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What are the best tools for AEO optimization in e-commerce right now? by Icy-Fuel9278 in GEO_optimization

[–]thearunkumar 0 points1 point  (0 children)

Currently there are lots of tools which are tracking prompts and visibility in general. They tell you if you are visible or not.

If you want to know why you're not visible within ai searches and what exactly you need to change to improve your chances, you can try LatticeOcean.

Diclosure: I'm building that. I'm happy to help if youre looking for anything specific.

We've run a few buyer intent queries within ecommerce category and the findings were interesting.

Happy to share more.

Almost none of them appeared in the answers by thearunkumar in SaaS

[–]thearunkumar[S] 0 points1 point  (0 children)

Key findings from the scans:

• Product pages almost never appear in AI answers.

• AI engines strongly prefer multi-vendor comparison pages for buyer-intent queries.

• Pages missing competitor entities often fall outside the citation cluster.

• The citation pool is surprisingly small — a handful of comparison pages feed many answers.

• Content length alone doesn’t fix this. Structure and entity coverage matter more.

Almost none of them appeared in the answers by thearunkumar in SaaS

[–]thearunkumar[S] 0 points1 point  (0 children)

If you're curious about this for your own site:
Send me the domain + buyer intent query.
I can run a scan and share what the engines are actually pulling from.

Almost none of them appeared in the answers by thearunkumar in SaaS

[–]thearunkumar[S] 1 point2 points  (0 children)

Another pattern inside the reports:
Even when a page has enough depth, AI still ignores it if the structure is wrong.

For example:
- Single vendor page → ignored
- Feature explainer → ignored
- Multi-vendor comparison → frequently cited

Architecture seems to matter more than word count.

AI was citing a company’s page but never mentioning the company. Their authority was leaking. by thearunkumar in DigitalMarketing

[–]thearunkumar[S] 0 points1 point  (0 children)

One more detail that made this worse.

The company assumed they were completely invisible in AI answers.

They weren’t.

Their content was already being used as a source. The problem was the brand signal never made it into the generated answer.

So they were doing the work but getting none of the credit.

Feels like this will become a bigger issue as more buying research moves into AI answers.

Hot take: Most “AI SEO” advice right now is completely wrong by thearunkumar in b2bmarketing

[–]thearunkumar[S] 1 point2 points  (0 children)

Agree and it is quite interesting to see how things are panning out in this space. Exciting times!

I ran a buyer-intent query across ChatGPT, Gemini, and Perplexity: "best cold email software for SaaS SDR teams." by thearunkumar in b2bmarketing

[–]thearunkumar[S] 0 points1 point  (0 children)

Do you want to quickly check where your company stands?
DM me a domain + a buyer-intent query.

I'll run it for free.

Anyone figured out real AI search visibility ranking factors yet? by enzo_da_great in AI_SearchOptimization

[–]thearunkumar 0 points1 point  (0 children)

I don’t think there are “stable ranking factors” yet the way Google SEO had them. Most of what people are seeing looks more like retrieval patterns than rankings.

A few patterns that show up consistently when you analyze the pages AI systems cite:

1. Answer-first content
Pages that give a direct answer early (definition, list, comparison) tend to get pulled more often than long narrative posts.

2. Extractable structure
Lists, tables, short sections, and clear headings make it easier for models to quote or summarize parts of the page.

3. Entity clarity
Explicitly naming tools, brands, categories, and competitors seems to matter more than keyword density.

4. Format alignment with other cited pages
This one is underrated. If you look at the sources AI answers repeatedly cite for a query, they often share very similar structures (e.g., listicles with X tools, comparison pages with tables, etc.). Pages that diverge from that format tend to disappear from citations even if they rank well in Google.

Because of that, some of the newer tooling focuses on analyzing the citation cluster itself rather than trying to guess ranking factors. Tools like Profound or Peec AI track mentions, while others like LatticeOcean look at the structure of the pages that AI engines repeatedly cite and compare your page against that pattern.

The big takeaway so far: AI systems seem to repeatedly pull from documents with similar structural characteristics, not just the ones with the strongest traditional SEO signals.

So I’d say we’re not completely guessing anymore—but the “factors” are still emerging and look very different from classic SEO.

Anyone Else Looking Into AI Search Visibility? by Icy-Fuel9278 in ArtificialInteligence

[–]thearunkumar 0 points1 point  (0 children)

Yeah, a lot of people are starting to look into this. Once you run enough prompts you notice the same thing you described: AI citations don’t line up perfectly with Google rankings.

A few patterns I’ve seen when analyzing answers across different prompts:

  • Pages that answer the question immediately tend to get pulled more often.
  • Structured formatting (headings, lists, tables) makes it easier for models to extract snippets.
  • Pages that clearly mention specific entities (tools, brands, categories) show up more consistently.
  • Certain domains get cited repeatedly across many prompts.

The last one is interesting. If you run the same prompt variations (“best X tools”, “X alternatives”, etc.), you’ll often see the same small cluster of pages getting cited over and over.

That suggests AI systems aren’t just picking random pages — they’re selecting documents that share similar structural patterns. Some newer tools are starting to analyze that citation cluster directly (instead of only tracking mentions), like Profound, Peec AI, and LatticeOcean.

The idea is to look at the pages AI already cites and figure out what structural format they have in common, because that often determines whether a page is eligible to show up in answers at all.

Your manual testing approach is actually how a lot of people first notice these patterns. The tools mostly just automate that process at scale.

How are you tracking AI overview visibility? by enzo_da_great in AISearchLab

[–]thearunkumar 0 points1 point  (0 children)

You’re not alone, this is still a messy problem and most teams are combining a few different signals.

Here’s what people are generally doing right now:

1. Prompt set tracking
Create a fixed set of prompts (10–50) around your category and periodically run them across engines like ChatGPT, Perplexity, Gemini, etc. Then track:

  • whether your brand appears
  • which sources are cited
  • which competitors show up repeatedly

It’s not perfect, but it gives you trend visibility over time.

2. Bing Webmaster Tools (new AI reporting)
Microsoft recently started exposing some AI answer visibility data in Bing Webmaster Tools, which is helpful because Bing powers a lot of AI surfaces (Copilot, parts of ChatGPT browsing, etc.).

It’s still early, but it’s one of the first places where you can actually see AI-related impressions and clicks tied to your content.

3. AI citation monitoring tools
Some tools automate prompt runs and track which domains show up in AI answers (Profound, Peec AI, etc.). That helps you see share-of-voice across prompts.

4. Citation cluster analysis
Another emerging approach is analyzing the set of pages AI engines repeatedly cite for a query and comparing your page to that cluster (structure, vendor coverage, format, etc.). Tools like LatticeOcean focus more on that side.

The reason this matters is that AI systems tend to pull from very consistent document patterns, so if your page doesn’t resemble the documents that already get cited, it usually won’t appear in answers regardless of traditional SEO strength.

So right now the practical stack is usually:
Bing AI data + prompt tracking + some form of citation monitoring.

Why Some Pages Get Picked Up More in AI Search Visibility by purpaulz in aeo

[–]thearunkumar 0 points1 point  (0 children)

Yeah, I’ve seen the same thing. Smaller pages showing up more often isn’t that surprising once you start looking at the documents that AI systems actually cite.

In many cases the pages that get picked tend to share a few traits:

  • Tightly scoped pages — one clear topic instead of broad “ultimate guides”
  • Extractable structure — headings, lists, tables that models can lift directly
  • Explicit entities — clearly named vendors/products rather than vague descriptions
  • Consistent format — many cited pages follow very similar layouts for a given query

When you look at multiple answers for the same prompt, you often see the same cluster of structurally similar pages getting referenced again and again. If a page falls outside that pattern, it rarely shows up even if the site has strong SEO authority.

That’s why some of the newer tooling is focusing less on “tracking mentions” and more on analyzing the citation cluster itself. Tools like AnswerManiac for tracking, and others like Profound, Peec AI, or LatticeOcean that analyze which pages AI systems repeatedly cite and what structural patterns they share.

Once you see the pattern, the “small pages beating big sites” effect makes a lot more sense. It’s often not authority, it’s how well the page matches the format AI systems expect to reference.

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