LLM Visibility Tools: SEO Insights
A recent LinkedIn discussion delved into LLM visibility tools and their role in SEO. The conversation revealed growing interest in optimizing for LLM-based search, though opinions vary on what metrics should be tracked.
Joe Hall (LinkedIn profile) sparked the debate with questions about LLM visibility tools’ effectiveness. His queries aimed to initiate a broader conversation.
“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Joshua Levenson (LinkedIn profile) responded, noting that current SEO tools are outdated:
“People are using the old paradigm to measure a new tech.”
Joe Hall agreed, emphasizing the need for new strategies.
Understanding LLM SEO
Lily Ray (LinkedIn profile) highlighted the importance of focusing on entities that LLMs rely on.
“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 inquired about the practical benefits for clients, to which Ray replied:
“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.’”
Tools for LLM Optimization
Dixon Jones (LinkedIn profile) introduced Waikay, a tool that uses entity and topic extraction for gap analysis.
Ryan Jones (LinkedIn profile) discussed his product, SERPRecon, and its approach:
“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.”
Jones elaborated on the challenges and benefits of using AI for SEO:
“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.”
He explained how this approach aids 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.”
Dynamic Nature of Search Results
Although Hall noted that traditional search results were static compared to LLM-based results, it’s important to recognize that traditional results have always been dynamic, especially post-Hummingbird update.
Billy Peery (LinkedIn profile) provided insights into LLM search stability:
“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.”
Peery emphasized the importance of focusing on brand mentions and source links rather than exact keyword matches.
Conclusion
Awareness of LLM tools for tracking visibility is growing. Marketers are reaching some consensus on what to track 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, integrating that data into their SEO strategies.
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