Google Analytics 4 (GA4)

1. Event-Driven Data Model

GA4’s most significant shift is that everything is an event.
Examples include:

• Page view

• Session start

• Scroll

• Outbound click

• File download

• Video engagement

• Form interactions

This model means you can measure behaviour far more precisely without relying on dozens of custom tags. It also provides more flexibility in defining what matters (e.g., prospect journey steps, interactions with research content, micro-conversions around course information).

2. Enhanced Measurement (Automatic Tracking)

GA4 automatically tracks a wide range of interactions without additional tag configuration:

• Scroll depth

• Outbound link clicks

• File downloads

• Site search terms

• Engagement with embedded videos

• Page views (including history-based navigation on single-page applications)

This reduces setup time and ensures consistent measurement across university sites, even when technical resources are limited.

3. Cross-Platform and Cross-Device Tracking

GA4 can track both web and app data in the same property, solving a major issue in UA.
For users who research on a mobile device and later apply on a desktop, GA4 provides a single, unified view of the journey.

GA4 also supports:

• User ID tracking (when available)

• Google Signals (for aggregated cross-device insights)

This is particularly useful for mapping prospect funnels or understanding how returning students interact with digital systems across devices.

4. Engagement Metrics (Replacing Bounce Rate)

GA4 moves away from bounce rate (which became meaningless with modern site designs) and introduces:

• Engaged sessions

• Engagement time

• Engaged sessions per user

Engagement is counted when a user stays on the page for at least 10 seconds, triggers an event, or visits more than one page—much more aligned with how real content behaves, especially on long-form academic or guidance pages.

5. Predictive Insights and Machine Learning

GA4 includes built-in machine learning to surface patterns that would be difficult to identify manually, such as:

• Users likely to engage

• Users likely to purchase (or equivalent goal)

• Trends in churn or return behaviour

• Outliers in traffic or engagement

These predictive capabilities can help understand which types of users, campaigns, or content lead to meaningful actions like course enquiries or sign-ups.

6. Improved Privacy and Compliance

GA4 is designed for a world with stricter privacy requirements. It supports:

• Cookieless tracking methodologies

• IP anonymisation by default

• Better data retention controls

• Controls for regional data collection

• The ability to disable personalised ads per region

For universities working across different jurisdictions or countries, the privacy-first structure reduces risk and improves compliance.

7. Custom Reporting and Explorations

GA4’s “Explorations” workspace is one of its most powerful features, allowing analysts to run:

• Funnel explorations

• Pathing analysis

• Segment comparisons

• Cohort analysis

• Free-form exploration tables

• User lifetime value analysis

• Custom attribution modelling

This allows deeper investigation into questions like:

• How do users navigate from a course page to applying?

• What content keeps prospective postgraduate students engaged?

• Which campaigns generate high-quality traffic rather than vanity metrics?

8. Integration with Google Marketing Ecosystem

GA4 integrates more closely with:

• Google Ads

• Search Console

• BigQuery (with free exports)

• DV360

• Merchant Centre

The BigQuery export, free for the first time, is a major advantage for universities with data analysis or BI teams. It allows long-term storage, detailed segmentation, and custom modelling that wouldn’t be possible in GA4 alone.