This article is not a 101-style introduction to Google Analytics 4.
It assumes you already understand the fundamentals of GA4. If you’re not yet familiar with the basics, I highly recommend reviewing the earlier episodes in this series:
GA4 Episode 1 – What is GA4, how to install it, and a walkthrough of the GA4 dashboard
GA4 Episode 2 – Understanding Dimensions in GA4
GA4 Episode 3 – Metrics: what they are and what you should track
GA4 Episode 4 – Understanding Events and Event Parameters
GA4 Episode 5 – Traffic & User Acquisition Reports
This episode builds on those foundations and moves into more advanced, technical applications of GA4.
Google Analytics 4: Intermediate to Advanced
Usage Guide
There are numerous use cases and practical applications for GA4. In this guide, I’ll walk through eight intermediate-to-advanced use cases that I’ve personally applied in business marketing scenarios:
10 Other Google Platforms You Can Integrate with GA4
Advanced Customization Capabilities in GA4
Audience & Segmentation
Explorations (Custom Reports)
Tracking AI Platform Referral Traffic with Regex (one of my personal favorites)
Using GA4 to Identify 404 Errors (another personal favorite)
GA4’s AI-Powered Search Bar
The Library Function
10 Google Platforms You Can Integrate with GA4
1. Google AdSense:
AdSense is Google’s publisher monetization platform. If you run ads on your website and earn revenue per impression or click, AdSense is what manages that.
When you link AdSense to GA4, revenue and ad performance data flow directly into your analytics reports. You’ll be able to see which traffic sources, pages, or user segments generate the most ad revenue — not just traffic.
Outcome:
You can optimize for revenue per user instead of just sessions. For content publishers, this shifts strategy from “traffic growth” to “traffic quality.”
2) Google Ads:
Google Ads is Google’s paid advertising platform (Search, Display, YouTube, etc.).
When linked to GA4, conversion events, audiences, and engagement metrics sync between platforms. You can import GA4 conversions into Google Ads and export GA4 audiences back into Ads for remarketing.
Outcome:
Smarter bidding, better attribution visibility, and more accurate conversion optimization. Your paid campaigns optimize based on real GA4 event data instead of limited ad-platform signals.
3) Ad Manager
Google Ad Manager is an advanced ad serving platform typically used by large publishers.
Linking it with GA4 connects ad impression and revenue data with user behavior data. This allows you to analyze monetization performance alongside engagement metrics.
Outcome:
Enterprise publishers can evaluate yield performance per audience segment and improve ad inventory strategy.
4) BigQuery
BigQuery is Google’s cloud data warehouse. Linking GA4 to BigQuery exports your raw, unsampled event-level data.
This is extremely powerful. Instead of relying only on GA4’s interface reports, you can run SQL queries on complete datasets — including user-level analysis and advanced attribution modeling.
Outcome:
Full data ownership, advanced modeling, predictive analytics, LTV analysis, and custom dashboards. This is where GA4 becomes truly enterprise-grade.
5) Display & Video 360
Display & Video 360 (DV360) is Google’s enterprise demand-side platform (DSP) for programmatic advertising.
When linked, GA4 conversions and audiences can be shared with DV360. Campaign performance data can also be analyzed inside GA4.
Outcome:
Cross-channel attribution visibility and more precise audience targeting for programmatic campaigns.
6) Floodlight
Floodlight is a conversion tracking system within Google Marketing Platform (primarily used with DV360 and Campaign Manager 360).
Linking Floodlight to GA4 allows you to unify conversion tracking across enterprise advertising systems.
Outcome:
Better cross-platform measurement and consistent conversion definitions across marketing tools.
7) Merchant Center
Merchant Center manages product feeds for Google Shopping and ecommerce ads.
When linked to GA4, ecommerce events (like purchases, add_to_cart, view_item) connect with product feed data. This enables deeper performance reporting by product category, brand, or SKU.
Outcome:
Improved e-commerce attribution and better Shopping campaign optimization.
8) Google Play
Google Play is Google’s app distribution platform.
When linked, GA4 can integrate app revenue and in-app purchase data from Google Play into your analytics reports.
Outcome:
Unified app + web measurement and more accurate mobile app monetization analysis.
9) Search Ads 360
Search Ads 360 is an enterprise-level search campaign management tool used to manage Google, Bing, and other search engines in one place.
When linked with GA4, conversion data and audience insights flow between systems.
Outcome:
Cross-engine optimization with consistent measurement logic.
10) Search Console
Search Console tracks organic search performance — impressions, clicks, and queries.
Linking it to GA4 allows you to see search query and landing page data alongside engagement metrics like bounce rate, engagement time, and conversions.
Outcome:
You can analyze not just which keywords drive traffic, but which keywords drive revenue or high engagement.
What You Can Customize in GA4
1) Custom Dimensions
In GA4, you can create custom dimensions based on event parameters or user properties that are not available by default.
Example:
user_type
content_category
plan_tier
GA4 only reports on predefined dimensions unless you register your own. Without registering them, your collected parameters cannot be used in reports or explorations.
2) Custom Events
GA4 allows you to create:
Fully new events (via GTM or gtag)
Derived events (created inside GA4 UI based on conditions)
Example:
form_submit_success
wallet_connect_click
high_intent_user
Default events (page_view, session_start) might not be enough for your business logic. Advanced tracking requires mapping real business actions.
3) Custom Parameters
Event parameters can be customized and attached to events.
Example:
plan_type
button_location
article_author
However:
You don’t “create” parameters inside GA4 first — you send them via GTM/gtag, then register them as custom dimensions or metrics.
Parameters give context. Events tell you what happened. Parameters tell you how, where, and under what condition it happened.
4) Custom Metrics (with conditions)
They are numeric event parameters registered as metrics.
Example:
scroll_percentage
token_amount
transaction_fee
Important:
Custom metrics must be numeric values. So you can aggregate business-specific numerical data (sum, average, etc.) beyond default GA4 metrics.
5) Custom Audiences
You can create audiences based on:
Event conditions
User properties
Predictive metrics
Sequences
Example:
High-value users
Users who visited pricing but didn’t convert
AI-traffic users
Audiences can be exported to Google Ads, DV360, etc. This turns analytics into activation.
6) Custom Segments (clarification needed)
Segments are created inside Explorations, not in standard reports.
They allow:
User segments
Session segments
Event segments
You can segment users who converted in a specific location. Segments allow deep behavioral comparison without altering raw data.
Difference between Audience vs Segment:
Audience = persistent & exportable
Segment = temporary & analysis-focused
7) Customize Report Snapshot
You can customize:
The Reports snapshot page
Individual reports
Navigation collections (Library)
This lets you:
Add/remove cards
Rearrange widgets
Add custom reports
GA4’s default reports are generic. By customizing the Reports snapshot, you can see the data that matters most to you at a glance. This also helps you become more familiar with GA4, making you more comfortable and confident when using it.
8) Custom Channel Grouping
You may create an “AI Traffic” channel grouping that includes:
-
chat.openai.com
-
gemini.google.com
-
perplexity.ai
-
copilot.microsoft.com
This is doable using:
-
Source
-
Medium
-
Campaign
-
Regex rules
Default channel grouping is outdated for modern traffic sources (especially AI tools). Custom channel grouping future-proofs attribution models.
Audience & Segment:
Defining your own Marketing Audience
In GA4, you can define marketing audiences based on user behavior, demographics, traffic sources, and specific events.
Examples include new users, returning users, visitors who viewed a particular page, or users who completed a key action such as making a purchase.
You can also create highly customized audiences for more granular targeting.
GA4 allows you to build audiences using flexible condition-based logic. This gives you significant control over how users are grouped.
For example, you can create audiences of users who have:
Visited your site three or more times
Viewed a specific blog post, product page, or landing page
Clicked on your contact page, but left without taking further action
Completed a purchase on your e-commerce website
Signed up for your newsletter
You can refine all of these conditions with time-based constraints (e.g., within the last 7 days, 30 days, or 90 days), making the audiences even more precise.
If your GA4 property is linked to Google Ads, these custom audiences can be shared directly with your ad account.
This enables you to use them in remarketing campaigns across the Google Search Network and Google Display Network to re-engage users who did not convert or to upsell existing customers.
Custom Audience Segmentation
GA4 offers extensive flexibility when defining custom audiences. There are countless combinations of conditions and parameters you can apply, allowing you to build highly specific and strategically valuable audience groups.
In addition to inclusion criteria, you can also define exclusion conditions. For example, you might exclude users from a particular country, users on a specific device category, or existing customers who have already completed a purchase. This level of control ensures your audiences align precisely with your marketing objectives.
Once you’ve configured the necessary conditions, simply assign an audience name and add a brief description for internal clarity and documentation. After that, click “Save” to activate the audience.
From that point onward, GA4 will begin populating the audience dynamically based on your defined rules, making it available for analysis or activation in any linked advertising platforms.
What’s the Difference Between Segments and Audiences?
Segment
A segment is a subset of your data used for analysis inside the Explore (Explorations) section of GA4.
Segments allow you to isolate and compare specific groups of users, sessions, or events. They are temporary and exist only within the exploration where they are created.
For example, you might create a segment of:
Users who converted in the last 30 days
Sessions from organic traffic
Events triggered on a specific landing page
Segments are designed purely for analytical comparison.
Audience
An audience is a dynamic group of users that meets specific conditions you define.
Unlike segments, audiences are persistent. Once created, GA4 continuously evaluates users and automatically adds or removes them based on your rules.
Audiences can also be exported to linked platforms like Google Ads for remarketing and campaign targeting.
TLDR:
| Feature | Segment | Audience |
|---|---|---|
| Purpose | Data analysis | Marketing activation |
| Where used | Explorations only | Across GA4 & linked ad platforms |
| Persistence | Temporary | Ongoing & dynamic |
| Exportable to Ads | ❌ No | ✅ Yes |
| Retroactive | Yes (analysis on past data) | No (starts collecting after creation) |
When Should You Use Each?
Use Segments when you want to analyze behavior differences between user groups.
Use Audiences when you want to take action — such as remarketing, suppression, or campaign optimization.
Exploration
The Explorations section in GA4 is an advanced analysis workspace that allows you to perform deeper, customizable data analysis beyond standard reports.
Unlike default reports, Explorations let you build custom analyses using dimensions, metrics, segments, and filters to answer specific business questions.
Google provides several pre-built templates to help you get started, including:
Funnel Exploration
Path Exploration
Free Form Analysis
Segment Overlap
User Explorer
Cohort Exploration
These templates allow you to analyze user journeys, drop-offs, behavior flows, and audience overlaps with greater flexibility and precision than standard GA4 reports.
Reports vs Explore (Standard Reports vs Custom Explorations)
The Reports section in GA4 contains pre-configured, standard reports. These are designed to provide quick insights using predefined dimensions and metrics.
The Explore section, located directly below Reports in the left-hand navigation, is where you build advanced, fully customized analyses. Explorations allow you to go beyond default reporting by combining your own dimensions, metrics, filters, and segments.
In short:
Reports = structured overview
Explore = flexible deep analysis
Build Google Analytics SEO Custom Report
You can use Explorations to create actionable SEO reports tailored to your needs.
Within Explorations, you select only the dimensions and metrics that matter — such as:
Landing page
Session source/medium
Organic sessions
Conversions
Engagement rate
You can also build custom segments to isolate organic search traffic, allowing you to analyze SEO performance without interference from other channels.
Path Exploration
Path Exploration is a GA4 exploration technique that visualizes the sequence of pages users visit and the actions they take.
You can configure it in two ways:
Forward path analysis – Start from a specific page or event to see what users do next.
Reverse path analysis – Start from a conversion or key event to understand what actions led up to it.
This makes Path Exploration valuable for analyzing user journeys, identifying drop-off points, and optimizing conversion flows.
You can use Path Exploration to:
Identify the most commonly visited pages after the homepage
Detect behavioral loops where users repeatedly cycle between pages
Analyze the most frequent actions taken after viewing a specific page
Understand how users progress toward (or away from) a conversion
What is variable?
In Explorations, a variable is a data element you add to your analysis workspace, such as a dimension, metric, or segment.
Variables act as the building blocks of your custom report. Once added, you can drag them into rows, columns, filters, or values to shape how your data is analyzed and visualized.
In short, variables define what data is available for your exploration.
Three SEO Anomalies to Watch in GA4
When analyzing organic performance, look for these three common patterns:
High-Traffic Pages
Focus on pages that already generate strong organic traffic. These are your proven performers.
Even small improvements — such as better internal linking, refreshed content, or improved conversion elements — can significantly increase overall traffic and conversions.
Low-Traffic but High-Quality New Pages
Sometimes, newly published, well-written, and detailed content does not immediately perform in search results. These pages may require optimization, internal linking support, or keyword refinement before they gain visibility in the SERPs. Monitor them so you can make strategic adjustments.
Pages with Significant Traffic Decline
SEO content naturally decays over time due to competition and algorithm changes. Identifying pages with declining organic traffic allows you to prioritize content refreshes, technical fixes, or keyword repositioning to recover lost visibility.
Note:
Your custom Exploration reports are saved automatically. Assigning a clear and descriptive name will help you locate them quickly later.
All saved Explorations can be accessed anytime by clicking “Explore” in the left-hand navigation panel.
Track AI Platform Referral Traffic with Regex
To accurately measure traffic coming from AI platforms (such as ChatGPT, Perplexity, or Gemini), you can use a regex-based filter inside GA4.
While setting up custom reports and custom channel groups is helpful, using a regex filter allows you to isolate AI referral traffic immediately for analysis.
Step 1: Navigate to Traffic Acquisition Report
In GA4:
Reports → Acquisition → Traffic acquisition
Step 2: Add a Regex Filter
Click “Add filter +” and configure it as follows:
Dimension: Session source/medium
Match type: Matches regex
Value: copy the below regex value:
.*(chatgpt\.com|openai\.com|perplexity\.ai|claude\.ai|gemini\.google\.com|bard\.google\.com|you\.com|search\.brave\.com|copilot\.microsoft\.com).*
Then click “Apply.”
This regex pattern captures referral traffic from major AI and LLM-driven platforms. You can modify the expression anytime to include additional sources.
Step 3: Identify Landing Pages from AI Traffic
To see which pages are receiving traffic from AI platforms:
Click the “+” button next to the primary dimension and select:
Landing page + query string
This allows you to analyze which URLs AI platforms are directing users to, helping you understand:
Which content performs well in LLM visibility
Which pages are cited or recommended
How AI-driven traffic behaves compared to other channels
You may also create a Custom Channel Group (for example, naming it “AI Referral Traffic”) to systematically group traffic from AI platforms like ChatGPT, Gemini, Perplexity, and Copilot.
Use GA4 to Identify 404 Errors
You can use GA4 reports to diagnose technical SEO issues — including identifying 404 error pages.
A 404 error occurs when a visitor tries to access a page that no longer exists or cannot be found. This creates a poor user experience and can negatively impact SEO performance.
These errors commonly happen when:
A page has been deleted
A URL has changed without a redirect
A link was incorrectly created
Here’s how to identify 404 pages in GA4:
Step 1: Open the Pages and Screens Report
Go to:
Reports → Life cycle → Engagement → Pages and screens
Step 2: Change the Primary Dimension
Click the dropdown arrow next to “Page path and screen class.”
Select “Page title and screen class.”
Step 3: Search for 404 Page Titles
In the search bar above the table, type:
not found
Then press Enter.
If your 404 page title contains “Not Found” (or similar wording), this will isolate those error pages.
Step 4: Reveal the Broken URLs
To see the actual URLs causing the issue:
Click the blue “+” icon to add a secondary dimension.
Select “Page path and screen class.”
You’ll now see the specific URLs generating 404 errors, allowing you to:
Fix broken internal links
Add proper 301 redirects
Restore missing pages if necessary
Note:
This method works only if your 404 page loads with a 200 status and has a unique page title like “Page Not Found.”
AI-Powered Search Bar in GA4
GA4 includes an AI-powered search bar (Analytics Intelligence) that helps you quickly find reports, insights, settings, and answers to specific questions.
You can type queries like:
“How many users this month vs last year?”
“Top users by city”
“How to create a landing page report”
The search bar can surface reports, generate quick comparisons, guide you to property settings, or show relevant help content.
As you become more familiar with GA4, this feature can significantly speed up navigation and analysis.
Library
GA4 Library is the area where you manage and organize your reports.
It allows you to:
-
Create and edit report collections
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Add or remove reports from the navigation menu
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Customize the structure of your reporting interface
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Publish changes so they appear in the left-hand sidebar
The Library features control how your Reports section is structured and displayed across the property.
What is attribution model?
An attribution model determines how credit for a conversion is distributed across different touchpoints in a user’s journey.
Types of attribution models:
GA4 supports several common attribution models.
Data-driven attribution (DDA) — the most widely used — uses machine learning to assign credit based on actual contribution to conversion.
Last-click attribution gives full credit to the final interaction before conversion.
While first-click attribution assigns all credit to the first touchpoint.
Linear attribution distributes credit evenly across all interactions.
Time-decay attribution gives more credit to touchpoints closer to the conversion,
and position-based attribution (also known as U-shaped) gives higher weight to the first and last interactions, with the remaining credit split among the middle steps.
Predictive audience:
Predictive audiences in GA4 use machine learning to identify users who are likely to take a specific action in the future. GA4 can predict audiences such as users likely to purchase in the next 7 days, users likely to churn, and users likely to generate high revenue.
To be eligible, your property must meet minimum data thresholds (e.g., sufficient purchase and churn events over a recent time period) so GA4 can train its predictive models accurately. Not all properties qualify automatically.
In practice, predictive audiences are commonly used for remarketing and budget optimization — for example, targeting users likely to purchase with higher bids, or re-engaging users predicted to churn before they drop off completely.