Migrating to Google Analytics 4: Opportunities and FAQs - Adsmurai

The digital ecosystem has undergone major changes, imposing new challenges on data measurement and analytics. With the rise of data privacy and the decline of user-level identifiers, measurement strategies must evolve. GA4 is designed for a privacy-centric world, where first-party data is critical to maintaining visibility into users' interactions with ads and digital properties.

TABLE OF CONTENTS

• Challenges and opportunities in migrating to Google Analytics 4
• Best practices in data collection and implementation in e-commerce
• Best practices for completeness and accuracy of data in Google Analytics 4
• Frequently asked questions about data collection and management in Google Analytics 4

Google Analytics 4 introduces innovative technology, capable of adapting to the volumes and challenges of today's data analytics. With advanced privacy controls, enhanced modelling and the advantage of Google's unique data, GA4 is positioned as an essential tool for analytics and marketing teams. This platform natively unifies web and application data, enabling a holistic view of user behaviour.

The evolution of GA4 is focused on delivering significant advancements in the platform, with a medium to long-term vision to continuously improve analytics capabilities. The platform promises to be a key component of Google's MarTech ecosystem, driving business impact and enabling advanced use cases that optimise performance across ads and the Google Marketing Platform.

Challenges and opportunities in migrating to Google Analytics 4

The migration to Google Analytics 4 (GA4) offers both challenges and opportunities. Here are the key aspects to consider:

• First-party data measurement: In an environment where privacy is paramount, it is essential to build a solid foundation for first-party data measurement. GA4 enables data collection with a flexible model that can be deployed in a variety of applications, facilitating detailed and secure measurement of users.
  
  • Flexible data model: GA4 uses an event-based model that replaces the session-based model of Universal Analytics. This approach allows for greater flexibility in data collection and provides a more detailed and accurate view of user behaviour.
  • Flexible tagging: The implementation of flexible tags facilitates data collection across applications and the web, and enables efficient management of user consent. GA4 simplifies the implementation of tags and ensures compliance with privacy regulations.
• Reinventing analytics: With GA4, Google has reinvented analytics by introducing an event-based model. This approach enables a holistic view of customer behaviour, from the first interaction to the final conversion.
  • Event-based model: This model captures every user interaction as an event, providing a more granular and detailed view of user behaviour.
  • Privacy-centric: GA4 incorporates solutions to collect user data securely, while respecting current privacy regulations. This includes new consent settings and tools for handling sensitive data.

Best practices in data collection and implementation in e-commerce

To maximize the effectiveness of GA4, it is crucial to follow best practices in data collection and implementation in e-commerce. Here are some key strategies:

• Collecting the right events: Identifying and capturing the right events is essential to obtaining useful and accurate data.
  • Inventory custom events and dimensions: Start by taking an inventory of the custom events and dimensions you are currently capturing in your classic web property.
  • Identifying uncaptured events: Determine which of your custom events and dimensions are not automatically captured in GA4 and need manual instrumentation.
  • Implementing recommended events: Use Google Tag Manager (GTM) to implement recommended or custom events and configure the necessary event parameters or user properties.
• Modification and creation of events: GA4 allows for the modification and creation of events without additional coding, which facilitates customisation and improved data collection.
  • Event modifications: You can add, delete or change event parameters and values to improve the accuracy and usefulness of your reports.
  • Custom events: Creating custom events allows you to generate new events based on other events you are already collecting, improving the usefulness of your reports.
    
    

Data completeness and accuracy best practices in Google Analytics 4

Ensuring the completeness and accuracy of data in GA4 is vital for effective analytics. Here are some best practices:

• Consent Mode v2: This mode reports the variables necessary to comply with privacy regulations while collecting useful data.
• Report identity and user data: Properly configuring report identity and using user-supplied data improves report attribution and accuracy.
  • Blended report identity: Configuring report identity in blended mode maximises conversion modelling and exports to Google Ads.
  • User ID implementation: Using User ID in both applications and on the web helps improve reporting accuracy and attribution.
  • User-supplied data functionality: Leverage first-party data to improve attribution and reporting capabilities in GA4.

Frequently asked questions about data collection and management in Google Analytics 4

What is the best practice for tagging: Auto-Tagging or Manual Tagging?

The best practice is to use Auto-Tagging whenever possible, as it ensures a correct grouping of channels following the established nomenclature. In cases where Auto-Tagging is not feasible, it is important to respect the nomenclature to maintain consistency in the data.

Why do I see discrepancies in the volume of key events when reviewing different reports in GA4?

Discrepancies may arise due to different attribution models applied in different reports. For example, reports in the advertising section apply the attribution model defined at the property level (by default, Data Driven Attribution), while traffic acquisition reports apply a session-based attribution model. It is crucial to understand that these different approaches do not indicate incorrect information, but simply a different perspective on the data.

What do the (other) values I occasionally see in GA4 reports mean?

The (other) parameter is reported when there are dimensions with high cardinality (more than 500 different values). To minimise the occurrence of (other), use predefined dimensions whenever possible and maximise the use of standard reports. 360 accounts have higher cardinality limits and additional options such as Expanded Data Sets to better handle these cases.

What do the (not set) values that are occasionally reported in GA4 reports mean?

The (not set) value appears when GA4 has not received any information for a specific dimension. In the case of Google Ads related dimensions, causes may include incomplete Auto-Tagging configuration or manual UTMs. They may also arise due to problems with event logging such as session_start or page_view.

Why are unwanted referrals being logged on my GA4 property?

Unwanted referrals are often registered due to external payment processors or user-journeys that include transitions between different domains. In GA4, it is extremely easy to exclude these domains as referrals directly in the user interface. Furthermore, the exclusion of self-referrals is automatic for all GA4 properties.

Why are some of the reports I generate in GA4 impacted by data sampling?

Data sampling is applied when data extraction exceeds the limits determined for each property type (10 million for standard accounts, 1 billion for 360 accounts). To minimise the impact of data sampling, adjust the volume of data requested by modifying the date range or re-evaluating the events being reported. 360 properties have Explorations without sampling and options to adjust the level of detail and speed of data extraction results.