GA4 in Five

Google Analytics 4 Explained With Five Questions, In Five Minutes, Like You’re Five

Were you once living an analytics zealot, but your current metrics just aren’t scratching the itch anymore? Has the pending shutdown of Universal Analytics got your palms sweating, fingers reaching for the ‘automigrate’ button? GA4 is likely to be the solution to your analytics woes! But – before you join the Google Analytics ecosystem for the first time, or jump from UA to GA4 – take a moment to read this quick refresher that covers the What, Who, Why, When, and Where of Google Analytics 4.


Google Analytics 4 (GA4) is the next generation of Google’s web-based analytics software. With cutting-edge, buzzword-busting features like predictive AI and enhanced conversion modelling, GA4 is such a meaningful update that the vast majority of businesses are going to really benefit by migrating from the older Google Universal Analytics (UA) set-up – which is just as well since Google is completely retiring Universal Analytics on the 1st of July this year (2023).


Google Analytics became part of the ‘Google Marketing Platform‘ brand in 2005, when the mulitnational Google LLC acquired the flagship product of a californian statistics software shop –  ‘Urchin‘.

The Urchin moniker was used until version 8, as the original team collaborated with Google to bring in greater functionality and integrate the system with Google Ads. Rapid iteration continued once Urchin was rebranded Google Analytics, and this web service has been in constant revision by some of Google’s top engineers since – really exploding in popularity with the addition of live metrics way back in 2011.


The primary reason to choose GA4 is its incredible functionality, particularly in comparison to the previous-gen UA platform.

Alongside major boosts to data accuracy and resolution, GA4 permits completely new analytics processes – including predictive reach and performance metrics powered by AI that eschew the need for bulding complex data models at high cost. Google’s AI build a cross-channel view of each step in your customer lifecycle, providing deeper behaviour insights to enhance marketing decisions and supercharge ROI.

Similar AI tech powers an overhauled multi-touch attribution system. This allows a unified view of user behaviour inside apps and on the web – serving more accurate behaviour, conversion and return on ad spend metrics.

These features are really just the tip of the iceberg: explore Google’s own exhaustive documentation for a more in-depth look – but suffice to say the platform is decidedly next-gen.

Of course, this all comes with a cost attached. If you’re really tech-savvy and your organisation is fairly small then GA4 might well work out to be more expensive than setting up your own custom pipelines for customer journey and behaviour metrics. For larger businesses, or teams who want a proven platform with strong user support, GA4 actually works out to be a great value option – costing as little as $40 for the most basic implementations. This means that generally speaking GA4 is going to be a cost-effective investment, providing another reason to choose this platform over a custom solution. when it comes to deciding between the standard and 360 versions things are a bit more complicated and extend beyond the contstraints of the ‘In-Five’ format –  but this great piece from Optimize Smart gives you all the info you need to make the right choice.

Obviously, the last major reason to use GA4 is that Google is completely retiring Universal Analytics on the 1st of July this year (2023) – so if you’re already in Google’s ecosystem then you have little choice. This might be frustrating, but the expanded utility and refined data approach of GA4, in our opinion, make this worthwhile. If you need support or advice to make the switch, check out our previous article on migrating.


GA4 generates meaningful metrics for a complete customer or user journey in-app or on-web. Real-time analytics are generated and served from the moment somebody interacts with your GA4-connected platform, collecting event-based metrics for each user behaviour and feeding this into detailed visualisations – providing actionable historic and live data.

This data can be plugged into advanced predictive models, meaning GA4 is incredibly useful when attempting to predict impacts of webpage/app redesigns on users. GA4 is clearly a temporally-holistic tool – which is to say highly useful in informing decision making with past, present, and ‘future’ data.


Although Google use a lot of ‘customer’ centric language when describing the functionality of GA4, its use cases are not restricted to businesses or individuals who are selling services online. In fact, any organisation with a web or app presence can extract a huge amount of value from Google Analytics by developing a detailed understanding of the behaviours and journeys of their userbase. The sheer breadth of data types that can be collected, and the universailty of an events-based approach also means that applications of GA4 are totally uncoupled from any specific industry or nation – Google’s webservice really can be applied in the broadest possible contexts. As such understanding GA4 use cases is best done by looking at the different methodologies with which Google’s clients apply the tool. These fall into two main groups – which are broadly inductive and deductive in their approaches.

Google Analytics 4 can be applied in a deductive, active sense – such as collecting highly specific data to inform a redesign of website layout.  Here, specific data is collected to guide a targeted outcome. With new ‘Analytics Intelligence’ AI modelling tech built in to GA4 as standard, it should be expected that this kind of research-style defined-goal usecase for analytics will see even more use with this new version of the Google Analytics platform.

Many users instead apply their analytics systems inductively –  using a ‘passive’  or ‘monitoring’ methodology. The goal here is less specific, with businesses using extensive historic data  to identify common user painpoints or offramps in the customer journey from website access to a sale, and then optimising their platform to solve these. Both approaches are generally applied in tandem, with an organisation’s focus on either shifting with their current priorities.


To surmise, GA4 is a next-gen analytics platform from the Google team that enables its users to obtain deep CEO, Behaviour, & Performance data using detailed event-based metrics and out-of-the-box predictive AI. Google’s veteran analytics webservice is even more powerful and relevant in its newest iteration, and looks set to continue to embody the industry standard into the future. Remember, Google is completely retiring Universal Analytics on the 1st of July this year (2023) – so if your organisation wants to remain in the Google ecosystem, make sure you migrate to GA4 before this date!

If you’re seeking some advice or support for your UA to GA4 migration, check out our new article on the subject.

To learn more about the GA4 platform, follow them on Twitter or head to Google’s own Dev Guide.

You can also join the conversation by commenting below – or click here to discover more Distributed Analytics BI blog posts!

Leave a Reply

Your email address will not be published. Required fields are marked *