I Tested My SEO Visibility Across AI Tools
I have a lot of work to do!
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Tracking visibility in AI
Google does not show us AI visibility in GSC, and other AI tools have followed suit. Although Barry Schwarz reported yesterday that Google Search Console maybe testing an AI contribution report, so we’ll have to wait and see.
In the meantime, SEOs are experimenting with all kinds of AI visibility tracking tool or even building their own. And honestly, we kind of have to. Without some form of data, even if it’s directional, it’s hard to generate insights that actually inform what we do next.
In today’s newsletter, I want to unpack both sides of this using my own courses as an example: What are we actually measuring? and How do we turn that into action?
Let’s dig in!
What are we actually measuring
The way I think about visibility tracking is this: it’s a measure of share of voice.
How likely is my brand to be mentioned compared to competitors?
Wil Reynolds recently shared this on LinkedIn: “We just asked a room of 200 marketers to enter the same prompt into ChatGPT incognito and I’d say 60% got the same 3 brands. I was shocked to see that level of repetition of brand names.”
That level of repetition is the signal. How often is your brand mentioned?
Because here’s the thing with LLMs:
They’re not deterministic. The output can change.
So the real question isn’t:
“Am I showing up?”
It’s:
“How likely am I to be mentioned?”
But here’s where it gets tricky…
How likely… for what?
We use prompts to check visibility—but prompts are just a proxy.
What you’re actually measuring is visibility for a topic.
The specific wording of a single prompt doesn’t matter nearly as much as the topic space it represents.
For example:
I asked ChatGPT, Gemini, Claude, and Google for:
→ “top python for seo resources”
→ “top python for seo courses”
For “resources”? No mention.
For “courses”? I showed up on almost every platform (except Gemini 🤷♀️).
Here’s an example of a mention on Claude:
What does that actually tell me?
It tells me my visibility is stronger around the “courses” topic than “resources.”
So if I want broader visibility, the opportunity isn’t to tweak prompts endlessly…
It’s to expand my presence in the “resources” topic space.
The takeaway
Individual prompts don’t matter.
Topics do.
So when you’re measuring AI visibility, ask:
What topic am I trying to win?
How often do I show up across prompts within that topic?
Where are the gaps?
Because that’s what actually turns visibility tracking into something actionable.
First party data, is the best data
One of the most useful things I did recently was mine my own GSC data differently.
I used this regex:
([^” “]*\s){4,}
to get keywords that are 5 words or more because that’s how I expect a prompt to look like and I found this search that I assume to be a prompt (there’s more):
“has anyone actually taken sara taher seo riddler course worth 299 dollars honest review of content quality”
I ran this by ChatGPT, Claude, Gemini and Google and I kid you not, none of them corrected the pricing for the course is not 299, but 100 😂😂😂 but that’s not the issue.
With the exception of Gemini, both chagtp and claude mentioned a big yellow flag for me to work on: there are no independent third party reviews of my course. Well, in actuality there is, but only on LinkedIn in some posts, but there’s non in the places/websites LLMs look for to validate a product e.g. reddit.
Here’s what chatgpt said:
Here’s what claude said:
So this is another workflow for you:
Check your GSC for prompt like keywords
Run them by LLMs
See how often you’re mentioned or not and if not, why.
Come up with a plan.
Note: I’m not entirely sure how to reliably get LLMs to correct incorrect pricing when it’s embedded in user input but I think now I have an idea for testing and research!
And That’s a Wrap (Almost 😄)
Tracking your AI visibility is great, but understanding it and knowing how to use those insights is what actually matters. And you may not need a lot of prompts to do that btw!
Also, always start your data journey with GSC. First party data is your best data (keeping in mind that GSC is very bugy 😄😄😄)
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Disclaimer: LLMs were used to assist in wording and phrasing this blog.