The Beginner's Guide To Google Analytics 4
Meet the new Google Analytics (GA4)
Introduction
There’s never been a more exciting time to be a digital marketer. Across industries and business models, customer journeys are shifting into digital channels, making marketing more measurable and personal than ever. At the same time, there’s never been a more challenging time to be a digital marketer. Digital customer journeys are taking place across an ever-increasing number of screens and browsers, making customer unification and attribution more difficult. Meanwhile, changes in the privacy landscape raise new questions about what data can be collected — and how reliable that data might be. As a result, 2022 is a time of both unprecedented opportunities and unprecedented obstacles. In this new world, marketers need a modern analytics platform specifically designed to address these challenges.
Google Analytics 4 (also referred to as “GA4”) is the latest generation of Google’s well-known analytics platform. It’s been specifically designed to address the challenges facing digital marketers today.
All advertisers will need to transition to GA4 in July or October 2023, depending on their current setup. In this guide, we’ll lay out how Google Analytics 4 differs from the previous version, how it can help marketers deal with tricky issues like attribution and privacy, and how it can give you an orientation within the reporting UI. First, though, it’s worth briefly recapping the backstory of Google Analytics.
Table of Contents
Why is GA4 happening now?
Google Analytics has a long history, particularly on an internet/technology timescale. Google purchased Urchin in 2005, released “Urchin from Google” shortly thereafter, and then rebranded the product to “Google Analytics” by 2006. The product has gone through a series of iterations over the years, with the current Universal Analytics version being released in 2012. While Universal Analytics has launched a wide variety of new features and product integrations over the years, the fact remains that Google’s fundamental analytics platform is now a full decade old.
As a result, it’s not natively suited to address challenges that have come up in recent years, like the decline of cookies, the adoption of tracking blockers, and the increasing scrutiny on data privacy. As such, Google decided to build an all-new platform for Google Analytics – and that’s what we know now as Google Analytics 4 or GA4. You can learn more in our introductory blog post, “What You Need to Know About Google Analytics 4.”
As a result, it’s not natively suited to address challenges that have come up in recent years, like the decline of cookies, the adoption of tracking blockers, the increasing scrutiny on data privacy, etc. As such, Google decided to build an all-new platform for Google Analytics – and that’s what we know now as Google Analytics 4 or GA4. You can learn more in our introductory blog post, “What You Need to Know About Google Analytics 4.”
How is GA4 different from Universal Analytics?
As you might expect with a from-the-ground-up rebuild of the Google Analytics platform, there are a lot of differences between GA4 and Universal Analytics. In this guide, we’ll focus on the big-ticket items – the ones that represent major departures from Universal Analytics, as opposed to relatively minor feature updates.
The GA Data Model
First, let’s talk about the underlying data model. This might sound like an abstract, academic topic, but developing a strong understanding of the data model will help you make your GA4 deployment as valuable as possible. As we noted above, Universal Analytics is a decade old, and Google Analytics as a whole is even older than that; in fact, it predates smartphones.
A consequence of this legacy is that GA’s data model was initially built for a world in which users browse web pages from desktop and laptop computers. To bring the GA data model into the modern world of smartphones, tablets, mobile apps, and many other ways to interact with brands, Google completely rebuilt it.
In Universal Analytics, you have a strictly hierarchical data model. Users have sessions, which are composed of page views or “events” (button clicks, video plays, etc.). Events themselves have a rigid model. You’re able to pass a category name, an action name, and a label name – and that’s it.
In GA4, the data model is event-based and is “flat,” rather than hierarchical. It has the concept of users, but users simply have events – and those events can be used to track any interaction that occurs on your digital properties, whether web or app. Furthermore, events now allow you to pass a wide variety of parameters that describe the event. No longer are you trapped within the Universal Analytics category/action/label framework.
What does this mean for you? If your organization runs both websites and mobile apps, you’ll now have a data model for analytics that intentionally makes data capture consistent across your ecosystem. Even if you’re only using GA4 for a website or mobile app, you’ll have an event-based model which is vastly more flexible than the previous version. This means you can reimagine how your data capture works, the level of detail at which you want to track, and more.
Consent Mode and Data Modeling in GA
Due to changes in the data privacy landscape, such as the passage of the well-known GDPR legislation, data analytics is now contingent upon the consent of the user. This creates an obvious issue: what happens to the quality and reliability of data when a fraction of users decline to consent to tracking? Google is attempting to future-proof GA4 in terms of privacy, and there are two major developments that Google has added.
First is Consent Mode. In a nutshell, Consent Mode allows you to dynamically adjust data capture based on the consent status of the people visiting your website. As a result, while a certain degree of data loss is inevitable when users decline consent for tracking, marketers can feel confident that they’re collecting as much data as possible while respecting users’ choices.
Second is Data Modeling. When marketers lose the ability to directly observe behavior on their sites or apps, Data Modeling is a technique that can help to fill the gaps by applying machine learning to the data that is being directly observed.
Consent Mode helps ensure that your data capture respects users’ wishes up front, and Data Modeling fills in the gaps in observed data so that marketers can be confident in making decisions based on their data. In this way, GA4 brings tools that help marketers make the best decisions in the face of privacy challenges.
Identity Management
Managing user identity is another foundational aspect of analytics that has been dramatically updated in GA4. To understand the change, it’s easiest to start with a refresher on how identity management currently works in Universal Analytics.
In Universal Analytics, every single website visitor or app user is assigned a Client ID, which is an anonymized, random string of digits that represents their identity. Google Analytics uses the Client ID to distinguish one user from another and to determine whether a given user is a new user or a repeat user. A long-standing challenge with the Client ID, however, is that it’s cookie-based, meaning it can only keep track of a given user if they don’t switch browsers or devices or clear their cookies. As a result, identity management via Client ID is very fragile, often resulting in inaccurate data on metrics such as unique visitors and total reach.
Universal Analytics also allowed organizations to deploy their own ID within Google Analytics. This was originally called the “User ID.” Organizations that had data on authenticated users could push their own ID to Google Analytics using the User ID function. This was helpful because it was much less fragile than the Client ID. Regardless of browser, device, or cookie clearance, if a user signed in, you could apply the User ID.
User ID is being preserved in GA4, but Google is also adding a third – and groundbreaking – layer to its identity management solution. For the first time, using a feature called Google Signals, organizations using GA4 will be able to tap into Google’s identity graph in a privacy-safe way. Consider: users who visit your website may be anonymous to your organization, but may not be anonymous to Google (e.g., a user browsing your website in one tab while signed into Chrome or Gmail in another tab). In a scenario like this, GA4 will use Google’s identity graph to manage the identity of your users. This has the potential to dramatically improve the quality of your data (e.g., by deduplicating your unique user counts), and in turn, the quality of the decisions you can make with that data.
There are numerous other changes to GA4, but the most important ones to understand are the ones outlined above, as they’re fundamental to how the analytics platform works. Identity management, data privacy, and the underlying data model itself have all been significantly revamped in GA4. Together, those improvements are bringing the Google Analytics product into the modern world.
Reporting and Analysis in GA4
Now that you understand some of the key aspects of how GA4 differs from Universal Analytics, let’s dive into the reporting interface itself.
The GA4 Admin Panel
GA4 has an extensive control panel and admin settings page. Beyond the usual account-type settings, GA4’s control panel enables the user to set up data streams and sources, create filters, define tagging settings, import data, and access other advanced features. It also has a “debug” mode that enables users to watch their data and filters in real time. The control panel offers extensive developer tools for advanced configurations.
Four Pillars of Reporting in Google Analytics 4
The left column of GA4 has icons that divide the interface into four sections: Reports, Explore, Advertising, and Configure. Each section starts with a snapshot page giving you an overview of that section. There is an abundance of reports in GA4 and many of them will look familiar to Universal Analytics users.
- Reports provide a view of the customer journey such as how users accessed your site, where they came from, what they are doing, and their retention.
- Explore is the advanced interface for custom reporting and sophisticated data visualizations.
- Advertising provides deeper insights into conversion performance and user journeys.
- Configure is where you manage your custom events and custom dimensions.
Reports
Lifecycle reports provide great insights into campaigns and user activity and include reports on acquisition, engagement, monetization, and retention.
Acquisition: Shown below is the acquisition summary screen which highlights the various acquisition reports. The acquisition report shows information such as how users are getting to your site and which channels they are using. It includes reports on existing and new users, users in the last 30 minutes, which medium users are using or have come from, session types, and lifetime value. Each report summary can be clicked on to take drill downs and deep dives.
Engagement: Engagement reports show what users are doing with your site or app. The summary screen shown below has reports on items such as their engagement time, number of engaged sessions per user, and time per session. It shows users over time, views, and which pages they are viewing. It also shows user “stickiness,” a measure of how often users return.
Monetization: The monetization tab shows how your site or app is creating revenue, either through purchases or advertising. This tab shows data like total revenue, number of purchasers, first-time purchasers, average revenue per user, top purchases by item, item views, coupons, and ad units.
Retention: The retention report provides an overview of how many customers are returning. It compares new users to returning users and retention rates. It measures how long they are engaged when they return, retention over time, and lifetime value.
User Reports
User reports show key information about the users who visit or use your site or app. Shown below is the summary screen which displays reports on key user demographics, such as users by country and city, gender, interests, age, and language. Google derives this data through a variety of means. Some metrics are gathered from hard data, such as a user’s language settings in their browser, and others are more abstractly inferred from best guesses (as informed by sophisticated AI), context, and past behavior. Each report can be sorted by different user types.
The user report also has a tech overview, which provides breakdowns of users by the platform they are using, their operating system (e.g., Windows, Mac, Android, etc.), device category (e.g., desktop, tablet, mobile), browser, screen resolution, app version, and other technical details.
Events Reports
Event reports show specific actions or outcomes from users. The two tabs in this report are conversions and events.
Conversions: The conversion event report shows actions the user took, such as entering the checkout process, making a purchase, or making a first visit after seeing an advertisement. It also shows the level of change or level of performance compared to the past. These can be customized to show your most important conversion events.
Events: Like conversions, the events show other actions a user takes, typically on a smaller or more tactical level, such as changing account information, clicks, errors, or adding an item to the shopping cart. These can also be defined and customized. Event data can be compared to other event sets or historical data to show positive or negative change over time.
“Explore” Reports
Explore reports reside within their own dedicated section, and come with templates that provide graphical, intuitive visualizations of your data. The report templates come pre-formatted for specific techniques (e.g., segment overlap, path analysis, user timeline), use cases (e.g., acquisition, conversion), and industry specialties (e.g., ecommerce, gaming). Explorations are highly customizable, and intended to help make GA4 data fit an organization’s specific reporting needs. The interface provides multiple filters and segments that can be assigned. For visualizations, the interface provides a drag-and-drop style interface allowing the user to intuitively drop data fields into the graphic. Explorations also enable users to go much deeper into their data, looking at different data cuts of the user’s choice.
Each visualization can be an exciting feast of information and a serious creative customization project. Take a deep dive into learning about these after you are familiar with the basics of GA4.
Getting Started with Google Analytics 4
If you’re ready to get started with GA4, the good news is that the setup process is similar to the process you used to implement Universal Analytics. The GA4 demo account can serve as a helpful learning environment along the way. The guide Implementing Google Analytics 4 on Your Website will take you through the process in detail. In a nutshell, to get GA4 deployed on your website, you’ll want to:
- Create a new “GA4” property within your existing Google Analytics account.
- Add a web “data stream” to that new property.
- Deploy the GA4 tag via your Google Tag Manager (GTM) account, if you’re using GTM…
- …or directly implement the gtag.js tag on all the pages of your website.
One important note: We recommend that organizations get started with Google Analytics 4 in parallel to a pre-existing Universal Analytics implementation. We call this approach “dual tagging.” In other words, rather than immediately replacing Universal Analytics with Google Analytics 4, you will implement Google Analytics 4 side by side. This will allow you to keep working with Universal Analytics as long as you need, while you build familiarity with GA4. In addition, GA4 is still under active development at Google, and many important features have not been released yet. Setting up GA4 in parallel allows you to be ready to take advantage of those new features as soon as they’re built.
GA4 Training Resources
The proliferation of multi-device journeys, the deprecation of third-party cookies, and the rise of stricter data privacy regulations have created new challenges for digital marketers in recent years. Google Analytics 4 provides marketers with an analytics platform that’s been intentionally designed to help surmount these challenges. While the finishing touches on Google Analytics 4 are still being developed, now is a great time to set up GA4 alongside Universal Analytics. You’ll get a head-start on building familiarity with the new platform, and you’ll be well-positioned to take advantage of new capabilities as soon as they’re released. To dive deeper into the world of Google Analytics 4, explore our ever-growing list of resources:
General Migration Information & Getting Started
- Google Announces Deprecation of Universal Analytics
- You Need to Transition to GA4 Now – Here’s How to Get Started
- 2021 GA4 Ask Me Anything: Your Top Questions
- How to Successfully Transition to GA4
- Reporting, Benefits, and Timing: Top 3 GA4 Questions, Answered
- GA4 Will Change the Internet: What CTOs Need to Know
- Implementing GA4 on Your Website
- What You Need to Know About GA4 (formerly Google App+Web)
Integrations
Functionality & Features
- Roll-Up Properties and Sub-Properties in GA4
- GA4 User Access and Administration
- How to Adapt to No “View” Feature in GA4
- How to Report on Google Optimize Experiments in GA4
- GA4 Won’t Support IP Addresses. Now What?
- Dynamic Event Precedence in GA4 with BigQuery and Data Studio
- Your Guide to Navigating the GA4 Demo Account
- GA4 & Firebase: The Evolution of Mobile Map Measurement
- Comparing Universal Analytics to GA4: 15 Key Differences
- Video: Google Analytics 4 Fundamentals
- Video: The Four Reporting Pillars of GA4
