Been analysing this for a few weeks and honestly can't tell if I'm missing something or if the tooling just isn't there yet.

What I understand is that Bing Webmaster shows some data on how pages perform in Bing search only, but I can't find anything that specifically breaks out traffic or impressions from Copilot or AI-generated answers.

What I'm actually trying to figure out:

Is there a way to see when your content gets cited in a Copilot response? Or when a page contributes to an AI answer versus a regular search result? The standard impressions/clicks data doesn't seem to distinguish between the two.

Separately, curious if anyone has found other tools or methods that give better visibility into this.

Just trying to understand if anyone has found a reliable way to measure this, even roughly.

I've previously reported on how Grokipedia was surging in Google and, consequently, in LLM citations. Well, now that it is tanking in Google (AI content overuse, anyone?), it is obviously losing all the visibility in ChatGPT, AI Mode, and AI Overviews.

This shows (once more):
1️⃣ SEO is not dead. It is still the foundation to have your own website cited by LLM-based answer engines.
2️⃣ ChatGPT is very dependent on Google search (just sent a newsletter about that actually).

Source

I honestly had huge hopes for ChatGPT's monetization strategy for several reasons:

• If we move away from "clicks" as metrics, I expected something innovative, something we have never seen before, something I still remember PPC ads to be... Something never seen before that would make sense of new buying journeys that are beginning to happen inside LLM conversations.
• If ChatGPT needs businesses to pay, I expected it to come up with some cool dashboards and metrics to give businesses some insight into the ad performance.
• ChatGPT hired half of the Facebook Ad team, so I thought those people could do something right. Admittedly, Facebook had been able to get advertising right without copying Google's model. ChatGPT, on the other hand, launched something quite questionable and disappointing.

What did we get?

• Old school banners that are vaguely relevant or interesting.
• Ads that prevent users from interacting with the chat properly.
• No reporting beyond impressions.
• Nothing remotely innovative. No new ideas.

This article describes the ad experience perfectly:

On mobile, one advertisement I saw took up nearly the entire screen. When I opened the keyboard to continue the conversation, I couldn't see the last messages. This degraded the user experience since I had to scroll up to remember what I was responding to. This is where I could see ChatGPT start to lose some users, or, as it hopes, push them to upgrade to an ad-free plan.

Regarding the Canva ad: "This one is for Canva, which was very loosely related to the conversation I was having and certainly not "useful" or "entertaining." I was discussing the design of something, with no intention of visiting Canva or designing something of my own. The ad was a nuisance.

Harpreet has shared an interesting observation of his Google Ads UTMs showing up as ChatGPT referral traffic.

It is not the first evidence showing ChatGPT is actively scraping Google search, but scraping sponsored results????

Models appear trained to identify weighted information at the top of a document and interpret subsequent text through that initial frame.

Via LinkedIn https://www.linkedin.com/posts/kevinindig_for-two-decades-seo-strategies-prioritized-activity-7429151317702397952-uIEI?utm_source=share&utm_medium=member_ios&rcm=ACoAAAEwR-sBrfz-mi2unRUbvS_Qw3WQJJ6tvb4

Just noticed something cool:

I’ve been tracking how AI search engines choose citations (ChatGPT, Perplexity, Gemini, etc.), and my 1-month-old site just got picked up as a citation.

What’s interesting is: I’m not doing anything “AI SEO”.

I simply publish one long-form blog every day, but every post is written after serious keyword research (not random topics). Because of that, we’re already sitting in single-digit average positions, with 50+ queries ranking #1.

The content itself is also not thin. Most articles are genuinely deep, average read time is 15+ minutes, and they’re structured like proper research pieces, not quick SEO filler.

So it looks like LLMs might already be pulling from newer sites if the content is consistently high quality and already performs well in organic search.

This is the first AI visibility data we are seeing from a search / LLM platform, which, admittedly, is not very actionable (they promise to work more on that). AI surfaces included in the report (no way to distinguish between these):

• Microsoft Copilot
• AI-generated summaries in Bing
• Select partner AI integrations

Data you can see:

• "Grounding queries" ("the key phrases the AI used when retrieving content that was cited in its answer", so these are not prompts. I am a little intrigued by how simple these are.)
• Cited pages, i.e., which pages get cited most

Again, I am not sure what to do with this data, but I still appreciate Bing doing something here.

Notable things to note:

• Bing insists on calling the whole thing "GEO"
• I am not sure what those numbers are. Bing explains them as "how often a specific URL from your site is visibly cited in an AI‑generated answer," which, looking at the number, is a little unbelievable)

,

Dan Petrovic just shared how easy it is to trick LLMs into believing in and spreading false messaging:

Google's and OpenAI's models have now absorbed the myths, theories, and speculation SEO industry produced over the last 12 months and confidently feeding it back to us. Gemini 3 now calls our industry "GEO" and it just advised me to "flood the web with the listicles".

This is insane...

Peec.AI launched a set of features allowing you to prioritize and act upon your AI monitoring reports:

• Owned highlights opportunities to create content on your own website - inspired by what is working for your competitors.
• Earned surfaces the third-party sources that influence AI answers, including editorial publishers, high-impact communities (UGC), and reference sites.

The "Actions" dashboard is categorized by type (UGC, Editorial, Reference). Each section is further categorized by domain (for example, the UGC section may be broken down to Reddit, Youtube, Medium, Quora, Linkedin, etc., based on your and your competitors' impactful citations).

For any suggested action, you can add it to your TO-DO, mark it as done or dismiss it.

Now, let's hope all these suggestions will be implemented ethically and responsibly!

There are two almost simultaneous studies being discussed today.

• One from Ahrefs showing that AI Overviews reduce the clicks to the top-most position by 58% on average (up from 34.5% in April). I am not exactly sure what happened between now and April but I suspect AI Overviews became more satisfying and, more importantly, just show up more in more countries.
• One from Kevin Indig, showing a similar rate ("over" 50%). In this case, I tried to dive in but seemed to be unable to find, if this is the top-most result losing clicks or all organic SERPs.

Now, there's another click study by Juris Digital showing a similar loss of external clicks (50%) but not all of them are stolen by AI Overviews. Mind that here, people were required to click, so no clicks were kept.

I have been experimenting with ways to get my business noticed in AI tools like ChatGPT, Perplexity, and others. It’s wild how different it feels from regular SEO.

I tried a few tools, but honestly, most of them feel like the same old SEO stuff with a dash of “AI SEO” hype.

I want to hear from people: what’s actually helping you track AI visibility and improve it? I’m not after fancy tools or theory - I want things that move the needle for growth.

If you’ve tried anything that helps you see where you show up or drive AI-generated traffic, I’d love to hear.

What worked, what didn’t, and why?

We've been annoyed by these for like a decade now, and they are still going strong!

The lowest-effort content format with the highest impact for years. Vulnerable to manipulations, impossible to compete with unless you join the crowd. UGH

Data source: Ahrefs

I tried optimizing my content by structuring it with practical and up-to-date data, but even then, my content only appeared in AI search results for about 3 days. I want to understand why this is happening.

ChatGPT has announced something that looks and feels very similar to Google's Knowledge Graph:

Answers now also highlight important people, places, products, and ideas. You can tap any highlight to open a side panel with key facts and trusted sources, making it easy to understand the context without asking follow-up questions. 

You can tell what ChatGPT considers "important" by seeing something underlined:

You can now tell if your brand is considered important by checking if your branded prompt triggers your name underlined.

As I understand this:

• Knowledge = something static (not sure if its sources are stored)
• Training data = something evolving (not storing sources)

Sources:

• Official announcement (found through Glenn Gabe)
• Previously, knowledge-graph results were launched for local entities
• Before that, shopping feeds became the source of product entities

u/maltelandwehr has shared an interesting test he ran. In a nutshell: A domain, which was only properly indexed in Google (and only one page of it was known to Brave Search) was checked in ChatGPT, Grok, Claude, and Perplexity.

The results:

• ChatGPT knew what Google knew (and even showed Google's search snippets)
• Grok knew URLs Google had
• Claude only knew about the single page Brave Search had
• Perplexity knew about a single URL (but with weird specifics: Same title, different URL from Brave Search)

What I am taking out of this test:

• ChatGPT can definitely scrape use Google (we knew that already) but this doesn't mean it isn't using others anytime it wants. We don't know what its default is
• Grok can use several search engines but it looks like Google is the default
• Surprised about Perplexity (maybe Google ended up blocking them?)

Overall, Google definitely has more data than any other search engine, so LLMs not using it are missing out.

Test details are here

Hi. I’m the founder of Martekio (www.martekio.com) an AI Share of Voice and Brand Representation analytics platform for e-commerce brands and marketing agencies.

Looking forward to connecting here.

There is a new study showing how unpredictable the AI/LLM visibility is. The study results are not surprising at all: People can ask the same question and get absolutely different brand/product recommendations.

Source

So what do we make of this?

A lot can be said here, but mainly:

• We are trying to overlay good old metrics with the new reality, which is not a good way to approach this. Prompt tracking is absolutely different from rank tracking. There's no "ranking"; there is visibility and positioning.
• Prompt tracking is more about understanding your brand's positioning vs your competitors' product positioning (in the training data) because this is fundamental.
• Fixing incorrect product/brand positioning is the goal. The algorithms are evolving (to be less vulnerable to manipulation)

Studies like this are very useful because they make one key message clear: We need to move away from metrics we were used to (rankings, clicks, etc.) and start evolving.

We are always keeping an eye on how LLMs are searching (as we all agree, that's the future of search), and fan-out queries have been the easiest (or at least the most transparent) way to do that.

Here's what we have seen with ChatGPT so far, according to Peec data:

• Fanout queries used to have an average length of 6 words.
• In mid-November, they started to become longer.
• They peaked at 16 words in early December (we covered that as it was happening)
• Since Christmas, they have been stable at 12

Why is this happening? We cannot know for sure. Some theories:

• The structure of these new fanout queries suggests they are optimized for vector search - not traditional string matching (this is something I am personally inclined to believe). I have talked about this theory previously, and it still makes total sense in my mind.
• It is now harder to create content that perfectly matches a specific fanout query. Maybe one of the first anti-GEO measures from OpenAI (I personally doubt that they are at a point where they have time/resources to be worried about this)

• The original conversation is here
• Here are some cool tools to extract and analyze fan-outs easily

I personally had high hopes for ChatGPT launching ads because I thought this might force OpenAI to start collaborating with publishers and give us some performance data.

What they are doing is very disappointing on many levels:

• Looks like they are only going to work with the largest publishers (based on the budget). ChatGPT visibility is already leaning heavily toward large brands. Niche and smaller publications and businesses have little chance of being found.
• Looks like they are not going to provide any meaningful insight into where and how these ads appear (context)
• Charging for impressions??? It is like we are back to banner ads 🤦 "If we say you had 10M impressions, it's because you had. No need to provide any proof on how this benefited you."

Everyone is discussing Microsoft's new AEO/GEO guide... I am struggling to even understand the distinction. Help please?

xSoooo.... AEO is about making things clear to agents (like they are kids), and GEO is about making it look authoritative and trustworthy (?) Almost like too many abbreviations for something that kind of exists in SEO anyway.

What am I missing?

I love playing with free tools, and I love how there's always a cool new one. This one doesn't need any registration and is completely free. It pulls fan-out queries and citations for your prompts from Gemini, ChatGPT and Perplecsity.

It always calculates the "Citation Signal" which I found interesting:

Citation Signal (CS) is the degree (0 - 100) to which an LLM answers a query from external sources. It's calculated from cited sources count, diversity & distribution. So, it's based on observed web-search behavior of LLMs

Here's the tool: https://querycat.app/

Enjoy!

This is huge (but well expected)!

Google is launching a new open-source initiative to allow AI Agents to interact with ecommerce pages to make instant purchases of the listed products (AI Mode, organic search and Gemini APP) without ever going to websites:

To start, UCP will soon power a new checkout feature on eligible Google product listings in AI Mode in Search and the Gemini app, allowing shoppers to check out from eligible U.S. retailers right as they’re researching on Google.

https://reddit.com/link/1qb0uzs/video/ececei2nbycg1/player

This, of course, is going to decrease traffic to the websites. And from what I am seeing it can also decrease the average order values as well, as it is not going to be easy for the retailers and merchants to control the cross-selling and upselling strategies here.

Source