Interview with Iñaki Gorostiza on the transition from Universal Analytics to Google Analytics 4

Google Universal Analytics will stop working in July of this year, giving way to Google Analytics 4. It is likely that you already knew this and that, with the news, a lot of doubts have arisen. 

Therefore, from Geotelecom we have chatted with Iñaki Gorostiza, co-author of the book "Google Analytics 4. Measure and You Will Win" and Head of Data at LIN3S, one of our strategic partners, to shed some light on this new tool.

 

1. To start from the beginning, what are the main differences between the Google Analytics we know now and Google Analytics 4 (GA4), what are the pros and cons?

It is important to clarify first of all that these are two different tools. GA4 is not a simple restyling of Universal (GA3), but rather a completely new event model and architecture. In a way, GA4 represents Google's commitment to adapt to the new times by adding functionalities of machine learning to the analysis and processing of the data, offering alternatives to the measurement with cookies and guaranteeing a measurement more respectful with the privacy of the users and the current legislation. 

The main obstacles to adopting GA4 as a measurement tool are its learning curve and the fact that many of the functionalities that were traditionally done in UA automatically and free of charge, now require a customized configuration and, sometimes, we will have to pay for them.

2. What are the top 5 custom reports that everyone should have configured?

It is not possible to generalize in this aspect, since the reports and KPIs will always depend on the nature of the sector in which we are working and the particular needs of the stakeholders of the organization. What I can recommend are five functionalities that you should always have localized and well configured:

  1. Google Consent Mode, for cookie consent management.
  2. Google Signals and User ID, for measuring users across devices.
  3. Data-driven attribution model for conversion analysis.
  4. Exploration techniques, for granular analysis of our users and customers.
  5. Integration with Google BigQuery for loading, transformation and processing of GA4 data.

3. And if we talk about other data sources such as META or email marketing, how will GA4 collect data and conversions?

When measuring our digital marketing actions with GA4 there are some important aspects to keep in mind. 

First of all, GA4 and any measurement tool tends to suffer from a significant myopia in identifying the origin of our traffic. As a result, the acquisition data reflected by the tool by default are very inaccurate. The solution is to manually tag the links in order to add a series of parameters in the url of our landing pages, known as utms, that provide GA4 with information about the campaign, the media and the source of each action.

Secondly, it is very important to know that the cookies and the adblockers are a big problem in the measurement and optimization of our campaigns. For this reason, companies such as Google or Facebook have proposed a new measurement paradigm based on sending pixels from the server. This is known as server-to-server in general and Facebook Conversion API in the case of Facebook.

4. What metrics and key performance indicators (KPIs) must be implemented?

KPIs depend on the business and each stakeholder. A sales manager will not focus on the same KPIs as a user experience manager. That said, there are some KPIs in GA4 that I find particularly sexy:

Acquisition metricsWe can cite two such as sessions and users. While UA focused on sessions, GA4 focuses on the user. User measurement is now much more accurate thanks to the combined action of Google Signals and User ID.

Behavioral metricsGA4 measures more accurately the behavior of our visitors thanks to enhanced event metrics. When measuring a user's interaction time with our content, GA4 is able to discern whether the browser tab is active or not, calculating much more accurately the average interaction time per session and user.

Conversion and investment metricsGA3: although it inherits all the metrics already existing in GA3, we can now set in our reports with restrictions for certain users. In this way, we can hide metrics such as revenue or investment for users outside the organization. 

Type metrics user lifetimeThe user cycle: they offer us information about the cycle of a user, that is, from the moment they met us until their last interaction with our website or app.

Predictive metricsGA4 applies ML models to the data to provide us with information about the likely future of our users. In this way, we can know what is the probability that a user will buy one of our products in the next few days or what risk we have of losing a customer in the immediate future.

5. How do you consider that the change will affect the ecommerce of our country?

I am afraid that the impact may be much greater than desirable. Mainly because we are often not very clear about what is involved in migrating a ecommerce to GA4, when it should be done and how we should start working from that moment on. Some of the aspects that an ecommerce should keep in mind are:

- The ecommerce event model and data layer structure changes completely in GA4. 

- Plugins and widgets that worked for Universal Analytics will no longer work for GA4.

- Most of UA's Enhanced E-commerce reports are no longer available in GA4 and we will have to build them manually and custom.

- We are going to lose the historical data from Universal Analytics so we will have to export all the reports we want to preserve. 

6. There has also been a lot of talk about events, what are the essential events that should not be missing in any ecommerce with web + app?

As I mentioned before, one of the most important new features of GA4 is its new event model. Much more flexible, powerful and sexy. Now we will find four types of events:

- Automatic events: they are automatically generated in GA4 to manage the internal operation of the tool.

- Enhanced events: allow us to measure a number of visitor interactions automatically: page views, video views, document downloads, link clicks, scrolls, interactions with forms, etc...

- Recommended events: GA4 will offer us a convention for the nomenclature of some common events in different types of websites. If we follow these recommendations, the tool in return will offer us very interesting functionalities and metrics, such as predictive analytics. In this way we can predict the future behavior of our visitors based on their historical behavior.

- Custom events: apart from the events mentioned above, in GA4 we can create and customize all the events we consider necessary to measure the behavior of our users.

7. Now, let's move on to more specific questions: is it necessary to modify the dataLayer from UA to GA4 to collect data correctly?

The data layer is by definition agnostic to any measurement tool. Most of the information that we would have defined for UA can also be consumed from GA4, but there is one exception, which is the structure associated with the enhanced ecommerce. Although both tools propose a different architecture, if we work with Google Tag Manager, we can easily reuse the data layer, minimizing the technical impact on the implementation process. 

8. How do I know which dynamic variables I need to include in the dataLayer for events such as purchase and Google Ads conversion tracking?

Very easy, just check the official Google documentation or buy our book Google Analytics 4, measure and you will win 😉.

9. Regarding integrations, what advantages does Tag Manager offer over other types of integrations?

Tag Manager offers many advantages over the traditional approach based on the insertion of Javascript code. To name a few:

- Orientation to the marketer.

- Version control system.

- Collaborative work in work environments.

- Native integration with Google Consent Mode.

- Enforcement of security policies through blacklisting and whitelisting.

- Simple and efficient management of Google and third-party tags.

- Scalability from templates.

- Dynamic management of information through variables.

In addition to these functionalities GTM offers the possibility of managing our labels server-to-server through server containers. This new implementation paradigm allows us to overcome the inherent restrictions of a web browser in terms of cookie dependency and the delivery of marketing and analytics hits.

10. And to finish with a look into the future, how do you think digital analytics will advance in the coming years, taking into account that more and more user data is being shielded?

Digital analytics along with measurement processes and technologies are obliged in the short term to dispense with cookies for user tracking. We will have the obligation to put privacy at the center of data analysis and be respectful of the legislation in force in each country.

Artificial intelligence techniques and machine learning you are going to revolutionize data mining and data collection. insights. This will force the digital analyst to become more technical in order to master all the analysis tools and techniques that will now be available to him. 

Digital analytics will tend to hyper-specialize in more granular knowledge areas such as CRO, marketing analytics, data science, business intelligence, data visualization, technical analytics, etc.

Finally, I believe that companies will be obliged to increase their measurement budgets, the "free 100% tools" will become a thing of the past and the profiles will become more and more qualified.  

 

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