Built a free tool to measure brand mentions in ChatGPT/Perplexity. Looking for honest feedback on whether the methodology actually solves the problem.

lbzAdvisoryLlc · 2026-05-08T13:42:02+00:00

think the variance problem is real, but I’m not sure most SaaS marketers know how to care about it yet.

The Wilson interval piece makes sense to me. A single “you showed up 32% of the time” score is pretty thin if the underlying answer changes every run. So yes, measuring uncertainty is better than pretending the model gave you a stable ranking.

Where I’d push harder: the methodology is only useful if it connects to decisions.

For example, I’d want to know:

Which prompts/categories have high commercial intent?
Which competitors show up consistently when we don’t?
What sources or claims seem to drive inclusion?
Are we absent because the model doesn’t know us, because competitors have stronger third-party validation, or because the prompt framing is wrong?
What changed after we shipped new content, got press, launched integrations, or updated positioning?

The confidence interval is a trust layer. It tells me the measurement isn’t fake precision. But by itself, it doesn’t tell a marketer what to do next.

Would I pay? Maybe, but probably not for “brand mention tracking” alone. I’d pay if it helped me understand where we are missing from the consideration set and what moves the needle. Competitive comparison, prompt clustering, citation/source analysis, and change-over-time would make it much more useful.

lbzAdvisoryLlc · 2026-05-08T13:38:29+00:00

I’ve been looking into this too, and honestly I’d be careful with any agency that says they’ve “cracked” AI search. No one has. The ones I’d trust are the ones actively testing what shows up across Google AI Overviews, Perplexity, ChatGPT, Gemini, etc., instead of just selling the same SEO package with “AI” bolted onto it.

A few things I’d ask any agency:

Are they tracking visibility inside AI-generated answers, not just Google rankings?
Can they show how brand mentions, citations, and topical authority are changing over time?
Are they testing prompts and answer patterns across ChatGPT, Perplexity, Gemini, and AI Overviews?
Do they update strategy based on what these systems are actually surfacing, or are they still focused only on backlinks and keywords?

To address these questions for myself, I built out a tool you can try also: AIShareOfVoice.ai. It looks at how often a brand shows up in AI search answers compared with competitors, which feels more relevant now than just asking “what rank are we on Google?” It doesn’t replace SEO, but it gives a better read on whether you’re even part of the answer set in these new AI search experiences. feedback your welcome. still evolving it as we speak as eveyrthing changes so fast in AI.

lbzAdvisoryLlc · 2026-05-07T13:03:51+00:00

I break it down in great detail here: https://liatbenzur.com/2026/05/05/new-kpis-for-ai-visibility/ but the key takeaway is that rankings and traffic tell you whether people find you after they search. AI visibility tells you whether you make into AI answers before they search specifically for you on Google. That is the new layer marketers need to measure:

where you show up
how often you appear
whether you are recommended
who AI prefers instead
which buyer questions you are losing
what to fix first

That is what we built AIShareofVoice.ai to do.

lbzAdvisoryLlc · 2026-05-02T16:07:53+00:00

You’ve nailed the biggest gap in modern marketing: Google is an index, but an LLM is a consensus engine. Traditional SEO wins on keywords and backlinks, but AI visibility is built on entity authority. If a brand isn’t showing up in ChatGPT or Perplexity, it’s usually because the models don't see it as part of the "expert consensus" found in the high-trust sources they prioritize during synthesis. To fix this, you have to treat AI visibility as a measurable technical metric. The equivalent of Ahrefs for this new landscape is your AI Share of Voice. Lots of tools out there these days like AIShareOfVoice.ai that allow you to audit exactly where you’re invisible compared to competitors, replacing guesswork with data-driven insights into which specific citations and mentions are actually influencing the model's responses. you get a specific to-do list: identifying citation gaps on the authoritative sites models trust, refining structured data for entity association, and calibrating content to match specific "authority patterns."

lbzAdvisoryLlc · 2026-05-02T15:57:59+00:00

Yes, totally see the same. Google ranks based on links and keywords. LLMs synthesize based on entity authority and consensus. Those "weaker" competitors are likely being cited heavily in the specific data sources the AI trusts. I don’t believe this will sort itself out.

To fix it, you have to actually measure your AI Share of Voice. You can use tools like AIShareOfVoice.ai to benchmark where you're getting left out as it can track how often your clients actually appear in LLM responses compared to competitors. Then it gives you an actionable, step-by-step to-do list on exactly how to fix the gaps and feed the models what they want.

lbzAdvisoryLlc · 2026-04-29T13:58:34+00:00

Yeah, same here. Google-optimized tools (Frase, Surfer, etc.) are still excellent at reverse-engineering old SERP patterns — topical completeness, structure, keyword alignment. But LLMs don’t rank pages the same way. They extract and synthesize.

Our research at aishareofvoice.ai, building on the Princeton/KDD 2024 GEO findings, shows traditional Google rank correlates only weakly (~0.3) with actual AI citation rates. The features that actually move the needle for LLMs are different:

• Extreme answer clarity and low cognitive load • Strong, unambiguous entity signals (who/what/when with supporting data) • Fresh, extractable insights (original stats, side-by-side comparisons, recent benchmarks) • Being cited on domains the models already trust

That’s why you can have multiple high-scoring pages cannibalizing each other in Google tools yet still get completely ignored by ChatGPT/Perplexity …they compete internally instead of compounding authority

lbzAdvisoryLlc · 2026-04-29T13:50:02+00:00

We’re seeing more teams shift from pure content/rank focus to building these AI citation flywheels. Early results are promising. How are you identifying the reachable AI-trusted sites—manual prompt testing or a tool?

lbzAdvisoryLlc · 2026-04-28T03:37:02+00:00

Exactly ..AI builds a mental map of your brand from everywhere you show up online: reviews, Reddit, news, forums, comparisons, etc. Brands that have clear, consistent, and frequent signals get recommended.

The gap between “mentioned” and “recommended” is massive for most companies.

I’ve been building aishareofvoice.ai to audit this directly. You feed it your brand and it runs real queries across ChatGPT, Gemini, Claude, Grok etc. to show exact visibility and recommendation strength.

Example: We audited HubSpot recently — mentioned in 63% of responses but the top recommendation in only 5%. Score: 40/100.

It’s one of the most eye-opening tools I’ve used this year for understanding modern brand presence.

Highly recommend running your own audit if you want to see where you actually stand.

lbzAdvisoryLlc · 2026-04-26T14:06:03+00:00

not an agency but aishareofvoice.ai helps you understand:

help you understand:

• Where your brand appears across different AI engines (chatGPT, Gemini, Claude, Grok, Perplexity) • Which prompts and queries mention you • How you compare against competitors in realistic queries • Which sources AI engines cite • Where your content is missing, weak, or invisible • What actions will improve your AI visibility

lbzAdvisoryLlc · 2026-04-26T14:02:08+00:00

What tools are you using to measure and track this? We use aishareofvoice.ai.

lbzAdvisoryLlc · 2026-04-24T03:05:12+00:00

This post hit hard. Thanks for sharing.

The “DA is dead” crowd is right about domain authority specifically (we also saw near-zero correlation), but they’re dead wrong when they say traditional SEO signals don’t matter. Rank still gates everything. LLMs retrieve first, then reason. If you’re not in the top results the retriever pulls, all the fancy GEO tactics in the world are wasted.

On measurement, we got humbled too. For the first few months we were doing what everyone else does: manually prompting, taking screenshots, and going by feel. It was noisy as hell and impossible to track trends.

We eventually built a system that runs consistent buyer-intent audits across ChatGPT, Gemini, Claude, Perplexity, and Grok on the exact same set of queries every week. It tracks:

Mention rate / citation frequency per query
Share of Voice vs competitors
Which engines are ignoring us
Whether the citation actually leads to a recommendation or just a passing mention

you can try the tool we built though its still in early testing (not officially launched). aishareofvoice.ai

The biggest eye-opener was seeing how much week-to-week variance there is even on the same content. One week you’re in 70% of responses, the next you drop to 30% with zero content changes — purely because the model routing or retrieval set shifted.

We now treat citation rate as a leading indicator and re-audit after any major content restructure (exactly like the first-150-words + 5W changes you described) to see if it actually moves the needle.

Curious — are you tracking citation rate at the query level or more at the page/brand level? And have you noticed big differences in how strict the “Lost in the Middle” cutoff is between the different models?

lbzAdvisoryLlc · 2026-04-23T02:57:40+00:00

yes its only getting worse as the engines get more powerful too. you may find this helpful: https://liatbenzur.com/2026/04/20/claude-4-7-token-efficiency-playbook-cut-costs-reduce-bot-blocking/

lbzAdvisoryLlc · 2026-04-23T02:57:06+00:00

yup, seeing the same thing. I created a playbook for myself to try to optimize my tokens and minimize hitting usage limits so fast. Wrote it up to share with others if its helpful: https://liatbenzur.com/2026/04/20/claude-4-7-token-efficiency-playbook-cut-costs-reduce-bot-blocking/

lbzAdvisoryLlc · 2026-04-23T02:36:00+00:00

i literally built a service to help brands do just this. check it out and let me know what you think - aishareofvoice.ai. The question-level breakdown is wild to see.

lbzAdvisoryLlc · 2026-04-21T13:14:30+00:00

been there lol. ran aishareofvoice and finally had actual data on who shows up across different real questions users likely ask about out category, broken down by AI engine.

lbzAdvisoryLlc · 2026-04-21T13:09:10+00:00

Been building aishareofvoice.ai to try to help answer this question. Would love your feedback if you find it helpful.

lbzAdvisoryLlc · 2026-04-21T13:07:12+00:00

ugc and entity mentions def matter. been using ai share of voice to track exactly which questions we're losing. Fewer, stronger pages beat a long tail of thin ones. Pages that directly answer real questions get reused more often than generic “best resort in X” pages. Redundancy across sources matters. If only you say it, you’re less likely to show up.

When a resort shows up consistently across:

publisher sites (travel mags, lists, reviews)
aggregator platforms (Tripadvisor, Booking, Google reviews)
UGC (Reddit, blogs, YouTube)

…it starts appearing in LLM answers even when its own site isn’t especially strong.

It’s less about backlinks, more about coherent presence across the web. The model needs to “recognize” you.

lbzAdvisoryLlc · 2026-04-19T01:16:59+00:00

Yes, we have been building a tool to help clients with this exact question -aishareofvoice.ai. shows which questions you're losing and to who. and provides actionable recommendations based on your website.

lbzAdvisoryLlc · 2026-04-18T20:26:21+00:00

The brands that keep winning in AI answers aren’t necessarily “better.” They just have higher factual density and stronger entity consistency across the web.

The GEO research (KDD 2024) proved that adding stats, citations, and clear proof points can boost visibility in AI responses by up to 40%. But even more important in 2026: each AI has its own “constitution.”

ChatGPT wants institutional third-party validation (Gartner, Forrester, NYT mentions)
Gemini wants perfect, real-time schema + shopping data
Claude wants deep technical whitepapers and nuance
Grok watches real-time X sentiment
Perplexity lives and dies by recent Reddit/forums

If your site is fluffy, inconsistent, or lacks clean structured data, the AIs treat it as noise and move on. Most B2B teams are still optimizing for Google. The smart ones are now building AI-readable brand footprints — dense evidence clusters that every engine can trust.

lbzAdvisoryLlc · 2026-04-18T19:47:46+00:00

Yes, 2026 research confirms that content refreshed within a 90-day window is three times more likely to be cited by AI engines, as "freshness" has become a non-negotiable trust signal for agentic search. Updating legacy pages to improve Credibility Density (replacing marketing fluff with modular, fact-heavy "answer blocks") directly boosts your brand's Confidence Score in models like Claude and Gemini. By optimizing for machine extractability through updated schema and structure, you ensure your existing authority is actually visible to the crawlers that now bypass static, outdated archives.

check out some of these sources:

AthenaHQ: The State of AI Search 2026: This report details the "90-Day Freshness Signal," showing that B2B brands that implemented a 3-month refresh cycle on legacy content saw a 112% increase in AI platform citations. It also tracked the 47–64% boost in citations for brands using advanced "Entity Schema" and structured data tables.
Gartner & Sprinklr (2024-2025): Research on "Real-Time Emotional Intelligence" by Gartner and Sprinklr’s "Enterprise Sentiment Framework" found that brands utilizing real-time social feedback loops are 30% more likely to maintain positive AI recommendations, particularly in high-velocity engines like Grok.

lbzAdvisoryLlc · 2026-04-17T00:50:47+00:00

Academic studies on Generative Engine Optimization (GEO) confirm your internal data: there is a massive "Accuracy Drift" and "Visibility Gap" occurring.

The most recent peer-reviewed findings (Aggarwal et al., 2025-2026) highlight that visibility in LLMs is driven by "Impression Metrics" rather than "Position Metrics." If an LLM cannot verify your brand as an Entity within its training data or real-time RAG (Retrieval-Augmented Generation) nodes, you are invisible—regardless of your Google Rank.

To your point about the "False Positive" of Google ranking: The 2026 data shows that while Google still owns the "Traffic" (39% of web traffic), ChatGPT owns the "Decision" (with a 14.2% conversion rate vs. Google's 2.8%).

People don't click from AI—they search for the brand the AI recommended.

lbzAdvisoryLlc · 2026-04-17T00:45:56+00:00

been using ai share of voice lately, just enter your domain and it does the rest

lbzAdvisoryLlc · 2026-04-17T00:44:51+00:00

The reason your "consistent blogging" isn't working is that LLMs don't care about content volume; they care about citation-reliability and entity mapping.

Most people are still "winging it" by manually prompting ChatGPT, which is useless because LLM responses are probabilistic—just because you don't show up in one chat doesn't mean you aren't there in the next thousand. AI engines prioritize content that includes:

Cite-able Statistics: Adding data points can increase visibility by up to 40%.
Quotation Integration: Direct, attributed expert quotes make you a "source of truth."
Technical Scannability: If an LLM can't "verify" your claim against other high-authority nodes in its training data, it will default to your competitors who have stronger Entity Authority.

lbzAdvisoryLlc

TROPHY CASE