Analytics should be core to your SEO strategy in 2026 - Jeremy Horne

If you want to grow as a business and if you want to save money on your marketing, use an MMM because you will save time and you will save money. I'm Jeremy Horn and this is SEO in 2026, additional insights. Jeremy, what's your number one SUTTIP for 2026? Analytics like MMMs or media mix models should be the core of your marketing strategy and budget to understand truly what is working and what isn't. I thought we were talking about MMMs there for a second, so what is a media mix model? I would say a media mix model if you're somebody that works in marketing and branding and advertising should be a suite. There's an MMM, so MMM stands for media mix model or marketing mix model. You might have heard it referred to as "economic tricks" beforehand as well, but that's also another long word that people find incredibly challenging to say. It's a glorified statistical model without going into the depths of mathematics and statistics, but it allows you to measure the impact that each channel has on your overall marketing performance. So if you're trying to get more sales or more leads through the door, if you've got 100 leads this week, how many of them have come from search, how many of them have come from social, how many of them come from offline media? But it also allows you to look at how many sales you're getting without marketing, because not everything that people buy or every lead that comes through the door is because we've done marketing sometimes people buy things because they buy things. And what does a typical media mix model look like? Is there a typical model and is it in any way similar to an attribution model? Or were the attribution versus "economic tricks" debate or "attribution versus mm-m debate" is a really interesting one? Is there a typical media mix model? I think there's a typical structure of a media mix model, but it's going to vary from one brand to the next. And it's also going to vary on the type of product you're selling. So if we go back to that example of people buy things because they buy things here, imagine you are one of the biggest bread retailers in your country, we won't advertise any of them here. But you don't walk into the supermarket shelves and say, "I'm going to pick up this brand of bread because I've just seen the ad for it outside. You pick it up because you need the bread." So you're actually a media mix model for a bread brand. We'll probably say most of your sales don't come from marketing. Whereas if you're selling something that maybe is a more considered purchase where it's a really highly competitive market, say a car, for example. And advertising is quite key in industries like that, can make the difference from one sale to the next. But the structure of that model is going to be very different. It'll be more marketing driven than non-marketing driven. But also the lack of time from lead to sale will be very different to bread. You know, you don't think about it, you just go and pick it up, whereas a car, either for one brand, you look at another brand, and then you make your decision three or four weeks later. So the structure is the same. It's the marketing plus non-marketing imp. But the models were slightly differed in that way. I've certainly seen lots of red adverts on TV old enough to remember though, the hovis adverts. Are you saying that marketing for bread doesn't work? Marketing for bread does work, but I think a lot of people don't really think about it when they're in the bread eye in the supermarket, do they? You know, if you usually buy how this may be a small proportion of people, look, all you just saw the end for Walbutton would try that instead. But you just only what you buy every single week, it's a bit like toothpaste as well. There's loads of toothpaste ads, but actually once you start to one brand, you don't really go and change. Whereas other things, so typically we built NMM recently for software brands. You know, that's quite a competitive market if your software is no good. Then somebody's going to go and buy a competitor next time. We've built it for measure brands. So if you think about things like gyms, there's a massive, massively competitive market for gyms and fitness. And if you're not getting the service, you want from one brand again, you can quite easily go to another brand. So this is where MMMs become really useful. And there's so many other questions that you can kind of dive deeper into once you've got a fully structured MMM built T. So where does one begin putting together a media mix model? This place is like this data. It's always the best place to start. And I think this is also where a lot of people get things wrong. Yes, yes. Data, let's analyze some data. And actually it's the wrong approach. It's, well, what's the question? So you start with some data and you start with what's luck keeps performance indicator. So normally it's lead or sales, but if you're maybe a charity donations. And how has that trapped over the past two to three years? The reason you look at two to three years of history is you want to understand what seasonality looks like. Typically for charities, there's going to be a massive season of trend around Christmas when everyone gives more money to charity. Bread, they can not so much see the amity, but every brand has a pattern and you need to understand what the pattern of that KPI looks like over time. Then you want to understand how you've been advertising over the same time period. So what does your advertising data look like? How much have you spent by channel by week over the course of that period? And then to build the non-marketing side, you'll look at economic factors. So Google Trends is really useful for this. But just Google Trends tell us about this market and how people are searching for it. Beyond that, what are things like consumer price index? So inflation, how's the market changing? How does this type of product respond to changes in the cost of living or changes in the market? Competitor advertising. So if your competitors advertise that better for you is that worse for you. In the case of something like cars, it could actually be better because if people test driving other cars and realizing, "And not quite as good as yours," they might come back and buy from you. So it's day to day to day to day. I would say 80% of a medium-ix modeling project is getting the right data. Once you've got that, the model is actually quite easy, but it's easy to build. And in terms of building it, any particular software that you favor at the moment? There's a few different ways of doing it. So there are what we will call a point and click technology where you can go out. It's a bit like an off-the-shelf product. You go and buy a product. You can put in all of your data and information that builds the model for you. That's what some people like called a black box. You don't really know what's happening under the hood. You put some data in, you get something out. It's the model good, who knows. We build all of our medium-ix models from scratch, and we do that in something called our Python. Probably people here have heard of Python are just another coding language that you might say is similar to Python, but it's slightly more niche. Maybe fewer marketers use it as a marketer. I've used the pure autofloy market to life. The advantage of doing that is you can actually write the model equation or formula or go into regression and statistics too deep. But you can write that yourself and you have more control over it. So if you understand how these models work, you can understand how to move parameters within that model to make it the best possible version of understanding the data. And we mentioned the word attribution. And you alluded to the fact that your medium-ix model and attribution was perhaps comparable but distinctly different. So what are the key differences and why would you want one over the other? I think attribution has had a long history of being used to measure the effectiveness of different marketing channels. The issue with attribution is that it's becoming harder and harder because if you think about, say, Google versus Facebook versus X versus other platforms, everybody's closing the walls. So it becomes harder to share data. So if you're looking at that full custom aderny, somebody's first seen an ad on Facebook, and they've seen something on X and then they convert by searching for your Google, you don't have that full journey because Google and Facebook and X are talking to each other. You've then got the added complexity that if you're advertising offline as well as online, if you had something like radio or TV and X and somebody's heard a radio ad or seen a TV ad, how does that interact with Google and Facebook and you can't measure that? But this is where a media mix model is better because it looks at the entire picture. And rather than say, we've got 100 sales, let's go through the one by one. This one's from Google, this one's from Facebook, this one's from TV. It does it the other way around, says, we've got 100. What proportion are from Google? What proportion are from Facebook? What proportion are from TV? And that's becoming probably, I would say, has become over the last couple years the new gold standard for measurement. Does attribution becomes harder? People are looking for other ways to robustly measure the impact of market contribution. We're seeing an uptick in the number of NIMs that we're building and the number of questions that we're getting to us and we want to build an MMM. Where do we start? Okay, and I guess from what you're saying, a media mix model could perhaps enable being a little bit more predictive in terms of what you should be doing in your future marketing strategy as well. So how do you use analytics to assist with determining what your marketing strategy should be? This is where MMMs really powerful when you look at the future marketing strategy. Because if you go back to the way I described it at the very beginning, all it is is an equation. So it's an equation that says this amount of your Google plus this amount of your Facebook plus this amount of your TV Etc. Etc. For every factor that you put into your model will give you the number of sales that you get this week and Because you've got that equation what you can do you can build it into a tool a dashboard I will call it as it's his spreadsheet my nomark to love a spreadsheet So let's keep it really simple You can put it into a spreadsheet and in that spreadsheet you can say if I spend 10k on this channel and 10k on this channel 20k on this channel what result do I expect to see and you can mock up different scenarios of Okay, if I've got my 40hp if I spent it 5 5 30 instead of 10 10 20 How would that differ in terms of the number of leads or sales coming through the door? So it becomes quite powerful because you can use it to plan the most optimal strategy Hence it's called a media mix model. What is the best media mix across all of the channels to get the result that we want? Because let's say you are just running search for example If you've just got Google ads or people search or something or on a search engine If you haven't got anything on another channel like Facebook or a TV ad or Can't do some YouTube ads What's feeding that search why are people going to search for you? Oh Google or any other search engine and that's where the media mix model is 10k Search when YouTube is on does this well, but when YouTube and Facebook are on it does this well? I think that SEOs naturally want to embrace analytics to see how what they're doing as performed as performing what where opportunities may lie for organic rankings in the future But obviously other marketing departments aren't necessarily so deep into analytics and how do you encourage other marketing departments to Embrace analytics and M&Ms and become more data driven in their approach Yeah, I think it's a challenge. We certainly see it a lot where people say I've got 20 years of experience in this industry and Because of that I know that the answer is X and Actually, sometimes you've got to break away from the personality and the experience and start to think about doing things in a different way One of the things we always encourage people to is be brave be brave and think differently because Data actually is your most crucial as data tells you what's happened in the past And if it's happened in the past, it's probably more than likely to happen again if you think about marketing in any form of In any form Marketing is about attracting people and you know whilst people might be unpredictable We are creatures of habit so we all behave in similar ways if we've been even a certain way in the past We're likely to behave in that way again So by looking at the data and analyzing how people are behaving You maximize your potential chances of success and this is why models like this are really really powerful and useful If you just say what we've done it the same way for 20 years, let's keep doing the same thing The most likely thing is your competitor will build the MMM your competitor will understand the data And they will be the winners because they're using data to inform their future decision-making Whereas you're staying in the past and not I'm talking about doing things a little bit differently. I guess One of the challenges with being truly data driven is that the past doesn't necessarily Always represent what is going to be the most effective thing to do in the future you have different Marketing channels that are up and coming and I may not necessarily have driven any traffic in the past How do you ensure that you're not missing out on those opportunities? Regular updates. So you know the whole point of the model is it's dynamic You don't just build a medium-mix model and say right that's done. This is how we're gonna plan media from now on You update the model regularly You update it every time you change your marketing strategy or if you're not changing your marketing strategy too often I'd probably say update it a few times a year So anything from like two to four times per year when you've got a relatively stable strategy Just to account for changes in the economy changes in the way the people are behaving But if you're marketing strategies changing all the time that regular update and now you to observe the change in people's behavior as a result of your strategy You know, let's say for example You wake up tomorrow morning said I'm gonna do something crazy I'm gonna put a zero at the end of my YouTube budget and I'm gonna spend you know whatever I'm spending before hand with a zero at the end of it If you wait six months to update that model you don't know if it's working so do it quickly Make the change update the model a few months later and see if it's work And if it does work keep doing it and if not Reverb back or go somewhere in the middle of previous strategy the current strategy Jeremy, what's the key takeaway from the tip you showed today? Key takeaway is that if you want to grow as a business and if you want to save money on your marketing Use an mm-m because you will save time and you will save money Your me on is director and founder and data cove find out more over at data cove.co.uk Jeremy, thanks so much for being part of SEO in 2026 Being your host David Bane get your copy of SEO in 2026 the book over at SEO in 2026 [BLANK_AUDIO]