The introduction of Google Analytics 4 (GA4) is an important step when it comes to tracking how effectively your website is operating and the audiences you are attracting.
For a long time, Google Universal Analytics (UA) was the go-to. But in order to address the evolving needs of businesses, as well as changes around user behaviour and privacy, Google has upgraded its analytics platform, with GA4 the result.
With a greater alignment with enhanced cross-platform tracking and a user-centric approach, GA4 is a watershed moment in online analytics. Add to this the privacy and data control, machine learning, advanced insights and integration with Google Ads, and GA4 is something that all online businesses should not only get up to speed with, but embrace as completely as possible.
In this blog, we take a look at how data tracking works in Google Analytics UA vs GA4, and explore all the best practices for migration, data collection methods, interpreting data and more.
Setting up tracking codes
To set up a tracking code in UA, you need to copy and paste the tracking code into every page of your website. You can use Google Tag Manager to do this, or you can install the tracking code directly.
In GA4, you can use Google Tag Manager or code installation to set up tracking codes. You can create a measurement ID and add it to the code. Then you need to paste the update code into every page of your website.
Data collection methods
When it comes to data collection in UA, it requires parameters to be set up and customised. Once established, users can track page views, events, ecommerce data and more.
GA4 on the other hand uses an event-driven data model, which can track page views and custom events, and offers enhanced management. GA4 can also capture actions that happen outside of the website that is being tracked.
We advise using both tracking methods to gain a comprehensive insight. Aim to set up precise events to track customer journeys and valuable engagements. Also, use the enhanced measurement feature available in GA4 to maximise data collection capabilities.
With regards to reporting capabilities, UA provides easy-to-understand reports with charts and graphs. UA can also generate custom reports with advanced filtering options.
GA4 on the other hand uses machine learning to analyse patterns and deliver insights. This provides more privacy protection options. Users can also customise and build custom reports.
In terms of best practices around reporting, we recommend using both reports to achieve comprehensive insights, and then place your focus on the report that delivers the most value to you. You can use machine learning to identify customer behaviour patterns and improve growth opportunities.
How you interpret all the data you have is a vital part of truly utilising Google Analytics. In UA, data interpretation focuses on page views and session metrics to understand user behaviour. Other key metrics include bounce rate, time on site, and conversion rate.
Data interpretation in GA4 emphasises event-driven data, with key metrics being user engagement, retention rate and conversion value.
Again, use both sets of metrics as a best practice. This will allow you to better understand user engagement and behaviour. Also look to focus on metrics that align with your business goals, and aim to customise metrics to fit with your KPIs.
Migrating from UA to GA4
When looking to migrate your analytics from UA to GA4, use the GA migration feature. Then create a property from scratch in GA4, and import all your data from UA to the new property in GA4.
Once set up in GA4, migrate in stages and use Google’s migration plans. Be sure to check tags and event triggers for website updates.
Be sure to plan any migration well in advance. Test your site with both tags active to ensure no tracking data loss. Also, be sure to migrate in stages to minimise risk.
Additional best practices
There are a number of additional best practices we recommend across both UA and GA4.
In UA, set up goal conversion to gain an increased understanding of user behaviour. Also create dashboards to track the most important metrics regularly. And clean up any spam referrals to ensure the most accurate data collection possible.
In GA4, use predictive analytics to forecast future trends, and use AI data modelling and segmentation to create meaningful insights. Also integrate GA4 with other Google services like Google Ads and Google Search Console.
How to check UA data in GA4
You may already know that UA and GA4 use different data models. The UA data model is based on sessions and pageviews, whereas the GA4 data model is based on events and parameters. This means that both UA and GA4 can collect, process and report the same data differently.
Here are five ways in which they differ:
1 – Reporting interface
The reporting interface in GA4 looks similar to that of Google Analytics for Firebase. This is because GA4 is built on Firebase analytics. It looks markedly different however from any reporting view found in UA.
2 – Measurement model
UA uses the measurement model, which is based on sessions and page views. GA4 however uses the measurement model but based on events and parameter.
Every tracked activity taken by a user in GA4 is considered an event, and these events can provide deeply detailed information.
As an example, let’s say we are tracking ‘page views’ as an event in GA4. This GA4 event would have additional information attached to it, such as the title of the page and the user location.
3 – Event tracking setup
When using UA, all the tracked events must follow the category-action-label-value schema. But in GA4, additional information is supplied to an event through parameters that you as a user set.
These events are automatically captured if the web page has gtag.js implemented directly on the page, or via Google Tag Manager.
It is possible to send up to 25 custom parameters per event, and each value can be 100 characters long. It should be noted that there is a limit of 500 unique event names per GA4 property.
4 – Ecommerce tracking
Ecommerce tracking capabilities provided by GA4 are still in their infancy. This means they are at present not as powerful as the ecommerce tracking capabilities provided by UA.
Additionally, GA4 does not provide Enhance Ecommerce tracking.
5 – UA vs GA4 Content Grouping
Content grouping is a rule-based grouping of related content groups. It is made up of one or more content groups.
A content group can be a set of web pages based on the same or similar topics, such as ‘attribution modelling’ for example.
In the case of an ecommerce website, a content group can be a set of web pages that sell similar products, like ‘shirts’ for example.
To demonstrate, since ‘content grouping’ is made up of one or more ‘content groups’, a ‘Men’ content grouping could consist of the following content groups:
- Men’s shirts
- Men’s trousers
- Men’s sportswear
Similarly, a ‘Women’ content grouping could consist of the following content groups:
- Women’s shirts
- Women’s trousers
- Women’s sportswear
Content grouping is especially useful if you have a big website with hundreds or thousands of pages. You can realistically measure the content performance at just the group level, and not at the individual page level. A content group can be setup in Google Tag Manager.
The future of analytics
We hope this blog has been an insightful read and helped you gain a better understanding of how UA and GA4 tracking works.
UA is still important due to its comprehensive reporting capabilities and its ability to track user sessions and pageviews.
However, GA4 is the future of analytics, with its advanced tracking capabilities, machine learning insights and advanced privacy protection.
In conclusion, use both UA and GA4 for in-depth insights. Choose the tracking method that aligns with your business goals and provides the most value to you. Also, migrate to GA4 in stages and with precision. This will allow you to maximise the user behaviour insights you can gain.
If you require any support or guidance around UA and GA4, simply contact Williams Commerce. Our team will be happy to help and allow you to get the most out of your analytics.