How To Measure LLM/AI Traffic In Google Analytics 4

Traffic from LLMs/AI, including ChatGPT, Claudie, Perplexity, and more, is on the rise. Here I will walk you through step-by-step how you can track LLM/AI traffic in Google Analytics 4.

Artificial intelligence (AI) and Large Language Models (LLMs) are rapidly reshaping how users discover content and engage with brands, and it's critical to understand and adapt to this new reality. As LLMs like ChatGPT, Gemini, and Perplexity become more integral to daily life, many are observing a shift away from traditional search engines, and this trend is just heating up.

As of May 2025, ChatGPT has almost 800 million weekly active users and processes over 1 billion queries per day. With ChatGPT being so young, this represents incredible growth and shows a fundamental shift in how users seek information and consume content.

As these shifts in user behaviors evolve and access to these platforms grows, brands need to start understanding how LLMs work, how to drive brand visibility within, and how to measure growth in user engagement, including traffic trends, user behaviors, page types, and leads.

As LLMs become more mainstream, I am getting more and more questions about how brands can learn to show up within LLMs, but also how to track traffic from these LLMs.

So, below I provide a step-by-step guide on how I am measuring traffic from LLM/AI in GA4.

Step #1 - Create Your Custom LLM Exploration Report

1. Open GA4 ➜ Explore ➜ + Blank exploration.

   1. Exploration Reports in GA4 give you ad‑hoc tables and charts that standard reports don’t, making them ideal for custom traffic analysis.

   2. Name the report “LLM/AI Traffic Analysis" or whatever you choose.

   3. It will look as follows:

   

   
   

Step #2 - Build your LLM Segments

1. With LLMs not being a standard channel, you need to create your own LLM segment in GA4.

2. On the left-hand side, you will see SEGMENTS

3. In the SEGMENTS column, click +  to Add segment → Create a Custom Segment → Session segment.

2. Under Add New Conditions, choose Session Source → matches regex

3. This next part is critical - copy and paste this regex:

   1. ^.*ai|.*\.openai.*|.*copilot.*|.*chatgpt.*|.*gemini.*|.*gpt.*|.*neeva.*|.*writesonic.*|.*nimble.*|.*outrider.*|.*perplexity.*|.*google.*bard.*|.*bard.*google.*|.*bard.*|.*edgeservices.*|.*astastic.*|.*copy.ai.*|.*bnngpt.*|.*gemini.*google.*$

   2. As other LLMs come online, you may need to update. For now, this should do it.

Step #3 - Add GA4 Dimensions to Your LLM Report

1. Next, add your Dimensions. I use - Session source/medium & Page path + query string:

   1. Session source/medium – shows which LLM sent the visit. Google Help

   2. Page path + query string (or Page path) – reveals the exact pages LLMs surface.

2. Once you have your dimensions added:

   1. Drag the Session source/medium into Rows, and if you want to see page-level data, add Page path + query string below it in Rows to break results cleanly.

Step #4 - Add Important GA4 Metrics to Your LLM Report

1. Once you have Dimensions, it's time to add your metrics.

2. For the metrics I use, I keep it straightforward. This gives me a pretty round-robin view of the traffic and their engagement.

   1. New Users & Total Users

   2. Sessions & Engaged Sessions

   3. Engagement Rate

   4. Avg. Session Duration

   5. Key Events - which should be leads

      
      

3. Add each metric via METRICS ➜ +, then check the metric you want to include and hit SAVE.

   1. Once you do, you will have the metrics listed as seen below.

3. From here, drag and drop each metric into the VALUES slot. With each value added, the report will update.

Step #5 - Visualize Your New Traffic From LLMs Report

1. This is where you can customize the data visualization, with the standard being the Table.

   1. You can find this under the Settings section of the report.

2. Personally, the table does the trick for me, but if you want to get pretty, try the scatterplot visualization

   
   

   

   
   

This is How You Measure Traffic From LLMs in GA4

Once you are done, your report should look like this - if your website is driving traffic from LLMs.

By following this workflow, you’ll have a reusable, insight‑rich LLM traffic report in GA4 that surfaces exactly which pages LLMs are driving traffic, and how those customer are engaging.

Frequently Asked Questions About LLM Traffic & GA4

1. What is LLM traffic, and why is it important to track in GA4?

   1. LLM (Large Language Model) traffic refers to website visits originating from AI-powered tools like ChatGPT, Perplexity, Claude, and Google's Gemini. These tools can reference or link to your content, leading users to your site. Tracking this traffic in GA4 is crucial because it helps you understand how AI-driven platforms contribute to your website's visibility and user engagement. By monitoring LLM traffic, you can assess the impact of AI tools on your site's performance and make informed decisions to optimize your content strategy.

2. How does LLM traffic impact my website's SEO performance?

   1. LLM traffic can influence your SEO in several ways:

      1. Increased Visibility: Being referenced by AI tools can boost your content's exposure to a broader audience.

      2. Traffic Quality: Analyzing metrics like bounce rate and session duration for LLM traffic helps assess the quality of visits from AI.

      3. Content Optimization: Understanding which content is frequently cited by AI tools can guide you in creating more relevant and authoritative content.

   2. By monitoring LLM traffic, you can refine your SEO strategies to better align with the evolving digital landscape influenced by AI technologies.