Be Prepared in Time for New 

Level of Cross-Device Tracking with

Google Analytics 4

The new version of Google Analytics (GA4) has been designed to better server in today’s world of cross-device user behavior, increased privacy focus and tracking prevention.

Why It's Important

Features of GA4 in a Nutshell

Current version of Google (Universal) Analytics will stop processing new data by the end of June 2023. If you don’t want to lose your data continuity and possibility of YoY comparison, setup of new GA4 and early adoption of the product is inevitable. See below what are the benefits of new analytics 4 from the data flow point of view:

Gathering

Analyzing

Reporting

Gathering

Analyzing

Reporting

How the Setup Looks Like

Google Analytics 4 Tailored to Your Needs

analytics.png

(1)

Identification of your needs for tracking

(2)

Audit of current measurement and creation of measurement plan

(3)

Creation of GA4 and implementation of new measurement code

(4)

GTM configuration

(5)

Setup of integration with GA4 (such as Google Ads, Search Console, or Big Query)

(6)

Adjustment of custom reports in GA4 to fit your needs

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FAQ

Find Out More about GA4

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:

  • sessions,
  • page views,
  • number of users and their engagement.

 

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:

  • scrolling on the page
  • click on outgoing links
  • download documents
  • interact with the video

 

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:

  • processing of raw data, its transformation and its subsequent visualization in BI visualization platforms,
  • combining / connecting data sources – for example, users behavior data from GA4 with your transactional data from ERP or CRM, or perhaps with data from other marketing platforms (Facebook, TikTok, etc.) In this way, you can get a comprehensive view of the performance of your marketing channels and activities and their contribution in terms of net orders, sales, margin and overall profitability, etc.
  • sending enriched data from BigQuery to other platforms (for example, enriching the Google Ads feed with signals such as margin at the product, category level, etc.)

 

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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Tailored GA4 Setup

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Free Data Export to Google Big Query

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:

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

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!

World Without Cookies and GA4

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.

Predictive Analytics in GA4

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:

  • Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
  • Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
  • Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.
 
You can create audiences based on these values and leverage them in your advertising campaigns – for example, you could exclude users who have a high purchase probability as you may assume that these users will buy anyways and you don’t have to spend additional marketing dollars on them (test it) or try to win back an audience with higher churn probability with a special campaign or communication.

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Predictive analytics in GA4

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:

  • Purchase probability: The probability that a user who was active in the last 28 days will log a specific conversion event within the next 7 days.
  • Churn probability: The probability that a user who was active on your app or site within the last 7 days will not be active within the next 7 days.
  • Predicted revenue: The revenue expected from all purchase conversions within the next 28 days from a user who was active in the last 28 days.

You can create audiences based on these values and leverage them in your advertising campaigns – for example, you could exclude users who have a high purchase probability as you may assume that these users will buy anyways and you don’t have to spend additional marketing dollars on them (test it) or try to win back an audience with higher churn probability with a special campaign or communication.

Data export to BigQuery

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

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

Example use cases might be

  • Joining GA4 data with other sources in your company – CRM, customer data, backend sales, and margin data.
  • Using GA4 data for advanced customer analytics for a better understanding of customer lifetime value or churn prediction.
  • Automate reporting in your BI tool.
  • Move the data to your on-premise or cloud data lake and data warehouse.

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!

Free data driven attribution in GA4

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.

World without Cookies and GA4

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.