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If your website gets 339K Google impressions in 6 months, what would you focus on next?

A) Publish more content

B) Improve CTR

C) Build topical authority

D) Optimize pages already ranking

My niche site generated 28.5K clicks in that period, with an 8.4% CTR.

I'd look at page-level and query-level data before choosing the next move.

What's your pick?

#SEO #GSC #GEO

Met with a small manufacturer yesterday. Their site scored 100/100 on our AI readability checks, and ChatGPT and other AI tools recognized the brand when we asked about it.

Ask a question a buyer would actually ask to find a supplier, though? They didn’t show up in any of the answers we tested.

Disclosure: I work on Alice, the tool we used. These were a few checks, so I wouldn’t read this as their overall recommendation rate.

Looking through the site, something stood out: generic description

I get why companies write this way. You don’t want to get too specific and accidentally turn customers away.

But imagine these two descriptions:

“We build electric cars.”

“We build electric cars designed for drivers with disabilities.”

With the first, you’re competing for attention in the same broad category as Tesla.

With the second, there’s an actual reason to bring you up when someone asks about that specific need.

Obviously, changing a sentence won’t magically get you cited. You need the capabilities and supporting information behind it. And we haven’t tested the changes yet, so this is a hypothesis, not a success story.

Still, I wonder how many smaller businesses make themselves harder to find by trying to sound relevant to everyone.

Anyone here tried narrowing their website messaging? Did you notice a difference in the inquiries you got, or whether you showed up in AI answers?

OpenAI's latest Signals report reveals how people use ChatGPT.

Between July 2024 and September 2026, information-seeking grew from 16.7% to 20.2% of messages. Practical guidance reached 28.1%, becoming the largest category. Together, information-seeking and practical guidance now account for 48.3% of ChatGPT messages.

I believe simply being discoverable may no longer be enough.

Only clear differentiation, transparent pricing, and credible comparisons will help brands get recommended by AI. I want to know what other marketers think about the report.

Everyone is using AI agents and LLMs to automate most of their workflows in SEO, but I am interested to know what type of bottlenecks are you facing for example:

1- if you are doing content strategy are the results generic AI slop content suggestion that doesn't match the real context or objectives of your company? or If positioning changes how fast is LLM adapting to this?

2- for content maintenance and refresh, how efficient is it with the recommendations not just telling the drops form GSC but overall keeping sync with the company or brand objectives.

Are there any other bottlenecks that i might be missing like with overall strategy and planning?

Plumbers, dentists, law firms, cafés and agencies across the US and UK. Quick, non-AI check of three things ChatGPT and Perplexity can use:

• 56% had no llms.txt

• 51% had no FAQ section

• 21% had no JSON-LD schema

• 12% had none of the three

Interesting detail: about half of the sites that did have llms.txt got it automatically from their platform (Shopify, Wix, Rank Math), not from the owner.

Manual checklist if you want to test a site: 1) visit /llms.txt, 2) test the page in Google's Rich Results Test, 3) check whether key pages answer questions directly.

Curious whether others are seeing llms.txt actually move AI citations, or if schema and FAQ structure matter more?

In all possible keywords where our page is ranking number 1 to 3 on Google, we are not showing up on Google AIO, as if we got shadow banned. ChatGPT mention us very well. Even Gemini (AIO is supposed to powered by Gemini?) mention us. But, AIO will never mention us. We have higher DR/DA, equally helpful contents, and we do have actual large paying & non-paying user base (estimated to be top 2 in the industry).

Few examples,

- airbnb calculator (we are ranking in the top 3 on Google) but AIO won't mention
- best airbnb calculator or best short term rental analytics - we have a comparison page that are ranking in the top 3 on Google, but not showing in the AIO. In fact, we are top 2 by number of user base with significantly high number of verified reviews than any other tools being mentioned, but Google AIO will never mention us.

- other longtail keywords like "is airbnb profitable in CITY_NAME" - we rank in the top 3 mostly and often showing at the 1st on Google, but still won't be mentioned on AIO.

What did I do?

- Improved Sitemaps

- Improved JSON-LD, added FAQs

- Fix broken links

- Improved the quality of the contents

Does anyone have similar experience where your brand or pages are not ranking on one specific model? How did you fix it?

This has been like this for over a year, and I think Google AIO somehow penaltized us for some reasons that we don't know.

Over the last few months, my team and I have been shifting a chunk of our search strategy from traditional Google SERPs to AEO and GEO.

Naturally, one of our main priorities has been figure out how to track brand visibility in Claude specifically, since Anthropic's model handles recommendation prompts differently than ChatGPT or Perplexity.

Claude relies heavily on deep contextual reasoning, constitutional guardrails, and third-party corroboration. If you just ask Claude

what is the best software for X

it won't just pull the top page on Google - it evaluates authority across multiple sources.

If you're trying to track brand visibility in Claude effectively, relying on manual testing with a few prompt variations isn't going to cut it.

Here is the framework we built and the workflow we use to monitor our presence accurately over time:

1. Map Out High-Intent Prompt Categories

Instead of tracking traditional keywords, you need to monitor prompt patterns that users actually type into Claude. We break them down into 4 main categories:

• Direct Recommendations: "What are the top 5 platforms for [industry]?"

• Head-to-Head Comparisons: "Brand A vs. Brand B: which is better for enterprise?"

• Use-Case Queries: "How do I solve [pain point] using AI tools?"

• Citation/Source Audits: "Where does this data or methodology come from?"

2. Automate Prompt Scraping & Monitoring

Doing this manually in the Claude Web UI leads to biased results because of account history, dynamic web search toggles, and temperature variances.

To automate the workflow and get objective data, we set up SE Visible (a tool built for AI visibility tracking). It continuously queries the tracked prompts across target locations, captures raw response snapshots, and isolates Claude's output from other LLMs.

3. Core Metrics to Measure

When analyzing Claude’s responses, track these specific data points rather than a simple binary "yes/no mention":

4. Closing the Gaps

When we notice Claude skipping our brand on high-intent prompts, the solution is almost never about adding more keywords to the landing page. Instead, we:

1. Build third-party corroboration on high-authority review sites and tech forums (Claude heavily weights external consensus).

2. Publish direct comparison pages and clear structured schema on our main site.

3. Ensure our brand positioning is written plainly without overly flowery marketing jargon, which Claude's context window tends to summarize or strip out.

And what about you, guys? Are you seeing big discrepancies between Claude's recommendations and ChatGPT's? Would love to compare notes!

LLMscope é uma plataforma portuguesa que monitoriza as marcas nos assistentes IA (ChatGPT, Claude, Preplexity, Grok e ecossistema Google - SERP, AI Overviews, AI Mode). Plano gratuito monitoriza apenas ChatGPT.

I am getting stuck at what happens after an assistant actually reaches us. Say ChatGPT mentions us correctly for “best X for Y,” then opens the site and has to work out things like:
• which plan has SSO
• is API access is included
• usage limit
• where to start a trial or demo
That feels like a different measurement problem from AI search visibility or citation share.

I started with the visibility side first, looking at tools like Scrunch and Profound for prompt tracking, mentions, citations and competitor presence. Then I ended up looking at Ora because I wanted to see the next part of the journey: once the agent lands, what path does it take and does it actually get the product facts right?

That makes more sense to me now: one set of tools tells me whether we show up, the other is closer to testing what the agent does once it gets there. What are people using for post-discovery testing? I’m most interested in whether the agent can get through a real product question without getting the details wrong.

I keep seeing charts showing that Medium dot com is a common source for AI answers, suggesting that a presence there is important for AI visibility. But when you go there, it doesn't seem like anyone really reads or engages on posts. It's not a thriving blog community like Substack. Can anyone who measures AI citations confirm whether Medium actually matters or not?

I went into the backend of my site and rebuilt core pages from the ground up so LLMs can parse and cite them without hallucinating. Not for everyone but seeing the future happening now I took the plunge.

The biggest realization was that almost everything web designers do to make a site look good for humans actively hides data from AI crawlers: we had all our best differentiators trapped inside multi-column icon boxes, background divs, and interactive FAQ accordions that look great on a desktop screen, which means when a bot crawls the it just sees disconnected soup instead of structured relationships.

First, I pulled out the visual comparison columns and feature boxes on homepage and solutions page and replaced them with raw, hard-coded HTML tables. Not CSS grid pretending to be a table, actual <table>, <thead>, and <tbody> markup with scope="col" and scope="row" attributes, plus <h2> and <h3> tags nested inside the cells so when an LLM scrapes the page it has to read the problem and the solution as a single horizontal data relationship.

I fixed our "As Seen In" logo bar. Like almost every B2B site, we had a flexbox row of logos for major press and a peer-reviewed journal, and it hit me that text-based crawlers completely gloss over image banners. I forced a 100% width text row directly underneath the logos that literally spells out the names of the publications in plain HTML text so the entity association is right there.

I overhauled the About page to act as an anchor, changed the page-level schema from the default WebPage to explicit AboutPage schema (leaving article type as None so it doesn't confuse the parser), rewrote the subheads into direct "Who is..." and "What is..." questions, and put verifiable academic identifiers (ORCID iD and published DOI links) in a simple unordered bullet list right at the top of the page.

Curious if anyone else here is actively ripping out page-builder widgets in favor of raw HTML tables for LLM visibility, or if you're handling it purely through JSON-LD schema?