GA4 Reports & Explorations · Standard Reports, Funnels & Paths

GA4 has two fundamentally different analysis environments that serve different analytical purposes:

Reports (the Reports section, left navigation) contains pre-built dashboards and tables that are always running — they update continuously as data comes in. Reports use aggregated data and are designed for regular monitoring: traffic by channel, engagement metrics, conversions over time. They are fast and always current, but limited in the questions they can answer — they show pre-defined dimensions and metrics in pre-defined layouts.

Explorations (the Explore section, left navigation) is an ad hoc query workspace. You define the dimensions, metrics, filters, segments, and visualisation type for each exploration. Explorations draw from raw event data (subject to the data retention period) and can answer specific analytical questions that standard reports cannot. Building a funnel requires Explorations. Analysing what users who converted had in common requires Explorations. Explorations are not continuously running — they query data when you open or refresh them.

The practical workflow: use standard reports for regular performance monitoring (weekly traffic reviews, conversion rate tracking); use Explorations when you need to answer a specific analytical question (why is checkout abandonment increasing? what do users who convert look at before converting?).

GA4's standard report collection is organised under five top-level sections:

• Acquisition. Where your users come from — traffic channels (organic search, direct, email, paid search, social, referral), campaign performance, and new vs returning users. The User Acquisition report shows the channel that first acquired each user; the Session Acquisition report shows the channel for each session.
• Engagement. What users do on your site — pages and screens, events, conversions (key events), and user engagement metrics (engaged sessions, engagement rate, average engagement time). The Pages and Screens report is the most-used: it shows which pages receive the most traffic and engagement.
• Monetisation. Revenue and e-commerce performance — available when e-commerce events are implemented. Purchase journey, product performance, checkout funnels, promotions, and order coupon performance.
• Retention. User return behaviour — the proportion of users who return in the days and weeks after their first visit. GA4's retention report shows return visit patterns for cohorts grouped by acquisition week.
• Demographics. User characteristics — country, city, language, device type, browser, operating system. The User Demographics report is available when Google Signals is enabled (requires user acceptance of Google personalisation).

Standard reports can be customised to show the dimensions and metrics most relevant to the specific use case. Click the pencil (edit) icon in any standard report to enter customisation mode. Changes are specific to the user and property — you can save customised reports as part of a custom report collection.

Key customisation options: add or remove metrics columns; change the primary dimension; add a secondary breakdown dimension; apply date comparison (current period vs same period last year); apply comparison segments; add report-level filters. The Report Customisation feature (Admin → Reports → Customise reports) allows creating custom report collections that become the default view for all users with appropriate access, replacing the default GA4 report structure with one tailored to the business's specific reporting needs.

Funnel Exploration visualises drop-off through a series of steps you define. It answers: of the users who started at step 1, what proportion completed step 2? Step 3? Where is the biggest drop-off in the conversion journey?

Building a funnel exploration

1. Explore → New exploration → Funnel exploration template
2. Define the funnel steps. Each step can be an event (e.g. "begin_checkout"), a page visit (page_location contains "checkout"), or any other condition. Steps can be open (users can skip steps) or closed (steps must be completed in order).
3. Configure the visualisation: open funnel (each step shows all users who completed it, regardless of previous steps) vs closed funnel (each step shows only users who completed all previous steps in order).
4. Add segments to the funnel breakdown to compare funnel performance between different user groups (e.g. mobile vs desktop, new vs returning users).

Funnel Exploration also shows the elapsed time between steps and what users did between funnel steps (the "Next action" column) — revealing the most common detours from the intended conversion path.

Path Exploration visualises the sequence of pages or events users navigate through. Two directions: forward paths (what users do after a specific page or event) and backward paths (what users did before reaching a specific page or event).

Forward path use cases: what do users do after visiting the pricing page? What events fire after the checkout begins? What pages do users visit after a product page? Backward path use cases: what did users who converted see before the conversion event? What was the most common path leading to high-value product pages? Path Exploration generates a tree visualisation showing the most common sequences, with each node showing the count and proportion of users following that path.

Segment Overlap shows which users belong to multiple segments simultaneously — visualised as overlapping circles. This is useful for identifying characteristics shared by high-value user groups: what proportion of converters also use mobile? What is the overlap between users who visited the pricing page and users who completed a purchase?

Segment Overlap is particularly valuable for audience analysis: by creating segments based on conversion behaviour and comparing them with channel, device, or demographic segments, you can identify which acquisition channels or user characteristics are associated with the highest-value user behaviour.

Cohort Exploration groups users by the date they first acquired them (or first completed a specific event) and tracks their behaviour in the subsequent periods. This reveals user retention patterns: of users acquired in week 1, what proportion returned in week 2? Week 4? Week 8?

Cohort analysis is most valuable for subscription businesses, apps, and e-commerce — where the question "are the users we acquire continuing to engage/purchase?" is central to business health. Declining cohort retention is an early warning of product or experience problems; improving cohort retention validates optimisation efforts.

User Explorer shows individual user event streams — a chronological list of every event fired by a specific user (identified by a pseudonymous user ID, not personally identifiable information). This is the most granular report in GA4 and is useful for understanding specific edge cases: what exactly did this user who completed a purchase do? What did this user who abandoned the checkout experience?

User Explorer is accessed through Explorations → User Explorer template, or by clicking through from other reports when a specific user ID is surfaced. Note: user IDs in GA4 are pseudonymous identifiers — they are not linked to personally identifiable information in GA4's standard implementation and cannot be used to identify specific individuals.

Free-Form Exploration is the most flexible analysis canvas in GA4 — drag any combination of dimensions and metrics from the Variables panel into the Rows, Columns, and Values cells to build a custom crosstab. Apply segments and filters. Visualise as a table, bar chart, scatter plot, line chart, pie chart, or geo map.

Free-Form is the right tool when the question does not fit any of the template explorations. Examples: what is the conversion rate by landing page for users from paid search? What is the average engagement time for users who read more than 3 articles? How does conversion rate vary by device type and acquisition channel simultaneously? These multi-dimensional analyses are impossible in standard reports and require Free-Form Exploration's flexible query builder.

For regular reporting to stakeholders who are not GA4 users, two options:

• Looker Studio (formerly Google Data Studio). Free Google tool that connects to GA4 and builds custom dashboards with charts, tables, and data visualisations. Dashboards are shareable via link — stakeholders can view live-updating reports without needing GA4 access. Looker Studio is Google's recommended reporting layer for GA4 data and is tightly integrated with the GA4 connector.
• GA4 custom report collections. The Report Customisation feature (Admin → Reports) allows creating a custom report structure that becomes the default for specified users — replacing the standard GA4 navigation with a structure tailored to specific roles (e.g. a simplified "Marketing Dashboard" collection showing only the metrics marketing needs, a "Sales" collection showing conversion and lead data).