Google Analytics 4: Unification of Firebase and Universal Analytics
When trying to unify both Firebase and Universal Analytics, the most significant stumbling block was always pushing to be the truth that both tools utilised different data models. Without getting into too much technical way, Universal Analytics and it’s predecessors consistently utilised a hit-driven data model, whilst Firebase utilised an event-driven data model.
The standard hit-driven data model utilised in Google Analytics was created on the concept of “hits” – which are split into pageview hits an event hits. The model concentrated on the idea of a Session which was a pack of pageview hits. The Pageview metric has consistently been the essential metric in Google Analytics and one from which other vital metrics were estimated like bounce rate, time on site, pages per session, etc.
The event-driven data model that has been utilised in Google Analytics for Firebase for mobile apps is a distinct way of looking at user action across mobile apps which aren’t necessarily just collections of page views. The concept of the “pageview” as the fundamental building block of sessions and other metrics is substituted with an additionally flexible system of “events”.
This event-driven model is that it will be utilised in Google Analytics 4. Events in Google Analytics 4 are entirely different from Events in Universal Analytics. The pr restrictive Category, Action and Label fields will not exist, and instead, Events will have a title and up to 25 flexible custom parameters each.
What’s New in Google Analytics 4?
Enhanced Measurement (Codeless Event Tracking)
By default, when you executed the old Universal Analytics code on your site, the single event that was automatically tracked was Pageviews and other Pageview connected metrics. Now with Enhanced Measurement, you can follow lots of different interactions automatically that earlier may have required hours, if not days, to implement via Google Tag Manager. These events are:
- File Downloads
- Outbound Link Clicks
- Video Engagement (for YouTube videos)
- Site Search
This is a huge advantage and will somewhat alleviate a regular cohort of DIY Google Analytics installers who expect more additional from the tool than what it provides them without significant technical re-configuration.
New User Interface
Another issue that I’ve previously complained about Universal Analytics is the User Interface – over the years it’s had small changes into the current UI as a new tracking feature was added. Yet, it hasn’t had an overhaul from the ground up. The result is a UI that has loads and loads of data available to the user, but in a very messy way which is not instinctive.
Google Analytics 4 tries to essay this issue with its new UI, which re-organises all information according to the company life cycle, user data and events. There are even some drill-down options in every one of these sections but nowhere near as many as in the old Universal Analytics UI– instead, letting you customise the reports on the fly via the “Customise Report” tool.
New User Identification Methods
Universal Analytics is how “users” are identified. By default, to associate traffic with a particular user, Universal Analytics associates a unique identifier with each user and transmits this with each hit. This identifier is usually a single, first-party cookie named _ga that stores a Google Analytics client ID.
This will assist distinguish the person as a “new user”. When the identical user visits your site at an after time, they will be calculated as a “returning user”.
This method of determining users is naturally flawed. If a person removes their browser cookies, uses incognito mode in their browser, or if they use the other browser or another device for a next visit to the site, they will seem like a ‘new’ respective user each time.
If you have a website where users can log in, you could even utilize the User ID feature which lets you assign a constant ID for that user nonetheless of the device or browser they are visiting.
In Google Analytics 4, there is a unique method of user identification which we expect will show us sufficiently user tracking across devices and browsers.
In the Default Reporting Uniqueness tab in your Property Settings, you can select to recognize users “By device only” or “By User-ID, Google signals, then device”. Basically what this does is prioritizes User ID if you have that set up and if you don’t, rather uses “Google Signals” – this means, as per Google:
New Funnel Reports
We understand that in Universal Analytics, we can make Goal Funnels with a distinct Goal as an endpoint and different other pageviews as funnel steps.
These were restricted in several forms. In Google Analytics 4, the “Funnel Analysis” choice in the “Analysis Hub” offers strong new features that were formerly only available to premium users of Google Analytics 360.
These will bring some getting utilised to but are effective analytical tools. A funnel can include up to ten steps. Each step may be one of a field of dimensions, metrics or events, and the funnels are applied to data retroactively. Funnels can be either “open” or “closed”.
New Simpler, More Flexible Conversion Tracking Options
As we’ve said already, Google Analytics 4 lets you track a lot more possibilities out of the box thanks to Enhanced Measurement. Not only that, but you can fast turn these events into Goals through the “Events” > “All Events” Selection by only clicking the toggle button beside the event.
Not only is it considerably easier to make Goals this method versus Universal Analytics, but you are also permitted up to 30 goals (vs 20 in Universal Analytics). Even further welcome progress is that these goals are used retroactively, AND when you toggle one Goal off, it frees up space for a new slot (which was not in UA).
Integration with BigQuery
Last but not least, Google Analytics 4 presents free integration with Google BigQuery, indicating you can gain access to your Google Analytics plain data without Google Analytics 360. For Google Analytics users that gain a huge amount of traffic, this permits for advanced analysis of huge, row data sets from Google Analytics – either in isolation or integrated with data from other 3rd party applications. You can also utilise BigQuery to, in favour, export data to visualisation media like Tableau and PowerBI.