What’s New With Google Analytics 4

Google Analytics is used by millions of organisations, large and small, to better understand customer preferences and develop better experiences for them. With more commerce shifting online and businesses under increasing pressure to make every marketing dollar count, digital analytics insights have never been more important.

Google Analytics 4 was released in October 2020, and it is the most recent version of the company’s analytics package (GA4). The new property launched with a slew of new features, and Google has already delivered numerous upgrades in the first quarter of 2021, so now is a good opportunity to review everything that’s new in GA4. We’ll also discuss why now is the best time to upgrade to GA4.

Google recently released a new version of Analytics, presenting it as the new default version of the company’s well-known data collection and online traffic analysis programme. What is Google Analytics 4 and how does it work? What does the new version imply for marketers, and how does it differ from the old one?

Millions of businesses and websites utilise Google’s Analytics reporting service to track user activity across web domains, mobile apps, and offline APIs. Most businesses are familiar with this platform as a tool for tracking web traffic, monitoring vital marketing channels, and measuring key performance indicators (KPIs). Google Analytics 4 is a new version that is more user-friendly.

The new Google Analytics 4 has a number of major improvements that set it apart from the previous edition. One of the most notable distinctions is the new data modelling function, which employs artificial intelligence to fill in data gaps where traditional analytics may be hindered by cookie consent requirements, banned JavaScript, and a privacy focus. Furthermore, the new default Google Analytics user interface is much different. So, here’s a rundown of some of the most significant distinctions.

What is Google Analytics 4 and how does it work?

The new Google Analytics is described by Google as a next-generation approach to “privacy-first” tracking, x-channel measurement, an AI-based predictive analytics all in one place. The new Analytics can fill in data for website traffic and user behaviour without relying on “hits” from every page by using Google’s powerful machine learning algorithms.

Google’s latest monitoring platform, is 4, was released recently. It’s a brand-new technology that’s unrelated to Universal Analytics (often referred to as “Google Analytics”).

If you’re new to Google Analytics, this isn’t the first time we’ve introduced a new platform. Google Analytics 4 is the culmination of several revisions of Google Analytics over the years.

Google Analytics has been issued and re-released several times since Google purchased the tracking platform Urchin in 2005. Classic Analytics, Universal Analytics, Google Analytics for Firebase, and other tools were used in previous iterations. Most of these technologies, however, only tracked web pages or app attributes, not both.

However, Google took a significant step forward last year. They introduced a Google Analytics “App+Web” solution, which was essentially Google Analytics for Firebase plus web-tracking capabilities. This was essentially a beta launch for Google Analytics 4, which is based on the same App+Web tracking approach as before—but with a slew of new features.

The new Google Analytics 4 highlights

  • It’s designed with machine learning as the primary method of data collection, with “modelling” that can extrapolate from historical data and generate predictions about site traffic and user behaviour. The new AI-powered “Insightstool is designed to highlight relevant information for marketers automatically.
  • It aims to provide marketers with a “more comprehensive understanding of the client journey across devices.” And it appears to be more concerned with tracking the entire buyer trip, rather than simply particular metrics across devices, pages, and segments.
  • It’s built to be “future-proof,” meaning it can work without cookies or identifying information.
  • Instead of the views and segments used by older Universal Analytics properties, Google Analytics 4 uses “data streams.”
  • GA4 does not have a “view” level section. Unlike classic Universal Analytics, which has three levels (Account, Property, and View), GA4 has only two levels: Account and Property.
  • Google Analytics 4 claims to allow editing, tracking, and fine-tuning of events within the UI, whereas “event tracking” in classic Analytics needed changed Analytics code or the gtag.js script. This includes behaviours such as clicks, page scrolling, and more.

GA4 is a model that is based on events.

Previously, all web-based Google Analytics versions were solely focused on tracking pageviews. Some events were supported by Enhanced Ecommerce, however, it was mostly limited to shop functions.

Part of Google’s new product’s objective was to establish a single tracking system that operated across all platforms, including web and mobile. To do this, Google Analytics 4 has completely switched to event-based tracking. This implies that all data is delivered to GA4 as an event, rather than a pageview, which is significantly more flexible.

Machine Learning Provides Insights

Machine learning is a technological revolution that is transforming practically every aspect of the digital world. Machine learning is now available in Google Analytics 4 through two features: automated insights and predictive metrics.

The Insights button in the top right corner of your interface can be used to get automated insights. A search area and a waterfall menu of data insights pulled by Google Analytics appear in the resulting sidebar.

What factors does Google Analytics consider when deciding which insights to display to you?

The automated insights function gathers information from major and secondary variables that have experienced substantial trend shifts. To put it another way, anything unusually wonderful (or terrible) will be brought to your attention right away. You’ll never have to look for data oddities again with automated insights.