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Current version of Google Analytics (Universal Analytics) will stop processing new data by July 2023. New version Google Analytics 4 brings generational shift from the previous version and had been designed to better server in today’s world cross-device user behaviour, increased privacy focus, tracking prevention etc. Some of the key features of the new generation of analytics:
New event-based data model focused on user journey across a website and mobile app in one system
Easy setup of events measurement such as scroll-tracking, outgoing link clicks, documents downloads etc.
Advanced Google AI modelling to fill the data gap cuase by cookie blocking and deletion
Free data-driven attribution allows your marketing team to optimize investments across your advertising
Export raw event-level data from GA4 into Big Query and use this as your data lake, data warehouse, or data science initiatives
Predictive analytics capabilities e.g. for revenue foreasting, purchase probability, churn probability etc.
Audit of current measurement
Creation of measurement plan based on your expectation and business needs
Implementation of new measurement code
GTM configuration and cleaning
Creation and setup of GA4 account plus adjustments of custom reports
Integration with selected platforms such as Google Ads, Google Big Query etc.
The short answer is simply: yes, you should start the process as soon as possible! Why? The current version of Google (Universal) Analytics will stop collecting data on June 30, 2023. To continue with measuring traffic, conversions, customer journeys or other events and campaigns on your website, there is no other choice but to switch to GA4. At the same time, the sooner you set up data measurement in GA4, the longer you will have the continuity of collecting data about your customers in the new event based system.
One of the best practice is to have concurrently the old UA tracking available and new GA4 tracking already implemented. At this moment you can begin to adopt GA4 for the use cases where GA4 has the features necessary – your team will gradually shift to the new data model, new UI etc.
During the process, we also recommend that you audit the current analytics setup (Evaluate your custom reports, MarTech integrations, segments, custom dimensions, etc) you use and these involve into GA4 equivalents. Also, use this opportunity to consider the future analytics + data collection + usage landscape in your organization due to changes in privacy, compliance, different business needs etc – some areas may no longer be relevant, others may need a fresh approach.
After deployment, events such as session_start, page_view, user_engagement will be automatically measured in GA4.
To be more concrete in the basic reports you will see the metrics originally marked as:
You can find a concrete list of automatically collected data in the official Google help.
GA4 brings simplification in the form of setting selected events directly in the interface. It works for events such as:
However, it is not always suitable to automatically switch on the measurement of the mentioned events. A customized measurement solution is still needed in some specific websites / web technologies, for example for scrolling on the page.
Google BigQuery is a data warehouse and is part of the so-called Google Cloud Platform. BigQuery, for example, serves as a data basis for advanced reporting on data from GA4 in various BI tools, such as Google Data Studio, PowerBI, Tableau and others. Alternatively, in BigQuery, with the help of the SQL language, you directly query – you could say filter – the information you want to obtain from the raw data. In other words, you are filtering answers to questions you can’t easily answer just by using the GA4 user interface.
Some practical usage examples are:
The advantage of BigQuery is in the ability to process large volumes of data, speed, low price and relatively easy work for users who know the SQL query language.
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GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.
Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.
For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.
Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.
DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.
The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.
It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.
DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.
Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.
Example use cases might be:
You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!
One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.
GA4 currently includes the following predictive metrics:
Get in touch with Marketingintelligence team
Get in touch with Marketingintelligence team
Get in touch with Marketingintelligence team
One of the new and exciting features of GA4 is called “predictive metrics” – with these you can learn about your customers and their shopping behavior. These are added automatically to your data and are based on Google’s machine learning expertise.
GA4 currently includes the following predictive metrics:
Google BigQuery is a cost-effective and highly-scalable cloud data warehouse optimized for high performance on very large data sets. With GA4 you can export all your event-level data to BigQuery for additional analytics or data science initiatives.
Example use cases might be
Example use cases might be
You will need to have a Google Cloud account set up and maintained for this purpose. That is why we are here and can solve all the infrastructure for you and with you!
Since 1/2022 GA4 has made a data-driven attribution model available to all users – unlike in Universal Analytics where the DDA model was only available to GA360 customers.
DDA is an algorithmic attribution model that quantifies the value of each touchpoint in the user journey (such as campaign click) and it does so by smart modelling behind the scenes without human bias that is always present in rule-based models.
The DDA model in GA4 is also better than the one on GA360 as it takes into account up to 50 touchpoints in the user journey vs. the GA360 only took 4 touchpoints.
It is also possible to set the default attribution model to DDA (or another model) from the old Last Non-Direct Click. And then all your reports and data exported to BigQuery will use the newly selected model as default.
DDA in Google Analytics 4 excludes almost all direct visits from receiving credit – you may or may not like that, but as it’s been always the case with attribution we recommend validating any model using marketing experiments.
GA4 is built for a world where more and more users opt out of cookie consent and other methods for data collection.
Google uses machine learning modeling to fill in the data gaps – some of these are already in the current GA4 others will be deployed in the future.
For example, modelling conversions allows GA4 to properly attribute conversions without user identification – this is crucial for optimized advertising campaigns and automated bidding. This covers situations such as some browsers limiting the time window for first-party cookies, conversions for unconsented users, Apple’s App Tracking Transparency (ATT) impacts, cross-device user behaviour, and others.