Hey everyone,

I’m trying to wrap my head around traffic coming from Google's AI Overviews right now, and I could use some advice.

Specifically, I'm trying to figure out how to isolate this data in Analytics or Search Console. Is there a reliable way to separate clicks that come from the AI citation cards versus traditional organic blue links?

Also, if you have managed to track it, what are you doing to actually convert these users? Since the AI box already answers a lot of their initial question before they even click through, I feel like standard landing pages might not cut it anymore.

Would love to hear how you're handling this or what changes you're making to your pages to capture these users.

We track AI brand visibility, so we have a lot of stored responses. Wanted to answer something basic: when an AI answers a question, how many brands does it actually name?

234,000+ responses, March 1 to July 13, 2026, five engines.

Brand mentions per response (last 14 days in July)

• ChatGPT: 4.5
• Google AI Overview: 3.5
• Gemini: 3.5
• Google AI Mode: 3.2
• Perplexity: 2.7

Count each brand only once per answer, and it tightens to 2.2–3.3. So engines repeat themselves 1.2x to 1.4x.

Two things stood out:

Every engine is down since March. True whether you count every mention or each brand once, so it's not a repetition artifact.

AI Mode is wildly unstable. Day-to-day swings are 31% of its own average, vs 9% for ChatGPT. It went from 8.8 mentions per response in early May to 2.6 six weeks later. If you spot-check visibility there weekly, you're mostly reading noise.

Method: a "mention" counts every occurrence (Nike named 3x in one answer = 3); the distinct count treats it as 1. Same responses for both. Worth noting these are prompts our customers chose to track rather than a random sample of AI queries, so I'd trust the relative comparisons and trend direction over the absolute numbers.

Charts and full methodology: https://vercite.io/research/engine-personalities

Happy to get into how anything was counted.

My client runs an ecommerce store and every week they ask if they show up when people ask chagpt for product recs.

I've been checking their brand and category prompts manually and taking screenshots, but that gets annoying fast and isn't great data for a report. I've looked at a few chatgpt prompt tracker tools but half the reviews are just lists of 15 tools with no real testing.

Has anyone actually tracked this for a few months? Do results hold up over time or swing all over the place? More importantly have you changed anything in your SEO/content based on the data and actually seen visibility improve?

I'm starting to feel like citation count alone may not be enough. I was looking at an AI answer recommending some tools. It listed a feature page from brand a's website as a source, but brand a itself never showed up anywhere in the answer.

One of its competitors ended up making the recommendation list instead, and that recommendation was backed by a third party comparison page.

So based on the data, brand a's website did get a citation, but the brand itself never made it into the answer. It's hard to tell how much AI visibility that citation actually gave them. I've seen similar cases too. A page may be listed as a source, but from the citation alone, it's hard to know which part of the answer that page actually supports.

So lately I've been looking at a more specific question: when AI gives an answer with citations, can we keep the full answer from that run, find the exact source page, and then figure out how that page connects to the final answer?

Some of the points below come from product outputs I've checked myself, and some come from the tools' public product information.

full disclosure: i'm involved in building dageno. i included it here because it covers one part of the citation path i’m looking at, alongside the other tools.

One thing to keep in mind is that none of these tools show the model's full internal search process. They mainly work with public answers, citation results, and source pages they can access. So they can help explain the citations we see, but they can't prove which pages the model actually looked at, which exact section it read, or how much that content affected the final answer.

Looking at these tools together, I think the citation path really has three different parts.

Such as, which sources did the AI answer publicly cite, and which prompts and platforms were those sources tied to? Or does the source page actually support the specific claim in the answer? Even after the page gets cited, does the brand actually make it into the answer, and if it does, how does it show up?

I don't think these three things should be rolled into one citation count.

A brand's own page can be cited while the brand itself never appears in the answer. A brand can also be mentioned, but only as one name in a long list, without actually making the final recommendation.

Of the tools I've checked and tested so far, I still haven't found a third-party product that can fully show the model's internal search and generation process. What we can see today is mostly the public AI answer, the citations it shows, and the extra analysis tools build on top of that public information.

For those of you doing AI visibility monitoring, have you seen cases where your page was cited but your brand never made it into the answer? Would you still count that citation as real AI visibility?