To understand the change, it’s easiest to start with a refresher on how identity management currently works in Universal Analytics. Managing user identity is another foundational aspect of analytics that has been dramatically updated in GA4. In this way, GA4 brings tools that help marketers make the best decisions in the face of privacy challenges. 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.Ĭonsent 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. 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. In a nutshell, Consent Mode allows you to dynamically adjust data capture based on the consent status of the people visiting your website. 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.įirst is Consent Mode. This means you can reimagine how your data capture works, the level of detail at which you want to track, and more.ĭue 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.
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. 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. No longer are you trapped within the Universal Analytics category/action/label framework. Furthermore, events now allow you to pass a wide variety of parameters that describe the event. 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. In GA4, the data model is event-based and is “flat,” rather than hierarchical. You’re able to pass a category name, an action name, and a label name – and that’s it. Users have sessions, which are composed of page views or “events” (button clicks, video plays, etc.). In Universal Analytics, you have a strictly hierarchical data model. 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.
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.Ī 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.
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. You can learn more in our introductory blog post, “ What You Need to Know About Google Analytics 4.”įirst, let’s talk about the underlying data model.
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.
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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.Īs 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.
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The product has gone through a series of iterations over the years, with the current Universal Analytics version being released in 2012. Google purchased Urchin in 2005, released “Urchin from Google” shortly thereafter, and then rebranded the product to “Google Analytics” by 2006. Google Analytics has a long history, particularly on an internet/technology timescale.
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