Google Analytics 4 Property Management and Optimization

Google Analytics 4 property management and optimization is a crucial aspect of getting the most out of your analytics data.

To create a new Google Analytics 4 property, you'll need to sign in to your Google Analytics account and click on the "Admin" button.

With Google Analytics 4, you can now set up multiple properties under a single account, making it easier to manage and track your data.

Property management involves setting up and configuring your Google Analytics 4 property, including setting up data streams and events.

In Google Analytics 4, data streams are used to collect data from various sources, such as web, app, and server logs.

Google Analytics 4 is Google's latest version of the leading analytics platform.

It's provided with several new features and functionalities that help you have better insights into how people interact with your website.

Google Analytics 4 uses AI to fill in gaps in data with its new data modeling feature.

This tool is highly used by many websites and businesses to track user interaction across various web domains, mobile apps, etc.

Google Analytics reporting tool is useful to track the amount of traffic on your website.

You can also use it to keep a watch on marketing channels and measure key performance indicators.

To create a Google Analytics 4 property, you must be in the correct account and have Editor or above permissions. You can create a new property by clicking on Admin, then Create, and selecting Property.

When creating a property, you'll need to enter a name, select the reporting time zone and currency, and choose your industry category and business size. You can also specify how you intend to use Google Analytics. If you want to start collecting data, you can continue to Add a data stream.

After creating a property, it's essential to finish setting it up using the Setup Assistant. This will guide you through recommended features and settings to help you fully set up your GA4 property. You can configure data collection, property settings, Google Ads, and advanced setup through the Setup Assistant.

Here are the key steps to create and manage a property:

A Google Analytics 4 property is a container that organizes data collected from a website or app. Within a property, you can view data through reports and manage this data.

You can find properties in the Property settings section, accessible by going to Admin → Property settings. This is where you'll see the various settings and features related to your property.

A property enables you to oversee data collection, attribution, privacy settings, and product links. These are all crucial aspects of managing your data.

Here's a breakdown of the key features you'll find in the Property settings section:

These features will help you manage and understand your data, making it easier to make informed decisions about your website or app.

Creating a property in Google Analytics 4 is a straightforward process that can be completed in just a few steps. To create a property, you need to be in the correct account where you want to add a new property, and you must be an Editor or above at the account level.

First, go to the Admin section and click on "Create" followed by "Property." Enter a name for the property, such as "My Business, Inc website", and select the reporting time zone and currency. If a visitor comes to your website on a Tuesday in their time zone, but it's Monday in your time zone, the visit is recorded as having occurred on Monday.

Next, click on "Next" and select your industry category and business size. Then, click on "Next" again and select how you intend to use Google Analytics. Finally, click on "Create" to create the property.

After creating the property, you can add a data stream to start collecting data. To do this, go to the Admin section and click on "Data Streams" followed by "New Data Stream." Select the type of data you want to collect, such as website or app data, and follow the prompts to set up the data stream.

Alternatively, if you already have a Universal Analytics property, you can create a Google Analytics 4 property from the existing property. To do this, go to the Admin section of your Universal Analytics property and click on "GA4 Setup Assistant." This will guide you through the process of creating a Google Analytics 4 property.

Here are the steps to create a Google Analytics 4 property from an existing Universal Analytics property:

1. Log into your Universal Analytics property and click on "Admin."

2. Click on "GA4 Setup Assistant" under the "Property" column.

3. Follow the prompts to create a Google Analytics 4 property.

By following these steps, you can create a property in Google Analytics 4 and start collecting data.

Google Analytics 4 data is event-driven, which means each row in the events table represents a single event. This is a stark contrast to the traditional Google Analytics 360 export where each row represents a single session.

The data model of Google Analytics 4 Properties closely resembles the updated data model of Google Analytics 4 Properties, with events placed at the forefront of analysis. This creates new metrics that enable unified cross-platform behavior.

Analysts working in the BigQuery events table will primarily focus on isolating specific activities across users and events, rather than sessions.

When you link your Firebase Project to BigQuery, there will likely be several supported and recommended product integrations for reporting, including Crashlytics, Cloud Messaging, Performance, Predictions, and Firebase A/B tests data.

A single dataset named "analytics_(your property id)" will populate for each Firebase and/or Google Analytics 4 Properties project that is linked to BigQuery. If you have Firebase and Data Streams in Google Analytics 4 Properties, each app and data stream for which BigQuery exporting is enabled will export its data to that single dataset.

The new Google Analytics 4 Properties format differs slightly from our traditional Google Analytics 360 daily and intraday exports. In our Google Analytics 360 exports, each dataset corresponds to a view within Google Analytics rather than a Firebase project.

Our events tables are structured very differently from our sessions tables, and therefore will be queried in different ways. We’ll dive deeper into our queries in a moment.

The events table schema of our new Google Analytics 4 Properties and Firebase data closely resembles the updated data model of Google Analytics 4 Properties, where events are placed at the forefront of our analysis.

Each row within our events table represents a single event. For example, in the image below we see a screen_view event, which houses several associated (and nested) event parameters and several columns of dimensions and metrics.

To reference a complete list of columns and data, you’ll want to review the Google Analytics 4 Properties and Firebase export schema.

The Google Analytics 4 Properties data model is a stark contrast to our Google Analytics 360 export where each row represents a single session. For example, in the image below we see a single session denoted by a visitID and a visit start time, followed by several associated (and nested) columns composed of dimensions and metrics.

Here are the key datasets you can expect to see in BigQuery for Google Analytics 4 Properties and Firebase:

Note that the exact datasets available will depend on your specific setup and configuration.

Working with Google Analytics 4 data in BigQuery can be a bit tricky due to data differences between the interface and BigQuery.

Google Analytics reports consist of pre-aggregated data, which can lead to small differences between our calculations in BigQuery and the Google Analytics interface reports, often around 1-2 percent.

This pre-aggregation is one of the reasons we need to recreate several common metrics in BigQuery.

The data model and export schema in BigQuery aren't actually "missing" data, but rather the Google Analytics reports are pre-aggregating data for us within the interface.

Most of this still holds true for our Firebase and Google Analytics 4 Properties export, especially relating to users.

In Google Analytics 4 Properties, events are associated with several custom dimensions, also known as parameters, which are key-value pairs that provide additional details about each event.

Each parameter value is assigned a data type, such as a string, integer, double, or float, which is essential to understand when querying for events in BigQuery.

To gain a comprehensive understanding of events and parameter keys in Google Analytics 4 Properties, you can run a quick query in BigQuery, which will return the event parameter values that you want to select by event.

Here's an example of a streamlined and readable output from such a query:

Alternatively, you can use the COALESCE() function in BigQuery to retrieve each event parameter value that is not null.

To access and isolate event parameters for analysis, you can use a query that UNNESTs events by event name and parameter with key-value pairs by user.

This user-level granularity is not available in the Analytics interface, and is one of the key benefits of Google Analytics 4 Properties.

Here's an example of a query that UNNESTs events and parameters for the post_score event:

This query will give you a unique row for each level_name, level, time, and score combination by user_id and acquisition metrics.

Note that you can modify this query to include other event parameters and metrics as needed.

Firebase and Google Analytics 4 can be connected to Google BigQuery, which is Google Cloud Platform's advanced database solution. This allows for deeper and more customized analysis of business data.

BigQuery provides the opportunity to uncover insights that are not possible without data transformation. It also enables the connection of Google Analytics and Firebase data with other third-party data sources.

Data Studio can be connected to BigQuery for more complex reporting options, offering benefits such as branding capabilities, enhanced sharing functionality, and calculated fields.

Firebase is a powerful tool that allows you to connect your Google Analytics and Firebase data with other third-party data sources.

BigQuery's advanced database solution enables you to conduct a much deeper and more customized analysis for your business.

Connecting Firebase to BigQuery gives you the flexibility to export your data to visualization platforms like Data Studio, Tableau, and PowerBI.

With Data Studio, you can create customized reporting tables and connect them to BigQuery for more complex reporting options.

Data Studio's native connector to Google Analytics 4 Properties is a great benefit, but using the Data Studio BigQuery connector can provide even more flexibility and reporting options.

In Google Analytics 4 Properties and Firebase, you'll need to isolate event data and parameters to create effective queries.

The data model in these platforms is simplified, consisting only of events and user properties, which reduces query complexity.

To handle key-value pairs, you'll need to create several subqueries.

This approach is a significant improvement over traditional Google Analytics BigQuery sessions tables, which often required more complex queries.

In Google Analytics 4 Properties, you'll notice that many metrics aren't automatically available in BigQuery, unlike traditional Google Analytics 360 sessions tables. You'll often need to manually calculate metrics like new users, count of sessions, or engaged sessions.

To recreate metrics like new users, you can isolate events like session_start, which is an automatically collected event in Google Analytics 4 Properties. This allows you to determine if a user is new or returning by looking at the ga_session_number.

The BigQuery export schema is a valuable resource to see what metrics are available to you, and you can use it to recreate metrics like new users. This means you can focus on isolating specific activities across users and events, which is at the forefront of the Google Analytics 4 Properties data model.

Tables are formatted and separated by date, and appear as "events_YYYYMMDD." These tables are imported within each analytics dataset for each day of export.

The events table is processed daily, and stored in an individual table per day. This means you'll see a separate table for each day, with a number next to the event table representing the number of daily partitioned tables available to you for analysis.

For example, if you see "events_145", it means there are 145 daily partitioned tables available. The events tables are on a one-day lag, meaning that your Tuesday, August 2nd data will arrive on Wednesday, August 3rd.

A table may also be imported for Google Analytics 4 Properties events received throughout the current day, named "events_intraday_YYYYMMDD" and populated in near real-time as app events are collected.

In Google Analytics 4 Properties, many metrics are not provided for you in BigQuery, so you'll need to manually calculate them.

You'll often need to reference the export schema to see what's available to you.

New users is a "missing" metric in our BigQuery export schema that can be easily recreated using the session_start event and the ga_session_number.

We generally have fewer metrics to calculate in Google Analytics 4 Properties compared to traditional Google Analytics 360 sessions tables.

To calculate new users, you can isolate the session_start event and determine if the user is new or returning based on the ga_session_number.