's incredibly fast, think about saying things that are much more verifiable, like our software returns a search query in under 200 milliseconds. And this exercise, it'll start training to think about your language that you use in verifiable facts rather than in vague subjective sort of non-realities. And that's the kind of language that AIs are gonna and customers are gonna start trusting. So now we've got this is the end of the theory part of entities. Next, what we need to do is to start a look at how we can actually get practical. So in the next episode, we'll start with part one of our four-part action plan, which will be a step-by-step guide on how to audit your own entity landscape. So that's it for this time. So until next time, keep optimizing. Stay curious, and remember, SEO is not that hard when you understand the basics.

Hello, welcome back to SEO, it's not like cloud, it's me here, Ep Dawson, as always, and today we are on to part seven of our mini-season on entities. So, over the last episodes we built what a really powerful theoretical foundation, so we started talking by defining an entity as a thing, not a string, just real-world concept search engines AI now prioritizing the sync keywords. We explored by machines and learned to read and link the identities to a universal database of knowledge. So we went inside Goodwall's entity database, the knowledge ref, and we saw how it uses the identity through its modern searching pages. Finally, we ventured inside the black boxes of those NLMs to learn how they represent entities on a vast mass. Of meaning that vector space and why grounding it in factual reality by verifying the information that comes from other websites, their own sources is so critical that factual grounding. So we've covered the what and the why. So now it's touring simple practical mirror time for the three. So this is where all this gets real. Over the next four episodes, we're going to build an action plan step by step. And this is your practical guide to making everything we've learned and anybody strategy in business or an SEO begins with one thing, like the analysis. So before we can optimize, before we can build, exactly what we're working with. So today we're going to conduct an audit. We're going to define and map out your personal or business's entity landscape. So I want you to think of your business as a universe. So the centre of that universe are the handful of core concepts that's going to give it shape and meaning. These are your core entities. So we're getting them is like trying to build a solar system without a sun. It's not going to have everything else. We'll just float aimlessly with no centre of gravity to space or island. So our goal of this episode is to get to your universe that centre of gravity. We're going to get incredibly clear on the people, the products, the concepts, which are the foundational pillars of your brand in the eyes of modern search engines like your pan BLNs. You want to pen and paper, or I put up a new document or a spreadsheet on a computer. We're going to go through this together. The process is pretty straightforward, but the currency provides to be real on it to really set game approval. So we're going to break down your entity universe into four key categories, or we can call the four pillars. So let's start with pillar one, which is your brand and product entities. This is the most obvious starting point, but it's important to be thorough. This pillar is going to define what you are and what you sell. First of all, we start by listing your official company name or your organization name. This is your primary organization. But don't start there athlete, think about all the variations. So do people using abbreviation, do we have a nickname? List all those two, however you are naming, list them. Next. Move on to our products and services and be specific. So if you sell shoes, don't just write down shoes. List the specific threat lines and model names. For example, if you would nine, your list won't be just shoes, it'll be included like Mike MX, Nike Douglas, Nike Rifcode. Each of these is distinctly its own audience and searching to different people, different business people, and to celebrate towards different shoes. So this is where you list any sub brands or the brands that you're closely associated with, such as your kept currently specific name of your flagship software, or for a restaurant, it might be the name of your signature dish. So typically just jot down sort of five to ten of the most important for under entities and be as specific as you can. This list forms called to which your core to your entity landscape as you're defining here. So that moves on to pillar two, your people entities. Now this pillar is really important, especially today where expertise and trust are just more important than that. And this pillar defines who you are. Remember, we heard me talk about Google's EEAT expertise, experience, or storage newness, and trust with us guidelines. People entities are really important and must a weight signal all four of these. Search engines want to see have a real credible experts to find your content and your brand. Plastic, who are the key people in organization? Obviously, start with your founders or your key executives, your CEO, your CTA, if you're operating at large, your head of marketing, these individuals or entities, and they're directly linked to your main organization entity. Next, probably the most important ones, these are your subject matter experts. Identify who the people are who are actually writing your blog posts, hosting webinars, speaking of coaches, the people who are authors, experts in the products and services that your business you're involved with. Every one of these coaches, personal entity that could build authority. And then think about anyone in your company who had a public-facing role who has recognized for their expertise. You're not just putting names on the page, you're creating a network of truth signals that tells people Race Keep Volunteer is backed by these credible, experienced individuals. So go ahead. If you're a solar founder, you are the most critical entity. Let's move on to pillar three. Your service and concept entities. So this is where we're moving away from physical concrete things into more definitions. So this pillar defines what you know and the problems that you solve. So for a service-based business, this is straightforward. It's a specific service you offer. So like a digital marketing agency, would it just be an agency? Its services would be search engine optimization, paid the clickover tours, content marketing, whatever the specialities you of the specific services are that you offer. And you can potentially break those down with it as well. So within such an organization, you might be a link building specialist, or you might be a content specialist, or you might be technical audit specialist. You can break down within these. And this pillar also includes concept. Now these are broader ideas. There are like things like theories of industry topics of the businesses like these are often the things that customers are searching for when they don't yet know what they need your specific products. So let's go back to coffee excerpt from episode one. The product entities for the coffee means the brewing equipment, but the concept entities are things like espresso, poro, sumatra, and barista. A website that demonstrates the knowledge of these concepts is going to be seen as a proper authority in the coffee world. For a financial advisor, though, you know, the same entities might be retirement planning or wealth management. The concept entities would be during the US, there'd be like 401k, R through, more investment strategies and market volatility. All things that you could be talking about, all these talking about on your website. No think about your niche. What are the core ideas, methods, or problems that you are extra on this to mail? These this is the pillar, is where you define what your topical authority is. Yeah, actually, topic that you authority are. And then finally bring us to pillar four, which are your audience-centric entities. This is this final pillar, this is what can really separate a good entity strategy from a great one. First three pillars were really focused on you, the focus on what you sell, who you are, and what you know. This pillar is really focused entirely on your audience, and it defines what they care. And this is one I've talked about really for the plus two, three years on this podcast. Understanding your audience, understanding the language they speak, questions they ask, and how to address their needs. And this means you should identify the entities that are most relevant to their interests, even if they are directly related to your product. So if you already find these, you gotta go and search for them, okay? You should go to online communities where your audience and you know what they're talking about on Reddit and Quora or in a further industry forums. The top picks of the discussions are your audio-centric entities. Go to Google, type in one of your core concept entities, then scroll down the people's box. Google's literally giving you a list of the ranked entities and questions you audio set. And obviously a little tip if you know a bit about me, if you'd listen to what's at the end of this podcast. Go to q people use.com. In your core concept entity, you will get back a whole drilled down list of 30-40 questions from those people's questions. We've also got the and Glink Quora, sorry, the Reddit and Quora searches and garrison searches where you should find it. You should find those questions. QSPPs is perfect. Well, again, you'd have to use it. And then finally think about the pain points your customers have before they find you. So a company that sells project management software, they might find their audience's interest is like team productivity. Come back to Bernie around remote work best practices. So by creating content that addresses these audience centricities, you're gonna meet your customers where they are. You're gonna prove that you understand their world, not just your own. And this builds trust, it draws them into your universe long before they're used to make a purchase. I take a few minutes to braid source on the topics and concepts that matter most to your target audience today. Do that research, find out the question there. So that's all four pillars, let's bring them together. So just to remind you, we have level one, the brand and product entities. So what you are, and what you sell. Secondly, you have your paper entities, who you are, and who you experts are. You put your service and concept into it. What you know and the problems you solve. Fourthly, finally, for your audience-centric entities, what your audience cares about, questions that your audience trying to get answered. Problems are trying to solve. And yeah, this brings us finally to your next steps for this week. So this is probably the most important piece of practical work I've suggested that you do so far. And that is always take that document or spreadsheet you built up while listening through this. And if you've just been doing it in your head as you go along, go back through, write all these down, get them on a spreadsheet, get them on a piece of paper, and then I want you to formalise it. Create four columns, one for each of your pillars. And your task is simple. Populate this list. For at least five or two entities in each capture which you start with. Don't worry about getting it perfect. This in the doctor is going to lit and it's going to grow. But it is going to list your foundational blueprint, the strategic map of your entity landscape. Every piece of content you create, every page, everything you do from this point for will be guided by this document. It will give your SEOFs clarity and a focus. That's impossible to achieve when all you do is just chasing keywords. That's it for today. In our next episode, we're going to put this lead proof into action. We're going to take your security and we're going to use it fine. Your prophets, and I'm doing analysis on them to reveal their strategies, their weaknesses, and the gaps in the market where you can compete and you can win. So that's if the stage over to Lex Torein. Keep optimizing, stay curious. You remember SEO is not the prod when you understand the basics. And then today we are going to look at distances, but let's just look back. So last week, what we did was they tried the first foundational work around sleep using entities. Uh we conducted an internal audit. We wrapped out your business's unique entity, landscape across four key pillars, your brand and products, your people, your core concepts, and the topics your audience cares. So you now should have that blueprint, a strategic map, the concepts that define your university important to your business, to your topics, to your website. So if you haven't done that exercise yet, I'd only refer in pause here, go back to episode seven, then list your trait there as an essential tool either mission. Because today we're going to turn outwards and we're going to look at your competition. Um take that blueprint to really dive into what's going on with your competition. So we're gonna we're gonna go through analysis on your type of like espionager and expanding on your competitors is what we're looking at. And this is entity-based competitive analysis. It's how you find the hidden gaps in your market and the precise opportunities to your rivals. For many years, competitive analysis in SEO meant just one thing, and that was like running a tool to see what keywords are surrounded for. It was useful, but also very one-dimensional. Keyword analysis shows you what the individual bricks you have competitors are using to build a wall. But an entity-based analysis shows you like the entire building, it gives you a higher level strategic view of the subject matter your competitor cover is not just show you a list of term, also reveals cover their employer, semantic network, that the web of concept of build them, the areas where they've established their topical authority in the eyes of Google and for so at least actually LLMs. Now this this way of looking at entities really is a game changer because it helps you answer much deeper questions, such as what is their true topical strength beyond just a few rocky articles, what are the precise topical gaps that enforce an entities that you've missed or only covered superficially? And also how do they structure their content and internal links around their core entities to signal that authority to the search engines? The answer to these questions provide proper data-driven roadmap for creating more code content that can help you leapfrog that in the search results, which is what you want. How do you actually do this? Might sound complex. But I'm going to walk you through a simple four-step process that you all can follow. All you need is your entity audit from Lustwing. URL of a key competitor, and access to your favourite AI LNMs, be that German LRA, ChatGPT, Floors, etc. Step one. Identify your true competitors. Well, this might sound obvious, and you might think yeah, they are already your competitors, but this is the crucial first step. Don't just think about your direct business competitors always. Think about your SERP competitors. The Swede. Go to Gable and search for all of your most important concept entities from your audit. So whatever that most important concept entity was, that's what we're going to search for. And then we're going to see the websites that are right for your first page for the two. And these are your direct competitors that even sell the same products as you, these are your competitors. So for today's exercise, just pick one. It could be your main business rival, or it could be a blog that really dominates the search results for the topic you want to add. We need to go to the search, find that one main business. Then step two, we need to gather the data. And this again, it's very simple, just go to your Convestor's Church website, pick one of the most important pages. Could be the home page, it could be a major service page, or it could be a popular blog post, whichever one they really wrote and work for from that core concept of yours. And I simply just copy the main body of text your page. You want to get at least the main heading, page one, subheading, page two, history progress content. The more you can get really, the better. And this is the core information that a search can do or anonym will analyze to understand this page's primary. And you want to paste that into the document ready on your clipboard. And in step three, we're going to extract the entities. Now, this is where the kind of the real magic behind the scenes happens. We're going to take the text that you've just gathered and we're going to use a large language model like ChatGPT, Germany, or Claude, see it the way a machine does. Now in the past you might like to point around the specialized tools to do this kind of entity extraction, which could be helpful message. Today, you have the LMs, they're actually really clearful natural language processing tools, just in themselves. And it's probably a tool. It's really the simplest way of doing it. LLMs are really good at main density recognition, because it's a whole part of how they were trades from the stone language. Open up whichever one is your favourite. And then you have specific props you can use and you can copy and paste this directly. I will read it out, but I will also click in the show notes, copy out of this. Here's the prompt. You are an experienced expert SEO analyst specializing in natural language processing and entity-based autorisation. I will provide you the texture of the competitor's web page. Your text is to perform a named entity recognition. NEO analysis on this text. These are identify all the significant entities mentioned in the text. Each entity specified one of the following categories Person, organization, location, product, event, or concept. Abstract ideas, the original topic. These could only find a simple list of table for workers, one color of the entity, one for the first category, and please sort them by their semantic relevance. Here is the text. And now is then we paste it with FTS text in them. So after you've pasted in the competitors text, AI and process it, it'll give you a really clearly structured list of the entities it's found in Skiff Questing. What you've done is you've just performed a really sophisticated entity extraction without any special software. And then we come to step four, analysing the results, following the story that's in the data. Now an entity list. Here's what you're looking for. So first, entity saturation. What are the top three to five entities? With the LM, I don't have quite a few of them. This will tell you what the AI believes is the main type of that page. How does their focus superior to your own? Or the emphasising entity you hadn't considered. Secondly, and most importantly, the topical gaps. This is where you're gonna find the real goal, essentially, in this data. You compare the list of entities, make a veteran's page against your own four pillar orders. Or they're mentioning the key concept of the tissue industry that you haven't read about, or the referencing influential people entities like photographs that you can also be citing or partnering with. Or they addressing audience-centric entities of those pain points of interest that you've overlooked. Every entity that appears in their list, but not on yours, but represents total content gap. Strategic opportunity there for you to create a more cognitive and authoritative results. Thirdly, look for structural clues. Look back at the pages URL, the main heading. How do they structure them around the main entities? Are they creating clear content hubs? For example, is the URL something like www.comp.com slash services slash entity SEO? Where you can see within that they have the services, all of their services as pages or the main services URL. And this gives you a really good clue just about toolballs, your own site, make it clear. Search engines. Finally, do a quick Google search for brand name. Do they have the knowledge portal on the right side of the results? If so, what information relate then does Google display then? This is like really a direct public-facing report card of sure well Google understands them as an entity. So, with all that said, this brings us towards the end of the episode. And what I want you to think between now and the next episode, and it's how to put your detective pattern on and conduct your own competitive choice. First of all, choose one key competitor. Secondly, select one of the most important pages, whether it's the home page or top service page, copy to main text. Use your favorite LM. The prompt I give you in to extract from that text, which is also in the show notes. And fourthly, to finally clear that list of entities to your own audit from episode seven and identify one specific entity gap, one important concept. Mayor taught you that you are not. And right entity down, because this is going to be the seed for our next project. So, in lect episode, we're going to take the gap that you've uncovered and we'll have had to build deeply authorized to check what level content. That's not only going to fill those gaps, it's also going to help us establish you as a new leader on that topic. So that's it for today. Until next time, keep optimising. Stay curious. You can remember S is not that broad when you understand basics.

Hello and welcome back to SUA Not That Hard. It's me here, Ed Dawson, as always. And today we're on to episode 9 of our series on entities. So far we've come quite a long way on this entity journey. We started by defining entities and understanding how machines read our content. We've explored Google's knowledge graph and insight how LLMs work. And in the last two episodes, we got practical. We conducted an internal audit to map our own entity landscape. And then we used that map to perform an analysis on our competition, identifying the strategic entity gaps in our market, in our topic area. So now you should have a specific entity written down. That concept that's crucial to your audience, but it's one that either you or your competitors are either ignoring or covering poorly. This is your opportunity. But how do we pick up on this opportunity? It's not enough just to write an article about that topic. The old way of doing SEO, sprinking a keyword into a 500-word blog post, that's dead. It's just too simplistic and it doesn't really help Google or the LMs understand what you're about. To win nowadays in the world of entities and AI, we need to do more. We need to build authority. So today we're going to learn how to create content that signals deep expertise and really undeniable authority to both your users and to the machines that guide them. We'll cover the strategic models and principles that are going to transform a simple website into a trusted go-to resource in its niche. So the first and most important shift we need to think is to stop thinking in terms of individual pages and start thinking in terms of interconnected topics. So for years, the standard SEO advice was to create one page for one keyword, and this just led to websites that were essentially a collection of disconnected siloed articles. You'd have a page on running shoes, another on marathon training, a third on injury prevention, and they might not even link to each other. And this structure just doesn't demonstrate deep expertise, it just shows you've targeted a few keywords. The new entity first model is called the topic cluster, or sometimes the hub and spoke model, you might hear it called also. So you've got to imagine you're building a small focused library on your website for each of your core entities. Instead of just writing like a single sort of short pamphlet, you're essentially going to create a little library around that topic cluster area. This model has got two key components. First of all, you might have heard me mention these before on the podcast. You have the pillar page. This is the hub around this topic cluster. It's going to be a broad, comprehensive, long-form piece of content that will provide a complete overview of your core entity. So if your entity is content marketing, your pillar page will be the ultimate goal. It'd cover the definition, the history, the key strategies, the metrics, everything from a high level. It's really like a foundational resource that introduces a user to the entire topic. Secondly, we then have cluster pages. Now these are like the individual books in a library. So if our if the pillar page is our library with a welcome area that's got the signpost to all the different topic areas within that cluster, the cluster pages are like the individual books. Or if you're linking in terms of hub and spoke, they're the spokes of your wheel. And these are more specific, in-depth articles that will explore subtopics or the sub-entities related to your pillar. So for example, with our content marketing pillar, the cluster pages might be how to create a content calendar, beginner's guide to SEO for blog posts, or 10 ways to repurpose your existing content, or measuring content ROI. Now here's the crucial part. It's going to be the linking structure. So the pillar page links out to every single one of its cluster pages. And just as importantly, every single cluster page links back up to the pillar page. This is going to create a tight, logical, antiqually rich web of internal links. And what this does is it signals to a search engine, it signals that you haven't just written one article about content marketing, but you've created a comprehensive, organized and interconnected resource that demonstrates a true mastery of the subject area. You're not just an expert on a keyword, you're an authority on a topic. And this is one of the most powerful ways to build topical authority today. Okay. So we have our structure. Simply mentioning an entity or entities is not enough. The internet's, you know it yourself, is drowning in repetitive, rehashed content, especially now people churning up more and more AI content that again is just rehashing and repeating what others are saying. To really stand out, you've got to provide what's called information gain. Now, I've done a podcast on this before. You can also search and go back to learn more about information gain. But basically, it's a concept from information theory. But in SEO, it means simply means this. Does your content add new, unique value to the web that goes beyond what's already available? When Google crawls and analyses your pages, they're set to effectively the saying, does this page teach me something new about this entity, or is it just repeating what a hundred other pages have already said? Content that provides genuine information gain is going to be seen as a more valuable resource and it's more likely to be rewarded. So how can we achieve information gain? One of the most powerful and simple ways is with original research and data. So instead of citing someone else's statistics, conduct your own survey. Analyse your internal data, publish the findings. When you do this, you become the primary source. You become a linkable asset, a piece of content that other experts, bloggers, and journalists will want to cite and link to, which is that is the ultimate authority signal. Another way is by publishing detailed case studies. Show, don't just tell. A case study with real data stories and results demonstrates how you solve the problems your audience is facing. It's a really powerful form of proof and it builds immense trust and authority. You can also add your own unique spin or commentary. If everyone in your industry is reporting on a piece of news, don't just repeat the facts. Analyse them. What does this news mean for your audience? What's your expert take? Your unique perspective is a form of information gain. And finally, you can simply be more comprehensive. This is the idea behind, you might have heard it called the skyscraper technique. You've just find the best piece of content that currently exists on your topic and then create something that is significantly better, more in depth, more up to date, with better examples, better visuals, and a clearer structure. So when you focus on information gain, you stop competing on keywords and you start competing on value. This goes back to everything I've been saying for the past few years on this podcast. So this brings us to the framework that ties all of this together. And you've gonna have heard of it before. It's Google's set of quality guidelines encapsulated in that acronym EEAT, which stands for expertise, experience, authoritiveness and trustworthiness. Now, EEAT is not a direct ranking factor, but it is the way that Google looks and evaluates content quality. And a strong entity-based content strategy like the one we've been discussing will directly support every single letter in that philosophy of theirs. So let's look at each one. Expertise and experience. How does Google know content is written by an expert with real-world experience through your people entities? That's why it's so critical to have those detailed author bios clearly attribute content to people and showcase their credentials and affiliations. You're not just publishing anonymous articles, you're presenting the work of a credible expert. Authoritativeness. How do you build authority? You build it through your topic clusters and your linkable assets. When you cover a topic more comprehensive than anyone else does, and when other authoritative websites start linking to your original research and case studies, then you are going to build undeniable authority and trustworthiness. How do you build trust? By being factually accurate and consistent, by supporting your claims with data, citing reputable sources and updating content regularly to keep it fresh and ensuring the information is verifiable. Going to build trust with both your users and the search engines. So now hopefully you've seen how it all fits together. An entity first approach isn't about chasing algorithm tricks, it's about building high quality, trustworthy, and expert driven resources. A well defined Entity profile gives Google greater confidence in your website as a properly reliable source of information, which is nowadays a crucial factor in how it's going to rank. So this brings us to the next steps. What I'd like you to do for this episode. It's now time, we've been doing a lot of analysis, we need now start to get to that point of creation. So I want you to take that single entity gap that you identified in the last episode, and instead of just planning one article, I want you to plan a small topic cluster around it. So first, you need to outline your main pillar page. What would a broad, comprehensive overview of this entity look like? What are the main sections you'd need to cover? Secondly, brainstorm three to four specific cluster pages. What are the deep dive questions or subtopics or related entities that deserve their own dedicated article? And thirdly, pick and finally pick one of those cluster page ideas and think about information gain. What unique angle can you bring to it? Is there a small server you could run? Is there a case study you could write? Is there data you've got that you could be using? Is there a unique perspective you have that no one else is talking about? Now you don't need to write this content yet. The goal really is just to practice the strategic thinking behind building authority and about designing a structure that's going to match the entities that you've got and how to demonstrate that typical authority. Because now we've had this plan for creating amazing authoritative content. We'll need to make sure that machines can understand it perfectly without any ambiguity. So in our next episode, we'll get technical, but I promise it's not going to be super technical. But we're going to dive into the world of schema markup and learn how to speak the language of machines. Until next time, keep optimising, stay curious, and remember SEO is not that hard when you understand the basics.

Hello and welcome back to SEO It's not that hard. It's me here at Dawson, as usual. And today we're on to episode 10 of our entity series, which is Speaking Machine, your practical guide to schema mark. We've now arrived at the implementation phase of our journey here with entities. We've done the strategic groundwork with MAFTA entities, audited our competition to uncover the gaps, and we've built a content blueprint grounded in information game, bringing something new, the story. So you now have a plan for creating technically deeply authoritative X-level content that's going to provide real value to people, but there's one step before all the hard work pays off. We need to make sure that machines, children crawlers, and increasingly models that read and summarize the word to understand our content just as clearly as humans did. In other words, it's time to speak their language. So stay with game a bit total cool, but don't worry, we're not going to get too deep. We'll keep it as simple and broad as we can. We're talking about one of the most powerful ways to get directly in search engines, and that is schema mark-up. So what exactly is schema markup? The simplest way to think of a schema is as like a universal translator for your website. It's a shared vocabulary tags, which are like a structured layer of code. You can add to your web pages to tell search engines exactly what your content is about. Imagine we talked about the analogy of using a lap your website as a library, you can consider each page as like a book. The content on the page, your words and images, videos, etc., that's the story inside the book. A search engine can read the story and guess what it's about. But when you add schema mark or you are also given a clear machine readable label to the cover. So you think things like this is an article. The headline is It's McDonald's to pour over coffee. The author is a person named Jane Doe. Jane Doe knows about coffee brewing. The publisher is our organization, the coffee collector. So what you do with this is you're removing what the ambiguity that natural language introduces, so go provide structured data. Explicitly, definitively defines the entities on your page and the relationships between them. Now there are a few formats spreading this code, but the one Google recommends, and the easiest to maintain, is called JSON LD, which stands for JavaScript Object Notation for Linked Data. JSON LD. Actually it's JSON Heiston LD, is how you see it spelled out. It's just a short script. You typically place it in your site's head section and it sits separate from invisible content, which can make it tidy, flexible, and make it easy to update. And the relationships between it popular. It won't magically improve everything exactly, but it will really dramatically improve where your entities are understood. The common mistake people often do in that is they add schema to individual pages, so article on a blog post, product on a product page, and then they just stop there. The real powerful magic happens when you start collecting those schemas together. So instead of isolated labels, you start to build a cohesive entity graph, which is like a web of relationship that produces your entire business and all the pages and products or everything within it. So you're not just typing individual books anymore, it's more it's more like having a map of your entire library. Think about how we build it by step. So the first step step one is your foundation. This is your site-wide identity. And this foundation could be your entire schemography as on your homepage. So this is where you can find who you are using two essential schema types, which is organization and website. The organization markup is like a digital business card, it includes your legal name, the URL, conducting things like that. But the most powerful properties you can give it is called SAMAS. And SAMAS tells the search engines, when you say a name, this isn't the same entity, it's these profiles. You can link to other authoritative external identities like your current LinkedIn page, your ex Twitter page, YouTube profiles, Croge Base, Wikipedia, Wikidata entries. And this helps Google and other systems disembicuate your brains. Connect your website to the correct real-world entity and global knowledge graph. Step two is then defining your expertise, your authors and your content. Once your organization is defined, establishing the expertise behind your content is your people, your authors. And this supports Google's EEAT experience, which is using Torridor. So every blog post use article schema inside it, including the author property, the link to a person object. So your person schema should include name, the author's full name, job title, their affiliation, which is the link back to your organization because they are affiliated with the organisation. And if you want to go a step further, you can use what this optional knows about property, which has helps specify what topics the author had expertise in. For example, knows about search engine optimization. That's not a Google requirement, but it is a smart way to help make your author expertise more machine readable. And then we move to step three, which is where we define what your your products or your services. Defining what your business actually offers. You use your product scheming for things you sell. The service scheming for offerings like consulting or design work. In these link back to your organise organisation that's using the provider properly. This shows your organization is the one providing these specific offerings. It more closely couples the semantic connection between brand and product. Just make sure you structure data, it always matches what's just on the page, Google Strip by that. You can make stuff up, and put it in the schema, hoping to fool people. It does cross-correlate what's on the two. So always make sure that you keep them in sync with each other and align. And then step four, the final step, this is where we connect all the dots. So you can link all our entities together. We use that ID attribute, which is it's a unique permanent identifier for each entity. So it's like a stable address, it's like having a phone number or a URL, whatever it is. It is the kind of canonical naming convention for each one of these things. So for your organization, you use your home page URL as the ID. For an author, you use that body page URL as the right ID. And for product, it's the product page URL for the right edition. So it's basically the canonical version. This is canonical version for this entity. So then whatever you reference to elsewhere, say the article schema reference in your organisation as a publisher, you can just point to that ID instead of repeating all the details, but instead the machine, if you want all the details of the organisation responsible with this article or this product, whatever, don't here. And you can just get all that matched up to repeat over and over again. This creates a really clean, connected structure that machines can easily follow. So the person you wrote this article published by this organisation for violetists. You're not just tagging your mapping relationship creating like so you had own your own little mini knowledge for the machines to read. We'll understand. So this week, what I suggest you do is do a quick non-intimidating technical checkup. So if you go to the schema mark validator, which is an official tool from schema.org. You drop the Troy Google's test if you want to see which features your markup might qualify for. Enter your page URL test. Then look for two settings. Designalisation and Detect when you expand it, does it contain the same as for Linux or profiles? Both are true, great, and got a solid foundation. But if not, you know what to fix. Now I know this is probably quite a lot to take in on a podcast episode. I've given you loads of different technology and schema entities and things to look at. Go to Google GoGio.ai, research these topics so you can get in much more detail on how to directly. I'm just giving you the high level both of the concept, how they work together, and why it's interesting to do. You really want to dig into this? You know what to do? Go and research, however, I'll put it. But it's a really powerful method here to link it all together and you link all the entities together and give the machines a real clear understanding of exploitry organization, entities, products, chances, etc., all linked together. So now you've got a plan for creating your authorities functions and for structuring it for a machine. I was telling there's just one PT. So the next episode we'll talk about failure to optimise your writing itself. So the AI, LLMs, which essentially nowadays are people call them answer engines, which do you should essentially they are like ChatGPT and Google's air, it needs to quote you directly, and that's what we'll look at in the next episode. So until next time, keep optimising. Stay curious, you remember. SEO is not that hard to anyone's done the basic.

Thanks for listening, it means a lot to me. This is where I get to remind you where you can connect with me and my SEO tools and services. You can find links to all the links I mentioned here in the show notes. Just remember, with all these places where I use my name, the ed is spelled with two Ds. You can find me on LinkedIn in Blue Sky, just search for Ed Dawson on both. You can record a voice question to get answered on the podcast. The link is in the show notes. You can drop my SEO intelligence platform KeywordsPuopleUse at keywordspeopleuse.com, where we can help you discover the questions and keywords people asking online. Posting those questions and keywords into related groups so you know what content you need to build topical authority, and finally, connect your Google Search Console account for your sites so we can crawl and understand your actual content. Find what keywords you rank for and then help you optimise and continually refine your content.eddawson.com. Bye for now and see you in the next episode of SU is not that hard. It's me here, Ed Dawson, as always, and we're now on to part 11 of our series on entity. So we're now deep into our action plan. So over the last few episodes, we've built a powerful on-pace strategy from the ground up. We started by auditing our entity landscape and really deep diving our competition. We then learned how to build deeply authoritative content using the topic cluster model and the principle of information gain. And last week we got technical, demystifying schema markup and learning how to build our own entity graph to speak directly to machines. At this point, you have a plan for creating content that is strategically brilliant, deeply authoritative, and technically perfect. It's designed to be understood by search engines and valued by your human audience. But there's one final layer to this on-page puzzle. The way people find information is starting to change. They're no longer just looking at a list of blue links. Many people still are. There is a shift happening. They're starting to seek the answers to their questions directly by asking them to AI assistance to LLMs, and they're getting back synthesized answers. Our job is to make sure our content is the source for those answers. So today we're going to look how to optimise our content for the new gatekeepers of this information, the generative AI engines like ChatGPT, Gemini, Google's AI overviews, Claude, and Perplexity. Now, for years the goal of SEO was to rank in Google and then get a user to click on your link. Now that's still important, but a new goal has emerged, and that's to be cited by an AI. Now when a user asks an AI what are the best techniques for brewing pour over coffee, the AI will synthesize an answer based on the most reliable, well-structured information that it can find. So becoming that source of information is the new position zero. It establishes your brand as a definitive authority in a way that even a number one ranking on Google can't. So how do we do it? How do we make our content not just readable, but easily digestible and reusable for an AI? It comes down to three core principles structuring for machine parsing, prioritizing factual accuracy, and framing your content as direct answers. So let's look at principle one, structuring for machine parsing. So imagine you're writing a textbook for a very smart, very literal machine, like a robot, doesn't appreciate literary flair or long meandering paragraphs. What it needs is clear signposts, a logical hierarchy, and information broken down into bite-sized, easily extractable pieces. So the first and most important signpost is your heading structure. This is something that hopefully, if you've been doing SCOL for a long time, is nothing new. And if you've been listening to my podcast for a while, then you'll have come across these, but it's worth reiterating here. So your main title should always be a H1, your main section should be H2s, and the subsections within those should be H3s, and so on. Now this isn't just for visual organization, which it will help for actual real people reading it, but it what it does is it creates a logical table of contents that a machine can pass instantly. It allows the AI to understand the topic boundaries and to navigate your content to find the specific segment it needs. Next, embrace short paragraphs and lists. An AI is far more likely to pull a concise three-sentence paragraph as a direct answer than it is to try to summarize a huge wall of text. Even better, a bulleted or numbered list. Each list item itself is a self-contained, perfectly formatted little snippet of information. And AIs can then easily lift a single bullet point answer to a specific question, making your content really incredibly useful for building AI-generated summaries. The overall mindset here is to think in snippets, not essays. Every section, every paragraph, every list item should be crafted as if it could be pulled out of context and still make perfect sense. Now, principle two is the currency of facts. So this is where, without a doubt, it's the most critical principle of all. So if we go back to episode six where we've talked about AI hallucinations and the rise of retrieval augmented generation, otherwise known as RAG. Now remember, RAG is like an open book exam for AI. It helps it combat into hallucinations. And it does this by first retrieving information from trusted external sources, and it then uses that information to formulate its answer. And your goal is to make your website the most reliable, fact-filled, and trustworthy book for that exam. So to do that, you must prioritize factual accuracy and unambiguous language. AA models they don't like vague marketing jargon and subjective claims. They're not really telling anything that they don't you can't extract a very well verifiable fact from that. So a statement like our product offers unparalleled performance is useless to an AI. It's subjective opinion with no data. What's the benchmark for that performance over what? So a statement like says our product processes data at 10,000 records per second, a 20% improvement over the previous version, now that's perfect, it's clear, 10,000 records per second. And it's backed by that claim of 20% improvement over the previous version. And that's a statement that a machine can pass and present as fact. So every claim you make should be verifiable. So if you publish statistics, cite your sources, link out to reputable research or official reports. This doesn't just build trust with human readers, it shows the AI that your content is part of a credible, interconnected web of information. Think of every page on your site as a potential source that an AI might quote directly. Your job is to make it airtight. Then principle three, this is where you need to frame your content as answers. So large language models are at their core, they're answer engines. They're designed to respond to natural language questions. So the most effective way to optimize for them is to structure your content to directly address the questions your users are asking. The most powerful tool for doing this is the FAQ section. Adding a frequently asked questions section to your articles or service pages or wherever on your site where it makes sense is a really strategic thing to do. It provides clear, self-contained, perfectly formatted question and answer pairs that an AI can easily lift and then repurpose with minimal efforts. Where do you find these questions? You don't have to guess. Google tells you exactly what people are asking. Go search for your main topic, look at the People also ask box. These are the discovery queries, these are the informational questions that users are asking about a topic. Build sections of your content or even entire articles that directly answer these questions. This is something you'll have heard me talking about again and again on this podcast. And obviously, you can go to Google directly or you can use keywordspeopleuse.com, which we built to easily mind these questions. It's that simple. If you frame your content as a series of clear answers, then you are aligning your website perfectly with what the primary function of an AI is. That's to make it incredibly easy for it to choose your content as its source. Finally, there's one last quick technical check. And this is like the equivalent of making sure that your door is unlocked for the LLMs to come in. Your content could be brilliantly structured, factually brilliant, but if AI crawlers are blocked from accessing it, none of it matters. So you need to check your robots.robots.txt. This is a simple file in the root directory of your site that gives instructions to web crawlers. So you need to ensure that you are not going to block crawlers accidentally from the major AI companies like OpenAI or Google or Gemini or Anthropic or Perplexity. And also check your content delivery network. If you're using one such as Cloudflare or something like Amazon AWS CloudFront, I actually found this on one of my sites. We were letting in all of the LLM crawlers via robots.txt, but then our content delivery network was blocking ChatGPT crawlers, meaning that ChatGPT couldn't answer the sites, access the sites. So we had to go and modify the settings in our CDN to make sure that it wouldn't block OpenIs robots so that it could see our content. And while you're there, checking out robots.txt, just check you've got a clean up-to-date XML sitemap. This file will act as a roadmap for your site, helping these crawlers discover and index all of your important content efficiently. If you're not sure what robots are or what XML sitemap is, search on our podcast because we've done episodes in on both of those individually. So that brings us to your next steps for this week. It's time to put these principles into practice and make your content truly AI ready. So I want you to choose one of your most important and popular blog posts or articles or web pages. Your task is to perform an AI optimization audit on it. So first of all, check the structure. Look at your heading hierarchy. Is it logical? Are you using proper headings? Can you break up any long paragraphs into shorter ones or turn a dense section into a bulleted list? Secondly, rewrite for facts. See if you can find any subjective marketing claims in the article and rewrite them to be specific, verifiable, data backed statements if you can. And thirdly, add an FAQ section. Go to Google or keywordspupleuse.com. Search for the main topic of your article and find two or three questions from the people or swash box and add a new FAQ section at the end of your article and provide clear, concise answers to those questions. This exercise will give you a tangible feel for how to refactor your content to be not just human-friendly, but machine friendly too. But that is the key thing. Always make sure it's human friendly, because that will make it machine friendly too. Now we have completed our deep dive into one-page entity first optimization. We've got a complete strategy for creating and structuring content that's going to build authority with users, search engines, and AI. And in our final episode next time, we'll look beyond the borders of our own website. We'll explore the world of off-page SEO and how to build powerful entity associations across the entire web. So until next time, keep topomising. Stay curious and remember SEO is not that hard when you understand the basics. Hello and welcome back to SEO is not that hard. It's me here, Ed Dawson, as always. And today this is the final episode in our special series all about entities. So we've reached the end of our journey, and it's been quite a journey. This is the first time we've done such a long series on a single subject, but we started by fundamentally redefining our approach to SEO, moving away from the old world of keywords to the new world of entities. We've audited our business, we've audited our competition, we've built a complete on-page strategy from the ground up. And we now have a plan for creating deeply authoritative content, structuring it with schema markup so that the machines can understand it, and optimizing it for the LLMs, the AI answer engines of tomorrow. If you followed along, your website will now be poised to be a real castle of authority, but like a castle, no matter how strong, it can feel isolated up there all by yourself. To truly establish your brand as a leader, you need to build bridges. You need to project your authority beyond the borders of your own domain and across the entire web. So today in our final episode, we're going to look beyond our websites and we're going to explore the world of modern off-page SEO. We'll learn how to build powerful signals of trust and authority that tell the whole world and every search engine, LLM or otherwise, that you are a credible, important and trustworthy entity. So for decades, off-page SEO meant one thing, link building. And the goal was to get as many links as possible from other websites to yours, where each link being seen as a vote, and the more votes you had, the higher you would rank. That's the simple version. I know it's more complex than that. But while links has an incredibly important, the thinking behind why they're important has evolved. The new goal is not just to acquire links in and of their own right, but to build entity association. And this is some kind of a crucial shift in mindset. The objective is no longer to just pass page rank or link juice. The broader, more strategic objective is to create a web of contextual signals that consistently associate your brand's identities with other established authoritative entities within your niche, within your topic area. Think of it like building a reputation in the real world. You can tell everyone you're an expert. Your reputation is truly built when other respected experts start talking about you, when or other organizations, the more prestigious the better, invite you to speak, and when your name consistently appears alongside theirs in important conversations. This is what we're trying to achieve online. We want Google and other AI systems to see that our brand entity is mentioned in the same context as other trusted entities in our industry. This pattern of association or co-occurrence teaches the AI to semantically connect you to that world of authority, effectively allowing you to borrow credibility and reinforce your topical relevance and authority. So, how do we do this? There's four key strategies. So, strategy one is digital PR and your linkable assets. This is the main strategy for building authority in the modern link building area. So let's we can connect this back to episode nine, where we talked about creating linkable assets. This is the cornerstone of your content strategy, okay? Your original research, your data-rich case studies, your definitive industry guides. A linkable asset is a piece of content that's so valuable that other people will want to cite it. But creating it is only half the battle. The other half is promoting it through digital PR. Now, there's whole episodes on digital PR that I've done, so if I'm more depth, go and listen to them, but for now, we'll cover it just in a nutshell. So digital PR is the process of doing outreach to journalists, bloggers, industry publications, and other content creators to make them aware of your valuable resources. The goal is to get them to reference your work in their own content, and this achieves two goals at once. First of all, it earns you a high-quality bat link, which is still a powerful ranking signal in its own right. Secondly, it generates valuable unlinked brand mentions from authoritative domains. Even if they don't link to you, just having your brand name mentioned in an article on a major industry site is a powerful entity association signal, just being mentioned, that co-occurrence. Google sees your name next to a topic you want to be known for on a site it already trusts, it's going to strengthen that connection. Strategy two, that's digital foundational consistency. Now, this next strategy is about building a really solid, trustworthy foundation for your brand across the web. Now you may have heard of the concept of NAP consistency, that's name, address, phone number. And for local businesses like a coffee shop or plumber, anyone who's local is really critical, big in local SEO. But for many of us, like SaaS companies, informational science, e-commerce, brands consultants, a physical address or a phone number isn't necessarily a core part of our identity. So we need to think about the digital equivalent. And this is what we can call digital foundational consistency. The principles it's the same. It's ensuring your core identity is perfectly consistent across every platform you're on. So this is going to create a web of signals that validates your entity's existence and legitimacy for search engines. So what are the core assets for a digital first business? Your official company name, is it MySAS or MySAS Inc., pick one, stick with it everywhere. Your website URL, always link the same Chronicle version. Your logo will use the same high-resolution file on all platforms. Your core messaging, like the one sentence description of what you do. It should be consistent across your social media bios, your directory listings, and your website. And where do you need to be consistent? On your core social profiles, so if you use LinkedIn or X, on any other major business reference sites that are relevant to you, like Crunch Base or sites like G2, Captera or Trustpilot, a comprehensive brand audit across these areas to make sure that you're being consistent across them all is a powerful trust building exercise. And then strategy three, strategic partnerships and co-occurrence. Now, this is slightly more advanced and it's about actively building entity associations. So it involves seeking out other people and other businesses to collaborate with who are non-competing but are highly authoritative entities in your industry. So this could take many forms. So co-authored research, I see this a lot in the SEO world. Partner with another company or an academic institution to produce a joint industry report, joint webinars, host a webinar featuring an expert from your company and an expert from a respected partner company that might have a product that complements the one you're trying to sell well, so you can complement each other. And then integration partnerships. So for example, if you're a software company integrating your product with another well-known tool which allows integrations, such as say with Screaming Frog in the SEO world, and then co-market the integration with that company that you've integrated with. And the strategic goal here is to create this co-occurrence, the repeated appearance of your brand entity alongside another trusted entity in relevant high-quality contents. Then when Google scrollers repeatedly see your brand and your trusted partner brand mentioned together in the same context of your industry topic, its algorithm is going to learn to associate the two. It's like being repeatedly seen with the most respected person at a party. Their authority and relevance begin to rub off on you. Then strategy four is becoming an expert source on modern platforms. This is our final strategy, and it brings us all back to your people entities. This is where we what we want to do is to relate people, your experts, to your topic area in mainly news platforms. So this is where journalists will be looking. To have experts on a topic compliment the story they're writing about. You'll see in most journalistic articles that they will always try and cite experts as third party people to give credibility to their articles, but but also being mentioned in the article gives credibility to the expert. It used to be for years for years and years the go-to tool for connecting experts with journalists was a service called Harrow. Now this landscape has evolved quite a bit, and there's a whole different world that you know to work in because Harrow was taken over, got shut down, launched again, might have been shut down again, but it's all got a bit messy. So what you need to do is discover which platform the journalists in your topic area are using to make these requests. Places that they might be are quoted, they might be on featured.com, they might be on source of sources, they might be on Facebook groups. Wherever the these journalists are, you need to find them. And the easiest way to do this in many cases is just to look at what journalists and which publications are writing about your topic area or covering news stories in your topic area, and then reverse engineer from them the journalists that are being quoted, and you'll find the places where they are. It could be worth building a list of the particular journalists if there are some specific journalists in your area and make contact with them and make sure that you're available. But it's better if you can find them in these marketplaces where they are pitching, asking people to be experts, provide expert quotes on news articles they're working on. Now, if you can crack this, it is a really powerful way of getting your entities, your people entities, and also often your organization entities mentioned in these high pro profile news sources, empowering that co-occurrence and that entity trust authority signals that you're looking for. Now and with that, we have now reached the end of our action plan and the end of our series. So let's take a moment to look back on where we've been. We started by fundamentally shifting our perspective from keywords to entities, things not strings. We learn to see the web as a network of concepts, not just a collection of pages. We've built a complete on-page strategy from auditing our entity landscape and creating authority topic clusters to structuring a database schema and optimizing our writing for AI answer engines. And today we've completed the picture by learning how to build our authority beyond our website through entity association. And this transition from a web of keywords to a world of entities is not a temporary trend. This is permanent and it's only accelerating now with the rise of AI and LLMs, which are the entity concept is core to them. And by embracing this entity-first framework, you're doing so much more than optimising for today's search algorithms, your future-proofing, your digital presence. You're transforming your website from a simple marketing tool into a strategic data asset, and you're going to position your brand to be a trusted go-to source of information for the LLM AI-powered answer engines of tomorrow. So the future of search is semantic, and the language it speaks is the language of entities. And now hopefully you're a bit more fluent than you were at the start of this series. So thank you for joining me on this special series. Go put these strategies into action and build the authority that you deserve. Now, until next time, keep optimising, stay curious, and remember SEO is not that hard when you understand the basics. Thanks for listening, it means a lot to me. This is where I get to remind you where you can connect with me and my SEO tools and services. You can find links to all the links I mentioned here in the show notes. Just remember with all these places where I use my name, the Ed is spelled with two Ds. You can find me on LinkedIn and Blue Sky, just search for Ed Dawson on both. You can record a voice question to get answered on the podcast, the link is in the show notes. You can try our SEO intelligence platform KeywordsPupleUse at keywordspupleuse.com where we can help you discover the questions and keywords people are asking online. Plus those questions and keywords into related groups so you know what content you need to build topical authority. And finally, connect your Google Search Console account for your sites so we can crawl and understand your actual content. Find what keywords you rank for and then help you optimise and continually refine your content. Targeted personalised advice to keep your traffic growing. If you're interested in learning more about me personally or looking for dedicated consulting advice, then visit www.eddawson.com. Bye for now and see you in the next episode of SU is not a hammer.