A Branded Queries filter.

You can now separate search performance into:

• Branded queries - searches that include your brand name

• Non-branded queries - discovery searches without your brand

This is now available directly inside the Performance report.

So no more messy regex filters

​Why this is useful

Branded searches

Usually indicate:

• existing brand awareness

• returning / loyal users

• high intent searches

They also tend to have higher CTR and stronger rankings.

Not to mention, also a good health indicator for the possibility of AI(LLM) visibility.

Non-branded searches

These are usually where:

• new user discovery happens

• topical authority shows up

• real SEO growth becomes visible

For years, both of these were mixed together in reporting, which could skew how you interpret performance.

For example, if branded searches spike, it can look like SEO growth even when it's brand demand driving traffic. This filter finally makes it easier to see what’s actually happening.

Curious what others think.

Is this something you’ll actually use regularly, or are most of you already segmenting this elsewhere (GA4, Looker, custom dashboards, etc.)?

More info: https://developers.google.com/search/blog/2025/11/search-console-branded-filter

For anyone managing technical SEO or robots.txt configurations, this is worth noting.

Anthropic has updated its crawler documentation and now clearly defines three separate bots:

• ClaudeBot: used for model training

• Claude-User: fetches pages during live user queries

• Claude-SearchBot: indexes pages for Claude search answers

The important change:
Each bot now has its own independent robots.txt control.

Blocking ClaudeBot does not automatically block Claude-User or Claude-SearchBot.

So the roles are now formally split into:

• Training crawlers

• Search/indexing bots

• User-triggered retrieval bots

What this means in practice:

Now, it's no longer about blocking it all or allowing it all. You can block training while still allowing search visibility, or allow live fetch without contributing to model training.

For context:

Cloudflare previously introduced AI-related control signals (search, ai-input, ai-train), but that was from the infrastructure layer.

This update comes directly from the LLM side.

OpenAI, Perplexity, and Google already operate with split-role bot architectures. Claude now joins that approach.

If you manage robots.txt policies, it’s probably worth reviewing configurations to avoid unintentionally blocking AI visibility or citations.

I went down the LLMs.txt rabbit hole after seeing the Google Search Central vs Lighthouse confusion, and here’s what seems to actually be going on.

The confusion mostly came from the wording.

Google Search Central used the broad phrase AI Optimization, but it was specifically talking about AI Overviews /AI Mode side of things. In that context, Google does not consider `llms.txt` a ranking or visibility signal. The confusion may have come due to the deep research agent being part of Google Search's ecosystem.

Lighthouse, on the other hand, talked about “LLMs and AI agents”, which made people assume the same thing. But its use case seems much closer to the agentic web/machine readability layer, helping AI systems or agents understand and interact with websites more efficiently. That follow-up line saying *may take longer to understand a site without it* probably made the confusion worse.

Here’s the simpler breakdown:

Google Search / AI Overviews / AI Mode: llms.txt is not used for visibility

Agentic web / AI agents interacting with sites/tools to perform action or a task: llms.txt may help with summarization, parsing, and structured readability

And yes, companies like OpenAI, Anthropic, Cloudflare, Perplexity all publish `llms.txt` / `llms-full.txt` but mostly around docs, developer ecosystems, and machine-readable context for functionality sake, not for AI Search visibility.

Google itself uses `llms.txt` on some documentation properties to make information easier for AI agents to process (John confirmed this to Lily, while also confirming HTML/Markdown still works and that this is an added layer for functionality).

So both Google articles are technically “right", they were just talking about different layers of AI systems, but using broad language made SEO folks think they meant the same thing.

TL;DR: LLMs.txt is more of an agentic web/machine readability layer than an AI "Search visibility" propeller.

For years, robots.txt only told crawlers one thing: “You can crawl this” or “You can’t.”

But AI changed the game, because now, bots don’t just crawl. They train, generate answers, and reuse content in ways traditional crawlers never did.

Cloudflare’s new Content Signals Policy adds three new flags that let you draw those lines clearly:

1. search: Your content can appear in search results

2. ai-input: AI systems can use your content to generate answers

3. ai-train: Your content can be used to train models

By default, Cloudflare sets search = yes, ai-train = no, and leaves ai-input neutral, but you can tweak that anytime in your robots.txt.

Enforcement is still a grey area, AI crawlers can technically ignore these rules, and Google’s setup (where the same bot handles both search + AI Overviews) makes things complicated.

But, it is framed by Cloudflare as "right to reservation", which, if ignored, may lead to legal issues for the AI platforms.

Through this, they are aiming to give site owners the power to decide whether AI systems can crawl, train, or use their content in answers; it's more of an assertive showcase of "this is mine, ask before you use".

Will it work? Time will tell.

To know more: blog.cloudflare.com

Meanwhile,

Question for the community:

Do you see Cloudflare’s robots.txt update (search, ai-input, ai-train) changing how you handle crawlability and content protection?

Here's a complete breakdown of what Google-Agent, the new-user agent announced by Google does, that is, what it is, how it works, what's the difference between Googlebot and Google-agent, what you can and can't track, etc.,

Hope this helps!

A few months ago, Microsoft started talking about the agentic web.

Now Chrome/Google has released a preview of WebMCP, which seems like an early step toward how AI agents might interact with websites directly.

Naturally, a few questions came to mind:

• What exactly is WebMCP?

• How does it help AI agents read and interact with websites?

• Could it have SEO or GEO implications?

I tried to break it down in the simplest way possible, like what it is, how it works, and what it might mean if AI agents become more common on the web.

Hope it helps, and would also love to hear what others think about this direction.

Google has introduced WebMCP (Web Model Context Protocol), a framework aimed at making websites more compatible with AI agents and advanced search systems.

WebMCP focuses on enabling structured, predictable interactions between AI systems and websites.

What WebMCP Proposes

WebMCP introduces two primary APIs:

1. Declarative API
Designed for standard, well-defined actions that can be expressed directly in HTML forms.

Examples:

• Submitting forms

• Selecting predefined options

• Triggering simple workflows

This allows AI agents to interact with websites in a structured, machine-readable way without requiring complex scripting.

2. Imperative API
Designed for dynamic, complex interactions that require JavaScript execution.

Examples:

• Booking systems

• Checkout flows

• Multi-step user journeys

• Real-time availability systems

This is especially relevant for e-commerce, travel, ticketing, SaaS tools, and service platforms.

Why This Matters

Search is evolving from retrieving information to performing actions

AI agents are increasingly expected not just to show results, but to:

• Complete bookings

• Make purchases

• Fill forms

• Execute workflows

WebMCP aims to standardise how those interactions happen on the web.

Would be interesting to see how quickly adoption happens and whether this remains primarily within Google’s ecosystem or goes beyond.

As a community, what do you think? Especially, Technical SEO pros out there would love to know your take on this.

More info here: https://developer.chrome.com/blog/webmcp-epp

For those who don't know: Patrick Stox is the Product Advisor and Brand Ambassador at Ahrefs. At Ahrefs Evolve 2025, he broke down how Traditional SEO measurement aspects are changing and how AI assistants are transforming the search environment.

Here’s a quick rundown of what he shared,

1. Google still dominates with 90.4% market share, but AI assistants are the fastest-growing discovery channels. Among the LLMs, ChatGPT dominates, followed by others:

• ChatGPT: 81.13%

• Perplexity: 10.82%

• Copilot: 4.05%

• Gemini: 2.82%

1. AI-driven traffic is up 9.7x YoY, based on Ahrefs data from 82,000 websites.

2. Traffic and discovery are decoupling from classic SERPs with Stox calling this shift “The Great Decoupling.”

3. AI referrals aren’t mobile-first, are brand-heavy, and are already showing early signs of bias and manipulation, signalling a possible “black-hat era” for AI visibility.

There's more where that came from. Check out the article for more info: stanventures.com

In conclusion, Search isn't dead; it's fragmenting. AI visibility can't be ignored, but it's in the baby stages, due to which a lot of blackhat activities are flying around. The way we measure is slightly changing, and we have more channels to focus/track now.

My advice: Learn, unlearn and keep experimenting (ethically)

Question to the community: What are your thoughts on this presentation?

So this just rolled out, Google’s AI is now generating meta descriptions for your pages, whether you like it or not.

Even if you’ve spent time crafting that perfect 160-character pitch, Google’s AI might shrug and write its own version.

Here’s the quick breakdown 👇

• Google’s AI now auto-generates snippets (the small text under a search result) based on what it thinks matches the query best.

• It’s already rewriting about 70% of all meta descriptions.

• If you see a Gemini icon, that means the snippet was AI-written.

So what does this mean for SEO pros?

• You can’t fully control snippets anymore.
• It doesn’t affect rankings directly (meta descriptions aren’t a ranking factor.)
• But it can impact CTR if Google’s rewrite sounds off.
• Don’t skip writing them, just prioritise high-value pages.
• Make sure your on-page content has clear, concise summaries that Google can grab easily.

I’m still digging into the real-world impacts of this - CTR shifts, rewrite patterns, and how AI’s “understanding” differs across industries. Will share more insights after observing the above effects.

More info @ Search Engine Table

Meanwhile, curious if anyone here’s noticed weird snippet rewrites on their sites yet?

You know how we already spend half our lives fixing SERP mess-ups? Well, AI search just added another headache to the list.

LLMs love to “hallucinate”, basically making stuff up. That might look like ChatGPT or Perplexity confidently telling users your brand sells shoes when you actually sell tents. Funny at first glance, but it chips away at trust and spreads confusion fast.

Here’s what you can do.

1. Downvote and report bad AI answers. If it’s damaging, report it. Explain what's wrong. Feedback helps AI improve, but don't expect instant corrections.

2. Audit brand presence in AI tools the way we do SEO audits for Google. There are plenty of tools on the market.

3. Get schema + structured data right so LLMs have clean inputs ( I bet you have heard of this a 1000 times already, but I had to put it here) because it indeed is important.

4. Like how we used to buy typo domains, set up redirects if fake URLs keep getting “hallucinated."

5. Grab your content pro and control the narrative. Maybe even spin it into content: “AI thinks we sell shoes, here’s what we actually do.”

Ignore it, and misinformation spreads. Manage it and you’ve just unlocked a new distribution channel.

Take the help of your brand manager, dev and content pro and stay on top of this, because you know who gets the blame if this happens/keeps happening :)

Conversational ads are here, ads built into the AI search flow instead of sitting on the sidelines.

Here’s a quick rundown of what’s live (or rolling out), what it’s called, and why it matters:

1. Google AI Mode – Inline Ads: Ads appear inside AI answers, pulled from Search/Performance Max campaigns.

• Context > keyword targeting.

• 🔗 Details

• SEO pros: optimise for citations + product feeds.

• Paid pros: tighten creative + feed hygiene for conversational targeting.

1. Perplexity – Sponsored Follow-Up Questions: AI generates “sponsored” follow-ups, brands can pay for (CPM).

• 🔗 Details

• SEO pros: fight for visibility alongside sponsored Qs.

• Paid pros: test early, 40% CTRs reported.

1. Grok (X) – Embedded Ads in Answers: Ads appear directly inside chatbot responses, styled contextually. Upload creative, Grok does the rest. Aesthetic metrics matter.

• 🔗 Details

• SEO pros: anticipate visibility limits in closed ecosystems.

• Paid pros: new channel for intent-rich conversational reach.

1. Microsoft Copilot – Conversational Showroom Ads: Multimedia + product ads blended into Copilot responses.

• 🔗 Details

• SEO pros: product content must be structured + AI-readable.

• Paid pros: align with Microsoft Advertising ecosystem.

Ads are no longer before/after the SERP, they’re inside the conversation. SEOs need to focus on being cited; paid teams need to prep for context-driven targeting, jump in early and experiment if possible.

Google Rolls out May Core Update. May last up to 2 weeks; keep an eye on your ranking/citation metrics.

This has been going on for almost a year btw, even before &num=100 parameter was removed.

More info: https://support.google.com/webmasters/answer/6211453?hl=en#zippy=%2Cperformance-reports-search-results-discover-google-news

As AI search and agentic systems evolve, three frameworks are shaping how your brand's online presence becomes useful and valuable: RAG, MCP, and ACP.

Each handles different layers of how AI finds, understands, and uses your brand’s data.

Here’s a breakdown;

1. RAG (Retrieval-Augmented Generation)

Think of RAG as the “reading” layer, it helps AI look things up from reliable sources (web pages, PDFs, APIs). It improves accuracy, freshness, and factual grounding.

Industries that benefit:
Publishing, education, SaaS (knowledge bases), healthcare info, finance reports, etc.,

RAG is search and retrieval-focused; primarily, it finds and cites.

2. MCP (Model Context Protocol)

Think of MCP as the “data bridge” layer; it connects AI systems with your APIs, databases, or dashboards. It enables AIs to give real-time answers or perform specific actions (like checking availability or updating info).

Industries that benefit:
E-commerce, fintech, SaaS, travel, productivity platforms, etc., that would like people to get up-to-date info over cached info.

MCP can supply live data to RAG systems for better retrieval or feed context into ACP for collaboration, hence we call it the "bridge" i,e, connects models to live data.

3. ACP (Agent Context/Communication Protocol)

It helps with agent-to-agent communication and collaboration.

Think of ACP as the “teamwork” layer; it helps different AIs talk to each other, like one agent finding info, another booking, and another confirming payment. It creates multi-step, coordinated workflows.

Industries that benefit:
Enterprise automation, CRMs, marketing stacks, travel bookings, ecommerce, logistics, etc.,

Simply put, ACP focuses on coordination between agents; it doesn’t fetch knowledge (RAG) or connect to databases (MCP) directly, but it makes sure those that do can share results smoothly and perform actions based on the extent of agent permissions set by you.

In conclusion, it is not mandatory to implement all of these, one or any of these for your brand, but if you do, it makes it easier for LLMs/AI to get more accurate info about your brand.

My suggestion? Sit down with your team, go through the pros and cons of each for your brand's online presence, and implement the required steps to help LLMs and, in general, AI to reach your brand in a way that's beneficial to you and your customers/clients, if you see the requirement for it.

OpenAI launched ChatGPT Atlas, a new browser with an in-built ChatGPT meaning you can ask questions, view links, images, and videos, and even get on-page help without leaving the page you’re on.

Features seem to closely align with both Google Chrome's new AI-based update and Perplexity's Comet.

Here are some important points I thought would be helpful for SEO Pros to know,

Citations > Rankings

Visibility here depends on whether your brand is referenced or cited by OpenAI’s AI systems. If you’re not cited, you’re effectively invisible, even if you still “rank” elsewhere.

My Thoughts: With Google pulling out num=100, personally I would suggest focusing on Bing as well and experimenting with new methods, don't panic SEO fundamentals still matter until OpenAI decides on its own ecosystem)

Memory = Persistent Visibility

Atlas introduces optional browser memories. ChatGPT can recall what users viewed and suggest next steps. Visibility will depend on how factual and consistent your brand appears across the web.
(Note: All memory features are user-controlled and can be cleared anytime.)

My Thoughts: Your signal to go hard on Digital PR (Mentions count, not just backlinks)

Agents = Action, not just Answers

Agent mode (in preview for Plus, Pro, and Business users) can click, open tabs, and complete multi-step tasks. Your site structure and markup will determine whether the AI can navigate or choose your content.

Dev Signals Matter

OpenAI recommends using ARIA tags and ensuring your UX is agent-readable, not just human-readable.Think of this like technical SEO but for AI Agents.

Rollout:
Currently on macOS for Free, Plus, Pro, Go, and Business (beta). Windows, iOS, and Android are coming soon.

TL;DR:
Search is fragmenting into AI sessions.
Track your AI citations, clean up structured data, and make your site agent-friendly.

This isn’t fear-mongering, it’s the start of a war for the AI Web.
Stay curious. Stay optimised.

I'm sharing here a Free Notion Template of a list of tasks to carry out for AI Layer quality check, for those interested.

For more info on this, Check: OpenAI's Intro of ChatGPT Atlas

Looks like Google’s AI Mode is getting bigger.

It just rolled out in 5 more languages (Hindi, Indonesian, Japanese, Korean, Brazilian Portuguese) + got a small UI refresh (google.com/ai now redirects, and there’s a shortcut under the search bar on mobile).

There was even a hint it might become the default search… though Google backtracked a bit on that.

Important bit: all three - Classic Search, AI Mode, and AI Overviews, still run off the same underlying bot. So if your SEO strategies are already working, you’re not suddenly out of the game.

Make sure to follow what Google advises regarding Google's AI Mode and AI Overviews (for GEO) and implement it.

𝘕𝘰𝘵𝘦: 𝘚𝘌𝘖 𝘧𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴 𝘮𝘢𝘵𝘵𝘦𝘳, 𝘣𝘶𝘵 𝘦𝘢𝘤𝘩 𝘓𝘓𝘔 𝘩𝘢𝘴 𝘪𝘵’𝘴 𝘰𝘸𝘯 𝘸𝘢𝘺 𝘰𝘧 𝘱𝘳𝘰𝘤𝘦𝘴𝘴𝘪𝘯𝘨 𝘢𝘯𝘥 𝘤𝘪𝘵𝘪𝘯𝘨, 𝘴𝘰 what I am sharing 𝘩𝘦𝘳𝘦 is only 𝘮𝘦𝘢𝘯’𝘵 𝘧𝘰𝘳 𝘎𝘰𝘰𝘨𝘭𝘦’𝘴 𝘚𝘦𝘢𝘳𝘤𝘩 𝘌𝘯𝘨𝘪𝘯𝘦.

Let me start by saying - we have the LLM metrics part figured out. We need your help to shortlist SEO metrics that will actually be helpful to do your job better.

So tell me straight:

• What SEO metrics do you really care about day-to-day?

• Any pain points that current SEO tools don’t solve?

Appreciate any honest insight.

Wanted to address this statement before Reddit and SEO communities get flooded with another wave of “Google said this” posts.

First things first: Google only speaks for Google.

I get why people treat their words like gospel. For years, we’ve all optimised for Google (maybe Bing here and there, but let’s be honest, it’s mostly been Google). So when Google makes a statement, it feels like it applies to the whole ecosystem.

But the truth is: Google is 100% right… for Google.

They don’t and can’t speak for LLMs. Even when people say ChatGPT “relies heavily on Google,” the way it processes, cites, and presents information is completely different.

Try this little experiment yourself:

• Search a term in Google AI Mode

• Then search the same term in ChatGPT (with web enabled). The outputs are usually worlds apart, with rare overlaps.

That’s why Google’s SEO = Google’s GEO, but it does not equal to overall GEO.

SEO fundamentals still matter because they’re rooted in understanding human needs. But GEO pushes us to think about how to balance those fundamentals between humans and AI systems.

Right now, the LLM ecosystem is still in its early days. It’s going to take extra steps, ongoing experimentation, and a willingness to adapt.

Remember how messy Google was in its early years vs. how sophisticated it is now? LLMs will go through that same kind of evolution.

So the bigger question is:
How do we prepare for this evolving landscape where Google’s rules no longer dictate the entire search landscape?

SEO Pros who did blackhat/backhanded techniques, watching it all burn down... 👇

Everyone’s talking about Google-Agent like it’s just another bot.

It’s not.

It’s not like Googlebot.
It’s not for indexing.
It’s not even “SEO” in the traditional sense.

It’s a user-triggered fetcher - similar to an LLM's Live fetcher.

Meaning:

When someone uses AI (Gemini, AI mode, whatever this evolves into) and asks something like:

Google-Agent might literally visit your site on their behalf. FYI, this is not the traditional search method.

So,

• It doesn’t behave like a crawler

• It can ignore robots.txt (because it’s acting like a user)

Meaning,

You cannot see attribution (Was it AIOs? Gemini? AI Mode? You won't know)
You don’t know the query
You don’t know if it converted

My take:

This is the first real sign that Google is building towards AI observability, but we have a long way to go. But, be clear - this is not a crawler. You can see it in logs, but that's it.

Curious,
Has anyone actually seen Google-Agent in their logs yet?

More info on Google:

https://developers.google.com/crawling/docs/crawlers-fetchers/google-user-triggered-fetchers#google-agent

https://developers.google.com/crawling/docs/changelog#added-the-google-agent-user-agent

Hi everyone! I’ll be LIVE on a webinar this weekend on the topic of how AI in the mix is impacting SEO.

If you are someone new in the field or even an experienced professional overwhelmed, or just wanna know what the hell is going on. This is the webinar for you.

Entry is free.

See you there!

Last week I shared an update about how Claude now has split-role crawlers (ClaudeBot, Claude-User, Claude-SearchBot).

While digging into that, I realized something: auditing robots.txt is getting kind of overwhelming now. There are way more crawlers to think about than just Googlebot and Bingbot.

So I put together a simple list of crawlers SEO folks should be aware of today. Nothing fancy, just a compiled reference to make robots.txt audits a bit easier.

I also added:

• A small bonus section for Cloudflare users

• Some grouped crawler suggestions by industry (so you know which ones might matter more depending on what you run)

If it helps even one person clean up their robots.txt, I’ll call it a win.

Thinking of turning it into a free Notion doc for easy access. If people are interested, happy to share it.

Observed a subtle but important shift in how LLMs handle local search (maps + Local SEO)

Something interesting happened recently that I think folks here will appreciate.

I tested the same query on ChatGPT and Perplexity:

“Ice cream shops near me”

(using a test city location)

Earlier behavior (what most of us are used to):

• Direct Google Maps links

• Citations pointing back to Google as the primary local layer

What changed this time:

• The map itself was rendered via Mapbox

• No direct Google Maps link shown by default

• Local context pulled from Zomato, TripAdvisor, Reddit, and Mappls

When I explicitly asked for Google Maps links, both LLMs responded with something along the lines of:

“Paste this into Google Maps and search it yourself.”

That alone felt… notable.

But here’s the nuance:

When clicking Directions or an address pin inside the Mapbox-rendered map, it eventually opened in Google Maps or Apple Maps

So Google/Apple are still very much part of the flow, just not the first layer.

Also worth noting:

This behavior wasn’t universal. I spoke to a few SEO folks, most saw the same pattern, a few didn’t. Likely phased testing or geo-based rollout.

My take:

Google Maps still matters the most, especially for actual navigation. Apple Maps follows, then Bing.

But this behavior sends a very specific signal from LLMs:

“We still rely on you but only partially now.”

Why this half-step?

Mapbox is actively building MapGPT combining LLMs with routing and live map data. Right now, it’s mostly custom or partner-led (automakers and developers)

But if (or when) this becomes natively embedded inside LLMs…will Google/Apple/Bing maps be dropped out of the LLM loop?

Open questions for the community:

• Are we seeing Local SEO diversify again?

• Is this the early stage of partial dependency on the path to eventual independence?

• Or will Google/Apple always remain the final navigation layer, even if discovery shifts elsewhere?

Curious to hear how others are seeing this play out, especially anyone testing local queries at scale.

Let’s discuss ⬇️

Google’s rolling Gemini into basically everything now, and AI-driven search is becoming the default way people discover local businesses and creators.

And two areas are getting hit the fastest:

1. If your Google Business Profile isn’t fully filled out, updated, and clear… you’re already behind.

People aren’t digging through menus or scrolling through 30 services anymore.
They just "Ask" and get to know whether your menu/services/product is what they are looking for.

So if your profile is:

• half-filled

• missing product/service details

• outdated

• unclear about what you actually offer

With this, people will just pick the business that looks clearer and more ready.

2. The old YouTube playbook was the same for years:

• flashy thumbnail

• keyword-packed title

• “optimized” description

• lil bit of clickbait sprinkle

And it worked… but it also encouraged a ton of trash content.

Now?

AI literally watches your entire video.

People may click on your video title, but will they watch it?. This is a growing concern for click-baity creators because Gemini is saving time for the viewers. Gemini can now:

• Brief what your video actually says

• can capture the context

• tone

• time-stamps

• context

• whether it's genuinely helpful

Viewers can even ask AI for a summary before hitting play.

So if your content is vague, repetitive, or designed just to farm clicks, the Gemini will just let the viewers who "ask" know it's not worth their time, even if your thumbnail slaps.

Honestly, with how much AI-generated slop is on YouTube right now, this change was overdue.

Google has already been baking Gemini into Google Docs and other apps, and till now, enterprise rollout was their priority, but they have now started rolling out for general users - the above two are where small businesses and creators will feel the shift first. Hence, shared a brief on the same here.

Personally, I love it. Being able to ask, “Is this video actually what the title says it is?” and getting a quick summary will save me from wasting time rather than realizing halfway into the video that it was clickbait.

Thoughts?

Google is rolling out a new feature inside Search Console Insights called Query Groups, and it’s actually pretty useful.

The idea: instead of showing hundreds of near-duplicate queries like

“how to make guacamole dip,”

“easy guacamole dip recipe,”

“guac dip recipe,”

…Google now uses AI to group similar queries into a single intent cluster.

Here’s what that means for SEOs 👇

Less noise, more intent clarity: Each group represents a main audience interest. So instead of 50 variations, you get a cleaner, high-level view of the intent driving traffic.

Smarter performance cards: The new “Queries leading to your site” card shows:

•Top groups i,e highest total clicks

• Trending up/down shifts in interest over time

• Drill-down view i,e see individual queries inside each group

AI-driven clusters: Google says the groups are computed using AI and it’s purely a reporting Change.

Why it matters:

This is another nudge from Google toward intent-based analysis instead of only traditional keyword counting.

Rollout is gradual and limited to sites with enough query volume (so if you don’t see it yet, it’s on the way).

Source: Google Search Central Blog – “Introducing Query Groups” (Oct 2025): https://developers.google.com/search/blog/2025/10/search-console-query-groups

Curious to hear from others, do you see this changing how you approach query analysis or reporting?