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- Data Analytics vs. Data Analysis
- Quantitative vs. Qualitative Data
- Data Analytics vs. Business Analytics
- Data Analytics vs. Data Science
- The Difference Between Data Analytics and Statistics
- The Difference Between Data Analytics and Data Visualization
- Data Analytics Lifecycle
- Data Analytics vs Business Intelligence
- What is Behavioral Analytics?
- What is Descriptive Analytics?
- What is Data Analytics?
- What is Diagnostic Analytics?
- Data Analytics Processes
- A top-level guide to data lakes
- Redshift vs Snowflake vs BigQuery: Choosing a Warehouse
- Data Warehouse Architecture
- What Is a Data Warehouse?
- How to Create and Use Business Intelligence with a Data Warehouse
- Best Practices for Accessing Your Data Warehouse
- Data Warehouse Best Practices — preparing your data for peak performance
- How do Data Warehouses Enhance Data Mining?
- Data Warehouses versus Databases: What’s the Difference?
- What are the Benefits of a Data Warehouse?
- Key Concepts of a Data Warehouse
- Data Warehouses versus Data Lakes
- Data Warehouses versus Data Marts
- Difference Between Big Data and Data Warehouses
- How to Move Data in Data Warehouses
- What Is Customer Data?
- Customer Data Analytics
- Customer Data Management
- Collecting Customer Data
- The Importance of First-Party Customer Data After iOS Updates
- Types of Customer Data
- What Is a Customer Data Platform?
- What is an Identity Graph?
- Customer Data Protection
- A complete guide to first-party customer data
- CDPs vs. DMPs
- What is Identity Resolution?
- What is Consent Management?
- Data Access Control
- Data Sharing and Third Parties
- Cybersecurity Frameworks
- What is PII Masking and How Can You Use It?
- Data Security Strategies
- Data Security Technologies
- Data Protection Security Controls
- How to Manage Data Retention
- How To Handle Your Company’s Sensitive Data
- Data Security Best Practices For Companies
- What is Persistent Data?
- Google Analytics 4 and eCommerce Tracking
- What Is Google Analytics 4 and Why Should You Migrate?
- GA4 vs. Universal Analytics
- What are the New Features of Google Analytics 4 (GA4)?
- GA4 Migration Guide
- Benefits and Limitations of Google Analytics 4 (GA4)
- Understanding Google Analytics 4 Organization Hierarchy
- Understanding Data Streams in Google Analytics 4
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GA4 vs. Universal Analytics
Google Analytics 4 (GA4) is the next generation of Google Analytics (GA). It’s the biggest change to GA since Universal Analytics (UA), which was announced in 2012. GA4 is substantially different from UA, so it’s better to think of it as a redesigned tool, rather than an upgrade from UA. Given that UA will stop collecting data in July 2023, it is important for you, as a digital marketer, to understand the differences between the two versions. This article will go over some of the key differences between UA and GA4 in order to make your transition to the new ecosystem smoother.
Google Analytics 4 vs. Universal Analytics: key differences
There are three key themes when it comes to understanding the differences between UA and GA4. GA4 is more compliance-centric, customizable, and specialized.
GA4 is built to satisfy much stronger privacy concerns than UA. Privacy regulations like GDPR and CCPA have recently changed the analytics industry. Because of these regulations — and the changes made by corporations, internet browsers, and mobile applications in response — analytics can no longer rely on third-party cookies. Since these third-party cookies were the main method UA used to track user interactions with sites, GA4 had to pivot away from relying so heavily on them. Google didn’t necessarily want to make this shift away from third-party cookies, given that they are the most direct measurement tools; but they had to do it for compliance reasons in a changing world.
Besides privacy-related compliance, Google encourages users to customize GA4 to fit their exact use case. UA had many different “hit types” and reporting templates. GA4, on the other hand, with its event-based model, has far fewer “'event types” and reports out of the box, but a lot more potential for customization. Google wants you to use its infrastructure to build your own tools rather than guess exactly what you’ll want to measure.
Another key theme is specialization. Instead of trying to do as many things as UA did, GA4 splits key functionality out into other Google tools. The primary example of this is Google Tag Manager (GTM). This plays a larger role in GA4 than in UA because it’s the recommended way to organize your events. In UA, events were not the main measurement tool. Another example of features being split out into other tools is GA4’s integration with BigQuery, Google’s cloud data warehouse solution. Previously, this integration was only available in the paid version of UA, Google Analytics 360.
Session-based vs. event-based model
One of the biggest differences between UA and GA4 is the change from a session-based model in UA to an event-based model in GA4. In UA, user interactions on a website (hits) were tied to a session, which was tied to a user. In GA4, these interactions are called events and are tied directly to a user, without a session in between.
One major reason for removing sessions from the data model is the impact of modern privacy concerns on third-party cookies. Another reason has to do with clarity of reporting. In UA, the different dimensions and metrics that make up reports had different scopes: user-level, session-level, or hit-level. Metrics and dimensions of different scopes could not be combined smoothly for reporting purposes. In GA, all dimensions and metrics now have the same scope, so there are no issues when combining different metrics.
With regard to privacy concerns, UA relied heavily on third-party cookie-based tracking, which allowed you to closely monitor entire sessions from users in the platform without you having to do more processing. For example, you could determine how many pages a user viewed in a given session through the pages/session metric. This gave a pretty robust view of engagement. However, the decline of third-party cookies limited UA’s ability to gather data via cross-domain tracking. On top of leaving you with less accurate data, this made it harder for you to remarket to users across different sites through UA’s Google Ads integration.
In UA, some of the more basic hit types (such as page view, screen view, transaction, etc.) were measured automatically, but this wasn’t possible for hits that didn't require a page reload (such as playing videos, external link clicks, etc.) This led to the creation of events — a new hit type in UA for these more dynamic hits.
In GA4, Google has done away with hits and generalized everything to events. Instead of using UA’s Category, Action, Label, and Value parameters, it’s now possible to create up to 25 event parameters of your own, making events much more customizable. For example, for a button click event like you might have recorded in UA, you could now choose to send a wider variety of parameters like the button URL, button text, or button CSS class, instead of just the label. These parameters can then be used to determine which button actually fired for the click event and take action accordingly.
GA4 has generalized all hits to events, but this does not mean that all events have to be implemented by users in a custom fashion, like in UA. Events in GA4 have different event types (just as hits in UA had different hit types), allowing GA4 to treat each event type differently. One type of event in GA4 is the custom event — these are events that you need to manage through the use of tags, similar to how all events in UA were managed. However, there are also automatically collected events — as the name suggests, these are collected automatically from your site and don't require any custom setup.
Google Tag Manager
Now that GA4 treats everything as events, GTM has become a more important part of the GA4 ecosystem. GA4 can technically be used without GTM, by using automatically collected events,, but other event types, such as custom events, rely on GTM to create in-depth tags that can be applied based on specific site interactions. When using GTM, you can customize your events to bring value to your specific workflow. We highly recommend implementing custom events in GA4, as the expanded Explorations section (an extension of Custom Reports in UA) can provide a wealth of information for different specific use cases.
In addition to the theme of customization, this move also shows the theme of specialization, as Google is now encouraging you to rely more on GTM for collecting data, while still relying on GA4 for consolidating, analyzing, and reporting the data.
UA’s account hierarchy had three separate elements: account, property, and view. GA4’s organization hierarchy has account and property elements, but no views. Views in UA served as filters for data before reports, but this functionality is not available in GA4.
Another notable change lies in how the different versions of GA distinguish between websites and mobile apps. In UA, you had to set up one property for a website and another for a mobile application, even if they were both part of the same web product. When using GA4, you can finally forget about this false distinction by setting up one property with multiple data streams – for example, one for web, one for Android, and one for iOS. Google wants us to think of web and mobile as two inputs to a single property, rather than two separate properties.
UA supported five types of goals: destination, duration, pages per session, smart goals, and event goals. Just as hit types in UA were generalized to events in GA4, these goal types have also been converted into a single, generalized conversion event. Now you can mark any event as a conversion, which can offer much more flexibility.
Another interesting nuance relates to how conversion metrics are now counted. In UA, only one goal for each type was counted per session. So, submitting the same form twice would count as one goal. In GA4, every conversion event is now counted separately. This is another consequence of the transition from a session-based model to an event-based model, in which all events are considered standalone, rather than part of a larger session.
Like events, reports have also been generalized to encourage more customization from users. Previously, UA had many different standard reports that could be selected to view data. There was also a Custom Reports section, which was less heavily used. Now, GA4 provides only a few standard reports out of the box — although more will likely be added over time — and encourages users to create highly-customized reports, called explorations. Although there is no view functionality in GA4, you can filter data like segments, dimensions, and metrics by creating custom explorations. More specifics for how to set up explorations in GA4 can be found in this GA4 Explorations documentation.
Big data and machine learning
GA4 places a stronger emphasis on machine learning models than UA did. To prepare for a future with less access to third-party cookies and tracking data, GA4 attempts to fill in any gaps with machine learning. Analytics insights help to spot trends like spikes or sudden decreases in usage, while predictive metrics help predict future behavior based on past event data. There’s also a trend toward using big data techniques for indirect identity resolution rather than the direct identity resolution that cookies provide. GA4 allows users to explore these big data techniques by exporting their GA4 data to BigQuery, Google’s cloud-based data warehouse. Previously, this functionality was only available with Google Analytics 360, the premium version of UA, but it’s now available with the free version of GA4.
Differences in metrics
Throughout this article, we’ve discussed some of the major changes between UA and GA4 in depth. Now, we’ll look at some of the smaller changes in metrics that users of UA might notice during their transition to GA4. Google’s UA to GA4 metric comparison documentation gives the most in-depth overview of the changes in metrics, but we’ll discuss some notable changes below.
One difference is the Views reporting metric in GA4, which counts the total number of app screens or web pages that your users see (unlike UA, which had separate metrics for pageviews and screenviews). This is another example of Google softening the distinction between web- and mobile-based data sources and encouraging users to think of their business as one property across various devices. Another change is that the term “users” in reports will now refer to the new “active users” metric in GA4, instead of the “total users” metric that it referred to in UA. Because it’s harder to measure sessions, the “active users” metric will count the number of distinct users for whom certain engagement events are logged. This clearly shows the shift from a session-based model to an event-based model.
GA4 is not merely an upgrade to UA; it also has a completely different philosophy behind it. It’s built with some new key focuses, including:
- Compliance: Google is building a future-proof tool for a world with less reliable third-party cookies and tracking data.
- Customization: GA4 provides a very general event model and report structure so that you can create highly specific customizations, whatever your specific use case.
- Specialization: this latest version of Google Analytics has outsourced functionality to other tools like GTM and BigQuery.
Now that you understand some of the specific differences between GA4 and Universal Analytics, it’s important to begin the migration process as soon as possible, as Google will soon be turning off data collection in your UA properties.
In order to gain a more in-depth understanding of GA4, consult the following articles from our learning center.
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