← Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Automating SEO with Experimental Strategies That Drive Growth
00:30:37
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- [00:00]
A
The Voices of Search Podcast is a proud member of the I Hear Everything Podcast Network. Looking to launch or scale your podcast, I Hear Everything delivers podcast production, growth and monetization solutions that transform your words into profit. Ready to give your brand a voice? Then visit iheareverything.com welcome to the Voices of Search Podcast. A member of the I Hear Everything Podcast network, ready to expedite your company's organic growth efforts. Sit back, relax, and get ready for your daily dose of search engine optimization wisdom. Here's today's host of the Voices of Search podcast, Tyson Stockton.
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B
In 2025, more than 60% of SEO teams reported integrating automation tools into their workflows, citing improvements in efficiency, faster insights, and increased scalability. But where do we start? And how should we be looking at evolving our overall processes? This is the Voice of Search Podcast. My name is Tyson Stockton, and today I'm joined by Tom Mensell, VP of Organic Performance at Crowd. Tom will be diving into how SEO's team can automate effectively using experimental strategies that drive measurable growth. Tom, welcome to the podcast.
- [01:16]
C
Thanks for having me. Excited to be here?
- [01:19]
B
No, I mean, I feel like the world of SEO has been kind of in a little bit of a. Almost like a frantic state, I would say. Like, you talk to some and it's more exciting. You talk to others and it's fear, doom and gloom, I guess. One, how are you feeling? Like, where, where's kind of your perspective at on these shifts in the environment that we're facing?
- [01:42]
C
I'm feeling. I'm feeling excited by it. I think all of these, all of these changes that have come over the last two years, it's arguably been more disruptive than the last kind of like 25 years of search. And I think, you know, that gives us an exciting challenge, right, to change what we've been doing, move into new areas, which I think is going to broaden our scope as SEOs. I think we're going to be talking to more departments within an organization. We're going to have more influence, certainly. And I think that does pose some challenges right, naturally around things like measurement, where the value trade of search engines coming to your website, crawling your content, and then sending you traffic in exchange for that, that's definitely going to change. But it's about how we overcome those challenges. And I, me personally, I feel very excited and energized by that, which I share.
- [02:40]
B
I share that total, like, sentiment of it, where it's like, yeah, there's a lot of uncertainty and yes, we have to, like, Change and evolve. But that's always been a pillar of our industry. And what we've, I mean, really, one of the things that attracted me to search was the fast pace and the change and the challenge of the new. And so it's like, yeah, this is a more amplified version, but we have this great opportunity of having the eyes and ears of upper management. It's like I've had more conversations with people outside of SEO about SEO in the last 12 months than probably like ever in my career, which is like, okay, now the spotlight's on, but at the same time, it's like for so long we've been complaining as almost being like forgotten in the corner and stuff. And so it's like now is kind of that time to lean into it and step up into that kind of more focal point in the company.
- [03:34]
C
Yeah, 100%. And therein lies massive opportunity. Right. To drive change and real, tangible commercial results for a business. So I absolutely agree with that, Tyson. I think, you know, SEO has always been in a bit of a silo in the corner, but I think to my earlier point now around, you know, the future of search, the different teams and specialisms that are going to impact the value that we can deliver for a business, I think it's a really exciting time to be working with those stakeholders and broadening our skill sets as well. Right. Which is super exciting. So everyone should feel energized by that, by that challenge.
- [04:20]
B
Exactly. Now with this, the way I often, I feel like think about this is kind of in terms of landscape and tactics. I mean, like, before we get into like the tactics, how are you looking at the space that we're competing in as it's evolving with AIO and these alternative search experiences?
- [04:44]
C
Yeah, it's a really interesting question. I think traditionally we as SEOs have been very focused on Google search. Right. And the reason for that is that Google's held a very tight grip over the search market over the last 20, 25 years. We are seeing evidence that that is beginning to change. We've got social search, we've got communities, we've got AI powered search experiences. And we're starting that landscape, becoming more diverse as people move outside of the Google ecosystem in order to discover content and find answers. So I think certainly from what we're doing at Crowd is trying to react to that with new propositions. One of them is a proposition called Search Anywhere, which is aimed at targeting the searcher irrespective of the platform. So it's a platform agnostic approach to search. And I think that's the direct direction we as an industry need to be heading in now. You know, taking the Google blinkers off, so to speak, and focusing on, you know, truly understanding what platforms our audiences interact and search on and making sure that we're visible across those areas.
- [06:02]
B
To me, it's also changing my perspective in how I look at the work because in a lot of ways SEO is no longer just what happens within your domain. And sure we had like backlinking and stuff, but I think from a strategy perspective now we're thinking more in terms of like other placements. Whether it's through, I don't know, Reddit or you know, other affiliate blogs and things. I think there's this increased value or almost like application where now we're applying some SEO strategies or ideas towards external efforts, whether it's partnerships or other kind of access that you can get to domains beyond just yours.
- [06:46]
C
Yeah, 100%. I mean, what I've kind of been, what I've been peddling over, over the last 12 months with, with all of this change is that I think, you know, brand mentions are definitely the new visibility currency, certainly within AI powered search experiences. And as a result of that, we've seen, you know, a real resurgence in, you know, digital pr. And I think that's going to be a super, super important strategic element to have over the next five, ten years, so. Absolutely right. I mean, the large language models themselves aren't just coming to your website or your own assets to build their understanding about your brand. They're looking at all of those off site sources, third party sources, what your customers are saying, all of that information is getting used to train these models. And we as search marketers need to think about first, understanding all of that information and that perception and secondly, how do we start influencing those in a natural way in order to improve our visibility. So 100% agree. Need to focus off the site as well as on site. And tactics like digital PR are going to be a hugely important thing for us over the next five to 10 years to do that.
- [08:07]
B
Yeah, there's this, it's like a resurgence on some of these, I mean almost in some ways like traditional factors, like I feel like I used to look at PR as more of this traditional kind of like marketing mix element. And I 100% agree that it's like it's back kind of like full swing and it's more, more prevalent now than I think I've seen it in at least the last maybe five, ten years.
- [08:33]
C
Yeah, yeah, definitely.
- [08:34]
B
100% now transitioning the conversation a bit. So we're operating and competing in new and emerging spaces. So our perspectives of task and bringing in things like digital PR and maybe like our prioritization shifts a little bit through this. But when we're thinking more of like how we are doing the work, what are some of like the automation foundations that you would be kind of, you know, wanting people to be really like, thinking about and what perspective should they have when we're thinking about how can I use some of these emerging technologies to either make my workflows more efficient and easier or even increasing like the quality of the output that we're able to achieve?
- [09:20]
C
Yeah, definitely. And I think if we just you know, take, take a slight step back and define what SEO automation means, to me, I think, you know, put simply, it's things that the machine can do that help us save time, they help us scale, they help us improve our output ultimately. And I think, you know, breaking that down into practical examples, definitely, you know, starting with research, you know, trending, top, identifying trending topics, keyword research topic clustering, you know, ingesting information that you can get from, you know, third party sources like Ahref, Semrush, etc. And rather than that being a manual process where you're, you know, you're going through, you know, large quantities of data, you can start to put that into large language models to extract a narrative from it. So I think, you know, there's a, there's a huge research element to this, right, where large language models and Agent Ki can start to do that, do that heavy lifting for you. I think also from an auditing perspective, think about clear, repeatable guidelines that you can almost translate into prompts to assess your SEO maturity, SEO sophistication at a very tactical level. Again, that's one of the things that we're looking at at CROWD at the moment with regards to SEO automation is how do we get the large language models to do a lot of those checks for us, which is super, super powerful when you think about scalability and standardization. And then finally, what I would say is strategy. So understanding your current baseline, understanding what the large language models know and perceive about your brand, and then extracting from there what areas you should focus on in order to improve performance. So picking out content, recommend, et cetera, are all things that you can now do with these large language models and Agent Ki. So three things. Research, auditing, strategy. Those are things that we have been very lent into over the last 18 months at crowd.
- [11:40]
B
I love that you let off too with the analysis piece because so often, especially in the early days as ChatGPT was coming out, content generation just dominated the conversations. And like in your three, I mean, I guess you can kind of pull in elements within those categories, but I wouldn't say like in any one of those three, content generation doesn't seem to be the main focus. Like tell us maybe a little bit about why your core focus of leveraging these technologies is kind of outside of that traditional mindset.
- [12:16]
C
Yeah, I think where you can start to fall into a bit of a trap is where you're trying to use it to speed up elements of your output and potentially sacrificing the quality. I mean, there's tons of research out there that suggests how these different large language models are preferring to lean into human generated content. You know, we've seen the rise of Reddit over the last 18, 24 months and I, I do think that there is still a very important part for a human, the human elements play within terms of, you know, content generation. So you can definitely use it to a point from kind of like research building briefs, understanding how you need to structure that content. But I wouldn't let it do the whole end to end process. Right. And my advice would be use it to a point, but with regards to the specific output and generation, that's where the human element is still super, super valuable.
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B
Yeah, it's like you're looking at your overall process and then kind of injecting at key points or kind of like leveraging for the heavy lifting at certain elements to then kind of make the human element more manageable at least.
- [13:39]
C
Yeah, exactly. Know where AI can help you become more efficient, it can improve your output, it can help you scale and plug it in in those areas, which typically for us centers around, you know, the research, the auditing, strategic kind of direction elements of, you know, building that, that process and that plan.
- [14:02]
B
Yeah. Now with this I want to get into kind of like how do we integrate kind of like into these systems and challenges that come from that. But we have then kind of like those three buckets that you mentioned. And so we're looking at areas of kind of the analysis, reporting side auditing and into like the strategy. What would some recommendations be for then? This some of like your favorite systems to use in actually bringing, you know, because anyone can just jump into chat GPT and put in a prompt, hey, I found something. Or insert a data set and find. Great. Now we have these findings from it, but from like an automation standpoint, now we're either looking towards Python scripts or I know Zapier has been getting more and more popular. Nan's been on the rise. Like what are kind of some of the areas that you would point some of the listeners to if they've used LLMs, but they haven't actually gotten to that automation point?
- [15:07]
C
Yeah, I think definitely using the large language models, knowing how to prompt them in the right way is crucial to getting a really good output. We're building different tools at crowd that automatically hook up into these large language models via API, cycling through different prompt patterns to extract, you know, the understanding and the perception of brands, which is giving us strategic guidance. But if I, if I kind of strip that right back. We're also having lots of success. You know, we've spoken about content. There are lots of great things that you can do with, with AI just in, you know, the web app, the interfaces to help accelerate your content program, you know, examples of that, you know, planning content in niche industries where you're, you know, you're working with clients in very specific areas where you haven't necessarily got the subject matter expertise. But you know, you can use AI, you can use these large language models to help give you that steer in that direction. It's great at understanding where you've got, you know, content gaps, where, you know, you're coming up with ideas, you can set it off across your website and you know, to try and scrape that content, identify content that's, you know, semantically relevant and where it would potentially improve content or where you need net new content to address that particular gap. It's also great at identifying trending topics from industry press and clustering those themes to give you tangible output. So those are some examples. If you haven't necessarily got engineering previous experience, you can definitely get very good outputs from those prompts to, you know, help help scale your output.
- [16:56]
B
And with these systems and I feel like an area that leads them to that maybe not enough people are like fully kind of embracing is in a lot of ways it's making elements of our job easier. So then what, where do we then kind of focus that newfound availability potentially? And one thing that we were kind of talking about before this is like the approach or kind of like how SEOs are leaning into experimentation of it. And so how do you see kind of like the intersection point of these emerging technologies and more of like, I don't know, let's call it like the mindset in an organization to lean into experimentation rather than just kind of, we're doing page titles this one way, and that's how we're doing it. And now we're going to move on to internal linking, like that kind of checklist approach versus the let's test like, let's see how things respond.
- [17:56]
C
Yeah, I think to answer the first part of that question around newfound availability, I firmly believe that. I don't think we're necessarily going to get to a point where it can pick out the insight, it can translate that into something that you can communicate back to senior leadership within an organization. I still think, you know, with all of the information and all the data that it has, it still requires that human analysis in order to translate that into a compelling narrative that you can go back to senior leadership with and, you know, translate that clear, actionable plan on what it is that you need to do specifically for that use case. So I think it can definitely do some of the heavy lifting, right, in terms of the, you know, the research, the insights, the analysis. But there's always going to be that human, that human element to it that needs to happen to translate that back into clear, measurable outcomes and actions in terms of balancing innovation and results. Personally, my belief is that we as SEO professionals and SEO leaders have to stay connected to the output of our teams and really maintain our craft skills. I think if you know the output, you understand the output, you know how to deliver the output, you can automate the output and that's when you can start to, you know, really leverage it in, in your day to day. I've said, you know, not everything can be automated, but it's about being able to take a step back from, you know, the day to day and being in those weeds and understand where something can be potentially automated. I think, you know, we spoke about it right at the start, that end to end process, understanding where these technologies can help you scale and standardize the output. There's also, when you are doing this, there's a risk level to it. With SEO automation, low risk is the report in the insights, the research, using it as your assistant to help you find answers in that data that can help guide your strategy. I think where you start to get into the high risk elements is the tactical automation, the content creation, as we've mentioned, you know, the optimization. If you are going to try those things, right, you need to first make sure you've managed expectations with, with leadership, be transparent about what you're testing and map out the different outcomes and scenarios. Right? You know, high risk, there comes, you know, potential high impact on performance, performance both positively or negatively. So Understand what you know, the business appetite, Senior leadership's appetite for risk is be transparent about what you're testing, fail fast and have contingencies in place to react.
- [20:55]
B
I love that you call out kind of these the risk as well, because balancing that like immediate results and innovation is such a critical piece of this. And it's like we do have constraints in managing expectations with leadership. Like you mentioned, those are key pieces that will either let you keep doing these experiments or have that potential to really limit the potential of it. It's also, I think, a good call out that you're differentiating essentially the analysis portion and the portion that's hitting, you know, that actually I guess a search engine would see or the external facing component where it's like, yeah, search engine will see your page titles, your content. And so with that you have that question mark of am I, is this gonna benefit or kind of harm the performance? But it's a great advice of like, if you are uncertain or you need that additional piece, start in that area that may have less risk, that then you can work up to some of those more, I don't know, potentially either like, there's a lot of value in those and you can reap the rewards of it, or you know, it's the area that you have to kind of recover and battle through.
- [22:17]
C
Yeah, absolutely. But always, you know, be transparent and manage those expectations. I mean, there's nothing worse than launching something that's, that's high risk. You know, people around you aren't aware of this, there's a performance impact and then you've got to justify that after the fact rather than setting all of those, you know, parameters up at the start.
- [22:41]
B
Absolutely. And there's a part of it too that I think you, you're playing into kind of like, like what is the business culture of the organization. And you have some businesses that I've worked with and I've seen and it's like they really lean into testing and it's like they embrace that and I think a greater appetite, but then they also are, you know, more and more reluctant in certain ways as well. So I think like the culture of the business and sometimes also like, if you are in a business that maybe doesn't have that established culture of a testing environment, it's something that you may need to kind of chip away with at time and there's ways of like getting people more familiar with it rather than kind of just throwing them into, to the deep end and running some massive tests that could have a huge swing of like, you know, risk reward on the website.
- [23:37]
C
Yeah, absolutely. And I think, you know, having a clear measurement strategy around that, you know, if you don't necessarily have those, you know, experimentation processes in place and as you rightly call out, you know, start to chip away at those, but list out all of the different experiments, all of the, you know, different tactics have a clear measurement framework around them. I mean, one example that I can give here is, you know, very simply we were testing the impact of copy blocks on product lister pages, PLPs and the way that we measured the impact of that was to isolate, you know, control versus variant test groups. Right. We pick out some control pages that we leave as is variant pages where we inject the copy blocks. And then, you know, simply we just looked at the percentage share of revenue that came from organic search on those pages versus versus the control and it jumped from around 19% to 21% share of revenue, which was a 10% lift in revenue overall. So that's an example, right, of where you've got a test and then you think about the measurement framework behind it. And I think if you've got those things in place, it's an easier conversation to broach with the business that isn't necessarily as set up for experimentation as it, as it should be.
- [24:57]
B
That's great advice, which is, I think, at least in my experience, an area that I feel like maybe we talk a little bit less about is that component of, well, how do you actually get these projects picked up and established buy in on? And I feel like that's a great kind of tip or advice in there, going into a little bit of kind of like, you know, like an action plan or scenario type question. What would be kind of three tactical steps that you'd recommend SEOs take like right now, this month to begin in like automating processes while still maintaining kind of that focus on control and measurable performance.
- [25:39]
C
Yeah, I think it circles back to what we've been talking about, you know, understanding the risk. Right. And my, my advice would be start with, start with the low risk areas, which is, you know, the reporting, the insight, the research. And as you mentioned previously, if you can get those areas right, you can start to unlock some more of your time which is, you know, super, super valuable. And those areas are low risk from a performance perspective. So if you can demonstrate efficiency drivers in the low risk areas, then that sets you off on a good platform to roll that out further and unlock more experimentation, budget within a business. I think where I talk about research, insights, engineering. One example of this that we're looking at, crowd is a tool called brand AI. So the way that works is querying the large language models through a range of different prompt patterns. We're injecting the brand, the market, the category that they operate in, the different purchase considerations that a customer would go through. And we're extracting from the large language models, you know, how do you understand the brand, how do you perceive the brand versus competitors in these different areas? And I think that's definitely something that people can start to do just through the web interfaces of the large language models, through different prompt ranges, understanding how your brand is rated, understanding the sentiment that you can very easily extract with all of the information that the large language models have collected from the open web, what that sentiment looks like, where you beat or fall behind your competition across those different categories and considerations. It's really good actually at coming up with content recommendations from there and turning that insight into action and then back to the point around PR understanding and looking at the sources that are cited within the responses to know about what are the most influential sites related to my brand and my industry, which gives you some really tangible outputs that you can then take forward into your strategy. So that would be an example of where I would start now with regards to automating processes is definitely around those kind of like reporting insight and research areas before you run off into trying to get executional output from these platforms, which is, as we say, higher risk.
- [28:14]
B
And with that, you're hitting, I think, right on one of the square strengths or focal point of an LLM system where it's like, by nature they're able to go through large amounts of data. And so you're able to kind of have those, you know, pull out those pieces of information like the, you know, identifying content recommendations. You can get super clever on your prompts to combine both existing performance competitors, maybe even like upcoming marketing campaigns from the organization and then identify pieces that align to it. I think to that point I would encourage all the SEOs move beyond that like, milkshake method or whatever it's referred to as, where you're just taking the unique keywords of your competitor and then saying, hey, there's our, like, strategy for it. Like, we can do so much more cool stuff that allows us to be a lot more precise in where we are competing. Which is then I think brings us back to where we started the conversation of like, it's not just the tactics, but it's also the landscape of where we are competing. And you've said a few times that it's like we need to be more deliberate in that kind of where we are visible or what the sentiment is in those spaces to ensure that we're staying relevant with the systems.
- [29:38]
C
Yeah, 100%. And if I could put a strap line on it, it would be you need more content in more places. So the more of that that you can extract from the large language models in terms of how you actually go about doing that is going to give you a massive opportunity to win in AI powered search experiences.
- [29:59]
B
100%. Well with that, that's going to wrap up this episode of the Voice of Search podcast. Thanks again to Tom from Crowd for joining us. If you'd like to contact Tom and find out more about him, be sure to check out his LinkedIn profile in our show notes or also check out his company's website@crowd.com be sure to go over there and check out some of the things that mentioned in this episode like the Search Anywhere product as well as the brand AI. And if you haven't subscribed yet and would like a daily stream of SEO and content marketing, be sure to hit that subscribe button on your podcast app or on YouTube and we'll be back in your feed in the next following day. Now with that, that's all for today. Thanks for stopping by and we'll see you in the next episode. Sa.