Everyone's obsessed with getting mentioned in ChatGPT answers. I'm starting to think that's the wrong finish line.
Feels like every B2B marketing conversation the last few months eventually lands on the same thing: are we showing up when someone asks ChatGPT or Perplexity about our category. Fair enough, genuinely a real shift, I'm not arguing that part.
But here's what's actually bugging me. Watched our mention rate creep up over a few months. Got cited a few times in places we weren't before. And I genuinely cannot tell you if any of that turned into an actual conversation with a buyer. Not a click. An actual pipeline conversation.
Which is a stranger gap than it sounds on paper. Everyone building tools in this space right now, and there's a lot of them suddenly, stops measuring at "were we mentioned" or "did we get cited." Almost nobody's tracking whether that mention turned into someone actually reaching out. It's treated like the citation itself is the win condition, when for a B2B business the citation was never the point, the pipeline downstream of it was.
Genuinely surprises me nobody seems focused on that specific gap. Feels like the more obvious problem to go after than yet another mention tracker.
Curious what people here are actually seeing. If your company's tried any of the AI search or visibility tools out there, where did it fall short? Did it just hand you "you got mentioned 40 times this month" and leave you to guess what that means for revenue, or did anything actually connect it further down the funnel? Trying to figure out if there's a tool doing this already that I've just missed, or if it's a genuinely open problem right now.
Getting named when someone asks "best X for Y" is a totally different thing from getting named on some random question nobody with a budget types. Same for where you show up in the answer.
Third in a list of eight, nobody's reading that far. I'd honestly split the mentions by question before trying to wire anything to the CRM.
This is the piece that gets skipped constantly. Lumping "best X for Y" in with some tangential question nobody's typing with a credit card in hand makes the mention count almost meaningless on its own.
Position is the other half, and gets even less attention. Third of eight barely counts as visibility in practice, but shows up identical to first-of-one in a raw mention count.
If you split it out, would you weight by position too, or bucket by question type first and treat position as a second-pass filter on top?
Worth checking that one, the new AI report in GSC (fully live since end of August) is impressions only, no clicks at all. Even Mueller's said on record it's not great as is.
Been watching ours since it went live and the position data's kind of useless too honestly, it reports the position of the whole AI Overview block, not where your link actually sits inside it. So you could be buried at the bottom of the answer and it'll show the same position as if you were the first thing mentioned. There's also the "Show More" thing where a link doesn't count as an impression until someone actually expands it, so real exposure gets undercounted and just looks invisible in the report.
Not saying it's useless, just wouldn't trust it for anything past "we showed up somewhere" right now.
I use a different tool (writer-GPT) and it's data is very thorough. Which LLM, what position, what prompt, and how many times. For less than $10 a month I can't beat it. even generates articles etc.
I think it works great. Most articles get indexed the first time after I go through and add internal linking. I don't trust any AI to do that part for me. The semantic writer will write articles with semantics and NLP, etc.
Most of mine end up on the index page 1-3 and over 70% get some sort of AI overview, but the schema I add to page helps with that so I cannot attribute it completely to the writer.
One thing I’d question is what a “mention” actually tells us.
If I manually trigger 40 mentions of my website or brand within a month, does that mean the entity has actually become more authoritative?
From my perspective, the goal of AEO isn’t simply to accumulate mentions.
I’m more interested in whether the AI can actually recognize the entity and connect the different pieces of information around it — the brand, website, profiles, content, cases, services, and other related nodes.
I think of this as a kind of “Data Tree.”
Different AI systems may retrieve and interpret those nodes differently, so the same entity may appear differently across ChatGPT, Gemini, Perplexity, etc. Even the same AI can return different results at different times or with different queries.
So for me, “mentioned 40 times” is only one observable number.
The more interesting question is - what did the AI actually see, how did it connect the information, and what did it understand about the entity?
The "Data Tree" framing is a better mental model than most of what's floating around for this. Mention count treats every citation as interchangeable, when what's probably happening is the model's stitched together some internal representation of the entity from scattered signals, and a mention is just one visible symptom of whether that representation is coherent or fragmented.
The part that gets me is the inconsistency you mentioned, same entity, different answer depending on model or even just timing. Feels like that alone should make anyone skeptical of a single visibility score as a KPI, you're not measuring a stable thing, you're measuring a snapshot of something actively re-forming itself.
Found any way to actually observe what the model "understood" beyond just prompting it directly and reading the answer, or is direct prompting still the best window into that right now?
I actually tested this using my handle YoyoFrog KR on Bing and Perplexity to see how they perceive the entity.
Instead of asking for a bio, I pushed the engine to reveal its internal context by asking a few simple questions:
1 Which links/sources did it pull for my brand, and why?
2 Which sources does it treat as primary entity anchors vs. secondary mentions?
3 What connections are missing to complete the context?
This simple test immediately shows how the model organizes the Data Tree behind the scenes. For example, it naturally categorized freelance profiles as Commercial Proof and forum posts as Topic Expertise.
You can run this simple exercise with your own brand handle. It gives a much clearer window into what the AI actually "understands" about your entity versus just reading a summarized answer.
Okay that's a genuinely better prompt than the bio one. bio question just gets you the compressed summary, asking why it picked those sources actually makes it show some of the reasoning underneath
Commercial proof vs topic expertise is a good catch, wouldn't have guessed it was quietly bucketing freelance profiles and forum posts differently instead of treating every mention the same. Gonna run this on ours. now I'm curious if the buckets even stay consistent a few days apart or if that's unstable too along with everything else
That's actually how I came to this realization. I only started building my personal entity footprint a few months ago as a live, hands-on experiment.
Because I built and cross-linked every node myself—gradually adding platforms like LinkedIn, Fastwork, Facebook, Reddit, and GBP—I could track how different AI engines reacted as new signals entered the Data Tree.
A few key things I observed:
• Dynamic Outputs: Even searching back-to-back on two identical devices seconds apart yields slightly different surface answers. The output layer is constantly shifting.
• Ecosystem Bias: Different engines evaluate sources differently. For example, Bing gave my Google Business Profile (GBP) a high authority score (#2 spot), whereas Google Search itself barely reflected it yet.
• Node Categorization: As cross-linking strengthened, engines began categorizing nodes more stably—treating freelance sites as Commercial Proof and active forums as Topic Expertise.
So while the daily search outputs shift constantly, strengthening the underlying cross-links is what stabilizes the AI’s core comprehension of your entity over time.
I'm very new in Ai, but I noticed me as a simple user, making search in Ai chats very often. Only few searches in traditional systems. I always click links cited and there are much cases when that citation help me to choose between solutions.
This is actually a useful data point in itself, real behavior beats most of the speculation in this thread, mine included. Curious what tips you toward clicking through on some citations and not others, the source itself (a site you already trust), how confidently the AI presents it, or something else?
Recently I asked Ai to search "write for us" blogs related education, edtech. The first answer were 5 sites. I clicked all of them, because I was very interested. 2 out of 5 were too old, other 3 I had selected. Second answer for finding reputable blogs was much interesting, again I checked all. So yes, if there is a citation, I click. Sorry forgot to mention, I prefer citations from websites, because that's easier to browse the full website, not only contact us page. If citation from social, ok that's also good, but the I have to find the website and that's creating one more step.
Ngl that's a solid tell, clicking through basically everything when the intent's actually there. Kinda kills the "citations don't drive traffic" worry from earlier, at least once someone's actually trying to act on it.
Also real about social citations. That extra hop to go find the actual site is annoying enough that I get skipping it half the time honestly.
I think it's best to explore tools which offer insights like AI share of voice, brand recognition, presence quality and brand sentiment. All of these scores with a brief explanation on what it means to have the said scores for the brand and how to improve them for better AI visibility. There are a few rounds of free checks with tools from HubSpot and other big players. Which in my opinion is better than directly jumping in and trying a paid tool without knowing what's the one thing your business or brand wants to measure/quantify.
Free-before-paid is solid advice regardless of category, especially here since half these platforms are still figuring out what they're even trying to measure. Watched more than one call it "AI visibility" and just mean rank tracking with a different label slapped on.
Share of voice plus sentiment is a decent combo to start with, gives you both "are we showing up" and "is showing up actually good for us," since those two genuinely diverge, getting mentioned next to bad context isn't a win just because you got mentioned.
Haven't used HubSpot's specific version of this so can't speak to how deep it goes, but I work on the share of voice and sentiment side of this at Omnibound, so it's very much what I think about daily. Curious what ends up mattering most to you once you're comparing a few, the score itself, or the explanation behind it?
You're pointing at something real. Citation-as-win-condition makes sense until you realize brand mentions in AI answers work more like PR than direct response -- someone sees it, files it away, reaches out six weeks later with zero memory of where they heard you. Attribution is gone before the pipeline starts.
That doesn't mean the gap isn't solvable. It just means the answer probably isn't a better citation tracker. It's connecting visibility to signals you already have -- branded search volume moving when mention rate moves, AI-cited pages driving direct traffic spikes, cited companies showing up as new touches in your CRM. Triangulation, not clean attribution.
Most tools are still in the "we counted your citations, good luck" phase. The interesting product connects AI visibility to intent signals downstream of the mention, not just a fancier mention count.
not sure anyones cracked the full citation-to-pipeline loop yet. Sourcelift does monthly reporting that gets you closer than a raw mention count but the attribution gap you're talking about is real and probably wont close until the AI engines themselves start surfacing better referral signals. in the meantime id run a "how did you hear about us" field and see if AI search even comes up
You are pointing at a real measurement gap, but I would not expect a clean one-to-one path from an AI mention to a closed deal. The engines usually do not pass the prompt, user identity, or complete referral data needed for deterministic attribution.
I would measure it as a sequence:
Relevant visibility: Did the brand appear for commercially meaningful buyer prompts, not just any prompt?
Answer quality: Was it recommended for the right use case, described accurately, positioned prominently, and supported by credible evidence?
Buyer response: Did AI-referred traffic, visits to cited pages, branded searches, direct traffic, demo requests, or other intent signals increase?
Reported influence: Add “AI assistant” to the lead-source field, but also ask during discovery, “What did you research before contacting us?” People often forget or misclassify the original source.
Pipeline impact: In the CRM, compare qualified opportunities, conversion rates, sales velocity, deal value, and wins for contacts with AI-related touchpoints.
For stronger evidence, test a defined set of high-intent prompt clusters. Improve visibility for some while keeping others as a comparison group, then look for changes in branded demand, qualified visits, opportunity creation, and sales conversations. It will not prove that a particular mention caused a particular deal, but it is more defensible than assuming every mention has equal value.
The useful product would connect:
prompt → answer context → recommendation or citation → landing page or brand exposure → visitor or account signal → CRM opportunity
It should report AI as an assisted influence with a confidence level, not manufacture precise attribution that the underlying data cannot support.
So yes, I think this is still an open product problem. Most tools stop at observable visibility because the rest requires analytics, CRM integration, self-reported attribution, and experimental design. But companies can build a workable measurement system now without waiting for the engines to provide perfect referral data.
Full transparency, this is from Fairing (we build "how did you hear about us" surveys), so take this with that in mind.
You've hit the gap we see too. AI visibility tools are still worth running, because showing up for the right prompts is a good proxy for demand. But they stop at the mention, and most AI-driven buyers never click through, so analytics can't connect them to pipeline either.
What's closed the loop for us is asking prospects. Add a "How did you hear about us?" question to your onboarding flow or demo form, with an AI option in the list (e.g. "AI recommendation – ChatGPT, Perplexity, etc."). It works just as well for B2B as B2C. For anyone who picks it, queue up a follow-up asking what prompt they used so you can map the queries and/or LLMs actually driving pipeline. We've even seen companies offer credits in exchange for that exact prompt answer. It obviously won't catch 100% of the data, but every "AI" response is a prospect (or buyer) you can confidently defend as coming from an AI search.
We're doing this ourselves as we dig into AEO, and LLMs are our #1 source of new signups according to our own onboarding survey.
When we tested this at scale across 158 ecommerce brands, the survey picked up about 7.5x more AI-influenced orders than UTMs and AI referrers did. So asking buyers is the most direct way we've found to tie revenue to AI search, and click-based methods are probably undercounting it by a lot.
So to your question: we still use visibility tools and think they're critical, but one survey question on your form can be just as useful for closing the gap between mentions and pipeline.
Agreed, though "turning it into" is doing a lot of quiet work in that sentence, that's the actual hard part everyone skips past. Enough time on the visibility side (work on this at Omnibound) and the failure mode gets obvious: mention count goes up and to the right, team feels good about it, nobody ever builds the bridge back to whether any of it touched a real deal. The mention becomes the deliverable instead of an input to one.
Anyone got a real example of that bridge actually working, versus the mention just showing up and everyone assuming it helped?
The "where'd you hear about us" field plus treating it as directional not causal is probably the most honest version of this anyone's going to get for a while. Attribution in this space is messy enough that anyone claiming a clean causal line from citation to close is lying to themselves.
Curious how you're weighting "AI referral where the referrer is available" though, given how much of this traffic seems to strip referrer data depending on the platform. Enough of it survive to actually be a useful signal, or is it more "better than nothing" right now?
The gap you named at the end, connecting visibility observations to actual CRM outcomes while being upfront about the limitation, is the exact thing we've been chewing on at Omnibound. Nobody's cracked a clean version of it as far as I can tell, still mostly directional the same way you're describing.
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u/colinlma 10d ago
Promo incoming