How to Track LLM Referral Traffic in GA4 (Step-by-Step Guide)

Tracking LLM referral traffic is crucial for businesses looking to understand how users discover content via large language models (LLMs) like ChatGPT, Gemini, and Bard. Google Analytics 4 (GA4) offers powerful AI tools that allow marketers to gain traffic insights and monitor user behavior driven by AI search. In this guide, we’ll show you how to create a simple, effective LLM referral report in GA4 to track this emerging source of web traffic, helping you gain a competitive edge over reliance on traditional search engines.

Our SaaS clients are getting 𝟎.𝟓-𝟑% of total traffic from LLMs.

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Here’s how to create a simple LLM referral report in GA4:

𝐒𝐭𝐞𝐩 𝟏: 𝐀𝐜𝐜𝐞𝐬𝐬 𝐆𝐀𝟒 𝐚𝐧𝐝 n𝐚𝐯𝐢𝐠𝐚𝐭𝐞 𝐭𝐨 e𝐱𝐩𝐥𝐨𝐫𝐞

Start by logging into your Google Analytics 4 account. From the left hand menu, click on Explore — GA4’s feature for building custom reports and session segments. Then select “Blank” to create a new report from scratch.

This section of GA4 is ideal for tracking specialized traffic in GA4 that standard reports might miss. By leveraging the dimensions and metrics section, you can build a report tailored to monitor LLM referral traffic and analyze traffic trends over time.

𝐒𝐭𝐞𝐩 𝟐: 𝐒𝐞𝐭 u𝐩 𝐭𝐡𝐞 c𝐮𝐬𝐭𝐨𝐦 e𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧 r𝐞𝐩𝐨𝐫𝐭

Rename the report

▪️ Click on “Untitled Exploration” and rename it to something like:

“LLM Referral Traffic & Key Events”

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2. Add dimensions

Click the “+” button in the Dimensions section to add new dimensions. Search for and include:

Session source/medium — this is a crucial session segment that identifies where your traffic originates, including referrals from AI tools.
Page referrer — helps pinpoint exact pages and sources sending referral traffic.

Adding these dimensions lets you track traffic specifically from AI search platforms and other LLM powered tools.

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3. Add metrics

Next, click “+” in the Metrics section to add key metrics like:

Sessions — total visits driven by LLM referrals.
Key events — important user interactions tagged as conversions or engagement signals.
Any other relevant key metrics such as bounce rate, average engagement time, and session duration to gauge user engagement and conversion rates.

These metrics provide deeper traffic insights into how users interact with your site after arriving via AI-driven referrals.

You might be interested in: LLMs vs SEO: What’s Changing in Search Discovery

4. Add rows and values

Drag Session source/medium into the Rows area, and Sessions into Values to structure the report for clear visibility of traffic sources and volume.

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𝐒𝐭𝐞𝐩 𝟑: 𝐒𝐞𝐭 u𝐩 f𝐢𝐥𝐭𝐞𝐫𝐬 𝐭𝐨 i𝐧𝐜𝐥𝐮𝐝𝐞 o𝐧𝐥𝐲 𝐋𝐋𝐌 r𝐞𝐟𝐞𝐫𝐫𝐚𝐥𝐬

To focus your report exclusively on LLM referral traffic, drag either Page referrer or Session source/medium into the Filters section. Then apply a “Matches Regex” filter using this regex string to capture known LLM referral domains:

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

Using regex filters like this in GA4 allows precise tracking of AI powered tools driving traffic, filtering out unrelated sources to keep your data clean. This step is essential for understanding traffic in GA4 specifically generated from AI search and other LLMs beyond traditional referral sources.

Note: you can use the source/medium contains filter to exclude anything that you want to remove from the report.

𝐒𝐭𝐞𝐩 𝟒: 𝐀𝐝𝐣𝐮𝐬𝐭 v𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 (o𝐩𝐭𝐢𝐨𝐧𝐚𝐥)

GA4 offers multiple visualization options to present your LLM referral data:

Table View — for a detailed breakdown of sessions, key events, and other metrics by source.
Line Graph — to monitor traffic trends over time and see growth in LLM referrals.
Bar Chart — for quick comparisons across different ai tools or platforms.

Adding Date as a breakdown dimension helps reveal how traffic insights evolve, showing increases in AI search visibility and changes in user behavior over weeks or months.

𝐒𝐭𝐞𝐩 𝟓: 𝐒𝐚𝐯𝐞 & s𝐡𝐚𝐫𝐞 𝐭𝐡𝐞 r𝐞𝐩𝐨𝐫𝐭

Once your report is configured:

Click Save in GA4 to keep it accessible anytime for ongoing analysis.
Optionally, use the Share feature to create a link or export the report as CSV or PDF for team collaboration or presentation.

Tracking LLM referral traffic regularly enables data-driven decisions that optimize how your website engages users discovering content through AI-powered platforms.

You might be interested: 10 Content Formats That Get Picked Up by LLMs

Final notes on key metrics and data interpretation

Keep in mind, key events in this report might be inflated if your GA4 setup tags many interactions as key events by default. Monitor session duration, bounce rate, and engagement rate alongside conversions to get a comprehensive picture of user engagement.

By analyzing session source medium data alongside regex filters and key metrics, you can verify how much traffic is driven by AI search compared to traditional search engines. This emerging source of traffic represents a valuable opportunity to boost your organic traffic and adapt marketing strategies for the future.

With this step-by-step method to create a custom LLM referral report in GA4, you’ll gain crucial insights into how users discover content through AI tools, enabling you to track and optimize your website’s performance in the age of AI search.