Ready or Not, Here It Comes: How to Prepare to Embrace GA4

UA was popular among many businesses not only because it was free, but also because it was extremely powerful and easy to learn. UA was a fan favorite, with a user-friendly interface and robust documentation. It was accessible to businesses of all sizes and technical abilities and provided valuable insights into website and app performance. According to a survey by W3Techs in 2021, Google Analytics had a market share of over 84% among websites that use analytics tools, with Universal Analytics being the most widely used version.

As GA4 gains momentum as the latest version of Google’s analytics platform, it’s essential for businesses to understand the benefits, differences from earlier versions, and how to prepare for a smooth transition. The good news is, GA4 is more advanced and intelligent than its predecessor and provides improved data tracking and analysis, better integration with other Google products, and enhanced privacy features.

However, moving to a new analytics platform can be overwhelming. Last year, we shared steps to help you migrate to GA4, starting with the property set-up and data collection. Following these steps is where the actual work in preparing for the transition begins. The first and most crucial part of this transition is to understand the key differences between the two platforms. This includes changes in the data model, user ID, user interface, historical data, and third-party integrations. By understanding these differences, you can plan and execute the migration more effectively.

• The Data Model is one of the biggest differences between the two products. GA4’s data model is organized around events, while UA’s revolves around sessions. Session-based data relies on cookies to determine sessions and repeat visits. Session data groups user interactions within a given time frame whereas event-based tracking uses event signals of interactions done by users (including pageviews, sessions, clicks) to build a dataset. Understanding this new form of data model will lead the way for businesses to understand and evaluate their current tracking and analytics practices to ensure all relevant data continues to get captured and gaps are identified. This change is also pushing the threshold of technical abilities of teams. Older models categorized data into four different scopes, which characterized each dimension or metric. Each of those dimensions or metrics could only fall under one scope. This structure made it easy for users with any level of technical knowledge to ingest, define, and report. With the concept of scope removed from GA4, users will need an in-depth understanding of dimensions and metrics to define and report. GA4 also provides users access to raw data through BigQuery, which also requires knowledge to build and maintain queries to pull and analyze data.
• This leads us to the second big challenge in the transition toward GA4—its user interface (UI). Vastly different from that of UA, it requires those used to UA’s older interface to adjust and learn new workflows and processes. The UI of GA4 is based on the user’s workflow, featuring a modern navigation bar on the left side of the screen that provides access to different sections of the platform. The reporting in GA4 is based on a flexible analysis workspace that allows users to customize reports and dashboards based on their specific needs. GA4 also uses different terms for some of the features and functions compared to UA that longtime users may also find challenging (e.g Pageviews are just Views in GA4 and the availability of Channels is across multiple dimensions such as default, session, and first users). Certain dimensions and metrics such as Previous Page Path, Entrance, Bounce Rate, Pages/Sessions are also not natively available in the new GA4—these either need to be calculated via BigQuery or are available under the Explore section. While GA4 offers more customization options compared to UA—including the ability to create custom dimensions, metrics, reports and dashboards—it certainly presents a huge learning curve for users. That being said, the Analysis Hub in GA4 provides a powerful tool for data exploration and analysis. It can be used to answer specific questions about data by using machine learning and advanced data modeling techniques.
• Unlike UA, which supports multiple properties and views within each property, GA4 provides unified data for your website and/or apps. With only one property in GA4, tracking and analyzing data becomes simpler and more streamlined. This means less time spent managing multiple views and properties and more time spent on analyzing and optimizing data. It also makes it easier to integrate data from different sources. This can be particularly useful for companies that use multiple tools for data collection and analysis. One of the big challenges with this unified view of data is the limited customization options to emulate the concept of views within the properties. In GA4, creating a “view” is not the same as it was in UA. Instead of views, GA4 uses “data streams” to collect and analyze data. The option to “Configure” the data stream allows users to add “Filters,” which can be predefined or custom.
  • Follow the instructions below to set up a filter based on your specific needs. For example, you can filter out traffic from your internal IP address or exclude traffic from certain countries.
    • One important thing to note here is that there are two states that a filter can be in: testing and active. When a filter is in the testing state, it is not applied to your data. Instead, the filter is shown in a preview mode, which allows you to see how your data would be affected if the filter were active. This preview mode is useful for testing new filters or making changes to existing filters without affecting your data. When a filter is in the active state, it is applied to your data and affects the reporting in your GA4 property. This means any data that matches the filter criteria will be excluded or included in your reports, depending on the type of filter.
    • To achieve the equivalent of ‘views’, we recommend keeping filters in the testing state. This will allow users to create and compare data segments without impacting how data is collected.

• Finally, when it comes to third-party integrations, GA4 allows businesses to export their raw event data to BigQuery, Google’s cloud-based data warehouse. From there, businesses can use tools like Google Data Studio or other third-party applications to analyze and visualize their data. This approach provides businesses with more control over their data, as well as the ability to integrate with a wider range of third-party tools and services.
  • Access to BigQuery is one of the biggest advantages and upgrades with GA4. This was a feature previously only available to Analytics 360 users. With BigQuery export available via GA4, businesses can analyze their raw event data in more detail, and create custom reports and visualizations that are tailored to their specific needs. BigQuery is designed to handle large volumes of data, allowing businesses to scale their analysis capabilities and efforts. It is also a cost-effective solution for data warehousing and analysis, as businesses only pay for the data they store and the queries they run. This makes it an affordable option for businesses of all sizes. It also allows teams to expand their strengths with access to machine learning capabilities, such as BigQuery ML, which uses data to build predictive models and gain insights into future performance.

GA4 is a powerful analytics tool that offers many new and powerful features—making it a huge transition for so many businesses. With features such as default anonymization of IP addresses and data sharing between other tools in the Google ecosystem, GA4 is also more compliant with privacy regulations than its predecessor. This migration can serve as an opportunity to reevaluate and advance existing measurement strategies and gain deeper insights—with a more granular view of user interactions—to make more data-driven decisions.

We hope this guide has provided you with a better understanding of GA4. This rapidly approaching transition will prove to be a huge learning curve for many businesses and users. Follow along for more tips on this upcoming change and how you can get a jump start on understanding this massive shift in data analysis.

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