GA4 was designed to address changes in digital advertising since 2005, ameliorate marketer frustrations with existing analytics, and deal with immediate privacy concerns.
GA4 was designed to address several factors impacting the digital analytics field currently. First, the digital landscape has greatly changed since the first Google Analytics launch in 2005, but their platform had not. Second, marketers in general are frustrated with the difficulty in getting clear results from existing digital analytics tools. Google has invested greatly in the digital advertising and analytics business. Failure to improve their platform would embolden the many competitors vying for market share. Finally, the data privacy movement has changed the way identifying data can be used. Google needed to provide an analytics approach less dependent on traditional identifiers, such as browser-based cookies.
Google Analytics has gone through various changes through versions 1 through 3. Google launched their first analytics platform by acquiring Urchin, transitioned to Universal Analytics in 2011, added mobile app analytics with the acquisition of Firebase in 2014, then rolled out the beta of “App + Web” property in June of 2019. This last beta would become GA4 in October 2020. The current online reality of consumers is moving from social media, to e-commerce reviews, to finally making a transaction on another site. Instead of a linear customer journey, the consumer path may be a spiral, as they cycle through different types of sites investigating, comparing, listing to friends and influencers, and using a variety of devices as they visit. The new GA4 property helps track and create a more accurate picture of these diverse customer journeys.
The new event-based measurement model finally unifies the data model, so rather than collecting sessions + page views for web data versus event + parameters for app data, all data is event-based. As marketers build their event catalog with value-rich parameters, they are able to use these building blocks consistently across platforms, creating a customer-centric dataset.
Finally, cognizant of the privacy challenges in using identifiers to tie users to their data, Google created a set of approaches to help marketers continue their analysis of the customer journey within a privacy-focused environment. GA4 uses 3 identifier types, and depending on which is available, uses machine learning to tie different data paths together with a user. GA4 also separates the opt-in for using data for measurement versus targeting. And finally, GA4 made improvements to data security by no longer exposing IP addresses, ensuring that private identifier data is not retained beyond 14 months, and that a mechanism exists to remove data upon user request.
Learn more about Google Analytics 4 here.