How to Track LLM Traffic: Measure AI Search Visibility & Brand Impact

If you’re working in B2B marketing right now, someone on your team has already asked…

“Can we track how much traffic is coming from ChatGPT?”

Or maybe:

“My board wants to know what we’re doing about AI search.”

Or even:

“I think we’re showing up on LLMs, but how do I prove it?”

The problem is that traditional analytics tools weren’t built for this new world. Google Search Console doesn’t show LLM-specific performance. SEO tools are retrofitting AI features with little substance. And even the smartest attribution platforms struggle to stitch together the LLM influence on the pipeline.

But there’s a path forward, and this blog is your answer to how to track AI search traffic. We’ll walk through how to track LLM traffic, what tools actually work, what to ignore, and how to use all this data to actually drive pipeline, not just report on it.

Let’s dive in.

Not too long ago, someone messaged me: “Can’t we just use GA4 and HubSpot to track LLM search traffic?”

It’s a fair question. Actually, it’s a smart one. And honestly? You can start there. You don’t need to buy a new tool just to get started.

You can begin tracking AI traffic using your existing marketing stack. Yes, even if all you have is GA4 and HubSpot.

Here’s how it works.

Let’s start simple. If you’re trying to figure out how to track AI traffic coming from tools like ChatGPT, your first stop is Google Analytics 4. Here’s why: when someone clicks on a link inside an LLM-generated answer like ChatGPT, for example, most of those tools now include UTM parameters in the link. That means when a user lands on your website from ChatGPT, GA4 can catch that click and show you the session data.

So yes, this is how to track AI traffic in GA4: by checking those UTMs and filtering your data to see what sessions originated from sources like “chat.openai.com” or other identifiers tied to AI tools. You’ll see how many people visited, which pages they saw, and whether they converted. It’s a good start. But that’s just it - a start.

Once you’ve spotted those visitors, you’ll want to know more than just page views. You’ll want to understand what those users are doing post-click. That’s where your CRM comes in. You can push UTM data from GA4 into HubSpot, Salesforce, or whatever system you’re using. Now you’re connecting dots - not just who clicked, but who became an MQL, who got qualified, and which leads came from LLM traffic. 

It’s not mandatory, but it’s super helpful once you want to go from “who clicked” to “why did they convert.”

Yes, you can. In fact, how to track ChatGPT traffic is one of the most pressing use cases for AI search visibility today. Most B2B teams we’ve worked with have seen ChatGPT emerge as the top referrer among all LLMs. Not Gemini. Not Claude. ChatGPT. If you’re getting any sort of AI-generated traffic, it’s probably coming from there first.

Again, the trick lies in those UTMs. But ChatGPT doesn’t always label them perfectly. Sometimes the session might show up as direct or untagged. That’s why pairing UTM tracking with source-specific dashboards or attribution tools like Factors.ai can help identify AI referrals more reliably. Especially if you want to figure out which companies or industries are discovering you through LLMs.

So while GA4 gives you the "what," adding attribution gives you the "who" and when you combine those two, you're finally getting close to understanding how to track LLM traffic in a meaningful way.

Let’s address the elephant in the room.

Most marketers get tripped up. They Google how to track AI traffic, come across a bunch of SEO tools like Semrush or Mangools claiming to offer AI search features, and assume that’s what they need.

But most of these features are half-baked.

They run a single prompt on ChatGPT, grab the result, and turn it into a graph. They might call it a "visibility score" or "AI search presence report," but it’s all just surface-level data. You’re not getting stability. You’re not getting insight. And most importantly, you’re not getting anything actionable.

What you don’t get from these tools is real understanding of where you’re being recommended, why your competitor is getting the edge, or what exact information LLMs are pulling to build their responses. If you’ve ever looked at those dashboards and thought, “Cool… now what?”you’re not alone.

Tracking LLM brand visibility takes more than a keyword tool and a sentiment graph. It requires understanding how prompts work, how facts get pulled into answers, and how AI tools behave very differently from traditional search engines.

One of the most underrated sources of LLM insight is your own chatbot. 

Think about it. Your chatbot captures real, raw questions from your users. People are asking about things your site might not answer well: pricing, integrations, use cases, support SLAs, and so on.

But if your chatbot gets a question your site doesn’t answer, ChatGPT probably won’t find that answer either. LLMs don’t talk to your chatbot. They crawl your site and public sources. So if you’re missing key info, you won’t get recommended even if you’re the perfect solution.

This is where fixing content gaps becomes a visibility strategy. Take those chatbot logs, find the most common unanswered questions, and turn them into web content. It’s an easy way to increase AI search visibility without guessing what to write.

Yup. You absolutely can. While you won’t find a “ChatGPT” tab in your Search Console (yet), it still shows you what people are searching for and some of those terms are likely being reused in LLM prompts. Think of it as indirect insight. If a keyword brings you leads from Google, chances are people are entering variations of it into ChatGPT too.

Similarly, if your Google Ads account is generating bottom-of-funnel conversions from high-intent queries, you can turn those same keywords into prompts and test them in ChatGPT. That way, you start understanding not just the traffic from LLMs, but which prompts are driving it, and where you’re missing from the conversation.

This approach is scrappy but effective, especially when you’re just starting to track LLM traffic and don’t want to commit to a new platform just yet.

So you’ve done the smart thing: you’re tracking traffic in GA4, you’ve passed UTM data into your CRM, maybe even poked around chatbot logs or Search Console for clues. That’s great. But at some point, it stops being enough. These tools tell you what happened. They don’t tell you what to do next. And they definitely weren’t built with LLMs in mind.

That’s why we built Chosenly.com.

Chosenly pulls data from all the tools you already use:

- GA4 and CRM (to track actual traffic and conversions)
- Chatbot data (to find missing questions)
- Google Ads and Search Console (to convert keywords into prompts)
- Live LLM queries (to see who gets recommended and why)

But it goes beyond analytics. It transforms keywords into prompts. It runs those prompts across different LLMs, multiple times, to stabilize the results. Then it shows you who’s being recommended, what citations are driving those answers, and what gaps you need to fill to change your brand’s standing.

It helps you fix AI hallucinations (like when ChatGPT lies about your pricing). It shows you where your competitors are winning. It even tells you where to focus your PR and backlink efforts so you get cited in the right places.

In short, while your stack shows what happened, Chosenly tells you what to do.

You can. But it’s going to be painful.

Running prompts on ChatGPT and tracking results is fine if you’re doing one or two. But the moment you scale to 50–100 prompts across different use cases, industries, and buyer types, you’re going to hit a wall. LLMs are unstable AF and results vary. You’ll run into inconsistency, miss trends, and lose time.

So yes, you can try to track LLM brand visibility and traffic manually but the real question is whether that’s the best use of your time, especially when better tools exist.

If you’ve made it this far, you already know the answer to the first question: yes, you can track LLM traffic today. Between GA4, UTMs, your CRM, chatbot logs, and a few creative workarounds, and a little elbow grease you can start measuring how people are finding you through tools like ChatGPT, Perplexity, Claude, and Gemini. You can even tie that traffic to conversions, MQLs, or pipeline. It’s doable. It works. And it’s probably more than what most teams are doing right now.

But tracking is just the start.

You might see a few random visits or even some early conversions from AI search. But if your goal is to consistently show up when your ideal buyer asks something like, “What’s the best [category] tool for a company like mine?”, that takes more than a few dashboards and lucky prompts.

To really grow your pipeline from AI search, you need to move from reactive analytics to proactive visibility. You need to understand what prompts people are typing, what criteria LLMs care about, and how they’re choosing which companies to recommend. You need to identify misrepresentations before they cost you deals. You need to know when your competitor is winning attention you should be getting and what you can do about it.

That’s where Chosenly comes in.

We built it not just to track traffic, but to help you influence it. To help you win those critical recommendation moments. To move your brand from “randomly mentioned” to “consistently cited.” And to do it in a way that doesn’t burn 20 hours a week running manual prompts and guessing what to fix next.

So yes, start with what you’ve got. But when you’re ready to go beyond GA4 and actually take control of your AI search visibility, Chosenly’s here to help.

👉 Book a demo and we’ll show you how to make sure the next prompt recommends you instead of someone else.

It’s the traffic that lands on your site because someone asked a question on ChatGPT, Perplexity, Claude, or any other AI tool, and got recommended a link to your site in the answer. This traffic is growing and already converts better than many paid channels.

Use UTM parameters. LLMs like ChatGPT often include them when linking to your site. Set up custom reports in GA4 to track sessions that came from sources like chat.openai.com. It’s a little hacky, but it works.

Same method - check for referral traffic from chat.openai.com or any custom UTMs you’ve set up. Combine this with CRM data to track leads, deals, and pipeline from ChatGPT sessions.

Not really. They’re mostly SEO-first tools trying to retrofit AI features. The data is often one-time, incomplete, and doesn’t help you take action. Good for exploration, but not reliable for serious AI strategy.

Not at all. SEO is about backlinks, keywords, and rankings. AI search is about citations, factual accuracy, and structured context. That shift is why AISO (AI Search Optimization) needs its own playbook. We’ve broken that down step-by-step in our complete AISO guide for B2B companies if you want to go deeper into strategy.

Faster than SEO. Many companies see visibility changes and traffic increases within 1–2 weeks of implementation, especially if you’re fixing misrepresentations or plugging major info gaps.