Measure clicks from AI search results with Google Tag Manager

Tracking Google AI Overviews in GA4

By Ralph Grundmann · Last updated on 22.04.2026

Since Google AI Overviews has been rolled out in search, the way users access websites is changing. AI-generated summaries with further links to external sources appear in the search results. This raises a very practical question for many SEO, content, and analytics teams: How can traffic from Google AI Overviews be made visible in GA4?

There is no direct standard solution for this in Google Analytics 4. However, with a pragmatic setup in Google Tag Manager, such visits can still be captured. The basis is a so-called Text Fragment in the URL (#:~:text=). This technique allows browsers to jump directly to a specific section of text on a page and highlight it. Text fragments are part of the URL fragment and are processed client-side, not sent to the server.

This very behaviour can be utilised for tracking: When a user lands on your website via a link with a text fragment, a GTM setup can recognise the passed excerpt and send it as an event to GA4.

Visits instead of Impressions

What can really be measured with AI Overviews

The correct classification is important: this setup does not measure whether an AI Overview has been displayed in Google search. It exclusively measures whether a page view on your website has arrived with a text snippet.

This is a crucial difference. Therefore, you will not receive a complete impression report for AI Overviews, but a very useful measurement for specific visits that arrive with a highlighted text excerpt on your page. Google describes AI Overviews as part of its AI-driven search features, which assist users with more complex queries and include links to web content.

Measurable data at a glance

Which data the setup makes visible in GA4

With this approach, the following information can be evaluated in GA4:

whether a visit occurred with a text fragment
which target page was accessed
which excerpt was passed in the fragment
how often such visits occur

This is particularly helpful if you want to understand which content is referenced in AI contexts and which landing pages receive additional traffic as a result.

Limits of classical measurements

Why normal referrers fail with AI traffic

Traffic from modern search interfaces is often analytically more difficult to classify than traditional organic visits. Text fragments provide a practical approach here, as they are directly identifiable in the target URL. Since fragments are processed client-side, they are not suitable for server-side evaluation, but are very well suited for a client-side GTM setup.

The technical structure

This is how tracking works with GTM and GA4

The setup consists of three components:

a custom JavaScript variable
a page view trigger
a GA4 event tag

The variable checks whether a text fragment of type #:~:text= is present. If so, the snippet is extracted and processed. Subsequently, a trigger fires on a page view basis, and a GA4 event sends the information to your property. This is exactly how your existing GTM export is structured.

Extracting text fragments

1. Custom JavaScript Variable: Text excerpt from Google AI

Create a new variable in Google Tag Manager.

To do this, navigate to Variables → New → Custom JavaScript Variable in your container.

It is best to assign the following name to the variable:

Recommended variable name:
JS – AI Overview Text Fragment

Then, insert this code:


function() {
try {
// Retrieve all performance entries for the current page view
var entries = performance.getEntries();

// Collect matches with text fragment
var matches = [];

for (var i = 0; i  entries.length; i++) {
if (entries[i].name && entries[i].name.indexOf("#:~:text=") !== -1) {
matches.push(entries[i]);
}
}

// No text fragment found
if (matches.length === 0) {
return null;
}

// Extract text fragment from the URL
var rawMatch = matches[0].name.match(/#:~:text=(.*)/);
if (!rawMatch || !rawMatch[1]) {
return null;
}

// URL-decode and make it more readable for GA4
var fragment = decodeURIComponent(rawMatch[1])
.replace(/,/g, "")
.replace(/…/g, "…")
.replace(/*/g, ", ");

// Return to GTM
return fragment || null;

} catch (e) {
// Do not send values on errors
return null;
}
}

Create Fragment Trigger

2. Trigger - Page view with snippet detection

Create a trigger in GTM now.

Recommended Name:
PV – AI Overview text fragment detected

Trigger Type:
Page View or Window Loaded

Condition:
{{JS – AI Overview text fragment}} is not equal to null

This way, the trigger will only fire if a text fragment has truly been detected.

Send data to GA4

3. Create GA4 Event Tag

Recommended tag name:
GA4 – Event – ai_overview_click

Event name:
ai_overview_click

Event parameter:

ai_snippet_text = {{JS – AI Overview text fragment}}

landing_page = {{Page URL}}

Why these names make sense:

For custom GA4 events there are clear naming rules: Event names must start with a letter and can only contain letters, numbers, and underscores. Furthermore, consistent snake_case is significantly easier to maintain in practice.

Consent Check

4. Correctly consider consent

The event may only be sent to GA4 if the user has consented to Analytics. In GTM, the tag should therefore be linked to your existing consent logic, for example via analytics_storage.

This is not a nice-to-have, but a requirement.

5. Create Custom Dimensions in GA4

To use the passed values later in explorations or reports, you should register the event parameters as custom dimensions in GA4.

Recommended:

ai_snippet_text
landing_page

After that, you can analyse which URLs were accessed most frequently through this event and which text snippets were passed along.

Final Examination

6. Test setup

Check the setup with the GTM preview and the DebugView in GA4.

Important test points:

Does the variable return a value?
Does the trigger fire only when the text fragment is present?
Does the event arrive in GA4?

Are ai_snippet_text and landing_page passed correctly?

Limits of Tracking

What the setup cannot achieve

This tracking is deliberately pragmatic and has clear boundaries.

It measures no impressions from AI overviews, but only page views with text fragments. Furthermore, the setup depends on the presence of a corresponding fragment in the target URL that can be recognised on the client side. Not every visit from an AI interface necessarily has to be passed in this way.

Nevertheless, the approach is valuable in practice: it creates transparency in an area where classic referrer data alone often does not help.

Visibility & Visits Combined

Evaluate SISTRIX and GA4 data together

A meaningful extension of the setup is the alignment with SISTRIX data. The AIO analysis in SISTRIX shows for a domain, which keywords Google displays an AI Overview for, which URL is cited, at which position it appears within the box and whether the cited URL also ranks organically.

Additionally, metrics such as search volume, competition, and CPC are available . On this basis, the evaluation in GA4 can be better contextualised: SISTRIX provides information about the fundamental presence of a domain in AI Overviews, while GA4 reveals which target pages actually receive visits with text fragments. By combining both data sources, it can be distinguished whether a URL primarily benefits from its AI Overview presence or already performs strongly organically, independent of that . This enhances the assessment of topic clusters, landing pages, and optimisation potentials in the context of AI Search.

Contact

Do you want to measure AI overview traffic in GA4 accurately?

Whether it’s GTM setup, event concept, consent mode, or clean evaluation in GA4: We support you in capturing AI-Overview traffic reliably and meaningfully categorising it. Speak to us if you would like to review, expand, or implement your tracking setup directly.