Google Tag Manager (GTM) has rolled out a long-awaited update for analytics teams working with GA4: it is now possible to directly access GA4’s core user and session identity data through built-in variables. This represents a significant transformation for both analytics and marketing technology (MarTech) integrations.
Previously, obtaining this data required developers or analysts to rely on more fragile methods such as complex Custom JavaScript solutions, cookie parsing, or gtag() calls. These approaches were vulnerable to changes in cookie policies, JavaScript execution order, or browser updates. The new built-in variables eliminate this need.
What the Built-in Variables Provide
GTM now offers the following three core GA4 identity data points as standard variables:
Analytics Client ID: The identifier created on the website that represents the user’s uniqueness.
Analytics Session ID: The unique identifier of the user’s current session.
Analytics Session Number: The number of sessions in which the user has visited the site (which session they are on).
These data points directly support identity-level measurement and more complex integration scenarios.
Strategic Importance: What Changes?
The practical impact of this update on analytics and marketing can be grouped under three main headings:
📌 1. Increased Data Reliability
Previously, data such as Client ID or Session ID accessed via custom code could break when cookie structures changed. Built-in variables pulled through GTM’s secure APIs offer a more stable and sustainable method supported by Google’s infrastructure.
📌 2. Speed and Accuracy of Integrations
When these identity data points are transferred to advertising platforms (for example, server-side configurations, CAPI integrations, or CRM systems) or to internal data warehouses, they are now directly accessible. This enables marketing teams to build faster, more accurate, and easier-to-maintain integrations.
📌 3. Scalability for Large-Scale Setups
In cases where GA4 is used across multiple properties or where different measurement IDs are managed, Analytics Storage–type variables make it clearer to isolate data for each property and route it to the correct channel/property. This provides a significant advantage, especially for enterprise environments.
Use Cases: What’s Possible?
With this new structure, the following use cases become much easier to implement:
✔️ CRM and Customer Profile Matching
For example, when a user logs in to an online store or fills out a form, the obtained Client ID can be matched with the CRM system to create richer customer profiles.
✔️ Server-Side Tagging
The data processing pipeline can use identifiers such as Client ID not only on the client side, but also through a server-side GTM setup, enabling a more reliable data pipeline.
✔️ Identity-Level Analysis Across Marketing Channels
Thanks to built-in variables, it becomes possible to send data with consistent identity information to third-party platforms (such as advertising APIs or server-side conversion APIs), ensuring consistency in cross-channel reporting.
Points to Consider
Although these new built-in variables are powerful, there are some important conditions to keep in mind:
✅ The Google Tag (gtag.js) code must be correctly implemented on the website. A standard GA4 configuration tag added via GTM usually fulfills this requirement.
⚠️ Each of these variables is not the same as the User ID or User-ID in GA4 reports. The Analytics Client ID is an identifier that represents a user only within a single browser; it is not used to match the same user across different devices. Therefore, for more comprehensive user matching, a strategy combining User-ID or other systems is required.
Conclusion: Making Measurement More Stable and Scalable
These built-in variables introduced by Google Tag Manager provide a standardized way to access identity data that previously required custom solutions. This creates tangible benefits such as:
✔ More sustainable data flow
✔ Faster and more error-free integrations
✔ A clearer and more manageable measurement infrastructure for large-scale setups
With this update, GTM takes measurement and integration infrastructure one step further toward a more professional level for analytics teams.
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