Google Analytics 4 officially replaced the famous web traffic analysis tool "Universal Analytics" on July 1st 2023, and promises to offer greater depth and completeness of analysis than its counterpart.
But, what's new in Google Analytics 4?
In this article, we will explore GA4's features, the differences compared to Universal Analytics, and the advantages it offers.
GA4 offers a more advanced view of user behavior on the website through event analysis and the use of machine learning technology.
It also guarantees better integration with other Google tools, greater protection of user privacy and greater flexibility in creating customized reports.
In addition, GA4's advanced attribution model allows marketers to track the complete user journey, from their first contact with the website to the final purchase and to gain a better understanding of the effectiveness of online marketing activities.
Let's take a closer look at the main differences:
The key difference between Google Analytics 4 and the previous version, Universal Analytics is the underlying technology.
The new GA4 platform is based on the event and user data model, which differs from the session and hit data model used in Universal Analytics, which means that GA4 is able to capture and use more data, including events and user interaction with the website.
• Account structure:
Google Analytics 4 uses a more flexible account structure than Universal Analytics.
In GA4 you can create multiple properties and data streams within a single account, allowing simultaneous monitoring of multiple websites and apps.
It is designed to integrate app and web property analytics into a single platform, giving you the ability to monitor user activities across different digital marketing channels in a single dashboard.
• Advanced analytics:
Google Analytics 4 uses machine learning technology to provide advanced analytics, such as traffic trend forecasting and user lifetime value calculation.
• Customized reports:
In addition to the predefined reports, GA4 offers a wide range of filtering and segmentation options to display the data in a specific and detailed way across various types of reports.
Google Analytics 4 allows for more flexible customized reporting than Google Universal Analytics, thanks to the new event-based data structure.
For example, you can create a report that tracks the conversion rate for a specific website page or the number of visits from a particular marketing channel.
Customized reports can be created to monitor any metric or aspect of the website, using the "Explore" function or the "Custom Reports" function.
With “Explore”, you can select the desired data and metrics and create a customized visualization of the data.
With “Custom Reports”, you can create specific reports based on your needs, creating customized charts, tables, and metrics.
GA4 for conversion analysis:
The last, but not least aspect that radically changes web data analysis is certainly the new advanced attribution model.
GA4's attribution model uses machine learning to determine the role of each marketing channel in the conversion process, providing a better understanding of the effectiveness of online marketing activities.
To choose the most suitable attribution model for your needs, it is important to understand the characteristics of each model and how they can be used to analyze data more effectively:
Data-driven (recommended):
This model uses your account's actual data to determine the most appropriate attribution credit for each interaction. The model spreads fractional conversion credit based on data for each conversion event, such as organic search, paid search, email, or direct.
Last Click:
This model assigns 100% of the conversion value to the last channel the user clicked on before converting. In other words, the credit for the conversion is attributed exclusively to the last interaction the user had with the website.
Ad Preferred Last Click:
This model assigns 100% of the conversion value to the last Google Ads channel the user clicked on before converting.
First click:
This model assigns all conversion credit to the first channel on which the user clicked before the conversion.
Linear:
This model evenly distributes credit for conversion across all channels on which the user clicked before converting.
Location-based:
This model allocates 40% of credit to the first and last interactions, with the remaining 20% of credit evenly distributed to central interactions.
Time decay:
This model gives more credit to touchpoints that occurred closer in time to the conversion. The model uses a seven-day half-life, which means that a click eight days before a conversion gets half the credit of a click one day before a conversion. This way, the model takes into account the importance of the user's more recent interactions with the website.
In conclusion, Google Analytics 4 represents an important step forward in understanding user behavior on the website and in evaluating the effectiveness of online marketing initiatives.
With the use of machine learning, a greater focus on user experience, a more advanced attribution model and better protection of user privacy, GA4 offers a range of new features that distinguish it and position it as the best, compared to previous versions of Google Analytics.
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