Getting to Know GA4: Google Analytic’s Next Generation
In March 2022, Google officially announced the denouement of Universal Analytics (UA). From July 1st 2023, UA standard properties will stop processing web hits, but don’t worry, you’ll be able to access your historic data until January 1st 2024. For Google Analytics 360 users the timeline is a bit longer – data will no longer be processed from October 2023, with data available in a view-only mode for 6 further months. As the UA sun sets, Google has recommended migrating to its updated version, Google Analytics 4 (GA4), as soon as possible. Though the platform was introduced in 2020, GA4 remains a mystery to many. What makes GA4 different from UA? Why should you bother migrating? What can you do about your historic data? And what steps do you need to take to move on to GA4?
In this article, our Senior Data Analyst Vanessa Fitarelli breaks down the new programme to help you stay ahead of the shift.
App and Web Data, All in One Place
While UA catered to a desktop-based web experience, arguably the main feature of GA4 is the possibility of looking at website and app data in a single property. Previously, app activity could be exclusively measured through Firebase – a tool designed for app developers that included some analytics features – but in October 2020, Google fused elements of Google Analytics’ interface with Firebase’s backend and rebranded it as Google Analytics App+Web, now known as GA4. Looking at both forms of data side by side is now possible in GA4 as the data schema for both is the same. It’s events-based, like it was in Firebase, as opposed to session-based as it was in Universal Analytics. Data collected and processed in GA4 is different from UA properties, which means we can’t move data from one type of property to the other. Therefore, all GA4 implementations are a fresh start from a data standpoint, introducing shiny new metrics and new ways to calculate some of the old ones you’re probably already used to.
Reflecting the Fluctuating User
This particular integration between app and web data is a reflection of changing user behaviour. User journeys have become more complex in the past couple of years, with an increasing number of touchpoints and nuances within the experience. Being able to understand how audiences move between different digital platforms has become a necessity for forward-thinking businesses. And the best way to do that is via a tool that brings together these touchpoints through seamless integration. Enter GA4.
Machine Learning, Privacy and Integrations
Some other noteworthy GA4 features:
- GA4 introduces machine learning to deliver predictive insights about your data, which you can also use to create predictive audiences for Google Ads. Machine learning is also used in data-driven attribution to understand how different touchpoints impact user journeys
- According to Google themselves, ‘Google Analytics 4 is designed with privacy at its core’, offering more comprehensive controls for data collection and usage
- All properties can now set up a daily export of data to BigQuery free of charge, which was a 360-exclusive feature in Universal Analytics. This allows companies to join GA data with other datasets for a more comprehensive understanding of user journeys
- Search Ads 360 and Display & Video 360 integrations are now available for standard properties, allowing for easier activation of insights between tools
Privacy has been a big concern (and a bit of a headache) for Google since GDPR (General Data Protection Regulation) came into effect in 2018. Questions around data processing, storage and transfer outside of the EU have been in the spotlight since then, with Google being one of the first companies to become the target of legal complaints for non-compliance, alongside Facebook and Instagram. Apart from scrutiny over how big G uses the data collected, GDPR also posed another challenge for Analytics. Its security laws prevent companies from collecting personal identifiable information without explicit consent, and because browser cookies are considered ‘online identifiers’, consent for them is required. So there is a purpose to those annoying pop-ups after all. In the case of cookies, GA4 also represents a transition between observed and modelled data. When UA (and its previous versions) were rolled out, privacy concerns were still under discussion, without clear rules and guidelines on consent. The core workings of UA rely on ‘setting a cookie’ – a unique string that identifies that particular browser – allowing GA to track activity back to that particular ID (or what it calls a ‘user’). Put simply, in UA, if you don’t consent to have that cookie set up, the business won’t get any data from your activity. While this is the appropriate action to protect users’ privacy, this also creates ‘gaps’ in the volume of data collected, with some businesses seeing drops of over 50% of traffic after implementing consent management platforms. This is also one of the reasons why machine learning and data modelling are such important parts of GA4 – they act as ways to try and fill in the data gaps left by users that don’t consent to analytics cookies.
What About Historic UA Data?
In the announcement, Google also mentioned you’ll be able to access historic data in standard UA properties for at least 6 months after hits stop being processed – up until January 1st 2024. But the widespread worry is unwarranted – there are ways to keep hold of your historic aggregated data. Multiple, actually. You can do so through a CSV download, Google’s API export or using an ETL tool, and the best way depends on your specific reporting needs. But for all of these solutions, you still need a defined reporting structure in place as it’s not possible to export all dimensions and metrics. Still, this shift is a great opportunity to review your existing reports and assess what type of historic data is actually necessary and useful. Then take your learnings and start fresh with a new set, fully optimised and backed by your UA export.
Migration to GA4, ASAP!
As daunting as it may seem, making the move to GA4 as soon as possible is essential. The sooner you migrate, the sooner you’ll have available data using this new collection model to report on, which is especially important for year-on-year analysis. Implementing GA4 now, while we still have a little less than a year til UA’s shutdown, also allows you and your team to get familiar with the new interface and data without the pressure of not having alternative data sources. As the recommendation is to run UA alongside GA4 until its deprecation, it’s a good moment to review what you are tracking in UA and assess if you’re actually using all those indicators, if anything is missing, and if there are new tracking opportunities since your last implementation. You may even think of this process as a new implementation rather than a migration. Truth is – the process varies for each company based on their needs as GA4 is a highly customizable tool, but our overall approach is as follows:
- Review your business goals and objectives to assess which indicators can be tracked through Google Analytics
- Review the existing UA setup and plan what needs to be migrated, what is no longer necessary, and define custom event taxonomy
- Implement the tracking and data quality assurance in each step
- Rebuild dashboards/reports that were previously feeding from your UA property as a way to start getting used to GA4 data
Navigating the Migration
Investing in implementing Google Analytics as soon as possible gives you the advantage of running more efficient analysis through historic data, and also gives teams more time to get used to this new processing model while UA continues collecting data in the background. It also enables teams to understand the differences and similarities between the data collected in UA and GA4, and make full use of Google’s machine learning intelligence. Predictive audiences can help to increase your ROI/ROAs, the export of data to BigQuery can make it easier to blend your marketing data with other data sources and looking at your web and app data side by side can bring to light insights around user behaviour you had no visibility of before.
The amount of time and investment for a migration varies according to your company’s particular requirements. If you want strategic support in taking this step, contact us and we can set up a call to go over your specific needs.
