Many marketers want to know if GA4 displays Google AI Mode as a referrer in analytics reports. AI-powered search is changing how people find content online, and tracking visitor sources has become more complex than ever before. Google’s AI Overview and its competitors’ tools have made accurate traffic source reporting a vital part of analytics.
GA4 referral traffic doesn’t always make AI-driven visits easy to spot. The sort of thing I love about GA4 is how it handles referral traffic from AI sources – it’s quite different from traditional search or social media channels. AI tools sometimes show up correctly in GA4 referral traffic, but they might appear as direct traffic or get lumped in with organic search. This piece will help you identify, track and analyze Google Analytics AI traffic to learn about how users find your content through these new channels.
You’ll learn the quickest way to measure AI-driven traffic in your GA4 reports. The guide covers everything from custom exploration reports to dedicated channel groups for AI referrals. These practical techniques will improve your analytics and give you a better understanding of this growing traffic segment.
Using GA4 Acquisition Reports to Spot AI Traffic
GA4 acquisition reports give you quick ways to spot AI tool traffic without complex setup or customization. AI traffic needs specific filtering methods to show up in your analytics data, unlike regular search or social media referrals.
Filtering by Session Source/Medium in GA4
You can find AI referrals in your GA4 interface through these simple steps:
- Go to Reports → Traffic acquisition in your GA4 dashboard
- Select Session source / medium from the primary dimension dropdown
- Look for AI sources using the search box above the table
- Search for terms like “chatgpt,” “perplexity,” or “gemini” to find these referrers
This quick filtering method shows you which AI platforms send visitors to your site. You can add Landing page + query string as a secondary dimension to see the exact pages getting AI traffic.
“When paired with a metric like Event count, the organic dimension value shows you the number of new sessions that came from an organic search and in which users triggered any event”. This principle works the same way for AI referrals – you’ll see both traffic volume and user engagement.
Identifying ChatGPT and Perplexity Referrals
Each AI tool leaves its own unique referral signature in GA4:
- ChatGPT traffic: Shows up as “chatgpt.com / referral” from paid ChatGPT versions
- Perplexity: Appears as “perplexity.ai / referral”
- Google Gemini: Lists as “gemini.google.com / referral”
- Microsoft Copilot: Appears as “copilot.microsoft.com / referral”
ChatGPT has a specific pattern. “OpenAI often adds a tag like utm_source=chatgpt.com to the URL. GA4 will then log that visit as Referral traffic, with the session source showing as chatgpt.com / referral”.
You can also create a regex filter to catch multiple AI domains at once. A pattern like ^.*(chatgpt\.com|gemini\.google\.com|perplexity\.ai|copilot\.microsoft\.com).* in your search or filters works well.
Limitations of Referral Traffic in Google Analytics
GA4 has key limitations in tracking AI traffic completely. “People using the free version of ChatGPT don’t send referrer data, so their visits show up as Direct traffic instead”.
Your reports show only some of your actual AI-driven visits. Many AI platforms remove referrer information, which miscategorizes these sessions. The factual data notes, “No Referrer = Direct Traffic – Many AI platforms strip referrer data. Those visits show up as ‘Direct,’ making attribution difficult”.
GA4’s handling of Google AI Mode referrals raises questions. Sources indicate that “Google’s AI Mode hides referrer data, making traffic invisible in Google Search Console and analytics platforms… Analytics tools classify these visits as Direct or Unknown“.
These limitations mean your GA4 reports likely show less than the true AI tools’ effect on your traffic. Bot traffic adds another layer of complexity – “Bot traffic often appears with zero engaged sessions and zero engagement time”. This makes it hard to tell real AI referrals from automated crawls.
The next section will explore advanced custom reporting methods to better understand AI’s role in your traffic patterns.
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Building Custom Exploration Reports for AI Referrals
GA4’s custom explorations give you better flexibility than standard acquisition reports to track AI referrals. These reports help you isolate AI traffic patterns and learn about how visitors from AI platforms use your site.
Creating a New Exploration in GA4
The process is simple if you’re new to custom explorations:
- Sign in to your GA4 account and click on Explore in the left navigation menu
- Select the Blank exploration template to start from scratch
- Name your report clearly (e.g., “AI Traffic Analysis” or “LLM Referral Report”)
- Set your date range—the last 30 days works well to spot recent trends
Custom explorations let you break free from standard reports’ limits and focus on AI-driven traffic patterns.
Adding Page Referrer and Landing Page Dimensions
After creating your exploration, add these specific dimensions:
- Click the + icon next to the Dimensions section
- Search for and select these important dimensions:
- Page Referrer (shows the exact URL that sent the user)
- Landing Page or Landing Page + query string (reveals which pages receive AI traffic)
- Under Metrics, add useful engagement measures like Sessions, Engaged Sessions, and Engagement Rate
AI visitors might use your content differently, so these dimensions help you spot which pages work best with AI-referred traffic.
Using Regex to Match AI Domains
The secret to isolating AI traffic lies in creating good regex filters. Add a filter to your exploration:
- Drag Page Referrer into the Filters section
- Set the condition to “matches regex“
- Enter a regex pattern like:
^.*(chatgpt\.com|openai\.com|.*perplexity.*|(gemini|bard)\.google\.com|copilot\.microsoft\.com|edge(pilot|services)\.bing\.com|claude\.ai|meta\.ai)$
This pattern catches major AI platforms like ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, and Claude.
Note that GA4 regex is case-sensitive with a 256-character limit. Spaces count as characters to match, so use them carefully.
Visualizing AI Traffic with Heat Maps
Heat maps are an easy-to-use way to see AI traffic patterns:
- In your exploration report, scroll down to Cell Type in the settings panel
- Select Heat Map from the options
- Your data transforms into a color-gradient visualization that highlights high-performing content
Heat maps show which landing pages get the most AI traffic quickly, making patterns easier to spot than regular tables.
You can create multiple tabs in your exploration to dig deeper:
- One tab shows AI traffic volume by source
- Another displays engagement metrics like session duration
- A third compares conversion rates between AI and other traffic sources
GA4 doesn’t tag Google AI Mode as a separate referrer automatically. These custom explorations help you work around this limitation and track this growing traffic segment. You can save your exploration and check back often to see how AI traffic patterns change over time.
Creating a Custom Channel Group for AI Traffic
A dedicated channel group in GA4 will give you a permanent solution to monitor AI traffic continuously. This applies to both historical and future data. Your AI referrals won’t get buried within generic referral traffic and you can track these visitors in any report.
Steps to Access Channel Groups in GA4 Admin
The channel group settings are accessible through GA4’s admin section:
- Open your GA4 property and click on Admin in the bottom left corner
- Under the Data display section, select Channel Groups
- You’ll see the default channel group provided by GA4
- Either click Create new channel group or click the three dots menu next to the default group and select Copy to create new
You’ll maintain the standard channel definitions while adding your custom AI channel this way.
Defining AI Traffic with Regex Conditions
The correct identification of AI traffic sources needs precise configuration:
- Name your new channel group (e.g., “AI Traffic Channel Group”) and provide a description
- Click Add new channel and name it “AI Traffic”
- Select Add condition group and choose Source as your dimension
- Set the condition to matches regex and input a pattern to capture AI platforms
The regex pattern below identifies major AI tools:
^(.*chatgpt.*|.*openai.*|.*perplexity.*|.*gemini.*google.*|.*copilot.*microsoft.*|.*claude\.ai.*|.*bard.*|.*edgeservices.*)$
This pattern captures traffic from ChatGPT, Perplexity, Google Gemini, Microsoft Copilot, Claude, and other popular AI platforms.
Reordering Channels to Prioritize AI Referrals
The next vital step involves reordering your channels:
- Click the Reorder button after saving your channel
- Drag your new “AI Traffic” channel above the “Referral” channel
- Click Apply to save the new order
GA4 processes channels sequentially from top to bottom. Your AI channel should be above Referral to prevent incorrect categorization as general referral traffic.
Viewing AI Traffic in the Traffic Acquisition Report
You can view your AI traffic in the acquisition reports:
- Go to Reports > Acquisition > Traffic acquisition
- Scroll to the data table and change the primary dimension to your new channel group
- Look for the “AI Traffic” channel in your reports
The AI traffic appears separated from other channels. Adding “Session source/medium” as a secondary dimension helps distinguish between different AI platforms. This shows whether traffic appears as “referral” (legitimate visits) or “(not set)” (potentially bot traffic).
The channel group applies retroactively to historical data. This gives you immediate understanding of past AI traffic patterns without waiting for new data.
Analyzing AI Traffic Behavior and Engagement
Custom channel groups help you learn about AI visitor behavior on your site. These patterns show the difference between real human engagement and automated traffic.
Tracking Key Events and Conversions from AI Visitors
GA4 now labels what we used to call “Conversions” as “Key events”. You can track these key events for AI referrals to see what actions these visitors take. Here’s how to set it up:
- Go to Admin > Data display > Key events
- Click “New key event” and enter the event name
- Toggle existing events by going to Admin > Data display > Events
This setup will track AI-driven sessions for your most valuable actions. Users who find content through AI assistants show higher intent. They stay 2.3 times longer and are 40% more likely to download resources.
Comparing AI vs Organic Traffic Engagement Rates
AI traffic interacts with your content differently than traditional organic traffic. Studies show that AI chatbot referrals have better engagement metrics than standard Google search traffic.
These differences show up as:
- Longer average session durations (extra 2.3 minutes)
- Higher pages-per-session metrics
- Increased content consumption
ChatGPT’s conversion rate matches Google’s according to Similarweb research. This suggests that AI platform visitors come with specific questions and clear intent after their initial chatbot research.
Detecting Bot-like Patterns in AI Referral Sessions
Not all AI-related traffic comes from real human interactions. Your analytics might show these bot warning signs:
- Zero engaged sessions with 0% engagement rate
- Very high session durations (10+ minutes)
- Too many page views (20+ in single visit)
- Multiple page requests within milliseconds
- Strange navigation patterns or session paths
Bad bots make up about 32% of all website traffic, not counting AI assistants like GPTBot and ClaudeBot. We analyzed AI traffic at the session level instead of user level because these interactions rarely show continuity.
Challenges and Future of AI Traffic in GA4
GA4 reports face unique challenges when tracking AI traffic. This needs both technical knowledge and careful analysis.
Why (not set) Appears in AI Traffic Reports
You’ll often see “(not set)” value in AI traffic reports because of technical reasons. AI tools sometimes add UTM parameters without including utm_medium parameter, which makes GA4 display “(not set)” as the medium. On top of that, AI chatbots try to copy human interactions while they create non-human traffic, and this leads to incorrect tracking data. To understand “(not set)” traffic better, you should look at engagement metrics. Real ChatGPT traffic usually shows “referral” as the medium, while zero engagement rates point to bot activity.
Session-Level vs User-Level Analysis for AI
We need to analyze AI traffic at the session level instead of user level. Yes, it is important because AI visits don’t have state and can’t be linked to specific users. Each AI-driven interaction creates a new session from sources like “perplexity.ai” or “gemini.google.com“. Since there’s no continuity or way to identify users, looking at session-based data gives us better evidence-based information about AI traffic patterns.
How Google AI Mode Blends with Organic Search
Google’s AI Mode has changed how we measure search completely. The performance data from AI Mode shows up in Google Search Console but mixes with regular search data. This makes it hard to see exactly how AI affects the results. Users now get answers right in the search interface, so old metrics like CTR don’t tell the whole story anymore. Companies now need to watch their search impressions and how often AI-generated answers mention their brand.
Conclusion
Knowing how to track AI traffic has become everything in website analytics as these platforms bring more visitors to your site. This piece shows you the best ways to spot and analyze Google AI Mode traffic among other AI platforms in your GA4 reports. You can get quick insights from standard acquisition reports by filtering AI domains. Custom explorations let you dig deeper with regex patterns and heat map visualizations. Your AI traffic will never get mixed up with general referrals if you create dedicated channel groups.
In spite of that, some big problems still exist. Google AI Mode and free ChatGPT versions show up as direct traffic because their referrer data gets stripped away. This makes complete attribution nowhere near possible. Bot traffic makes things even more complex. You need to look carefully at engagement metrics to tell the difference between automated visits and real human interactions.
AI-referred visitors show better engagement rates and stronger intent than traditional organic traffic. People who find your content through AI assistants spend more time on your site, look at more pages, and convert just as well as Google search traffic. Your analytics approach needs to adapt to capture this growing segment that will give you informed insights into new search behaviors.
GA4 will change as AI search tools evolve. The techniques in this piece are the foundations to track this vital traffic source right now. These methods help you measure AI’s effect on your site, make your content better for these platforms, and take informed decisions as AI changes how people find your business online.
