6 Powerful Features of Google Analytics

Google Analytics 4 provides features that transform it from a basic reporting tool into a foundation for serious data infrastructure. Unlocking that value, however, requires understanding how to extract and operationalize GA4 data beyond the standard interface. 

This article covers the six GA4 features that matter most for data teams, with practical guidance on moving GA4 data into your warehouse.

TL;DR: Google Analytics Features at a Glance

• Event-based tracking captures every user interaction as a discrete, queryable event with up to 25 custom parameters
• Explorations provide flexible ad-hoc analysis but sample data at high volumes and cannot be scheduled or exported
• Predictive audiences use machine learning to identify likely purchasers and churners, though they require 1,000+ users to qualify
• BigQuery integration exports raw, unsampled event data for free up to 1 million events per day
• Cross-platform measurement unifies web and mobile app data in a single property using User ID or Google Signals

What Makes Google Analytics 4 Different from Universal Analytics?

GA4 uses an event-based data model instead of session-based tracking. This architectural shift enables cross-platform measurement and predictive analytics that Universal Analytics couldn't support.

In Universal Analytics, sessions and pageviews were the primary data units. GA4 treats every user interaction as an event with customizable parameters. A page view is an event. A button click is an event. A purchase is an event. This consistency simplifies tracking logic and makes cross-platform analysis possible.

Several changes matter specifically for data teams. GA4 tracks web and mobile app users within a single property. Machine learning powers predictive metrics like purchase probability and churn likelihood. And the BigQuery export, previously limited to GA360 enterprise accounts, is now free for all properties.

What Are the 6 Most Powerful Features in Google Analytics 4?

The following six features separate GA4 from basic analytics tools and provide the foundation for building robust marketing data pipelines.

1. Event-Based Tracking and Custom Events

Event-based tracking captures any user interaction as a discrete event with customizable parameters, giving you granular data without forcing everything into session abstractions.

GA4 collects three categories of events:

• Automatic events: Fire without any configuration, including firstvisit and sessionstart
• Enhanced measurement events: Track common interactions like scrolls, outbound clicks, and video engagement when you enable them in the admin settings
• Custom events: Let you track anything specific to your business, from form submissions to feature usage

Each event can carry up to 25 parameters, adding context that makes analysis meaningful. A purchase event might include item id, item name, price, and quantity. A sign_up event could carry the registration method and referral source.

Consider a multi-step checkout flow. With custom events, you can track each step: begin checkout, add shipping info, add payment_info, and purchase. Parameters on each event capture cart value, item count, and any applied discounts. This granularity enables funnel analysis that pinpoints exactly where users drop off.

There are limits to know. Each property supports 500 distinct event names, and each event can carry 25 event parameters plus 25 user properties. Most teams will not hit these limits, but high-volume properties should plan accordingly.

When you export GA4 data to a warehouse, events become rows you can aggregate, filter, and join with other datasets. This unlocks analysis at a scale and flexibility the native interface cannot match.

2. Explorations and Custom Reporting

Explorations provide flexible analysis beyond standard reports, letting you build funnel analyses, path explorations, cohort comparisons, and free-form visualizations without being constrained by pre-defined templates.

GA4 offers several exploration types, each suited to different analytical questions: 

• Free-form explorations: Let you drag and drop dimensions and metrics into custom tables and charts
• Funnel explorations: Show conversion rates through defined steps
• Path explorations: Visualize how users navigate your site or app
• Cohort explorations: Track behavior over time for user groups defined by acquisition date

These tools have limitations, however. Explorations sample data when queries exceed certain thresholds, typically around 10 million events. You also cannot schedule explorations to run automatically or export them directly to external tools.

For ad-hoc questions, explorations work well. Data teams building repeatable dashboards, on the other hand, need raw data in a warehouse where they control aggregation, sampling, and scheduling.

3. Audience Segmentation and Predictive Audiences

GA4's audience builder segments users by behavior, demographics, and AI-predicted outcomes like purchase probability and churn likelihood.

Standard audiences use dimensions you would expect: demographics, geography, device type, and acquisition source. Custom audiences let you define conditions based on any combination of events, parameters, and user properties. For example, a custom audience might include users who viewed a product page three times but never purchased.

Predictive audiences are where GA4's machine learning becomes practical. These audiences automatically identify users likely to purchase in the next seven days, users likely to churn, or users with high predicted lifetime value. The models train on your property's historical data without requiring custom development.

Predictive audiences do have data requirements. Purchase probability needs at least 1,000 users who purchased and 1,000 who did not within the last 28 days. Smaller properties may not qualify for predictive features.

Audiences sync to Google Ads for remarketing campaigns. Data teams, however, often need audience membership exported to the warehouse for analysis alongside CRM data, product analytics, and other marketing sources. This requires moving GA4 data out of Google's ecosystem.

4. BigQuery Integration

GA4's native BigQuery export sends raw, unsampled event data to Google's data warehouse, making it essential for serious analytics work. For standard properties, the daily export is free up to 1 million events per day.

Two export options exist:

• Daily export: Batches the previous day's data and loads it into BigQuery, typically completing by mid-morning.
• Streaming export: Sends events within seconds but incurs BigQuery streaming insert costs.

The export includes raw event data with all parameters, user properties, device information, and traffic source details. The schema uses nested and repeated fields, so working with this data requires SQL knowledge and BigQuery syntax for unnesting arrays.

The critical advantage is that BigQuery export data is not sampled. When your GA4 interface starts showing sampled results at high traffic volumes, BigQuery still contains every event.

The tradeoff is ecosystem lock-in. The native BigQuery link only works with BigQuery, so teams running Snowflake, Databricks, or other warehouses need additional pipelines to move data from GA4 or BigQuery to their destination.

5. Cross-Platform Measurement

GA4 tracks users across websites and mobile apps within a single property, providing unified user journeys instead of siloed platform data.

You configure data streams for each platform: one for your website, one for iOS, one for Android. Events from all streams flow into the same property, letting you analyze cross-platform behavior in a unified view.

User identity stitching happens through two mechanisms. User ID lets you assign your own identifier when users authenticate, linking their activity across devices and platforms. Google Signals uses signed-in Google users' data to connect sessions across devices.

Consider a practical example. A user browses products on your mobile app during their commute, then completes the purchase on their laptop that evening. With proper User ID implementation, GA4 shows this as a single user journey rather than two separate users.

Cross-platform measurement only works when all data flows through a single GA4 property. Teams with data in multiple analytics tools still need a warehouse to unify the full customer journey.

6. Privacy Controls and Consent Mode

GA4's Consent Mode adjusts data collection based on user consent status, using machine learning to model conversions when users decline tracking.

Consent Mode v2 became essential for EU compliance under the Digital Markets Act. When a user denies consent, GA4 still sends cookieless pings that register basic interaction data. Machine learning models then estimate conversions that would have occurred if full tracking were enabled.

Behavioral modeling fills gaps from non-consenting users, but it is estimation rather than measurement. Accuracy depends on having sufficient consenting users for the models to learn from.

Data retention settings affect what is available for analysis. Standard properties can retain event data for 2 or 14 months. BigQuery export bypasses this limitation, so data exported to BigQuery stays as long as you keep it.

Privacy controls directly affect what data reaches your warehouse. Teams in regulated industries need to understand consent status and retention implications before building pipelines on GA4 data.

How Do You Get Google Analytics Data into Your Data Warehouse?

The native BigQuery export works for teams running Google-native stacks. Organizations using Snowflake, Databricks, or other warehouses, however, need additional pipelines to get GA4 data where they need it. Three primary approaches exist:

• BigQuery export route: Send data to BigQuery first, then build a separate pipeline to your warehouse. This adds infrastructure and latency but gives you access to unsampled event-level data.
• GA4 Data API: Access data programmatically, though with constraints. The API returns aggregated data subject to sampling rather than raw events, and quota limits restrict request frequency.
• Pre-built connectors: Data integration tools handle API authentication, pagination, rate limiting, and schema evolution automatically. When GA4 changes its data structure, maintained connectors adapt without pipeline rewrites.

Data integration platforms with GA4 connectors eliminate this maintenance work. You configure the connection once, and the connector handles ongoing extraction while you focus on analysis.

How to Move Forward with GA4 Data?

GA4's event-based architecture, BigQuery integration, and predictive capabilities give data teams the foundation for serious marketing analytics. Getting that data into your warehouse alongside other sources, however, is where the real analytical work begins.

Connect Google Analytics 4 to your data warehouse in minutes. Try Airbyte for free or talk to sales to set up your first GA4 sync with 600+ pre-built connectors.

Frequently Asked Questions

How does GA4 handle data sampling?

GA4 samples data in the interface when queries exceed thresholds, typically around 10 million events. BigQuery export provides unsampled data, making it essential for high-traffic properties that need accurate counts.

Can you use GA4 data for machine learning models?

Yes. BigQuery export gives you raw event data suitable for ML training. GA4 also provides built-in predictive metrics like purchase probability and churn likelihood.

What is the difference between GA4 segments and audiences?

Segments exist for ad-hoc analysis in Explorations and apply retroactively to historical data. Audiences are persistent user groups that collect data from creation date forward and can sync to Google Ads for remarketing.

How long does GA4 retain data?

Standard properties retain event data for 2 or 14 months, configurable in admin settings. BigQuery export stores data indefinitely in your Google Cloud project, bypassing GA4's retention limits.