Improving your SEO with conceptual models - Mark Williams-Cook

So, thank you all for coming. I'm going to talk to you about possibly the most boring title we've ever had at Search and Knowledge, which is Conceptual Models of SEO and Google Exploits. And to start, I'm going to show you about conceptual models because it's easier than trying to explain. And I've brought my famous tennis ball that comes with me everywhere because I want you to take part in a science experiment. Oh, no. experiment with me, which is very simple. I just want you to imagine if I drop this normal tennis ball, how high you think it's going to bounce. So put your hand up if you think it will bounce this high. Put your hand up if you think it's going to bounce about this high. Lower. What, lower? Okay, I'm just going to do it. We'll find out. Look at this. Amazing. Underwhelming. About that. My point here is there is a way, of course, to calculate this with the coefficient of restitution, and I'm sure none of you did that in your head. You used a conceptual model that you have because you know floor hard, tennis ball kind of squishy, things accelerate when you drop them, and you've seen some tennis balls bounce before. And my pitch to you is you can do SEO in a similar way with conceptual models, and in fact, this is what advanced SEO is. Advanced SEO is all the tiny bits of information that you pick up understanding how certain things work, hearing what people like Brendan were saying, and stitching them together into a model. To me, that's what advanced SEO is. It's great understanding things like vector embeddings, but it's not going to tell you what is and what is not going to rank, and that's what I want to talk to you about. So, if you're brand new, fresh-faced in SEO, and you're like this chap, I'm going to hopefully change the way you think about SEO. If you have a few years under your belt in SEO, and you look a bit more like this, I'm going to teach you how to add to your conceptual models to build a reliable way of knowing what you should be doing in SEO. Or if you are like Brendan, a veteran SEO living their best life, enjoying all of the work they're doing— and by the way, hello to all the clients in the room that watch Brendan's talk. You must be the nice ones because you're still with us. I'm going to go through— for those that don't know, we found a Google exploit last year And we got lots of interesting information from that, which I'm gonna share some with you in this talk. But I wanna start off talking about games, 'cause I play lots of games, but I play games in a weird way, which is I think about them as optimization problems rather than having fun. So I will never play you at Connect Four, 'cause I know if you start and you go in the middle column, it takes 41 turns to force a win. So it's a solved game. I'll probably never play you at Monopoly because where you move is determined by the distribution of 2 dice rolls which has this bias. So you can make a heat map when you take into account the chance cards of where people are gonna land and therefore you just know where to buy properties. So it's not particularly fun to play. So what do we learn from this apart from I'm a very unfun person to invite to your board game evening? It's that there is a game actually, and you may laugh at this, that I think taught me more professionally than many actual qualifications I took. which is this game. It was released in 1996, and it's called Theme Park. It's kind of a simulation game about running a theme park. There's all kinds of really interesting variables you can affect, which change your optimisation outcome, which is you're meant to make as much money as possible in this game. And this game is really similar to SEO for lots of reasons. You're optimising to get the most amount of visitors. You're optimising for them to have a good time. You look at things like how long they spend in your park, lots of things SEOs talk about. And like Google, there are documented and undocumented things you can do to affect the outcome. So an example in theme park is you can change the ticket price, which if you put the ticket price up, you make more money up until a certain point where people are like, oh, it's too expensive, I'm not coming. Just like Google has provided us with documented advice about you can optimise your title tag and put keywords in it, but you know you can't put 500 words in your title tag. It doesn't work that way. But the really interesting thing about this game for me and why I ended up loving it was the undocumented things you can do to optimise. So in this game you can have little shops and they sell French fries, sell chips, right? But I found you can control the amount of salt you put on the chips, and if you put lots of salt on the chips, people get thirsty. Really thirsty. So you can make the chips really cheap, and then you put a drink shop just down the road and charge 5 times as much for the drinks, and people buy them because they're so goddamn thirsty. So it's like this undocumented way to change your outcome, and Google has these as well. So Google's— the most obvious example, I think, is anchor text, the words you use in the text when you link to web pages. Google really never told us about this, but as a community, we figured out it was a really powerful way to change how your website ranked. Unfortunately, in Theme Park, if you put way too much salt in your chips, people puke. I found that out as well. And if you really overdid the anchor text thing, you would actually get a penalty from Google. That was the Penguin algorithm we spoke about. But what really fascinated me was the advice, the strategic advice. In the manual, Theme Park said, just aim to build the most fun theme park your guests will love. And it really reminded me of what Google tells us, which is just make really good, helpful content for your users. And this is the framework of our conceptual model, right, which is Google tells you to make helpful content. And I'm certainly not saying this is a bad thing. You should absolutely do this. But the Google algorithm has a target. And if you think that target is helpful content, you will probably find out, like many people, you will spend ages making helpful content and find out you don't actually rank. This is because there are other forces pulling on this. Which in Google's case is making ungodly amounts of money, which we've learned. And the result is the algorithm reality is somewhere in the middle, right? Yeah, they want helpful content, but they also want to make as much money as possible. I did have examples of this, but then Google got found to be a monopoly, so I don't have to bother proving it because it's factually true now. But this is the same as theme park, right? Just make fun rides, but I'm going to salt the hell out of the chips because my optimisation goal is to make as much money. This is the way you can think about SEO. If this is the ranking reality, why is SEO not super simple? Part of the reason is it turns out it's very difficult to index and order all of the information in the world. It's a tricky thing to do. Google, while that might be their target, there is a margin of error, and these are addressed with Google updates. So, generally what will happen is Google will do an update and it will maybe veer itself off course. They'll gather data about why that is, or the web kind of changes. They do another update which fixes that and creates some other kind of issue. And the interesting thing is at each one of these junctures, sometimes they land near the middle line, but when they're way out, it normally means there's something exploitable in the algorithm you can do. And this is referred to— so if there are any gamers here, you'll be enjoying this— this is referred to in online gaming as the meta. Does anyone know what that term means in gaming? A few people. A few people obviously like worried to out themselves. So the meta is generally the term used when in competitive online gaming the optimal way of playing the game is discovered, the optimal equipment to use, the optimal strategy. And what happens is everyone starts playing with that strategy and doing that thing, and as a result, the game becomes less fun. So, the developers of the game release updates to change the rules slightly to make it more competitive. So, they change the meta. This is exactly what happens with SEO. We kind of figure out what the meta is, then over-optimise it, and then Google's like, hmm, the results probably aren't as good as they should be because all these SEOs are there. So they changed the rules. And I say this because I can provide evidence. At Brighton SEO in 2022, I gave this example of multiple sites going from 0 to over a million visitors in 16 weeks on brand new domains, just using zero volume keywords and basically spamming out AI content. And that was because of recent Google updates had left this as like a big open goal for them. We had a whole second renaissance in niche blogging, i.e., people just setting up blogs, and they found anything ranked really easily. Um, and Google was just kind of like, whoops, probably shouldn't be doing that. And their fix for this was the Helpful Content Update, which you can see the algorithm running here. This is behind the scenes, and it's selecting Reddit there. That's Reddit, that's Forbes, that's Quora, um, being carefully selected as the sites that should rank. So what happened was Google essentially started doing the opposite. It became very difficult to rank unless you had these big authoritative sites. So that is the current meta, which is this site reputation abuse. Currently, LinkedIn ranks for 94,000 loans keywords in the US, and by LinkedIn I mean these Pulse articles which are written by AI by people that don't exist. This is Judith, and this is all the content she's written about loans in the last 24 hours, right? And Google's just like, this is really helpful, I'm gonna rank this. It's millions and millions of traffic that's, like, valuable traffic. This is LinkedIn. This is LinkedIn, you know? So, this is where the current meta is. So, we're gonna talk about models and how we add to our own model of how Google works. There is this, which is a terrifying thing by Dr. Mario Fischer, which he put together. This is his model after working in the industry for many years about how he thinks Google works. I can see some people grimacing in the audience, and you should be. It's terrifying. And don't worry, what I'm going to say to you is you can use really simple models as well, and this will help you decode what Google is saying. So this is an example of a model you can use, which is, you know, there is a ranking stage to Google, which is this slow thing that's site-centric, and they're scoring sites and web pages. Then before you see the results, there is a post-ranking phase, which is query-dependent, so it depends on what people search for, where they might adjust the results based on things like user interaction. Why it's helpful to know something like this is you get Googlers such as Gary Illyes say, we don't use click-through rate directly in ranking, and then lots of SEOs come out and argue, because we've done tests and we found when we pay people to click on this result all the time, it shoots to number 1. And you say you don't use it in ranking. Now, both of these things can be true. Technically. Technically. It's all down to wording. So, there is a ranking phase where Google does not care how much your site was clicked on or was not clicked on. They do have a predicted click-through rate, which I'll talk about later, but in post-ranking, which is they've decided for this keyword, this is how it's going to— how I'm going to rank the pages, they do look at how people interact with the SERP. And they told us why they do this in the DOJ trial, the antitrust trial. They gave the example of if they have a search result for nice pictures and then there is a terrorist attack in France and people are Googling Nice pictures and they're clicking on something completely different, this defies what Google would expect massively. So they re-rank the queries. And just like every experiment you see with click-through rate manipulation, when the manipulation stops, the rankings return to what they were originally ranked as. So technically, we don't use click-through rate directly in ranking is true. Our questions are bad, and we think of everything before we see the result as ranking. So I always say, if a Googler is saying something to you that is opposite to how you believe Google works, it's probably some kind of nuance that you don't understand rather than the Googler is lying. I haven't ever personally seen a Googler intentionally lying, but they are sneaky. I will give them that. So, let's play a game of traffic lights, otherwise known as, is it a ranking factor? Because this is always something we talk about in SEO, right? I'm going to run through this example with bounce rate, Google Analytics bounce rate to be very specific, because whenever I post about this, I will always get people argue with me about it. So there are 3 rules that I apply to anything before I add it to my kind of mental model of how Google works, which is firstly, really obvious, does it make sense? Bounce rate on your Google Analytics is something fully under your control. You can make the bounce rate of a page 100%, you can make it a 0, you can make it 50. It doesn't make a lot of sense for Google to rank a site on something that you have full control over like that as a metric. So I don't think off the bat, and I can't even tell you what a good bounce rate would be, because if you're Googling some really specific bit of information, you load up a page, you spend 5 minutes reading it, you're like, that's what I wanted to know, and you leave. That's 100% bounce because you haven't done anything, but you've had a really good experience. So to me, it doesn't make sense. Does it scale is a really important question. Whatever you're saying Google uses as a ranking as a ranking factor, it has to scale across the web. Now, initially I was like, well, no, bounce rate doesn't scale because not all sites have Google Analytics on. When I thought about it a bit more deeply, they've probably got enough data that they could build a model to give a good guess on what bounce rate is, so I'd give that a maybe. Someone that knows more than me might be able to explain how they could do that. And lastly, is there any evidence? Evidence comes in all shapes and sizes. Evidence is things Googlers tell us they are doing or not doing clearly. It comes in patents. Now, while there might be a patent for something, it doesn't mean that's actually in search, but it's good supporting evidence. Tools like Sistrix, Semrush, Ahrefs do big data studies where they can show us correlation. Again, correlation doesn't equal causation, but it's supporting evidence. There is basically no supporting evidence that I've found that's compelling for bounce rate, so it's a definite no-no to me. These 3 questions, ask them for everything We have a discussion. I wonder if Google uses this as a ranking factor. So we're on very quickly now to Google exploit facts for your models. So this is for maybe experienced SEOs. These are things that we found. So our team found basically a Google endpoint. Google has these endpoints that talk to each other, that send data to each other, and They do this in a format called Protobuf. Does anyone know what Protobufs are? No. No, Protobufs are basically a super shorthand-like code that Google uses, where they have almost like a translation file, where they're like, yeah, if I say pigeon, it actually means this 100-word function. And it lets them send information across their endpoints really fast, 'cause it's like a compressed data source. We found a Google endpoint, and we very conveniently found that by manipulating some of the network requests, Google would just translate it into plaintext for us. Hooray! So, we got loads of data. We ended up submitting this a while after we found it to the Google Vulnerability Reward Program. They paid us $13,337 because it was a high exploitation risk, and it was highly likely to be abused. We gave that money to Brave Futures, so they've benefited from that this year. But we ended up getting 2 terabytes of data. We identified over 2,000 properties Google uses to classify queries and sites, and we got this data, over 90 million queries. So what did we find? Google has talked before about how they try and work out if information is reliable on the web, and they've talked about this notion of consensus before. And what we found out was Google actually counts the number of passages in content that agree with, contradict, or are neutral to the general consensus, and it will generate a consensus score. That consensus score is likely to impact whether you rank on a specific query or not, because we also found, interestingly, there are all these classifiers for queries, such as this one, which is, is debunking query. So this is basically Google deciding if what the user's asking is they're trying to debunk misinformation. An example for this is, is the Earth flat, would be classed as a debunking query, and what you'll notice about the results is they are all extremely with the consensus, which is— might be news to you— the Earth is roughly a sphere. For different topics where it's more subjective, for political questions, Google intentionally tries to have some consensus, some neutral, some non-consensus, which is also interesting because it may be that your content is perfect, your site is perfect, but the reason you are not ranking is Google just already has a good site which fulfils that need. You're not going to rank 3rd and 4th because Google wanted something specifically that didn't agree with the consensus there and you did. Think of it like making a good recipe. If you're baking a good cake, there's more ingredients, unfortunately, than just sugar. They have loads of these kinds of classifiers as well. There's a newsiness score for how related to news the query is. There is, unsurprisingly, a medical classifier trigger. Which Google have mentioned before when they talk about how they fight disinformation. So Google specifically told us about what they call YMYL queries, which is your money, your life, which is when someone does a query that is about something important where if Google gets it wrong, it could impact someone's health or finances. And they specifically tell us they weight their algorithm differently when they detect this is a YMYL query. So things that might not be so important somewhere are more important in these types of queries, which is why, again, there's no generic— what works for one website might work for another. We also found, and I don't believe this has ever been published before, Google seems to classify almost all queries into one of 8 query classes. RQ stands for refined query. Semantic classes which are on the screen. I won't read them out. What's interesting about this is we looked at the distribution of these over 90 million queries, and a huge chunk of them were short fact or bool, which stands for Boolean, which means a question that essentially has a yes or no, true or false type answer. I find this particularly interesting because if you are making kind of your— this is where you're getting a lot of your search traffic from, I think this is what's gonna evaporate with AI overviews that Google are pushing out. If it can be answered with a yes or no, or it can be answered with one paragraph, Google's just gonna generate an answer rather than send them to your website, 'cause they make more money that way, and that's on their wants. We're actually building some classifier models where you can type in a query and we'll tell you what we know about it, whether, what category it is, if it's why and well, et cetera. We're sending them out on our newsletter, which is coreupdates.com. We've already sent out something data. Lastly, the really interesting things we found was there appeared to be a click probability for every organic result, which is what I was talking about earlier, which is Google uses click-through rate to build a prediction model. They've been doing this for years. Actually, I owe a half an apology for Kez, who brought this up to me once on LinkedIn. I was like, no, that's bullshit, mate. Turns out it wasn't. So it appears that Google does factor in how likely it thinks someone is going to be to click on your result. This would change if we modified the page title. They have a tool that can give you hints about this in the Google Ads Planner because it will tell you an estimated click-through rate there. Again, this is something I'd really like to build a kind of a model for so we can optimise this ahead of time. So, again, Google doesn't use click-through rate directly in ranking, but they have a prediction, and you can perform against that prediction. The real thing that gave me my kind of shocked Pikachu moment was Google had what they called a Site Quality Score for every single website on a subdomain level. It gave them a score of 0 to 1, and We got this data for 800,000 domains. Firstly, it being on a subdomain level was really interesting because Google's always said they don't care about subdomains, but they do score them differently. We did this distribution of the scores we had, and you'll notice that site quality here starts at 0.4. It does actually go lower than this. This data was grabbed from when we were looking at a specific rich type of result in Google. Rich results are things like featured snippets, People Also Ask, and what we found is if you have a site quality less than 0.4, you are not eligible to get things like featured snippets or PAAs. It doesn't matter what guides you follow on optimising for these things, you won't get them. That's something that became apparent in this algorithm is there's lots of heat races you need to qualify to get to the next stage in ranking. So, it doesn't matter what you're doing over here if you're not even in that race, because you haven't filled this prerequisite first. So, what is Site Quality? This is the short version of this talk. Basically, there is a patent by Google called Site Quality Score, which is super helpful. To give you the short version, Site Quality Score is calculated based on how many times people specifically search for your website, along with other search terms, how often they select your website even when you're not number 1, and how often your website name or brand name, if you want to call it that, appears in anchor text around the web. So it's essentially kind of like a layman's, if you want to call it like, brand measurement in a way. But what about when there's no user data to tell them this? They have a predicting Site Quality Score patent, which was super helpful to find, and basically they take sites that they have scores for and they build what's what's called a phrase model, which is basically they take the content on the page and turn it into a load of numbers that apparently mean something to smart people. And they have like the numerical shape of this website, and they can predict how good that site's going to be in terms of how it compares to other sites they know. Which is why we saw so many visibility graphs like this over the months, which is— this is a famous AI content abusing site called Causal. That shot up the rankings, and then Google kind of worked out what was going on, and it got massively penalized. We've seen that happen a lot, and I believe this is because AI content broke this prediction model. So, previously, if you were spamming content, you'd be using techniques called Markov chains or spinning, which would be— if you read them as a human, you could kind of see it didn't feel quite right. That's incredibly easy to detect from a computer point of view. Because of how large language models like ChatGPT inherently work with token probability, the kind of content they generate because of their training set, I believe, will score highly on site quality because it's based on good content, right? It's based on good English. So the issue they had, and why I think we had the 2022 issue partly, was because all these brand new sites were coming on board and Google was doing this quick evaluation of how good do I think the site quality is Yeah, it looks good. Yeah, you published 100,000 pages. And Google was like, yeah, why not? Obviously, this became a big problem, so Google released the Helpful Content Update, which essentially makes it almost impossible, unless you are a big website, known brand, to rank. This is also backed up by— this is Tom Kapper. He did a brilliant study for Moz where he found the sites most impacted by the Helpful Content Update were ones with high domain authority metrics. That's pretty much like how many links you have, and low brand authority metrics. So, if it looks like you've been doing lots of SEO, lots of SEO success, but I've never heard of you, that's kind of fishy. And there was a really obvious correlation to those sites getting penalised. We've even got historic comments from like Eric Schmidt, the ex-CEO of Google, saying, brands are the solution, not the problem. Brands are how you sort out the cesspool. So, we've kind of hit our 3 things here. It makes sense, it scales, and there's all kinds of evidence. There's patents, there's real-world data, there's large data studies, there's Eric Schmidt quotes. And we've found this with clients where classic SEO metrics are less reliable. So this is one of our clients, Rehabs. It's ranking top for a YMYL-type query around addiction. It's outranking UKAT, which has been around way longer than them. It's got like 15 times as many links. It's outranking NHS, again, on all those other metrics. From a classical SEO point of view, they should be losing, but they're not. So finally, conceptual models help you understand why things are ranking. The why is sometimes more important than the how, although the how is fun. Please be cautious when people are telling you things are or are not ranking factors, and certainly if it's Kez, don't just be like, no, it's bullshit, mate, because he knows what he's doing. And lastly, definitely play more video games because they help. Woop woop. That's it. Thanks.