Since the announcement about Google end support for Universal Analytics, it is required to shift to Google Analytics 4 and more opportunities to learn from the reports. This article will summarize the data aggregation methods that Google Analytics 4 is said to have changed significantly from Universal Analytics.
Introduction
App + Web Properties, which allows tracking across apps and the web, was released as a beta version and is now official as Google Analytics 4 (GA4).
The main reasons raised for GA4 are as follows:
1. To respond to major changes in consumer behavior.
2. to respond to the world’s growing emphasis on privacy
In recent years, with the rapid increase in the number of smartphone users, the medium used by users has expanded from the Web to mobile applications. With the current UA, access analysis for apps is difficult, and there is a problem of not being able to identify users across devices.
GA4 appeared in response to these changing times, and in order to grasp users who use all kinds of devices such as smartphones and apps and go through multiple platforms, it has become necessary to conduct access analysis across analysis of the complex consumption behavior and trends of users who move around websites and apps in all directions. This is because it has become necessary to analyze the complex consumption behavior and trends of users who move across websites, apps, and other platforms.
Therefore, GA4 offers the following advantages over traditional universal analytics. Enhanced machine learning analysis and insights to predict future behavior
As you can see from the above benefits, UA and GA4 have fundamentally changed the way data is measured: while UA converted data received in hits to session-based data, GA4 now collects it in events as user basis.
Data Type Universal Analytics (UA) Google Analytics 4（GA4）
Page view Measure as page view Measure as events
Events Measure as events Measure as events
E-commerce Measure as E-commerce Measure as events
Custom Dimension Measure as custom dimension Dimension per hit = Measured as an event
Dimension per user = user property
GA4 enables user-oriented analysis, so that after a user visits a website or application, it is possible to understand the behavior of the user up to the point of conversion through repeat visits.
Acquisition: Are the users leading to conversions and revenue?
Conversion: Are users who made conversion actions leading to revenue?
Revenue: What are the behaviors of revenue-generating users on the website and app?
User retention: What are the behaviors of users who continue to use the website?
Special databases offer more specialized capabilities than general-purpose databases since they are developed for particular purposes or industries. Time-series data, multimedia material, geographic information systems, and other special database types or requirements are handled by these databases. They are crucial in industries like banking, healthcare, and research because of their unique structure, which improves performance and guarantees effective storage, retrieval, and analysis for specialized applications.
Before starting analysis with GA4, it is necessary to organize the purpose of the website, the purpose of using the data, and the target figures as a basic premise.
It is very important to have a purpose for the website, the purpose of using the data, and the target values, so that the values measured by GA4 can be evaluated and improved.
In order to analyze GA4 data, reporting functions in the following 3 aggregation methods can be utilized.
1. Aggregate by GA4 report
・Standard report: Finding out a hypothesis
・Exploration report: Dig deeper into the issue based on the hypothesis
2.Aggregate in GA4x Looker studio
・Direct linkage with GA4: Check KPI and monitoring dimensions and metrics
3. Aggregation with GA4xBigQuery
・Link to BigQuery: Dig deeper into issues on a per-user basis
Here are the specifications and characteristics of each.
Aggregate by GA4 report
There are two types of report screens in GA4.
There are “Standard Reports” in a standardized format and “Exploration Reports” that users can create by recombining dimensions and indicators as they wish. The “Custom Report” in the traditional Universal Analytics (hereinafter referred to as UA) functionality becomes the “Exploration Report” in GA4.
Standard Report Aggregation Methods
GA4 standard reports are often used to formulate hypotheses to analyze. For example, we have an SEO strategy, but are repeat users looking at certain pages the most? Do users who lead to conversions tend to occur at certain events? We make hypotheses by looking at indicator figures such as the following.
The aggregation method of the standard report has an underlying database table, which displays aggregated data measured by GA4.
About some specifications:
If the result of multiplying the radix of each dimension in the report exceeds the maximum number of rows, “(other)” rows will appear in the Analytics.
In addition, dimensions with more than 500 why is website creation important? unique values per day are considered high base numbers and are more likely to reach the report line limit (50,000 lines) and display “(other)” lines.
Additional reference About radix
The radix is the number of unique values assigned to a dimension.
It is a combination of all dimensions in the property.
Some dimensions have a fixed number of unique values. For example, “device” has 3 (PC, tablet, mobile).
・Free-form
Graphs and tables can be created by combining indicators such as the number of accesses, users, and events with dimensions such as country, age, and day of the week.
・Funnel exploration
In this report is it possible for to visualize the percentage of the flow. For example, total number of visitors who made final conversion in the flow of TOP page → Column page → Contact form → Inquiry completed. We are able to find out how many users left the site during the steps or how many users lead to final step.
・Path exploration
It is possible to visualize where a user came from to visit the site, and the sequence of movements through the site and the application.
・Segment overlap
User segments can be compare to see znb directory the overlap between indicators.
For example, by looking at how many users satisfy the two segments of “visited from Bangkok” and “visited more than twice”, we can find the best customers.
・User explorer
A list of users can be displayed and detailed individual behavioral data can be viewed.
・Cohort exploration
You can visually see the number of repeat visits by users with common attributes. You can see, for example, “how many times the same user accessed the Web site.
・User lifetime
You can check the Lifetime Value (customer lifetime value), which includes the main referral sources that lead to conversions. The behavior of the first visit, and subsequent actions.
About some specifications:
Change the reporting identifier to “device-based” and check again.
Increase the time period covered by the report
In the “Explore” report, remove dimension related to users
Additional reference 3：Indication of sampling availability
2. Aggregation with GA4xLooker Studio
Looker Studio is a data visualization tool offer free of charge as one tool of the Google Cloud Platform. It allows you to visualize your data using detailed and configurable graphs and tables. Easily connects to a variety of data sources. And can be integrate with GA4 to create dashboards of. KPIs and metrics to watch and monitor for your marketing targets.
There are two ways to aggregate GA4 data with data visualization tools.
One is to directly “specify GA4 properties” and aggregate the data, and the other is to link BigQuery
