“Where do we even start with AI visibility?”
I hear this constantly.
Everyone knows they should be tracking something, but nobody agrees on what actually matters. There’s a ton of data out there that you could be tracking, but without a solid framework—it’s all just noise.
Most approaches I see are scattered, focused on vanity metrics, or missing the connection to outcomes entirely.
So where do you even start?
I’ve landed on a 4-category framework that answers different questions about your AI presence. This is the exact framework I’m using at Backstage SEO. Nothing top secret—take it if you want it.
A framework for AI visibility measurement
Traditional SEO came with a fairly clear map—rankings, impressions, clicks. AEO and AI search operates in a probabilistic space where you’re measuring influence and implied connections, not rankings and directly attributable actions.
This framework organizes metrics by the question they answer:
- Visibility — Are you showing up in AI answers?
- Context — How are you showing up?
- Citations — What’s influencing the response?
- Impact — Is any of this driving results?
Most people measure the first three and ignore Impact—which is where the actual business case lives. If you’re layering AI visibility measurement onto an existing B2B SEO strategy, Impact is how you prove it’s working.
Let’s start with the basics.
Visibility: Is your brand showing up in AI answers?
Before anything else, you need to know if you’re even in the conversation.
1. Brand presence
Brand presence measures what percent of relevant AI-generated responses include a mention of your brand name.
This is the output of solid brand work and AI SEO efforts and it doesn’t (usually) happen overnight.
Why is this a lagging indicator?
Because by the time your brand’s visibility goes up, a lot of upstream work has already happened.
Don’t watch this number alone—you’ll drive yourself crazy refreshing dashboards. Think of it as a health check, not a daily optimization metric. It tells you IF you’re showing up, but not HOW you’re being positioned.
2. Competitive share
Competitive share measures what percent of AI responses mention your competitors (and which ones).
This gives you a snapshot of your “position” in the AI-generated landscape—who else is getting mentioned alongside you, and who’s showing up in queries where you’re absent.
What do you do with this?
Spot gaps.
If a direct competitor is appearing in prompts where you should be, that’s a signal worth investigating. Maybe they’re getting cited more. Maybe their brand mentions are showing up in sources the models trust.
But visibility only tells you who’s in the room. It’s the context that tells you what’s actually being said about them (and you).
Context: How are you showing up?
Knowing you’re mentioned is one thing. Knowing HOW you’re being positioned is another. Two metrics here: the actual response content and the sentiment around your brand.
3. Exact responses
Exact responses capture the actual text that large language models say when they mention your brand.
You need to see the AI-generated responses themselves, not just counts. This is where you catch hallucinations, outdated info, and positioning issues that could be hurting you.
You’re looking for factual errors, weird competitive framing—anything that doesn’t match your actual positioning. I’ve seen models confidently state incorrect pricing, list discontinued features, and make comparisons that make no sense.
Keep a log over time to track how responses evolve as models update too. What they say about you in January might be completely different by March.
Still, the content is only one piece of the puzzle. The sentiment is another.
4. Overall sentiment
Overall sentiment measures whether the AI positions your brand positively, neutrally, or negatively when you’re mentioned.
In my experience, most brand mentions are generally positive. But watch for false negatives like:
- Slimy competitor comparison pages
- Fake or misleading review sites
- Outdated forum posts
- Inaccurate summary articles
If your brand is being positioned poorly or inaccurately because of third-party content that’s either wrong or just outdated, that’s something worth chasing. Sentiment analysis at scale helps you spot these issues before they compound
Citations: What’s influencing the response?
AI responses are shaped by the sources they pull from. This is where you see your upstream content efforts actually working.
5. Owned citations
Owned citations measure whether YOUR URLs are included in AI search responses and influence what the model says.
Long-term, you want to become the go-to source. When your content gets cited, you’re essentially a contributor that’s shaping the response, not just appearing in it.
Why does this matter more than just pure brand mentions?
Because citations give you some level of control over the narrative. If the model is pulling from your pricing page, your comparison content, your thought leadership content—you’ve influenced what gets said downstream.
6. Source mentions
Source mentions track if your brand is mentioned in the URLs that ARE getting cited, even if they’re not your pages.
Short-term, you want presence in the go-to sources. More mentions in authoritative content equals more credibility, which equals more visibility. It’s a flywheel.
What’s the play here?
Get mentioned on industry roundups, analyst reports, comparison sites—the places AI models are already citing. If a handful of websites are consistently showing up in responses, you being mentioned there matters.
Impact: Is AI search visibility driving results?
This is the category most people skip (even though it’s ultimately the whole point).
Visibility, context and citations are upstream metrics. They tell you what’s happening inside the AI platforms.
Impact connects them to actual business outcomes. It’s the same principle behind tying SEO to CRO—visibility without conversion infrastructure is just expensive awareness.
Why track upstream metrics at all if they don’t connect to pipeline?
7. AI referral sessions
AI referral sessions measure traffic coming from AI platforms like ChatGPT, Perplexity, Claude, and Google AI Overviews.
Important caveat here:
AI visibility doesn’t always equal traffic, and that’s 100% okay.
Most users don’t click links in AI answers—they get what they need and move on.
So why track it?
Because the people who DO click are often high-intent.
They want to go deeper. Treat this like traffic coming from peer recommendations. These visitors already have some pre-visit framing and intent.
Check your analytics for sessions from chatgpt.com, perplexity.ai, and others. The volume might be small, but the quality tends to be strong.
8. AI-assisted conversions
AI-assisted conversions measure inbound leads and pipeline that can be attributed (even loosely) to AI touchpoints in the buyer journey.
This is obviously much harder to track than AI referral sessions and all of the pure visibility metrics because—let’s be honest—most attribution sucks.
Someone might start in ChatGPT, then go to Google, then switch to their phone, then forget about the problem for a week, then go back to ChatGPT, then Google your brand name and convert.
That whole mess?
Last-touch attribution would give it to “Organic Search” even though it all started with (and was later supported by) ChatGPT. Traditional search attribution was already imperfect, and AI-driven search makes it worse.
So how do you actually track this?
To be honest, it’s tough.
I recommend two things that most B2B SaaS teams can do without needing a 5-6 figure attribution software subscription:
- Good ol’ Google Analytics or your CRM — Don’t treat it as gospel, but it is still worth looking at AI attribution in GA4 or your CRM. It can be directionally helpful even if the numbers aren’t perfect.
- Self-reported attribution — Ask a simple “How did you hear about us? on your inbound lead forms, then look for AI-related responses. Again, it won’t be perfect, but it’s something.
If you’re seeing AI referral traffic but no conversions, that could also be a conversion infrastructure problem, not an AI visibility problem.
How to track your brand’s visibility and AEO impact (without losing your mind)
You can do this manually—run queries, log responses, build spreadsheets. It works for spot checks. But it breaks down fast when you try to track trends over time or cover every prompt variation that matters.
The alternative?
Custom prompt tracking software.
The truth is, AI visibility tools have exploded. There are 70+ specialized platforms as it stands, and most handle the tracking layer fairly well.
I’ve been using Scrunch for the past few months. Not affiliated or anything—it’s just been working well for us. Also starting to test a few others since the space is moving so fast.
Along with all of the essentials you’d expect, Scrunch lets you set up “personas” that mimic LLM personalization (not perfect, but better than nothing) which is why I’m a fan.
The key is to pick a tool that tracks what matters to you, based on a framework like this one. Don’t let a vendor’s dashboard define your metrics.
Whatever you use, the point is:
What gets measured gets managed.
(Stale quote, yes, but accurate)
And if you’d rather not rely on a “full service” marketing agency’s cherry-picked reporting and want to own your own visibility data—this is how you do it.
Of course, if you’d prefer…
Or skip the spreadsheets entirely and work with an AEO consultancy like Backstage SEO
You could run all of this yourself…
Or you can hand it off.
What does working with Backstage SEO look like?
We start by running our Search Blueprint process to define the prompts that actually matter, get a current situation snapshot, and build out a 90-day roadmap. Then we handle the tracking, the reporting, AND the work to actually move the numbers.
- Ongoing optimization across traditional SEO and AI search
- End-to-end content production
- Daily AI visibility monitoring
- Monthly reporting
Plus, every tier includes 24/7 access to a live AI visibility dashboard—the same thing you’d otherwise pay at least a few hundred a month for as a standalone SaaS subscription.
If that sounds better than trying to DIY it all, book a 30 min discovery call to see if we’d be a good a fit.
