GA4 Dimensions & Metrics: Complete Reference 2026

Understanding the full landscape of GA4 dimensions and metrics is essential for anyone analyzing website or app performance. Whether you're building custom reports in Looker Studio, pulling data through the GA4 Data API, or configuring explorations in the GA4 interface, knowing exactly what data is available — and what each field means — is the foundation of effective marketing analytics.

This guide provides a complete reference of every dimension and metric available in Google Analytics 4 as of 2026. We've organized them by category, included the API field names for developers and analysts, and added practical context on when and how to use each one. We also cover the critical differences between GA4 and Universal Analytics for teams that are still adapting to the new data model.

What Are GA4 Dimensions vs Metrics?

Before diving into the full reference, it's important to understand the difference between dimensions and metrics — two concepts that are often confused but serve fundamentally different purposes in analytics data.

Dimensions are descriptive attributes that define what you're looking at. They are the labels, categories, and identifiers that let you organize and filter your data. Examples include page path, session source/medium, device category, country, and event name. Dimensions answer the question: "How do I want to slice this data?"

Metrics are quantitative measurements that tell you how much or how many. They are the numbers: sessions, users, event count, revenue, engagement rate. Metrics answer the question: "What happened on my site or app?"

Scopes are a critical GA4 concept. Every dimension and metric has a scope — user, session, or event — that determines how it's attributed and aggregated. User-scoped dimensions (like first user source) persist for the user's lifetime. Session-scoped dimensions (like session source) apply to a single session. Event-scoped dimensions (like page path) apply to individual events. Mixing scopes incorrectly produces misleading reports.

User Dimensions

User dimensions describe the individual visitor — their identity, acquisition source, and lifecycle stage. GA4 distinguishes between first-touch (user-scoped) and session-touch (session-scoped) attribution for traffic sources, which is critical for understanding both acquisition and re-engagement.

Session Dimensions

Session dimensions describe individual visits to your site or app. GA4 automatically creates sessions from event streams — a session starts with the session_start event and ends after 30 minutes of inactivity (configurable). Session-scoped traffic source dimensions are critical for understanding which channels drive specific visits.

Event Dimensions

Event dimensions describe individual interactions. In GA4, everything is an event — pageviews (page_view), purchases (purchase), button clicks (click), scrolls (scroll), and any custom events you define. Event parameters carry additional context about what happened.

Page / Screen Dimensions

Page dimensions describe the content users interact with. For websites, these are page paths, titles, and referrers. For apps, these are screen names and classes. Content grouping lets you organize pages into logical categories for higher-level analysis.

Geographic Dimensions

Geographic dimensions tell you where your users are located. GA4 derives location from IP addresses (anonymized) and uses Google's geolocation database. These dimensions are essential for geo-targeting analysis, localization strategy, and understanding regional performance differences.

Technology Dimensions

Technology dimensions describe the devices, operating systems, browsers, and screen sizes your users access your site with. Essential for responsive design decisions, browser compatibility testing, and understanding the technical context of user experience issues.

Traffic Source Dimensions

Traffic source dimensions are among the most important in GA4 — they tell you where your users come from. GA4 has a critical concept of scope for traffic sources: user-scoped (first acquisition), session-scoped (how this visit started), and event-scoped (most recent touchpoint). Understanding scope is essential for accurate channel attribution.

E-commerce Dimensions

E-commerce dimensions describe transaction and product details from your online store. These are populated by the GA4 recommended e-commerce events (purchase, add_to_cart, begin_checkout, etc.) and their associated item parameters. Accurate e-commerce tracking requires implementing the full items array with each event.

User Metrics

User metrics count and measure your audience. GA4 introduces a critical distinction between active users and total users — the default "Users" metric in GA4 reports is actually active users, not total users. Understanding this difference prevents misinterpretation of your audience data.

Session Metrics

Session metrics measure visit-level behavior. GA4's session model is fundamentally different from Universal Analytics — sessions are derived from event streams, and the key concept of "engaged sessions" replaces the old bounce rate logic. An engaged session lasts 10+ seconds, has 2+ page views, or triggers a conversion event.

Event Metrics

Event metrics measure interactions at the most granular level in GA4. Since every interaction is an event, these metrics form the building blocks for all higher-level analysis. Conversion events are simply events marked as conversions in the GA4 admin — there is no separate conversion tracking mechanism.

E-commerce Metrics

E-commerce metrics track the monetary outcomes from your online store. These automatically populate when you implement the GA4 recommended e-commerce events (purchase, add_to_cart, begin_checkout, etc.) with the required parameters. The items array within each event provides product-level detail.

Page Metrics

Page metrics measure content performance at the individual page level. These help you understand which pages attract visitors, hold attention, and serve as entry or exit points for your site.

Audience and Cohort Dimensions

Audience and cohort dimensions enable advanced segmentation and longitudinal analysis. Audiences are groups of users defined by conditions you set in GA4 admin. Cohorts group users by shared characteristics (like acquisition date) for retention analysis. User properties store custom attributes about your users.

How to Use GA4 Metrics for Marketing Optimization

GA4's event-based model provides more flexibility than Universal Analytics, but it also requires a different analytical mindset. Here's how to leverage these metrics and dimensions for each optimization goal.

For content optimization

Focus on engagement rate, average engagement time, scroll events, and views per user at the page level. Use the page path dimension to compare content performance. An engagement rate below 40% on blog content signals weak hooks or mismatched intent. Track entrances and exits to understand page flow — high exits on a pricing page may indicate friction.

For acquisition analysis

Use session source/medium with new users and engagement rate to evaluate channel quality. Compare first user source (user-scoped) vs. session source (session-scoped) to understand whether channels drive new users or re-engage existing ones. The default channel group provides a clean summary, but drill into source/medium for granular insights.

For e-commerce optimization

Track the purchase funnel: view_item → add_to_cart → begin_checkout → purchase. Use cart-to-view rate and purchase-to-view rate to identify product-level conversion issues. Monitor average purchase revenue (average order value) alongside transactions — optimizing for transaction count without tracking AOV can reduce overall revenue. Break down by item category and item brand to identify top-performing product segments.

For campaign measurement

Match session campaign and session source/medium with conversions and purchase revenue to measure campaign effectiveness. Use landing page with engagement rate to evaluate ad-to-page relevance. Compare post-click conversion data in GA4 with your ad platform's reported conversions — discrepancies reveal attribution model differences.

For retention and loyalty analysis

Use cohort analysis with cohort Nth day/week/month to measure retention curves. Track DAU/MAU ratio as a stickiness indicator. Segment by new vs returning and compare engaged sessions per user to understand loyalty depth. Use audience dimensions to compare behavior across customer segments you've defined in GA4 admin.

What Changed: UA vs GA4 Differences

The transition from Universal Analytics (UA) to GA4 represents the biggest change in Google Analytics history. Understanding these differences is critical for accurate interpretation, especially if you're comparing historical UA data with GA4 reports.

Data model: sessions to events

UA used a session-based hit model with specific hit types (pageview, event, transaction, social, timing). GA4 uses an event-based model where everything is an event with parameters. A pageview is the page_view event. A transaction is the purchase event. This means GA4 event counts are naturally higher than UA event counts because GA4 counts pageviews as events too.

Bounce rate vs engagement rate

UA's bounce rate measured single-page sessions — a user who spent 10 minutes reading one page was counted as a bounce. GA4's bounce rate is the inverse of engagement rate: a session is "bounced" only if it lasted less than 10 seconds, had fewer than 2 page views, AND triggered no conversion events. GA4 bounce rates are typically 15-30% lower than UA bounce rates for the same content.

Goals vs conversion events

UA had a fixed set of goals (max 20 per view) configured in the admin. GA4 lets you mark any event as a conversion (now called "key event") with no limit. However, GA4 counts every conversion instance (a user can convert multiple times per session), while UA counted goals once per session. This means GA4 conversion counts can be higher than UA goal completions for the same activity.

User counting

UA's primary "Users" metric counted all users (total users). GA4's primary "Users" metric counts active users (users with an engaged session). This means GA4 often shows fewer "Users" than UA for the same traffic. If you need the UA-equivalent count, use GA4's Total Users metric.

Attribution model

UA defaulted to last non-direct click attribution. GA4 defaults to data-driven attribution, which uses machine learning to distribute credit across touchpoints. This means the same campaign can show different conversion counts in GA4 vs. UA. GA4 also introduced three scope levels for traffic sources (user, session, event), adding complexity but also more analytical flexibility.

Removed or replaced features

Several UA features were removed or replaced in GA4: Views (replaced by data filters and comparisons), Content Grouping (replaced by custom dimensions using the content_group parameter), Segments in standard reports (moved to Explorations only), Custom Channel Groupings (replaced by GA4's configurable default channel group), and Multi-Channel Funnels reports (replaced by Attribution reports and data-driven attribution).

Common Mistakes When Analyzing GA4 Data

Even experienced analysts make these mistakes when working with GA4 metrics. Avoiding them will save you from flawed analyses and bad optimization decisions.

1. Confusing active users with total users

When GA4 shows "Users" in standard reports, it means active users — users with an engaged session or first_visit event. This is always less than or equal to total users. If you're comparing GA4 to UA, use the totalUsers metric for an apples-to-apples comparison. Many teams panic about "lost users" when switching to GA4, not realizing the default metric simply changed.

2. Mixing traffic source scopes

GA4 has three scopes for traffic sources: user (first acquisition), session (how this visit started), and event (most recent source). Using firstUserSource to measure campaign performance of a retargeting campaign will show the original acquisition source, not the retargeting campaign. For campaign measurement, always use session-scoped or event-scoped source dimensions.

3. Comparing GA4 bounce rate to UA bounce rate

GA4's bounce rate is fundamentally different from UA's. UA counted single-page sessions as bounces. GA4 counts non-engaged sessions (under 10 seconds, 1 page, no conversion) as bounces. A long single-page read is a bounce in UA but not in GA4. Never compare the two directly — they measure different things.

4. Not setting up custom dimensions for event parameters

GA4 collects event parameters but does not automatically make them available as report dimensions. You must register custom event parameters as custom dimensions in GA4 Admin > Custom Definitions. Until registered, parameter data exists in raw events (viewable in DebugView and BigQuery) but cannot be used in standard reports or the Data API.

5. Using event count without filtering by event name

Since everything is an event in GA4, the raw eventCount metric includes page_view, session_start, first_visit, scroll, and every other event. Looking at "Event Count" without filtering by event name is meaningless — always pair event count with the eventName dimension to analyze specific interactions.

6. Ignoring thresholds and data sampling

GA4 applies data thresholds when Google Signals is enabled — it removes rows from reports when the user count is too low to maintain anonymity. This can make reports appear to have missing data. Additionally, GA4 standard reports may show sampled data for large date ranges (indicated by a green checkmark turning orange). Always check the data quality indicator and consider using BigQuery export for unsampled, complete data.