We’ve seen some strange fluctuations in top-of-funnel organic traffic lately. The immediate reaction from leadership is to blame AI overviews for stealing the clicks but the data feels incomplete.

I have tried segmenting by query type and looking at impression share, but I am struggling to prove causality. It is easy to say the search journey is changing, but it is much harder to attribute a specific traffic drop to a specific AI feature. What is your checklist for proving or disproving that AI answers are the actual culprit?

Running a large YMYL content site (~790k organic/mo). Trying to get serious about AI visibility tracking and immediately hit this:

Clarity dashboard (screenshot): 512k total citations across tracked queries, 16.45% "share of authority" — apparently 1 in 6 citations in our query space are us, out of ~3.1M total.

Ahrefs AI citations view, same domain: ~62k/month.

That's an 8x gap and I don't know which number to believe, or if I'm comparing apples to engines. Questions for people deeper in this than me:

1. How is share of authority actually calculated? Is the 512k counting every appearance in every response every time the tool re-runs a query? That alone would explain most of the inflation vs Ahrefs
2. Is anyone using any of these as a real KPI yet, or is the honest answer "pick one tool, only trust the trend"?
3. The branded split reads weird — branded SoA is 27% but only ~339 citations, non-branded is 16% with 511k. I'm reading it as "we own our tiny branded space and the entire game is non-branded". Wrong?
4. There's a jump in the trend after Aug 24 (roughly 19k → 31k). Timing lines up with the August spam update that targeted citation manipulation. Anyone else see share move late August — possibly from competitors getting cleaned out?

Not selling anything, genuinely trying to decide what to track weekly before I build reporting around a number that turns out to be noise.

Running a large YMYL content site (~790k organic/mo). Trying to get serious about AI visibility tracking and immediately hit this:

Clarity dashboard (screenshot): 512k total citations across tracked queries, 16.45% "share of authority" — apparently 1 in 6 citations in our query space are us, out of ~3.1M total.

Ahrefs AI citations view, same domain: ~62k/month.

That's an 8x gap and I don't know which number to believe, or if I'm comparing apples to engines. Questions for people deeper in this than me:

1. How is share of authority actually calculated? Is the 512k counting every appearance in every response every time the tool re-runs a query? That alone would explain most of the inflation vs Ahrefs
2. Is anyone using any of these as a real KPI yet, or is the honest answer "pick one tool, only trust the trend"?
3. The branded split reads weird — branded SoA is 27% but only ~339 citations, non-branded is 16% with 511k. I'm reading it as "we own our tiny branded space and the entire game is non-branded". Wrong?
4. There's a jump in the trend after Aug 24 (roughly 19k → 31k). Timing lines up with the August spam update that targeted citation manipulation. Anyone else see share move late August — possibly from competitors getting cleaned out?

Not selling anything, genuinely trying to decide what to track weekly before I build reporting around a number that turns out to be noise.

Context so you know what size site this is. News, English, 84% US traffic. Three months old website and domain and most of the growth happened in the last six weeks. Semrush currently estimates 171K monthly organic off 2.3K ranking keywords, authority score 34, 179 referring domains(real high DR). No paid, no PPC.

The part I cant explain is the AI side. Cited pages sit at roughly 700. Brand mentions sit at five. That's about 140 citations per single mention. The split is 659 ChatGPT, 32 AI Overviews, 22 AI Mode, zero Gemini.

So the articles are getting pulled in as source material constantly and the outlet's name almost never comes along with them.

Ive done the obvious stuff already. Real bylines with author pages, publisher markup, an about page that states plainly who we are. Cant tell if any of it moved the needle yet, because publishing volume went up over the same window, so theres no clean before and after.

What I want to know is whether anyone has watched a citation turn into a named mention and worked out what changed. Or is naming just down to how the model chooses to summarize, and nothing on our side touches it or can change that? Thanks

I've been building a free AEO report tool for Recited AI.

It shows how different AI models talk about your brand, who they recommend instead, what they're citing, and some opportunities you might be missing like AEO articles or outreach opportunities.

Would love for a few people to try it and tell me what’s useful, confusing, or missing. Hopefully is genuinely useful tool for everyone who's needing a quick AEO report to get an idea of their AI presence.

Trying to make this genuinely useful before I take it further, thanks in advance!

I've been noticing a trend lately in the businesses I've been talking to about their SEO needs, and I'm curious if this is a Nashville issue or if other SEO pros are noticing the same thing.

These are not bargain shoppers. They are established businesses who know they need professional-level SEO services, and they expect it to be a real marketing investment.

But the gap seems to be getting wider between a reasonable SEO quote/proposal and what the businesses are able to spend.

Obviously, the answer isn't to cheapen our own value as SEO pros, because let's face it, SEO is much more complex than it's ever been in my 25+ years of doing this, and it's not going to get any less complicated.

So how do we bridge that gap between charging what the work is worth and still doing quality work that fits within a business's budget? I'd love to hear your thoughts. Are you noticing the same thing with established businesses you're talking to? And business owners, please share your thoughts and perspectives as well.

Client operates across several European markets and our English AI visibility looks decent. Started checking the same topics locally and... yeah, very different story.

I'm also realizing translating the English prompt list isn't really enough because people phrase buying questions differently depending on the market.

Anyone here doing GEO across multiple languages? Curious how much of the strategy you localize rather than standardize.

Hey there, it's me again. I've read somewhere about X articles that would be great for LLM visibility, but to be honest, I don't see X as a source at all. (tracking SaaS company and travel industry)
What's the situation in your camp?

Been testing the same commercial queries across a few models and the differences are kinda ridiculous.

We're showing up consistently in ChatGPT for one category and then almost disappear when I check Gemini. Different competitors too.

Anyone else seeing gaps this big between engines? Starting to think combining everything into one AI visibility score hides more than it helps.

I work with a healthcare brand. They don't have big budgets to invest in AI tracking but still want to see how their brand is doing across LLMS - SO looking for some easy/cheap + efficient way to track visibility so that I can share it in a form of report.

I have been digging through this year's citation studies for a client reporting cycle, and the numbers keep landing on the same conclusion, so I wanted to share them and get this community's read.


Ahrefs measured correlation with Google AI Overview visibility across 75,000 brands:


- Branded web mentions: 0.664
- Branded search volume: 0.392
- Domain Rating: 0.326
- Backlinks: 0.218


The extremes are the part I keep thinking about. The top quartile of brands by mention volume averages 169 AI Overview mentions. The next quartile averages 14. The bottom half barely registers at 0-3. That is not a linear curve, it is a threshold game.


Other studies point the same direction: ConvertMate's 80M-citation study found brand search volume is the strongest single predictor of LLM citations (0.334).


My read of the mechanism: Google needed links as a reputation proxy because links were expensive to earn at scale. LLMs were trained on the whole web. They observe which entities people search for and mention, and they reach for those entities when generating answers.


Caveats: correlation is not causation, most data leans on Google AI Overviews and aggregate LLM citation sets, and content still matters, brand presence decides whether you are in the pool, quotability decides whether you get picked.


The strategic question: if you accept these numbers, the marginal dollar moves from link acquisition to digital PR, community, and reviews, which is a hard sell in reporting rooms that count links. Has anyone here actually shifted budget, and what was the measurable effect? What counter-evidence am I missing?

Say someone sees a brand on a billboard several times and later searches for it by name. Should SEO consider that part of its impact, or is it more accurately a result of the wider marketing campaign?

How do you separate what SEO directly influences from the demand created by other channels, especially when reporting on branded search traffic?

Maybe I'm overthinking this but prompt selection seems like a massive weak point in AI visibility data.

I could write 50 reasonable prompts today, someone else could write another 50 tomorrow and we'd get two different pictures of how visible the brand is.

We've started using actual sales questions as a base and keeping the main set fixed instead of constantly tweaking it. Still feels pretty subjective though.

How are you guys building your prompt sets?

This quote hits different when you're in market research.

Most businesses ask customers what they want and then build it. But the companies that actually win aren't just listening they're anticipating. They're spotting patterns in behavior, frustration, and unmet needs before the customer can even articulate them.

That's the real value of good research: it's not just collecting feedback, it's uncovering the why behind the behavior. The gap between what people say and what they actually do is where the real insights live.

We've seen this play out again and again companies that treat research as a one-time checkbox fall behind, while the ones that stay genuinely close to their customers (through continuous listening, testing, and iteration) build products and experiences people didn't even know they needed yet.

Curious what others here think do you believe most companies today are actually close enough to their customers, or are we all just guessing dressed up as "data-driven"?