GA4 Regulations: Adapting Retention and Analysis in 2025

The arrival of Google Analytics 4 coincides with an unprecedented tightening of data protection regulations. For marketing and analytics teams, this context requires a complete overhaul of practices: where Universal Analytics allowed data to be retained for up to sixty-four months, GA4 by default limits retention to two months, extendable to a maximum of fourteen months. This technical constraint directly stems from the data retention limitation principle enshrined in the GDPR. How can these new limits be reconciled with the need for long-term insights?

Regulatory Requirements Redefining Retention

The General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and CNIL recommendations in France have reshaped the web analytics landscape. The fundamental principle remains the same: personal data can only be retained for the time strictly necessary for the purposes for which it was collected.

In practice, most organizations today must configure a retention period of twelve months or less in GA4. This duration corresponds to legitimate needs for analyzing annual cycles (seasonality, campaign performance), while respecting the proportionality required by law. Beyond this, justification becomes difficult to defend against data protection authorities.

“The transition to GA4 represents a complete re-education of teams on an event-based data model, but above all, a profound adaptation to privacy requirements.”

The CNIL has also issued formal notices to several French website managers for their use of Universal Analytics, deemed illegal due to data transfers to the United States and the lack of sufficient guarantees. Companies like Sephora, Décathlon, and Leroy Merlin have been forced to review their practices, according to information reported by CCM Benchmark.

Essential Compliance Mechanisms

To align GA4 with regulatory requirements, several technical measures must be activated:

• IP address anonymization: GA4 now anonymizes IP addresses by default, unlike Universal Analytics where this option had to be explicitly activated.
• Consent Mode: This mechanism allows the behavior of tags to be adapted based on the user's choice expressed via a consent management platform.
• Consent Management Platform (CMP): Compliant with the Transparency and Consent Framework (TCF), it must collect consent before any analytical cookie is placed.

Data transfers to Google servers in the United States also require the implementation of Standard Contractual Clauses (SCCs) or other safeguarding mechanisms validated by European authorities. These legal guarantees aim to compensate for the absence of a stable adequacy decision since the invalidation of the Privacy Shield.

Configuring Retention in GA4: How-To Guide

Configuring the data retention period in Google Analytics 4 takes just a few clicks, but its strategic implications are considerable. By default, user events are retained for two months, a period largely insufficient to analyze trends or measure performance over a full quarter.

In the GA4 property settings, the administrator can extend this period to fourteen months, as recommended by Agence KZN in its comprehensive guide. However, be careful: this extension must correspond to a real and documented need, recorded in the processing activities register. Any excessive duration exposes the organization to penalties in the event of an audit.

It is also crucial to configure the exclusion of internal traffic (visits by internal teams) and spam referrers (like PayPal) to avoid distorting statistics. A site can experience up to 10% artificial inflation if it does not properly filter these sources.

The BigQuery Alternative for Controlled Retention

Faced with the fourteen-month limit in the GA4 interface, automated export to BigQuery is the preferred solution for organizations with long-term analysis needs. This architecture allows raw data to be stored in a separate warehouse, where customized purge policies can be applied.

Specifically, every event captured by GA4 is transferred daily to BigQuery. Data teams can then apply transformations, create anonymized aggregates, and define deletion rules adapted to each data type. Personally identifiable information can be purged after twelve months, while aggregated metrics (e.g., traffic by channel, conversion rate by region) can be retained longer for benchmarking purposes.

This hybrid architecture meets GDPR requirements while preserving historical analysis capabilities, provided that the purposes and durations are precisely documented in the processing register.

Adapting Analysis Strategies to the New Framework

Retention limitations require rethinking dashboards and reports. Cohort analyses, multi-touch attribution studies, or customer lifetime value (CLV) calculations now require an aggregated approach and increased automation.

To fully utilize the retention window, it is necessary to:

1. Design aggregated metrics: Instead of tracking individual users over several years, focus on anonymized segments and global trends. 2. Automate insight extraction: Before data expires, scripts can export cohort analyses, monthly retention rates, or performance by channel. 3. Leverage GA4's predictive AI: Integrated machine learning models can anticipate conversions or churn rates, even with limited retention.

One of the major upheavals lies in the transition from a session-based model (Universal Analytics) to an event-based model. GA4 now tracks every user interaction as a distinct event, offering superior granularity while facilitating anonymization. However, this evolution requires team training and a redesign of historical KPIs.

Server-Side Tagging to Bypass Blockers

The increasing use of ad blockers — over 40% of internet users worldwide — and browser restrictions on third-party cookies weaken client-side data collection. Server-side tagging allows these limitations to be bypassed by capturing events from the company's server before transmitting them to GA4.

Solutions like RudderStack, mentioned in technical documents on GA4 limitations, facilitate this hybrid architecture. Server-side tagging offers several advantages:

• Increased reliability: Data is no longer blocked by browser extensions.
• Reduced latency: Fewer scripts loaded client-side improve page speed (each second of delay reduces conversions by 20 to 22%).
• Enhanced control: The company decides what data to send to GA4, thus limiting the collection of personal data.

This approach fully aligns with privacy by design principles and allows for reconciling compliance with analytical performance.

Consent Mode and Conversion Modeling

Consent Mode v2, deployed by Google in response to GDPR requirements, introduces a distinction between consent granted for analytics and consent granted for advertising. When the user refuses cookie placement, GA4 switches to degraded mode: it sends anonymized signals (pings) that allow for modeling missing conversions.

This modeling relies on artificial intelligence: GA4 extrapolates the behavior of consenting users to estimate that of non-consenting users. If 30% of visitors refuse cookies, the platform partially compensates for data loss by relying on observed trends in similar segments.

This approach raises ethical and methodological questions. Modeling offers only an approximation, and some organizations prefer transparency by displaying partial data rather than resorting to estimates. To learn more about leveraging event data in GA4, you can consult our article on GA4 and personalization.

Towards Strengthened Data Governance

Beyond technical aspects, regulatory compliance requires robust governance of analytical data. The processing activities register must precisely describe:

• The categories of data collected (identifiers, browsing behavior, location data)
• The purposes pursued (audience analysis, UX optimization, marketing attribution)
• The recipients of the data (Google LLC, third-party providers)
• The retention periods for each category
• The security measures implemented (encryption, access controls)

The appointment of a Data Protection Officer (DPO) becomes strategic for steering this governance. In collaboration with marketing and IT teams, the DPO must conduct Privacy Impact Assessments (PIA) for each new risky processing, identifying vulnerabilities and recommending remediation measures.

This approach is part of a privacy by design logic: personal data protection is no longer a constraint imposed retrospectively, but an architectural criterion integrated from the design of tools and processes. Organizations that adopt this stance gain user trust and reduce their exposure to penalties.

Anticipating Future Regulatory Developments

The legislative framework continues to evolve. The Digital Services Act (DSA) and the Digital Markets Act (DMA) in Europe, the progressive adoption of laws comparable to the CCPA in other US states, and proposals for artificial intelligence regulation (AI Act) paint an increasingly demanding landscape.

Generative AI and machine learning solutions raise new challenges for personal data protection. When an AI model is trained on analytical data containing personal information, questions of consent, transparency, and re-identification risk arise. Companies must adapt their tools and procedures to integrate these dimensions, as highlighted by RiskInsight Wavestone.

In this context, adopting a privacy-respecting customer data platform (CDP), combined with pseudonymization and encryption mechanisms, helps prepare for the future. Analysis strategies must also integrate the ethical dimension, prioritizing transparency and limiting collection to what is strictly necessary.

GA4 vs. Universal Analytics Retention Comparison Table

Characteristic	Universal Analytics (UA)	Google Analytics 4 (GA4)
Default Retention	26 months (configurable up to 64)	2 months
Maximum Retention	64 months	14 months
Data Model	Session-based	Event-based
IP Anonymization	Option to activate	By default