Google Analytics 4 Answers to Common Questions

Google Analytics 4 is a game-changer for marketers and website owners. It offers a more comprehensive understanding of user behavior and provides actionable insights to improve website performance.

One of the key benefits of Google Analytics 4 is its ability to track user behavior across multiple devices and platforms. This is made possible by its integration with Google Signals, which uses machine learning to identify and connect user data across devices.

Google Analytics 4 also introduces a new data model that focuses on user-centric data, rather than session-based data. This shift allows for a more accurate understanding of user behavior and preferences.

With Google Analytics 4, you can easily set up cross-device tracking and get a unified view of your users' behavior across all their devices.

Let's start by installing Google Analytics on our website. We can do this by copying the code and setting up the Reporting Identity.

First, no data is flowing into our account yet, so we need to set it up now. We can find more information about our users in the Tech Overview report, which includes details about devices and operating systems.

To get started with Google Analytics 4, we need to create a Firebase project and add Firebase to our apps. This will allow us to create apps that appear as distinct data streams in GA4.

Let's start with the basics of Google Analytics 4 setup. Google Analytics 4 provides a way to explore existing business data and experiment with Google Analytics features.

To begin, you need to install Google Analytics on your website, which will allow you to see the number of users that visited your website in the last 30 minutes and which countries they come from. This information can be found in the reporting section of Google Analytics 4.

You can also get insights into your data, such as what devices your users use to arrive at your website and which operating system they're using. This is all available in the Tech Overview report.

To access this data, you'll need to set up Google Analytics on your website. No data will flow into your account until you do this.

To set up Google Analytics 4 with your mobile app, you'll need to create a Firebase project and add Firebase to your app. This will allow you to create apps within your Firebase project, which will appear as distinct data streams in GA4.

The first step is to create a Firebase project if you don't already have one. From there, you can create apps and add Firebase to each one.

All events need to be logged from your app to Firebase, which will then appear in your GA4 property. You can find instructions on how to get started with this process in the relevant documentation.

If you need to trigger other tags based on your Firebase events, or if you want to be able to add or modify events without re-submitting your app's binaries, Google Tag Manager mobile containers are the way to go.

Moving your event tracking to Google Analytics 4 (GA4) can be a seamless process. To start, you'll need to map your existing events to the GA4 data model, which involves designing your event schema with some event naming considerations in mind.

You'll also need to tag your app or website with the new events, and the process differs depending on whether you're using Google Tag Manager or the Global Site Tag (gtag.js) for your website. For apps, you must install Firebase and log events that way.

Google provides step-by-step guides for the tagging process on your website, and general instructions for your app. The Firebase documentation offers more detail on logging events for both Android and iOS data streams.

You can use event-scoped campaign parameters for conversion events, which will inherit the session_start's campaign parameters. This is useful for tracking source, medium, or campaign information for specific events.

For example, you can mark an event like file_download as a conversion, and its campaign parameters will inherit the session_start's campaign parameters.

At GA4, attribution is a crucial aspect of understanding how users interact with your website. The Comparisons feature allows you to change the attribution type reflected in the report.

You can choose from four attribution types, each with its own approach to attributing conversions. The default selection, Last Non-Direct Click, attributes conversions to the last non-direct click.

The Last Non-Direct Click attribution type gives credit to the last non-direct click, while the Last Non-Direct Click with View-Through Conversion type also gives credit to an ad impression if there is no recent click. This is a key difference between UA and GA4 attribution models.

Google Analytics 4 (GA4) has changed the way attribution works compared to Universal Analytics (UA).

The default attribution type in GA4 is Last Non-Direct Click, which attributes conversions to the last non-direct click.

UA attributes goal completions to the last non-direct click, but GA4 offers a Comparisons feature to change the attribution type reflected in the report.

There are four attribution types in GA4: Last Non-Direct Click, View-Through Conversion, Last Click, and Last Ad Click.

The Last Click attribution type gives credit to the latest click, regardless of whether it was from Google Ads or another source.

The Last Ad Click attribution type gives credit to the latest Google Ads click, even if there was a more recent engagement via another source.

I recommend parallel tracking, which means continuing to collect data in your Universal Analytics property and adding GA4 tags to your container.

This will allow you to see a new tag template in the Google Tag Manager (GTM) interface and piggyback on triggers and variables that already exist in your container.

In GA4, you can create a maximum of 200 analyses per user and 500 shared analyses per property in the Analysis Hub. This limit may serve as an upper bound depending on your report configuration.

To use segments outside of the Analysis Hub, you need to turn them into audiences. There is a limit of 100 audiences per property.

Google publishes collection limits on their website, so be sure to check those out if you're curious about the specifics.

If you're looking for more advanced features and higher data collection limits, you might consider upgrading to GA360.

Exploration Reports in GA4 are a powerful tool for building custom visualizations of your data. You can create funnel exploration reports to visualize user journeys through a sequence of steps, such as a purchase process.

There are two types of funnel exploration reports: standard and trended funnels. Standard funnels show the sequence of steps, while trended funnels show how each step trends over time.

Free-form exploration reports allow you to build custom tables from scratch using various event parameters. This option is ideal for tailoring the data analysis to your specific needs.

Currently, exploration reports cannot be integrated directly into the main GA4 reporting interface, except for custom funnels. This might change in future updates.

To get the most out of your analytics investments, you can use the eCommerce template in GA4, which provides a great starting point for commercial eCommerce reports.

Here are the types of exploration reports available in GA4:

GA4 is bridging the gap between event-level and session-level metrics, and it's already started to do so. This means you can expect more comprehensive and detailed insights from your data.

According to the Google Analytics blog, we should stay tuned for visibility into the feature roadmap, which will give us a better understanding of what's to come.

Event-level metrics are being combined with session-level metrics, providing a more complete picture of user behavior and interactions. This is a significant development in the world of data analysis and reporting.

The exact timeline for this integration is not specified, but we can expect it to happen eventually.

The standard reports in GA4 offer a range of useful insights into your website's performance. You can find reports on funnel visualizations, which allow you to track user journeys through a sequence of steps.

You can create standard or trended funnels to visualize how each step trends over time, or an open funnel to see the sequence of steps a user needs to take.

The Pages and Screens report is a key tool for understanding how users interact with your website. You can use it to see the number of 'session_start' events for each page.

To access the Pages and Screens report, select the 'session_start' event from the drop-down in the Event count column or create a custom report for it.

Unfortunately, page exit metrics are not available in the standard reports in GA4. However, you can find the metric 'Exits' in the Explorer.

To find exit pages and exit % in GA4, create a new Free-Form exploration, use Page Path as the dimension and Exits as the metric.

Here are the types of funnel exploration reports you can create in GA4:

Getting an ROI is crucial for any business. It's a measure of how well your investments in data analysis and reporting are paying off.

To get a clear picture of your return on investment, you need to link your Google Analytics 4 properties to Google Ads. This will help you understand the impact of your ad spend on your website's performance.

The comparison of GA360 to GA4 is a bit misleading. Instead, consider comparing GA360 to the upcoming enterprise version of GA4, which is expected to offer similar features.

By tracking your website's performance in Google Analytics 4, you can identify areas where you can optimize your ad spend and improve your ROI. This is especially important if you're using Google Ads to drive traffic to your website.

Creating accurate custom reports in Google Analytics 4 (GA4) can be a challenge, especially when it comes to traffic type. To avoid issues, make sure to share the report with a colleague and ensure they have access to the GA property.

You'll also want to check if the data has thresholds applied, as this can impact the accuracy of your report. If thresholds are in place, try reducing the number of dimensions and increasing the date range.

It's also essential to check your data retention settings, as this can affect the explorer views in GA4. Data retention should be set to 14 months to ensure accurate reporting.

If you're working with large datasets, be aware that data sampling thresholds apply in the Explore workspace when creating custom reports with over 10 million events.

Finding Bounce Rate can be a bit tricky in GA4, but it's now available thanks to Google's July '22 update.

Bounce rate is a percentage of sessions that were not engaged sessions.

If a user visits your website, reviews content on your homepage for a few seconds, and then leaves, the session will count as a bounce.

You can learn more about bounce rate in GA4 to get a better understanding of this metric.

Finding 'Time on Page' is no longer a straightforward task in Google Analytics 4, as it's been replaced by a more meaningful metric.

The 'average engagement time' is now the go-to metric for understanding how much time users spend on your website. It can be found in the 'Pages and Screens' report in GA4.

This change was made because the old 'Time on Page' metric had its flaws, particularly when it came to calculating time on exit pages or pages where users bounced.

The 'average engagement time' is a more accurate representation of user engagement, making it a valuable addition to your data analysis and reporting.

You can view traffic by channel in GA4 by navigating to Reports > Acquisition > Traffic acquisition and changing the primary dimension to 'Session default channel grouping'.

This will give you a breakdown of your traffic by default channel groupings, which are defined in Google's official documentation.

To see the definition of each channel, check out Google's official documentation.

Unfortunately, you can't edit these channel groupings in GA4.

However, you can create custom channels in Data Studio using a CASE WHEN formula, as explained in this blog post.

Data Studio is a powerful tool for creating custom reports and visualizations, and it's worth exploring if you need more flexibility in your channel reporting.

User reporting in GA4 is a bit different from what you're used to in UA. GA4 uses up to three different identifiers to unify a user's activity.

One of these identifiers is the User ID, which is an optional, manually inputted ID. This is the most accurate way to identify users, and it's commonly associated with a login ID.

Google Signals is another identifier used by GA4. This is data from users who are signed in to Google and have consented to share this information.

If the above options aren't available, GA4 will use a device ID, which is a randomly generated ID stored in a cookie on the user's browser and sent with all analytics hits.

Here's a breakdown of the identifiers used by GA4:

In Google Analytics 4, site search reports haven't been built yet.

This means you'll need to find alternative ways to track site search metrics. One option is to use BigQuery to re-create these metrics, as Google provides a walkthrough for doing so.

Site search metrics are important for understanding user behavior on your website. They can help you identify what users are searching for and what they're looking for on your site.

You can use BigQuery to create custom site search metrics, but it requires some technical know-how.

Easy filtering options are available for standard reports. You can click the "Customize report" button to access these options.

To filter by data stream, click on the vertical dots next to the "All Users" comparison and click Edit Comparisons. From here you can include or exclude based on the dimension Stream ID.

Filtering by data stream is a straightforward process that can be completed in just a few clicks.

You can customize reports in Google Analytics 4 by clicking the "Customize report" button from any standard report, then clicking the vertical dots next to "All Users" comparison and editing comparisons.

Filtering by data stream is a breeze - simply click the "Customize report" button, then edit comparisons to include or exclude based on the dimension Stream ID.

Hardcoding on the website is an alternative to using Google Tag Manager for events, but Google Tag Manager is recommended as it's easier and more efficient.

The new Google Tag Update is a significant change that affects how you configure your tags in Google Analytics 4. Google will automatically upgrade your GA4 configuration tag to the new Google tag.

You won't see the page view option if you use the new Google tag, but it's enabled by default. This means you don't need to worry about setting it up.

To configure your tag, you'll need to provide your measurement ID or tag ID, which can be found inside GA4. This is a crucial step to ensure your data is sent to Google Analytics.

Google provides a lot of support for this tag migration, especially for eCommerce tracking. This is a complex process that requires careful setup.

The new Google tag works the same as the GA4 configuration tag, so you don't need to worry about changing your workflow. It's a seamless transition that will help you take advantage of Google Analytics 4's advanced capabilities.

In some cases, you may need to re-tag your entire website for GA4. This is because GA4 uses a different data model, which requires a different method of collection.

Filters are a great way to tailor your reports to your specific needs. You can easily filter by data stream in standard reports.

To do this, click the "Customize report" button from any standard report. This will open up a range of options for you to choose from.

Clicking on the vertical dots next to the "All Users" comparison and selecting "Edit Comparisons" allows you to include or exclude specific data streams based on their Stream ID.

Deleting custom dimensions will remove them from your GA4 platform, and they will only be available when you reinstate the custom dimension.

If you delete custom dimensions, you'll need to set up your BigQuery connection to review all events available in your property, as this will provide a permanent store of your GA4 user-level data for every triggered event.

Deleting custom dimensions is a permanent action, so make sure you have a plan for reinstating them if needed.

Importing custom reports from GA4 into Looker Studio can be a bit of a challenge. However, it's not impossible, and with the right approach, you can make it work.

Ten-second session lengths are a common benchmark for measuring landing screen and engagement, but it's essential to consider the context of your website or app. If you're seeing consistently short session lengths, it's worth investigating to understand what's happening.

Geo.location fields in Google can be a bit tricky, and if you're using them, be aware that they're mediated to prevent identifying users. If you're unsure about the implications, it's a good idea to consult with a GDPR expert.

Analyzing client behavior and website or app goals on specific pages can help you better understand the data and make informed decisions.

Connecting Optimize to GA is a bit tricky, but don't worry, I've got you covered. There is no connector for Google Optimize and GA4, but this is on the roadmap and should be released soon.

Google has shared that they're working on it, so it's just a matter of time before you can connect the two.

Setting up content grouping in Google Analytics 4 (GA4) requires a few steps. You need to set up content grouping via Google Tag Manager.

To do this, go to your GA4 config tag in Google Tag Manager. Add a row to the Fields to Set section with the field name 'content_group'.

Create a regex table with dimension {{Page Path}} to develop a set of rules for your content grouping. This is similar to creating content grouping in the UI for Universal Analytics.

Preview and publish your changes, and the data will become available under Content Grouping in your GA4 property.

You can't directly compare Universal Analytics data to GA4 data due to their different data structures. The data model in GA4 allows for more intuitive structuring, but this also means you can't automatically draw direct comparisons.

Marketers can replicate the structure of UA reports in GA4, but it requires careful planning and a thoughtful approach to event taxonomy. This will help you get the most out of your GA4 data.

Each analytics setup comes with unique challenges, so it's hard to give a one-size-fits-all recommendation. However, setting up all localisations in the same property can provide accurate aggregated reporting and better attribution.

You can compare aggregated reports from UA and GA4, but this process relies heavily on manual data processing and mapping events from UA to their GA4 counterparts. This can be a time-consuming task, but it's doable with the right approach.