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SEO tools for LLM Search are maturing as marketers better understand what to measure and how those measurements support client goals.
A discussion on LinkedIn about LLM visibility and the tools for tracking it explored how SEOs are approaching optimization for LLM-based search. The answers provided suggest that tools for LLM-focused SEO are gaining maturity, though there is some disagreement about what exactly should be tracked.
Joe Hall (LinkedIn profile) raised a series of questions on LinkedIn about the usefulness of tools that track LLM visibility. He didn’t explicitly say that the tools lacked utility, but his questions appeared intended to open a conversation
He wrote:
“I don’t understand how these systems that claim to track LLM visibility work. LLM responses are highly subjective to context. They are not static like traditional SERPs are. Even if you could track them, how can you reasonably connect performance to business objectives? How can you do forecasting, or even build a strategy with that data? I understand the value of it from a superficial level, but it doesn’t really seem good for anything other than selling a service to consultants that don’t really know what they are doing.”
Joshua Levenson (LinkedIn profile) else answered saying that today’s SEO tools are out of date, remarking:
“People are using the old paradigm to measure a new tech.”
Joe Hall responded with “Bingo!”
Lily Ray (LinkedIn profile) responded to say that the entities that LLMs fall back on are a key element to focus on.
She explained:
“If you ask an LLM the same question thousands of times per day, you’ll be able to average the entities it mentions in its responses. And then repeat that every day. It’s not perfect but it’s something.”
Hall asked her how that’s helpful to clients and Lily answered:
“Well, there are plenty of actionable recommendations that can be gleaned from the data. But that’s obviously the hard part. It’s not as easy as “add this keyword to your title tag.”
Dixon Jones (LinkedIn profile) responded with a brief comment to introduce Waikay, which stands for What AI Knows About You. He said that his tool uses entity and topic extraction, and bases its recommendations and actions on gap analysis.
Ryan Jones (LinkedIn profile) responded to discuss how his product SERPRecon works:
“There’s 2 ways to do it. one – the way I’m doing it on SERPrecon is to use the APIs to monitor responses to the queries and then like LIly said, extract the entities, topics, etc from it. this is the cheaper/easier way but is easiest to focus on what you care about. The focus isn’t on the exact wording but the topics and themes it keeps mentioning – so you can go optimize for those.
The other way is to monitor ISP data and see how many real user queries you actually showed up for. This is super expensive.
Any other method doesn’t make much sense.”
And in another post followed up with more information:
“AI doesn’t tell you how it fanned out or what other queries it did. people keep finding clever ways in the network tab of chrome to see it, but they keep changing it just as fast.
The AI Overview tool in my tool tries to reverse engineer them using the same logic/math as their patents, but it can never be 100%.”
Then he explained how it helps clients:
“It helps us in the context of, if I enter 25 queries I want to see who IS showing up there, and what topics they’re mentioning so that I can try to make sure I’m showing up there if I’m not. That’s about it. The people measuring sentiment of the AI responses annoy the hell out of me.”
Although Hall stated that the “traditional” search results were static, in contrast to LLM-based search results, it must be pointed out that the old search results were in a constant state of change, especially after the Hummingbird update which enabled Google to add fresh search results when the query required it or when new or updated web pages were introduced to the web. Also, the traditional search results tended to have more than one intent, often as many as three, resulting in fluctuations in what’s ranking.
LLMs also show diversity in their search results but, in the case of AI Overviews, Google shows a few results that for the query and then does the “fan-out” thing to anticipate follow-up questions that naturally follow as part of discovering a topic.
Billy Peery (LinkedIn profile) offered an interesting insight into LLM search results, suggesting that the output exhibits a degree of stability and isn’t as volatile as commonly believed.
He offered this truly interesting insight:
“I guess I disagree with the idea that the SERPs were ever static.
With LLMs, we’re able to better understand which sources they’re pulling from to answer questions. So, even if the specific words change, the model’s likelihood of pulling from sources and mentioning brands is significantly more static.
I think the people who are saying that LLMs are too volatile for optimization are too focused on the exact wording, as opposed to the sources and brand mentions.”
Peery makes an excellent point by noting that some SEOs may be getting hung up on the exact keyword matching (“exact wording”) and that perhaps the more important thing to focus on is whether the LLM is linking to and mentioning specific websites and brands.
Awareness of LLM tools for tracking visibility is growing. Marketers are reaching some agreement on what should be tracked and how it benefits clients. While some question the strategic value of these tools, others use them to identify which brands and themes are mentioned, adding that data to their SEO mix.
Featured Image by Shutterstock/TierneyMJ
I have 25 years hands-on experience in SEO, evolving along with the search engines by keeping up with the latest …
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