← Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
Voices of Search // A Search Engine Optimization (SEO) & Content Marketing Podcast
How AI Optimization Is Redefining Search PR and Influence
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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, Jordan Cooney.
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B
I'm Jordan Cooney and joining me today is Whitney Hart, Chief Strategy Officer at Avenue Z. Whitney, welcome to the Voice of Search podcast.
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C
Thank you so much for having me. It's a pleasure to be here today.
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B
Yeah, I'm thrilled to dive into not only the research that you have all done, but also to get to know you more and your background in the search space. So why don't you tell our listeners a little bit more about Avenue Z yourself and, and then we can dive into some of this research.
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C
Thank you so much, Jordan, and it's a pleasure to be here today. I'm thrilled to be here to speak with you and your listeners to talk about AI search. Avenue Z is an AI accelerated hybrid agency for influence. We partner with brands focused on driving revenue and reputation across all channels with a specific focus on large language models. So how are we able to drive influence for our clients across places like Chat, GPT, Gemini, Perplexity, Claude, Grok, etc. There are a lot of them these days though. Power is concentrating around Chat, GPT and Gemini and that's where we are tending to spend most of our time and focus for clients with some other use cases around Claude and Grok that are quite industry specific. I am the Chief Strategy officer here, so my role is focused around how large language models are evolving, how consumer usage of those large language models is changing, and how those two work in tandem to change, change the search landscape.
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B
Awesome. Love it. And you know, we talked a lot about concentration both, both in our opening and you mentioned it here, that it's, it's really a changing landscape that, that is force functioning, this concentration. Right? We're moving away from these rankings where consumers had some choice and maybe how they navigated or what they visited or learned from. And, and now it's basically an answer, right? You get a formulated answer that kind of limits your options or your selections. How much of this concentration is just a shift in how we're consuming knowledge and Information versus the fact that these brands have always been winning or always been at the top of their, let's call it, game. For now.
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C
I think that large language models are now pulling, pulling together so many sources of information and a lot of brands actually struggle to understand what is feeding the answer that it's giving to a consumer and how can I potentially influence or control or direct that answer and those information sources? One of the things that brands really need to grapple with is how widespread and diverse the data sources are. And it requires a level of coordination and strategy that most organizations don't have at this point. So a lot of organizations, for example, will have an SEO team that is very coordinated with the content strategies of what's going into the blog. How does that extend into media, social, social media, things like this. But then PR will sit on an island all of its own and there can be a lot of disconnects there. So ultimately I think that brands that are able to bring all of these discrete areas of marketing together under one content strategy and brand strategy are those that are going to weather the large language model storm and revolution the best and come out ahead in the months and years to come.
- [04:34]
B
Absolutely. And what's your perspective on, on the, the size of these organizations that are winning? Is it, is it that there's space for small brands to find ways to compete? Is it, is it all just the, the big, you know, top well known, well established leaders that are only winning? Like what's been your experience in looking at this connection point of these disciplines like SEO, PR and all these other mechanics, but then that translating into how a small versus large brand wins.
- [05:10]
C
So what's really interesting is I'm working with some very large brands right now and the hurdles that they face internally with legal and compliance checkpoint points and approval processes in order to publish anything on the website. It's so arduous that it can take them months to get simple pieces of content updated, published, approved, iterated, and it puts them on the back foot. And so in a lot of cases, young startup new entrants to the scene can make a huge splash in large language models because they don't have that level of governance, they don't have the level of legal and compliance checkpoints and frameworks. And so they can act much more nimbly and when it comes to publishing content on the site, iterating formats of that content to be more large language model friendly, making those adjustments, they can move so much quicker that they then gain that visibility that much faster. I think the thing that on the flip side holds those brands back then becomes pr. Do they have robust PR programs in place? Are they able to secure those mentions from large reputable outlets like the Wall Street Journal for example, or are they struggling to get visibility with major outlets? And therefore while they're able to publish amazing content on their site quickly and nimbly to meet the needs of a prompt strategy for the large language model, they don't have that third party verification through PR placements and therefore they're kind of held back. So it's really playing those things, holding those things in balance and ultimately if you have those things, a new entrant, a startup brand can make a huge, huge splash and really secure visibility over a stalwart brand.
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B
Yeah, I want to dive even a little bit further into this because I, I, I totally agree with what you're sharing and, and I love the, the perspective of how, you know, smaller brands can win in, in the current market. Does this just mean though that we're going to be moving more and more towards this winner takes all perspective like our LLMs and just an answer based world, Whether it's in traditional search or, or new discovery outlets like ChatGPT, are they just going to become more and more concentrated in the sense that there's just going to be one answer? There's not going to be as much choice as we used to have. And so small brands need to make and be more aware of this channel sooner when it comes to their marketing strategy.
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C
I think that there is going to be greater and greater levels of program personalization that we haven't really taken into account in discussions about this topic. So ultimately, yes, from a very generic point of view there is the potential for that consolidation and the winner takes all or takes most dynamic. But the flip side is that as you pour more and more of yourself into Chat, GPT or Gemini or insert your personal companion of choice, it is going to be able to offer more and more personalized recommendations. And so that is where there will be wider areas of discrepancy and larger opportunities I think for brands to be really specific in what they specialize in, what they're really good at, what they're really great use cases are, and ensuring they're able to assemble influencer strategies, listicle earned media strategies around that really core use case that will be able to resonate with the large language models memory of the user in question. So the responses or the answers that the large language model is giving are very specific to that individual.
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B
I mean, I think that this is One of the interesting shifts that the market is going to continue to unpack and, and better measure is not just the, the fact that we need to understand what is working within these responses, but also just generally, are we going to an ecosystem where there's less real estate? Right. Can you just put less McDonald's on every intersection? You know, because there's just, there's just not going to be the same as we used to call it, SERP visibility. In our intro we talked about visibility and we talked about visibility measurement. You know, Avenue Z and yourself, you guys have built a lot of visibility index, index indices and have measured what's happening in terms of different brands and different categories. And I think that this measurement piece is, is where we are today. How, how should marketers be thinking about this concept of visibility? How is it different from rankings in traffic? And, and tell us the story about how you guys came to start building some of these indices for sure.
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C
So we have six larger visibility reports, AI visibility index reports that rank about 50 to 60 different competitors and then we have nine mini reports that are a tighter concentration of competitors and examining those, those niches really specifically. And what we're doing is looking at a wide variety of different inputs to assess share of voice and essentially who is winning in, who's winning the share of voice in these specific industries. One of the things that I think is most interesting about where this is going is this was publish in 2025 is actually that consumer preferences for large language models has dramatically changed in nine months. So we published these last summer and since then perplexity has fallen out of the mainstream in the way that it used to be. And instead Gemini has really come up strong and is on the rise. So when we're looking at redoing the reports this summer and doing a year over year analysis of what's changed not only within the metrics of themselves, but also within how consumers are using large language models and which ones they're using. So that is really insightful to me and I think when we rerun them, we are going to see how consumer behavior has transitioned away from these smaller startup entrants in the large language model world like Perplexity, into larger legacy behemoths like Gemini, which is part of Google's ecosystem and how we're seeing that transition back towards a Google oriented search experience.
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B
I love this. This is actually kind of one of the, one of the things that we've talked a lot about on our show and we've had a lot of guests who talk about Verticalization, right, And verticalization has come through various different forms, whether it be in, in shopping, with platforms like ebay, Etsy, Amazon, Stealing share from Google. In terms of shopping, search or in travel, you have the OTAs like Expedia and booking. And you know, there are all these vertical experiences that consumers have a preference towards. LLMs, just like anything else, there are these preference shifts and trends that evolve over time. You know, in your role, working with different clients or supporting different brands, how important is that prioritization to your overall strategy? How does your strategy change if there's a model like ChatGPT or Gemini or, you know, llama, whatever the model is. What, what, which one is, what is the actual strategic value in understanding if your audience is using that model?
- [13:36]
C
It is crucially important and this is the initial starting point for all of the strategies that we work on for our clients. So ChatGPT at this point in time has about 60% of market share based on various reports. That's followed by Gemini, that's at about 15% and then co pilot trails at about 13% and other models follow thereafter. So for most of our clients, we are focused on ChatGPT and Gaming Gemini as the two workhorses of large language model visibility. After that, we are very specific and selective with which models we integrate into our strategies. So for example, for all of our blockchain clients, we recommend integrating Grok. Why? Grok is the only large language model that can index x Twitter and so crypto Twitter, as its affectionately known in the industry, is of the utmost importance for anything in the blockchain and crypto industry. And therefore users within that industry are typically using GROK as their large language model companion of choice to do their research because they want to tap into that really crucial data source and all of the trending sentiment and content that's posted into X day in and day out. So for us, measuring and crafting programs that really specifically integrate GROK data is high, high priority. So for clients like that, we will look at ChatGPT, we'll look at Gemini, and we'll look at Grok. Increasingly we will also look at Claude very specifically, because Vibe coding users are asking, Claude, what blockchain should I build on? What blockchain should I integrate? What does that look like? And so those prompts are also really important. One of the other biggest shifts that we didn't talk about when we look at updating the AI visibility reports is that it's hard to fathom that this is the case. But about a year ago, Claude didn't have live web search. It was very limited. It was only focused on its training data. And so how far the industry has come, come in 13, 14 months is remarkable. And so Claude is something that we are increasingly looking at, particularly for a lot of our finance clients that are very focused on how industry researchers are using Claude to assemble their reports and issue reports for their end clients. And so Claude, Claude and being able to measure visibility, sentiment, competition is absolutely a requirement. So we do find that Claude has very specific niche use cases that are of the utmost importance to that client base. And same with grok.
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B
So I don't want, I don't want to belittle the value of visibility. I think that there's still so much that we're trying to learn about visibility. But one of the things I really enjoy about our conversation and even our prep conversations we were getting ready for our show is this concept of connecting other sets of data, other insights we have. So the beauty of being able to take blockchain insights about where consumers are and then that connection to where LLMs are consuming content or training on content through either Twitter X or other other forms to then understand that that's where our users are going to be because that's where the best answers are, the best direction is going to be formed from an LLM. As you have explored this through the different clients and partnerships, what, what were some of the key learnings that you've had in being able to connect some of the more conventional or traditional sets of digital media metrics to how LLMs are now working and unpacking responses for consumers?
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C
So we are measuring success for AI optimization programs through a traditional funnel type of concept. Do you have basic visibility at the top of the funnel? Is the large language model recommending you at all? From there it is it a positive sentiment or a negative sentiment? How is it recommending you? Is it saying what you want it to say? And then from there we, we go down the funnel to think about those clicks through to the site. We do find that large language models drive significantly less traffic than traditional search. Like that cannot be underscored enough. The search volumes are very low. However, the conversion rate is very high. So we are seeing that for some of our clients, the conversion rate is 10x what an organic search conversion rate looks like. Typically, the user that lands on a client's website is so educated, they've done so much research, they've done so much competitive research in their large language model journey that once they arrive at your site, they're ready to purchase. They, they're ready to go. They don't really need to continue browsing, seeing what's going on, seeing what data you have, reading your blog posts. They've already consumed some proxy of all of that information in their gen AI experience, so they want to convert. So I think that that's really important. Another important metric that I am going to cite comes from similar webs study they published in November December 2025 that 95% of chat GPT users are still using Google. So a lot of chat GPT users are then going back to Google to get to the brand site that they want to find, to find the specific products that they want to purchase. So it really isn't a case of ChatGPT is the new reigning king and traditional Google experiences are dead in the water as much. All of the LinkedIn, you know, LinkedIn posts want to claim this and, and incite outrage among the industry. Quite to the contrary really. Search is evolving into a two track multimodal system where users are blending traditional search and generative AI interactions as complementary workflows. So I think that's something else to consider. Like you can't just start optimizing your marketing and communications ecosystem purely for ChatGPT and completely cut off your nose despite your face ignoring Google because Google is still absolutely crucial.
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B
Yeah, I love that it's such a interesting time like right now, whereas we're recording because we're recording just like a month or so before Google IO. You know, to your point earlier, Whitney, like, you know, Gemini has been one of the fastest growing models in your research and so many people just forget that this is still the behemoth that drives your awareness, drives your growth, is how people find things in Google. And that dual multimodal experience of discovery is such a really valid point of where things are going on. This transformation as we're seeing things change in the market. Another one of the key, you know, evolutionary components that we've seen over the last six to 12 months is the shift towards major media shift towards kind of these awareness outlets. In some cases very traditional ones like Returners, New York Times, Wall Street Journal, and then in other cases, some, maybe untraditional ones like Reddit or other community forums or Wikipedia as a source is, is the world changing towards more of a need for pr, a need for brand awareness in order to be successful in what, what we call traditionally search 100%.
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C
So I have a few points on this. So when we talk with our clients about how to optimize marketing and communication, communications Ecosystems For AI search, we talk about three core pillars. First and foremost is technical optimization. You need to ensure that your site can be easily consumed by the large language model by the AI bot that it is sending through to read your site. This is very similar to the search engine spider, but with a few extra components or expanded components components. From our standpoint, we're monitoring over 200 technical points at any given moment for our clients to ensure that not only is a large language model able to send in its AI bot, but that it's able to appropriately consume that content and understand what is the brand, what do they offer, what are their strengths. You know, that baseline of information, information. From there we talk about building content. So this is content on your site, content on your blog, on your product pages, on your services pages. This is also then extends into what is the content in Reddit, Quora, Wikipedia, influencer content, things like this. It's a very large realm of social content. YouTube is also a great one. Making sure that you have appropriate transcripts, really well built out notes or information, documentation about the YouTube video. All of that is pulled in and then PR earned media is the other pillar. Earned media. Those reputable media placements are driving results. Large language models love citing those sorts of third party earned placements and recommending it to their customers, to the user, prompting the large language model. But what is most important is how you are deploying your earned media coverage through those potential reputable media placements. So in your question, you asked about the New York Times or you referenced the New York Times. So the New York Times actually has a huge lawsuit open against OpenAI. It started in late 2023 and it is still ongoing and when it will wrap up is completely TBD and unknown. But basically the New York Times alleged that OpenAI copied millions of their articles without permission to train its large language model and that ChatGPT can reproduce or substitute New York Times journalism in ways that infringe copyright. So this is part of this overall debate about what does copyright include and how far does it extend extend in the world of large language models. So ultimately Chat GPT and the New York Times don't get along. No large language model is able to train on the New York Times at this point in time. So this is not just a question of oh well, I have this press release and let me put it in any large reputable media organization. It actually needs to be really strategic. So we know in the case of ChatGPT that OpenAI has partnered with 18 media organizations that represent nearly 450 publications worldwide. And so understanding out of those 450 or so publications, what is most relevant for your brand and your organization? And ultimately, if you have a press release, if you have a scoop, if you're launching a new product, a new service, understanding okay, well, what is most important for my audience that AI bot? Where could I place this to ensure that I can get traction within these large language models? And then how do I hold that in conjunction or how do I hold that in balance with then where my core audience is consuming information? So my actual consumer that is buying my goods or services, that is investing in my company, that is buying my stock, whatever the case may be, you need to hold those different audiences in balance to understand, okay, I need to be able to strategically select where I am launching new information in this new realm of public relations for the benefit of all of my audiences. Everywhere they're doing research, not just where they're consuming content. Content in an older pre AI content consumption model.
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A
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B
All right, I want to get petty for a second. Like you brought up this New York Times thing and I think this is a fun one because for, for our listeners, you know, this is, this is where the world is going right, we're shifting away from this. Google is a good company that wants to help the world find stuff to we're protecting the assets we have. We're not allowing LLMs or these big tech companies to consume the best thing we have, which is our journalism in the New York Times case, and we're going to block them off from ever using it. Let's just be petty for a second though, and pretend we're an advertiser and that we like spend tens of millions of dollars with the New York Times as an advertiser, as a marketer, and maybe as a brand that's mentioned many, many times in the New York Times. You want to be seen by the LLM. You want, you want to know that company A is associated with company B, the New York Times, and that this is an important vehicle by which we advertise, promote, co author, provide opinions. Whatever it is that you do with the New York Times, you want that known by ChatGPT or any other LLM in order to grow your business and to grow your readership or your audience or your sales, whatever it is. Like, wouldn't you be upset as an advertiser?
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C
So I think there are two different things. One is the earned media coverage, like the journalism, the true journalism coverage that is published in the New York Times is not advertising, right? So this is true earned media coverage. I think that this opens the opportunity to have a discussion with whoever manages your website to say, hey, we actually do need to have a section on our website that lists out our most prominent earned media coverage that will be able to replicate that content in some way. And you know, you can have all of the appropriate SEO technical components to cite back to the original source to avoid duplicate content penalties within traditional search engine optimization techniques. But at the same time then you are able to showcase to the large language model, hey, I am getting this coverage. Journalists are reviewing this. This is very important and here's all of the amazing coverage I've been able to get. So I think that this is another area where you can see new opportunities for partnership between the PR team and the digital team or the website team, however that's structured within your organization to pull those pieces together. Another great option is then to surface that content into social media channels. So do you have a Reddit strategy? Is it possible to deploy links to this type of content coverage that maybe isn't prioritized by OpenAI or is not able to be consumed legally by OpenAI, but now you can put a link to it with A few key takeaways in Reddit and then the large language model is able to consume those snippets through Reddit and kind of get the gist of, of what that article said. So there are ways to still leverage that type of content and work around it so you can still get benefits from it. But at the end of the day, you know, it is out of the 450 publications that that OpenAI has partnered with, I mean they're incredible organizations. Time, Conde Nast, Scott Dash, Meredith, News Corp, ft. I mean you have a lot of options of other places where you can get content published within your publisher strategy.
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B
Yeah, I mean I think that that's a really a, a amazing message which is like you got to take control of where you're winning. Right. So if you are winning in an EARN strategy and you, you're not seeing that become valued by LLMs, then then there's a whole like you should be talking about it on your website. You should be promoting this, this message on your own channels to ensure that the models are, are capturing this. You, you brought up in that, in that example because I love that example and I think it is one of the big misnomers. Everyone's chasing after hype cycles. They want to be seen more in Reddit and they've never even talked in Reddit to a single customer ever in their life. So like, why start now? Like these organizations are, are, are full of different types of department departments, but these departments are going through a tremendous amount of transition and change as to how to do their work. Whether it be adopting new channels like Reddit or becoming more capable of taking your earned media strategy and publishing content and assets about that on your own website. These are, these are skills that typically require coordination. They require cross functional partnership within large complex organizations where you have say a PR department or SEO team, a content team, a web publishing team, and the list of departments and teams can go on. In terms of your experience and some of the projects that you've worked on, what's making organizations successful in this current time of change?
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C
The first thing that I recommend organizations do. Okay, let me take it back up. The very, very first thing is actually figuring out the names of the people responsible for all the other departments and their email addresses and their slack slacks or whatnot and set up.
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B
Find the people first.
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C
Find the people, figure out what are they doing, where are they reliant on agencies or clients, contractors. You know, just understand the landscape of what your colleague in another marketing and communications department does or has in terms of resources. And then from there, once you understand who the people are and what it is that they're doing and how they're doing it at a very basic level, what I love recommending and seeing play out in real time is getting everyone to sit down in a room together if possible or over a zoom or video call and have someone pull up chat GPT and just start asking it questions about your brand. Ask it questions about things your brand provides, the goods and services that you're selling from a non branded prompt perspective and then start asking it about your brand and see what it says and see if you like it, see if you show up and then start clicking through because at the bottom of every provided that the large language model uses a live web search to do its to give you an answer, it will give you the links of where it has sourced the information. So pull up those links and look at all of those sources to understand what is driving this result and what needs to be done to change that result. So are there 20 Reddit links and no one has, no one in the room has responsibility over Reddit. Then you know, coming out of that meeting you need to designate someone who's, you know, fame and fortune or demise is going to live on the Reddit program. You know, in the case of is it earned media and is it all, you know, Forbes or affiliate links? And who needs to figure out how to architect a new program to be able to influence those results. So I really think that sitting down and doing that joint collaboration is crucial. Crucial. And once you do that and you're able to gain camaraderie amongst this broader team by looking at the results together and having a shared sense of ownership over what it says and a shared sense of pain over the potential outcomes that drives people to greater levels of collaboration and new ways of working.
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B
So one of the great challenges is that metrics are just very murky right now. And so this cross functional opportunity, I fully agree, is centered around being able to look at shared goals, shared metrics, shared results. One of the things that we're still trying to get clarity on in terms of LLMs is how does visibility connect to revenue? Right. And is this a direct connection of I'm more visible here and now, I'm thus going to get more customers. In your experience in working within these teams or working with clients, how do you make that a successful journey of LM visibility to some sort of financial or business outcome for a client or organization?
- [37:53]
C
That's a great question. So depending on what industry you're in. When you're listening to this podcast, assuming most people are in search, you are going to have tools that you rely on like semrush or Ahrefs or Moz that are your sources of truth for things like keyword rankings and traditional search visibility. There are different tools that exist that are equally specialized. For large language models. We use three, four. Sorry, we use four different tools like this as well as the AI visibility tools that exist within those legacy search players and then use a variety of supporting tools as well for various specific niche requirements. So I would say that being able to dig into these tools and understand what is my visibility for the prompts and they will give you a percentage so it for a given prompt strategy, they will say you are visible 30% of the time, 50% of the time, 90% of the time. But it all really comes down to that prompt strategy because it's only going to show you the visibility for those prompts that the tool is monitoring. So for our clients, we spend a lot of time and focus on that initial strategy phase of defining those initial prompts. And again, we think of this in like a traditional marketing funnel. What are those top of funnel non branded super broad prompts that someone is going to open ChatGPT because they're just starting their research journey versus then at the bottom of the funnel, what are those branded super specific prompts that someone is typing in because they're ready to make a transaction and they just need help deciding between product A or product B? So we're monitoring that whole funnel and then able to look at for non branded prompts, what is the visibility for branded prompts? We're focused more on what is the sentiment? Is the large language model strongly recommending you? Are they recommending one product over another product? Are they surfacing any brand challenges, any problematic things that have happened in your past? Are those coming up, you know, brand crises, things like this, you know, and so that's what we're really monitoring and looking at to be able to talk to our clients about what the right tactics are that we need to do to implement improve visibility for that prompt set. But ultimately it all comes down to that prompt strategy. And what do we want to be known for? Where do we want to be known? How do we want to be known? And then making sure that we're appropriately analyzing the data consistently after that.
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B
All right, since you brought it up, we're going to go into this last topic here around citations and kind of just all these sources of, of how to be more recognized through LLMs. And this is the substance that these models are being trained on and learning about a brand or their products or services. So I mean, one of the great, I think challenges, and you mentioned this right from the beginning, is that these models are changing so fast. Right? And as the models update, so do the sources that they depend on or rely on. From your experience, having worked a broad set of roles, not just in search, but in PR and in other organizations that, that that are heavily dependent on one channel or the other, are there certain types of sources that no matter what, no matter the LLM, a search engine, social media, that they just are tried and true value places to have your brand seen, heard and connected to with customers or users of a product?
- [42:01]
C
Yes, I would say there are definitely sources that are preferred overall and there's overlap across large language models in their preferences. I will say before I jump into that, we have seen for our clients that if and when you our brand is able to get coverage in some of these sources, you can see the large language model change its recommendation or start citing that source literally overnight. So this is not like the age of search where you need to go get a whole bunch of backlinks, you change the content on your site, and if you're lucky, in three months, organic search results start changing. This is literally within 24 hours. So you know, this is an area that you need to be very on top of. What are the changes? How quickly is it changing? You know, and just as quickly as you get a piece published in Forbes and all of a sudden your brand is highly recommended. Just as quickly you can be dethroned because your competitor now got a similar article in Forbes and now they are the most recommended. So, so it requires a level of monitoring and vigilance that I think is really unfamiliar for a lot of marketers because they're accustomed to potentially slower life cycles. I think paid media is the only realm where people are accustomed to things moving that fast and how quickly they need to be on top of it. So for those of us in the traditional search or PR realms, it is a really exciting, exciting new frontier. And I think there's a lot that we can learn from our paid media colleagues about their monitoring frameworks that really can positively influence how we're approaching aeo. With regards to the your core question of like, which websites are most important? There are various studies. For example, Semrush has done various studies on this. Forbes, Nerd Wallet, Bankrate and CNBC are four of the highest priority sources that are prioritized across ChatGPT and Google AI mode. So they're looking at both of these ecosystems are looking at financial and business authority sites that carry very high editorial trust. The information is very well structured so it's easy for the large language model to consume it. And the large language model knows that these sites have really strong authority. So particularly if your brand is in the finance space, Nerd Wallet is like a must have good positive in positive citations in Nerd Wallet are crazy crucial for visibility.
- [44:58]
B
And when we talk about like the, the places to be seen that that's one component but like the other component or the next like layer to this onion is the sentiment, right? Like you might be in Nerd Wallet, but it might be pure on negative, right? It's like this is, this is a dumpster fire credit card to get or it might be positive and they say this is the best rates and their, their customer service is just, you know, flowers and rainbows. But like what, what does sentiment play in this whole stream of citation and mention? And is it more important to try and influence the volume of citations or is it more important to try to influence the type of sentiment that is being shared in the citations?
- [45:46]
C
I mean, I think that you need the two hand in hand. If you are getting consistently negative press or reviews, whether that's product reviews in Amazon or Walmart or something like this, or you're having consistently terrible reviews in authoritative third party studies like, you know, Gartner, something like this. Like, I mean you need to fix your business problem and that is really crucial. And the large language model is only going to amplify the problem. It is going to make it clear so much faster how big your problem is. And it's crucial that your entire organization rallies around the core problem to fix it. And this could be, I've seen examples where it truly comes down to misinformation because there are not clear linguistics on the site. There's not clear, clear linguistics for influencers, there's not clear linguistics for journalists and podcasts and things like this of what the brand or organization does or does not provide, what a product can or cannot do. And so the question is really, does it come down to an issue of information and content and needing to have clarity around that, or is there truly a product problem that is a much larger challenge to address and how you solve that becomes an organizational wide operations and legal project?
- [47:25]
B
Yep, absolutely. All right, we're going to move on to my favorite part of the podcast, which is what we call our lightning round. Whitney, I'm Going to ask you five questions about the different themes we discussed today and in 30 seconds or less you're going to give me a kind of quick response on, on your point of view on those. All right, Whitney, you ready?
- [47:45]
C
I'm ready.
- [47:46]
B
Awesome. What's the biggest SEO metric that no longer matters?
- [47:52]
C
Oh, okay. Stopwatch is on the metric that no longer matters. We are no longer looking at average position. Position. I am much more focused about how often we're chosen and cited in AI answers than like a blended blue link rank. I already can hear the naysayers out there though. Position does matter. We do know that that ultimately where you rank in traditional search has an effect on how prominently you show up in in large language model answers. So position still does matter as a proxy for prominence. But overall looking at a rolled up average position value or metric is just really noisy and distracting because we're really then blending like low value and high value queries, markets, SERP layouts into a single number. And I think that metric came to importance for clarity clients who just needed something simple. But at this point I think simple is really diluting what matters.
- [49:05]
B
Yep, absolutely. All right, One signal that matters more in AI search than in Google third party validation.
- [49:15]
C
And this can take so many different forms. It could be consumer reviews, industry or analyst report reports, earn media and journalist reviews, expert or influencer citations. Like there's so many different types. Ultimately, those types of third party validation all need to work together, align to the overarching brand strategy and content strategy rather than siloed marketing teams pushing out different messages. The AI cares. Who else truly trusts you, not just how well you stuff your own site with claims.
- [49:53]
B
All right, as you think about content or citations, if you have to invest in one, which do you invest in? More
- [50:05]
C
content. But because content is the source that everything else feeds on, citations are really just a delivery and validation mechanism for the content. So in a cheeky way, I'm actually saying you're trying to get both very important. Both are really crucial. But you can't have the citations without the content. So if I have to pick budget in one or the other, I'm going to say the content.
- [50:34]
B
Love it. Absolutely. Okay, give me one mistake brands are making with AI visibility. Right now.
- [50:42]
C
Brands are treating AEO as their SEO strategy with a few extra bells and whistles tacked on instead of really digging in, understanding the challenges and opportunities presented by large language models and then crafting new strategies, data paradigms and measurement frameworks, as well as collaboration frameworks to move
- [51:03]
B
the needle, all right. And lastly, if you had to prioritize one channel for AI visibility, what would it be?
- [51:13]
C
I would say earned media, because ultimately that brand coverage is what AI models are leaning on when they decide who to trust, and because it is usually largely the channel that is most disconnected from the realm of SEO and content strategies. And so addressing earned media and building those bridges is very crucial and needs to be prioritized by most most brands today.
- [51:40]
B
Okay, that wraps up this episode of the Voices of Search podcast. A huge thank you to Whitney Hart, Chief Strategy Officer at Avenue Z, for joining us. If you'd like to contact Whitney, you can find a link to her LinkedIn profile in our show notes or on the voicesofsearch.com you can also visit our company website, avenuez.com if you haven't subscribed yet and want a daily stream of SEO and content marketing knowledge in your podcast feedback, hit the subscribe button in your podcast app or on YouTube and we'll be back in your feed every week. Okay, that's all for today, but until next time, remember the answers are always in the data.