The Reports snapshot in GA4 can be a great starting point for your next analysis. I say can as with many other reports and settings in GA4, you need to customize it to take full advantage of it.

As you can see, it is the ‘main’ overview report in the navigation bar displayed on top in the Reports section in GA4.

In this blogpost I will run through this report in great detail and share 10 steps to set it up in an optimal way for your organisation.

Table of Contents

• Be mindful with access permissions
• Create a copy of the Reports snapshot
• Define a list with your main KPIs
• Learn all about summary cards in GA4
• Remove any unrelated summary cards
• Ask for input from the internal team
• Choose one of these three routes
• Create the new Reports snapshot
• Validate the new report internally
• Implement the new Reports Snapshot

Following the steps below will not only help you get more out of this specific report, but also in general out of GA4.

1. Be Mindful with Access Permissions

It doesn’t take a lot of work to mess up the GA4 default reporting UI. Exactly, it is very easy to do and in many cases difficult to undo.

Deleting, modifying and/or changing the Reports snapshot requires Editor or Administrator access to the GA4 property involved.

Make sure to provide the Editor level access (or above) only to a small set of users who require this level of access and are very capable to work with GA4.

Here you can see the difference between a ‘Marketer’ and ‘Editor’ (in relation to this report).

Anyone with ‘Marketer’ or lower access level permissions won’t see the ‘Customize report’ button on top.

2. Create a Copy of the Reports Snapshot

There is one tricky thing you should know. The initial report is permanently gone if you decide to set another overview report as the Report snapshot.

In the overview below (Library GA4) you can find the Reports snapshot and the available overview reports in GA4.

• Clicking on any link containing the text ‘Set as Reports Snapshot’ will set that report as the new ‘Reports snapshot’ in GA4.
• The consequence is that the original ‘Reports snapshot’ report is permanently removed from the library.

You can prevent this from happening by copying the original ‘Reports snapshot’ first.

In this case you will always have a copy available (with the same report cards) after setting a different overview report as the Reports snapshot. It takes less than one minute, but can save you a lot of time.

Note: You can always rebuilt the original Reports snapshot, i.e. by reviewing this report in a different GA4 property, but it can take quite some time to do so.

3. Define a List with Your Main KPIs

If you haven’t yet, now it is time to set up a list of KPIs, containing your most important metrics and dimensions in GA4.

Back in 2016 I interviewed 42 Analytics experts on this topic. Reading this blog post is a great way to get started.

Need some more inspiration? Make sure to check out this blogpost from Avinash Kaushik about ‘the most important business KPIs’.

In the past I have attended quite a few so-called KPI brainstorm sessions and in my experience they aren’t that effective in many cases.

Stacey Barr approaches it from an alternative way called the PuMP method. Watch the video below to learn more.

4. Learn All About Summary Cards in GA4

The Report snapshot report and all other overview reports in GA4 consist of one to sixteen summary cards.

In short, summary cards highlight the main ‘insights’ from a detailed report. You need to have ‘Editor’ or ‘Administrator’ access to create, modify, apply or delete these cards.

At the time of writing, this is the default set up of the Reports snapshot.

• The Reports snapshot report contains thirteen summary report cards.
• Eleven cards contain a ‘deeplink’ to a detailed report, specified in ‘green’.
• All report cards summarize metrics and dimensions that might or might not be relevant for your business.

In the screenshot below you can see the available options when creating a summary card.

More specifically, you have the following options when creating a card:

• Add one or more dimensions from the ‘linked’ detailed report.
• Add one or more metrics from the ‘linked’ detailed report.
• Choose one of the visualization options:
  • Bar chart
  • Pie chart
  • Line chart
  • Table
• Add a filter – one that is currently active in the detailed report or create a new one.
  • Include or exclude statement based on one or more dimensions.
  • You cannot use metrics in filters.

Now you know more about the building blocks of a summary card, let’s discuss why you want to change the default setup in most cases.

5. Remove Any Unrelated Summary Cards

Here are three recommendations based on the default Reports snapshot setup:

• Remove the ‘Conversions by Platform’ card if you only have a Web or App data stream, but not both
• Remove the ‘Items purchased by Item name’ card if the E-commerce module is not applicable to you
• Remove any other card that doesn’t provide sufficient value for you

For now, I would leave the rest as it is and park this until step 7 where we discuss everything in greater detail.

6. Ask for Input From the Internal Teams

Do you fully ‘own’ this area of GA4/Analytics or might it be worth asking input from the internal team(s)?

This is a question you can only answer, but in most cases you will want to have a quick chat with other team members.

It doesn’t have to be specifically aimed at KPI setting, but can cover a lot of practical questions regarding the Reports snapshot:

• Overall structure – what topics to cover?
• Most suitable visualizations
• Include all data or specific channels/segments
• Number of report cards

7. Choose One of These Three Routes

I recommend choosing one of these three options when setting a new Reports snapshot for your website and/or app.

1. Modify the default Reports snapshot
2. Use a default overview report
3. Create an entirely new version

Let’s discuss each of these in more detail.

1. Modify the Default Reports Snapshot

The first option requires you to think about a suitable, (slightly) modified version of the default Reports snapshot.

• This route is most suitable if you think that you can re-use many of the cards in your new report.
• It’s a mixture from all – the default report pulls data in from many areas (covering Acquistion, Engagement, Monetisation and Retention).
• You can choose to delete certain cards as discussed in step 5.
• You can add new cards that are available by default.
• Another option is to define a new summary card to include here.

Note: as mentioned, always create a copy of the Reports snapshot first as it is easy to mess up things in GA4.

2. Use a Default Overview Report

The other option that you have is to set one of the (default) overview reports as the Reports snapshot.

You can easily do this in three steps:

Step 1: click on ‘Reports’ in the GA4 property.

Step 2: at the bottom, click on ‘Library’.

Step 3: search on ‘overview’.

Step 4: click on ‘Set as reports snapshot’ (overview report of your choice).

You can also copy an existing ‘overview’ report and make some slight changes before setting it as the Reports snapshot.

This route can be beneficial if you want to have a Reports snapshot covering the report topics from one collection (i.e. Monetisation).

3. Create an Entirely New Version

This route is for you if you want to create a new version from scratch.

I think it especially suits those of you with more experience in GA4 and customizing the GA4 reporting UI.

Also, this is probably the best choice if you have an entirely different KPI list and thoughts on how to set up the Report snapshot.

It takes three steps to get started and many more to finetune your new report version.

The first two steps are the same as you saw previously:

1. Click on ‘Reports’ in the GA4 property.
2. At the bottom, click on ‘Library’.

The third and last step to get started is to click on ‘Create new report’.

Select ‘Create overview report’ to create a report that suits as the Reports snapshot.

• You can add up to 16 cards to this blank, ‘unconnected’ template.
• Important to understand about cards:
  • If the detail report through which the card is created is part of a collection, you can find the card in the Summary Cards tab.
  • If the detail report through which the card is created is not part of a collection, you can find the card in the Other Cards tab.

It is very likely that additionally you want to set up extra cards in detail reports to include in the new version of the Reports snapshot.

You will find the option to create new cards at the bottom of all detail reports (example below).

8. Create the New Reports Snapshot

By now you should have a good understanding of:

• The role of the Reports snapshot in GA4.
• The main KPIs for your business.
• How you can customize the Reports snapshot.
• The three routes you can take and which one suits your situation best.

I recommend reading the previous sections again if not everything is clear yet.

And then it’s actually time to create the new version and that’s the part I can’t and won’t fill in for you!

Good luck and take the time to create the version that fits your business’ needs and goals best (you can always improve the initial, new version at a later time).

Tip: always create a copy of the Reports snapshot and any other overview report you like to potentially set as the new version first. By doing this you won’t risk losing any templates that might come in handy later on.

9. Validate the New Report Internally

I recommend talking to a few ‘relevant’ co-workers in your organization before implementing the new version of the Reports snapshot.

Choose wisely and only involve people who have sufficient experience with the GA4 UI and visualizing data in reporting dashboards.

You might want to involve some of the team members that supported you in step six. Always be open to make any changes if good suggestions are shared!

10. Implement the New Reports Snapshot

We are now at the final step, implementing the brand new version of the Reports snapshot.

Again, we will look at these three scenarios:

1. Modify the default Reports snapshot
2. Use a default overview report
3. Create an entirely new version

1. Modify the Default Reports Snapshot

You need to overwrite the current Reports snapshot if you implement a new version after modifying the current one.

But again, it is better to work with a ‘copy’ and set that one live after making all required modifications.

2. Use a Default Overview Report

You can change the Reports snapshot from the Library section if you want to implement a different overview report as the Reports snapshot.

3. Create an Entirely New Version

And this is how it looks like if you create an entirely new version of this report in the GA4 reporting interface.

The action you need to take is similar to the second scenario. The only difference is that this is a new overview report gathered from existing and new summary cards.

Note: additionally, I recommend informing everyone who has access to the same GA4 property about this change. Changing the structure and contents of this report affects all users who have access to the same GA4 property.

This is it from side. Happy to hear your thoughts on this section of GA4. Did you already modify the Reports snapshot and if yes, what changes did you make?

The post 10 Key Steps to Leverage the Reports Snapshot in GA4 appeared first on Online Metrics.

Currently, Google Analytics 4 provides you with a small reporting section dedicated to conversion paths. You can find it within the ‘Advertising’ section under Attribution.

At the time of writing, this reporting section is less comprehensive compared to Universal Analytics and Attribution.

The fact that attribution is becoming increasingly difficult and the relatively small section in GA4, shouldn’t hold you back from getting the most out of it.

In this blogpost I will specifically talk about conversion paths in GA4 and how to leverage them in the best possible way.

“A conversion path is a guided journey a potential customer takes on your website to meet your goal. This path is different for all brands and aspirations but typically involves multiple touch points, pop-up reminders, email notifications, and other tactics to encourage an action.” by ClearVoice

Table of Contents

• Why is Attribution Increasingly Difficult
• Conversion Paths Report in GA4
• Available Conversion Models
• Report Prerequisites
• Touch Points Definitions
• Conversion Path Setup Options
• GA4 Conversion Path Insights
• Concluding Thoughts

We have lots to cover, let’s dive right in!

Why is Attribution Increasingly Difficult

The (non-direct) last click attribution model is there for centuries, but does it tell the complete story?

Potentially, two or more channels are involved and attribute to the same conversion.There are many more things that make attribution a real challenge.

A quick overview of related factors:

• Multiple browsers (often) lead to inaccurate attribution / user journey and conversion data.
• Multiple devices (often) lead to inaccurate attribution / user journey and conversion data.
• Cookie deletion skews user-level data and thus attribution path analysis.
• Data privacy challenges i.e. with iOS Apps lead to capturing incomplete/inaccurate data.

And this is just a short list, many more things play a role here and the list is increasingly growing.

Still, it makes a TON of sense to think about attribution if you want to learn about and act on user behavior and conversion paths.

The next chapters all focus on one specific report and how it can help you to understand a bit more about the user journey.

The limitations above (by far not a complete list) are no reason to skip the GA4 conversion paths report and the attribution section as a whole. Just know they have an impact on the data that you work with.

Conversion Paths Report in GA4

The Conversion Paths reports shows how conversions are attributed to different sources of traffic.

Available report components to play with that will impact the report outcome:

• Selected conversion event(s)
• Path length included, by default all touch points (max. 20)
• Date range, by default last 28 days
• Reporting dimension, by default Default channel group
• Attribution model, by default data-driven model
• *Optionally: you can include a filter to narrow the report data down to a subset of users

In addition, you can find out how conversion credit is attributed to one or more sources of traffic, example below.

The example above (row 41) shows that 35% of all conversions / conversion credit (1,002.00) is attributed to one Cross-network touch point and the other 65% to the three Organic Search touch points.

Later in this article I will explain about how to set it up correctly so you can gain valuable insights.

In the Google Merchandise Store, there are many conversion events defined and you need to modify the settings to get meaningful data to work with.

Available Conversion Models

From September 2023, Cross-channel data-driven attribution, Cross-channel (non-direct) last click, and Ads preferred last click are the only available models in the Attribution reports of GA4.

Additionally, there are ways to leverage BigQuery to build your own attribution models.

Report Prerequisites

The main prerequisite for using this report is to have data collected for one or more conversions.

This can either be Ecommerce transactions (via ‘purchase’ event) or non-Ecommerce events marked as conversions or both.

Three more things:

• I recommend to wait for at least 30 days (after conversions start tripling in) to have meaningful data to work with.
• In general, the more conversions on your website or app, the more meaningful this report becomes.
• Make sure to connect Google Ads to GA4 if applicable for your business. Not doing so will result in inaccurate data and attribution.

Touch Points Definitions

Google Analytics distinguishes three types of touch points in the conversion paths reports:

• Early touch points: First 25% of touch points in the path rounded to the nearest whole number.
  • This segment is empty if the path has only one touch point.
• Mid touch points: The middle 50% of touch points in the path.
  • If the path has < 3 touchpoints, this segment is empty.
• Late touch points: The last 25% of touch points in the path, rounded up to the nearest whole number.
  • If the path consists of just one touch point, this segment has the touch point.

Here is an example when I only select the ‘purchase’ conversion.

You can hover over each of the visible channels to get more details on conversion credits.

In terms of conversion attribution, Organic Search has a stronger impact in the middle of the conversion journey and towards the end. It doesn’t seem to initiate the conversion.

Of course, you would need to dig deeper to find out whether there are differences between branded and non-branded Search as that potentially has an impact as well.

If you hover over Cross-network, you can see:

• Early touch points: 7.58
• Mid touch points: 3.59
• Late touch points: 84.22

You can conclude here that the Cross-network channel is primarily closing the sale, but doesn’t have a (strong) contribution at the top of the funnel (seeding awareness).

The analysis is made based on the Data-Driven attribution model.

Keep in mind:

• The early touch points and mid touch points remain empty if you select Cross-channel (non-direct) last click as the attribution model.
• The early touch points and mid touch points show a very small number of data/channels (negligible) if you select Ads preferred last click as the attribution model.

Conversion Path Setup Options

You have different options when setting up the conversion path report for maximum insights. I will discuss all the features below.

1. Date Range

You are flexible to select the required date range similar to other reports.

Keyboard shortcuts might come in handy here.

You can use them to quickly select the required dates. In general, I recommend using a long enough period (at least a few weeks) when doing this conversion path and attribution analysis.

2. Conversion Events

In most cases you want to limit your conversion paths analysis to one single conversion event.

For example, your analysis wouldn’t make a lot of sense it you include 11 different conversion events. For Google, an E-commerce store, it sounds logical to only select the ‘purchase’ event.

The macro goal of your website is most often the leading one in this type of analysis.

3. Path Length

On default, all user purchase journeys are included (up to 20 touch points).

You can modify the content and outcome of the report. Let’s assume you want to only review the conversion paths for user purchase journeys having less than or equal to three touch points.

And below you can find the new report.

4. Filters

You can make a more granular conversion paths analysis by applying a filter with one to five conditions.

Here is an example of a filter with two conditions:

• First user default channel group = Organic Search
• Country = United States

It’s always important to ask (yourself) questions before setting up an analysis. And this is even more important the more granular you get with attribution analysis or another advanced data analysis.

5. Conversion Source Dimension

At the time of writing you have four different options when it comes to the conversion source.

On default, the conversion source is set to ‘default channel group’. You have three other options:

• Source
• Medium
• Campaign

It depends on your implemented (UTM) tracking, but getting too granular here might not bring the desired insights in performance.

6 Applied Attribution Model

On default, the Attribution model is set to Data-Driven attribution.

From September, the only two other options are Cross-channel (non-direct) or Ads preferred last click.

In a lot of cases I like to work with Data-Driven attribution instead of the other two models still available from September’23.

7. Rows per Page

Google allows you to increase the rows per page from 10 to 250 max.

In addition, you can use ‘pagination’ to start at a certain row, i.e. 12.

GA4 Conversion Path Insights

Potentially, you can get many different insights from the conversion path report in GA4.

Here are three types of insights:

1. Channel role in purchase journey.
2. Device-level attribution per channel.
3. Top conversion paths.

I will discuss them in more detail below.

1. Channel Role in Purchase Journey

For the top channels, visible on top (screenshot below) it is possible to get a good understanding of channel role and how it attributes to conversion.

In the example above I hover over ‘Cross-network’.

• The data visualization clearly indicates that ‘Cross-network’ is strong in closing conversions.
• On the other hand, its role as an initiating or assisting channel is very weak.

By doing this exercise you can quickly learn more about your channels and each predominant role(s).

The downside of this visualization is that Google doesn’t display the exact values of all channels, but only the top five in each chart.

2. Device-Level Attribution per Channel

Let’s assume you like to see whether performance on ‘Desktop’ deviates from ‘Mobile’ for the Cross-network channel. In this case User ID (measuring across multiple devices is not implemented).

You can use filters to include only data from a specific segment.

Here are the results (absolute and percentage).  

And here is the visualization for easy review.Conclusion:

• On both Desktop and Mobile, the Cross-network channel is by far the strongest in closing conversions (purchases).
• On Mobile, it tends more strong towards closing where on Desktop more assisted conversions are measured.

3. Top Conversion Paths

The chart below allows you to analyse the top (10) conversion paths in more detail.For the Google Merchandise Store (27 Apr – 24 May, 2023) it turns out that:

• Direct takes credit for over 50% of the purchases (sounds like an implementation issue).
• Many conversion shows a (relatively) long path to conversion (in terms of days and touch points). ‘Organic Search x 20’ also sounds like a measurement issue.
• The top 10 conversion paths (less than 10% of all paths) are good for 80% of all conversions (impacted by skewed Direct conversions).

This is just the tip of the Iceberg and you can gain many, many more insights if you ask the right question before digging into this report in more detail.

Concluding Thoughts

The GA4 Conversion Paths report is a welcome addition to the set of native reports available in the standard GA4 reporting UI.

Most companies can benefit from using this report and the Attribution section in general, but it is not the holy grail. Keep in mind that Attribution is becoming increasingly difficult.

It would be great if the options for analysis grow over time so that you can further customize it and analyse everything in more depth. Compared to Universal Analytics, the Attribution section is relatively small and you won’t find the same reports as in UA.

By now you know how the report works, what settings are available and how you can gain particular insights by applying several analysis techniques.

This is it from my side! What do you think of the Attribution section in GA4 and in particular the Conversion Paths report?

The post In-Depth Guide on Conversion Paths in GA4 (Google Analytics 4) appeared first on Online Metrics.

As with many other topics, you need to dive deep into the ‘reporting identity’ feature to learn exactly how it works and impacts your reports and data.

I will start with a basic introduction to the ‘reporting identity’ feature before discussing key concepts you need to understand when working with this feature in GA4.

Table of Contents

• What is the Reporting Identity in GA4
• Where to find the Reporting Identity
• Impact of Changing the Reporting Identity
• Exclude Google Signals Data
• Concluding Thoughts

Let’s dive right in and start with a basic introduction to GA4’s ‘reporting identity’ feature.

What is the Reporting Identity in GA4

The reporting identity in GA4 refers to how Google Analytics measures the behavior of users across multiple platforms and devices.

Here is an example of Jennifer’s purchase journey when buying a new pair of sunglasses.

• In the morning – at the office – she browses on Google Chrome (desktop) to stumble upon your sunglasses store online.
• Late afternoon she checks in again via her mobile phone (Safari) and makes a note on three potential sunglasses to buy.
• In the evening – after having her coffee – she takes her private tablet to eventually buy one pair of sunglasses.

Each of these three sessions is measured separately, but based on the reporting identity set and the GA4 implementation, they could potentially be all stitched together into a single cross-device user journey.

I say ‘could’ as there are many factors impacting how these measurements come through in GA4.

Identity Spaces

GA4 comes with four so-called identity spaces to measure users across multiple platforms and devices. And to (try to) unify sessions from the same person into a single user journey.

• User-ID
• Google Signals
• Device-ID
• Modelling

I will now explain how each of these methods work.

User-ID

The User-ID is a unique identifier that you assign to your users when they authenticate during their session (i.e. when logging in to your website or app). The great thing is that this User-ID remains the same regardless of what device or platform is used when accessing your website or app.

In general, the User-ID is the most accurate method to identify unique users, but it is only available for a (small) subset of users (after authentication). Also, keep in mind that implementing this User-ID feature requires a strong privacy check up.

“Note: You’re responsible for ensuring that your use of the user ID is in accordance with the Google Analytics Terms of Service. This includes avoiding the use of impermissible personally identifiable information, and providing appropriate notice of your use of identifiers in your Privacy Policy. Your user ID must not contain information that a third party could use to determine a user’s identity.”

Google Signals

Google signals is data collected from users who are signed in to Google. When this data is available, GA4 associates event data it collects from users with the Google accounts of signed-in users who have consented to sharing this information.

Also, for this feature I recommend reviewing your privacy policy and specific requirements in your country.

Device-ID

The Device-ID is a unique identifier for the device you are using to access a website or app.

This is how the Device-ID is visible across websites and apps:

• Websites pass the client ID to GA4.
• Apps pass the app-instance ID to GA4.

The Device-ID is less reliable than the User ID, as users can clear their cookies and/or use different devices. In that case the same person is visible in GA4 as multiple users (which is often the case).

Modelling

The last identity space is ‘Modelling’. This is a model where Google uses machine learning to predict the behavior of users who don’t accept the analytics cookies. It helps to fill in these gaps using the data of similar users who accept the tracking cookies from the same property.

You might see this in your GA4 property under reporting identity:

This is because ‘Modelling’ comes with a long list of requirements:

• Consent mode is enabled across all pages of your sites and/or all app screens of your apps.
• Consent mode for web pages must be implemented so that tags are loaded before the consent dialog appears, and Google tags load in all cases, not only if the user consents (advanced implementation).
• The property collects at least 1,000 events per day with analytics_storage=’denied’ for at least 7 days.
• The property has at least 1,000 daily users sending events with analytics_storage=’granted’ for at least 7 of the previous 28 days.

Also, whether or not to implement Google Consent mode, is a topic that would require an entire blogpost by itself.

Where to Find the Reporting Identity

The ‘Reporting Identity’ settings is visible under the property settings (below).

By default, when creating a new GA4 property, the reporting identity is set to ‘Blended’.

This means that – when appropriate – Google Analytics will evaluate all four identity spaces to associate events with users.

And this is how each of the methods work:

• Blended: Google Analytics uses the user ID if it is collected. If no user ID is collected, then Analytics uses information from Google signals if available. Analytics will use the device ID if both identity spaces don’t yield any results. If no identifier is available, Analytics uses behavioral modeling.
• Observed: Google Analytics uses the user ID if it is collected. If no user ID is collected, then Analytics uses information from Google signals if available. Analytics will use the device ID if both identity spaces don’t yield any results.
• Device-based: Google Analytics only uses the device ID and ignores all other identity spaces.

In the next section we will evaluate the pros and cons of changing the reporting identity.

Note: ‘Editor’ or ‘Administrator’ access to the GA4 property is required to make changes to the reporting identity setup.

Impact of Changing the Reporting Identity

It’s very important to note that changing the reporting identity doesn’t impact the underlying data in GA4.

But, it does retroactively impact the GA4 data that you see in your reports for all users that have access to the same GA4 property. Meaning you can experiment with the different reporting identities to see how it impacts the reports and data, but be aware of the impact on everyone who is accessing this property.

Especially, in larger organizations with many people having access to the same properties, this requires (in my opinion) a careful approach when making these type of changes to GA4 settings.

What Reporting Identity is Best?

In my experience, most companies need to be very careful taking these two steps:

• Enabling Google Signals.
• Primarily using the ‘Blended’ or ‘Observed’ reporting identity setting.

Google Signals

Whether or not to link Google Signals partly depends on the User-IDs in the platform and if you plan to leverage that feature.

• Go ahead with linking Google Signals if you don’t need the User-ID.
• Think twice before linking Google Signals if leveraging the insights from the User-ID within the GA4 UI are important to you.

Impact of linking Google Signals:

• To protect the privacy of Google’s proprietary data (device-graph), Google will threshold your data. The threshold means that if a report contains rows with a small number of users (less than +/- 40 per row), Google will ‘hide’ that row from your report.

Note: For your reports to include Google-signals data you need a monthly average of 500 users per day per property.

‘Blended’ or ‘Observed’

Unfortunately, once Google Signals is linked, it can cause permanent thresholding in your GA4 property using User-id identity methods (Blended or Observed).

Google Analytics applies ‘thresholding’ to your report if these three conditions are met:

• Google Signals is linked.
• The reporting identity is set to Blended or Observed.
• A report contains rows with small user or event numbers.

In short, I recommend using ‘device-based’ in most cases and only switching to ‘Blended’ or ‘Observed’ when appropriate.

Update October 2023: you now have an option to exclude Google Signals data from your reports and explorations.

How to Avoid Thresholding in GA4

I will create a more thorough blogpost on this topic in the future.

For now, these five methods will help you to remove or limit the thresholding in GA4:

1. By default, use the device-based reporting identity.
2. Reduce the number of dimension values with low user/event counts by increasing your date range.
3. Turn off Google Signals or exclude Google Signals data in reporting identity.
4. Use BigQuery (in addition to the GA4 UI).
5. Set up two GA4 properties: one with Google Signals enabled and the other one without.

Exclude Google Signals Data

Early October 2023 Google launched a new feature.

“If you’ve activated Google signals for your property, you can now turn off Include Google Signals in Reporting Identity on the Data Collection page in Admin to omit specific demographics and interest data from reports—specifically, data from signed-in, consented users. It can help to reduce the likelihood of data thresholding if your property uses Blended or Observed.”

It sounds like a great feature, but in larger organizations – with many GA4 users – switching on and off Google Signals (if needed) might lead to more confusion.

It’s great to see Google listening to the community, but in my opinion there are still some pros and cons when looking at this feature.

You can see your data through different angles which is great, but everybody in the organization needs to be aware of what setting is implemented at a certain point in time.

Concluding Thoughts

Many companies are unaware of the actual impact of turning on Google Signals in relation to the reporting identity feature in GA4.

By now you should have a better understanding of the reporting identity and related concepts as Google Signals and data thresholding.

As mentioned, turning on Google Signals can have a permanent impact on your data.

As a ‘solution’:

• You can set up two distinct GA4 properties, one with Google Signals turned on and the other without Google Signals. Not ideal, but for some companies it is currently the best option available.
• Since early October 2023 you can go a different route and turn off Google Signals in the Reporting Identify (via data collection). A great new feature, but you need to understand the implication it has for all users of the same GA4 property if your setting is not permanently defined and you regularly change it over time.

Also, I recommend experimenting with the different reporting identity options available and analyzing the impact on the metrics and dimensions in your GA4 reports.

This is it from my side. As always, let me know your thoughts and any tips or questions you have in the comments below!

The post How to Best Leverage the Reporting Identity in GA4 appeared first on Online Metrics.

In my experience, the majority of Ecommerce shops and non-Ecommerce shops with GA4 Ecommerce implemented, want to report on Conversion Rate metrics. Currently, you need to take a couple of steps before you can see this metric in the GA4 UI.

In the previous blogpost we ran through conversion rates and GA4 where in this blogpost I will introduce you to Ecommerce conversion rate metrics in GA4.

Table of Contents

• Ecommerce Conversion Rate
• Standard Reports
• Explorations
• Looker Studio
• Concluding Thoughts

I will start with a general introduction to Ecommerce conversion rate and GA4.

Ecommerce Conversion Rate

The Ecommerce conversion rate (Ecom CVR) in Universal Analytics (GA3) is based on sessions. In other words, in what percentage of sessions does the user convert?

In my blogpost about the conversion rate in GA4, you can read about both session as well as user level conversion rates and the benefits of using both. You will get much more context around user behavior and conversions if you analyze these CVRs on both scopes.

Our goal in this blogpost is to demystify the Ecommerce conversion rate in GA4 at the session and user level.

Standard Reports

Adding the Ecommerce conversion rate metric to GA4 in standard reports requires a few, easy steps.

Review this part of my Conversion Rate in GA4 article to exactly learn how to do this.

In short, here are all steps you need to follow:

• Navigate to ‘Traffic acquisition’ within the reporting UI.
• Click on ‘Customize report’ in the top right corner.
• Click on ‘Metrics’ under report data.
• Click on ‘Add metric’ link.
• Search for and add ‘Session conversion rate’; click on Save.
• Save changes to the current report.
• Review the new metric in your report.
• Under ‘Session conversion rate’: change ‘all events’ to ‘purchase’.

Keep in mind:

• Edit or administrator access is required for making these changes.
• You need to add this metric to every report where you want it to appear.
• You can change the order of the metrics; i.e. you can list it next to the ‘Conversions’ metric.
• The ‘user conversion rate’ metric is also available for you to include in a report of your choice.

Explorations

At the surface, it seems impossible to have the Ecommerce conversion rate metric in an Exploration in GA4.

Currently, there is no native Ecommerce conversion rate in GA4.

But, you can apply a few tricks to see this metric in your reports.

You will find more details about the setup below, but this is how the final report looks like:

Let me explain a bit more about the setup (you can tweak it and include additional/other metrics and dimensions where needed).

Ecommerce Conversion Rate Report Setup

Here you will find the exact setup of the GA4 Ecommerce conversion rate report.

Dimensions

• Event name – required, as you need to use it as part of the filter
• Session default channel grouping – optional, you can include it to segment the Ecom CVR at the channel level

Rows

Add the ‘Session default channel grouping’ here. ‘Event name’ is not required as it solely functions as a filter.

Metrics

• Session conversion rate – required, as you transform this metric into the Ecommerce conversion rate in GA4
• Conversions – optional, you can include it to provide more context around the Ecom CVR
• Sessions – optional, you can include it to provide more context around the Ecom CVR

Values

Similar to ‘Metrics’; the only difference is that by having them here you actually see them in the report.

Also, the order is a bit different:

• Conversions (sort order = descending)
• Sessions
• Session conversion rate

The above setup reflects the importance of each channel in terms of transaction volume.

Filters

This is the most tricky part.

• Event name matches regex ‘^(session_start|purchase)$‘

Note: you need to add the ‘purchase’ AND ‘session_start’ event to include all sessions and not only those with a purchase.

Caveats with ‘session_start’ Event

Most people might not know, but not all sessions include the ‘session_start’ event (below).

You will see this when you create a segment within the Exploration.

And here is the segment (you can review the details before saving).Basically it means that the reported numbers in your Exploration might deviate a bit from what you see in the standard reports, but the difference is minimal.

Here is an example (numbers are based on six months of data):

Exploration vs Standard report

• Conversions (Transactions): 38529 vs 38529
• Sessions: 1158973 vs 1164512
• Session conversion rate (Ecom CVR): 3.32% vs 3.31%

As mentioned, the impact is minimal, but as an Analyst it is always good to know what is causing minimal differences in some cases.

User Ecommerce Conversion Rate Report

Simply replace the ‘Session conversion rate’ metric by ‘User conversion rate’ and add a few user-scoped dimensions and metrics to analyze the user Ecommerce conversion rate in GA4 in an Exploration report.Here are the numbers based on six months of data:

Exploration vs Standard report

• Conversions (Transactions): 38529 vs 38529
• Total users: 756971 vs 761059
• User conversion rate (Ecom CVR): 4.89% vs 4.87%

You will see that the difference between the User-scoped Ecommerce conversion rate in an Exploration and Standard report is negligible again.

There is one more detail I need to share:

“You can’t calculate the Ecom CVR as ((Transactions / Total users) * 100%); you would need to create a segment reflecting the Users w/ Purchase and divide it by Total users instead.”

I hope you are still with me! Let’s dive into Looker Studio in the next section.

Looker Studio

Tip: use GA4SPY to find out details about metrics and dimensions available in GA4.

It shows us there are specific Conversion Rate metrics available in GA4. And that they are not available in Explorations (yet).

Opening Looker Studio will also reveal these metrics. But, the report breaks when you try to add these type of metrics:

In this case, instead of trying to fix the error above, I prefer to create a calculated metric directly in Looker Studio.

You can do this via the ‘Chart setup’ environment as shown below.

Step 1: remove the metric that throws an error and click on ‘CREATE FIELD’.

Step 2: create the new metric definition and click on ‘APPLY’.

Step 3: review your report and Ecommerce conversion rate.

There you have it! No error and everything works as expected.

As a final step, I recommend changing ‘Number’ to ‘Percent’ if you like to use it in other reports as well.

You need to go to ‘Manage added data sources’ to do so.

You will see the screen below after clicking on ‘edit’ GA4 – Google Merch Shop.

• Change ‘Number’ into ‘Percent’.
• Add a ‘Description’ if helpful for you and your team.

Creating an accurate ‘user-scoped Ecommerce conversion rate’ would require a few additional steps which go beyond the scope of this article.

Read this article if you want to learn more about calculated fields in Looker Studio.

Additionally, you can work with Google Sheets and first feed your GA4 data into Google Sheets before connecting it to Looker Studio. Your options are endless!

And, if you are skilled enough, BigQuery might come in handy as well.

Concluding Thoughts

Working with Ecommerce conversion rate metrics in GA4 isn’t that easy as long as they are not natively available.

By now you know what strategies to apply to set up and analyze Ecommerce conversion rate metrics in a variety of place:

• Standard reports
• Explorations
• Looker Studio reports

Getting a deeper understanding of GA4 and how it works will empower you and your organization in getting the most out of this platform.

This is it from my side. As always, I am curious to hear about your experience and struggles (if any) when trying to analyze and optimize your business and conversions with GA4.

The post How to Analyze and Optimize on the Ecommerce Conversion Rate Metric in GA4 appeared first on Online Metrics.

For quite some time it was not possible to see and analyze performance based on these type of metrics in the GA4 UI.

Thankfully, conversion rate metrics are now available in Google Analytics 4, but based on my experience not everybody knows how to access and leverage them.

In this blogpost we will dive deep into the conversion rate and GA4 and how to leverage related metrics in various ways.

Table of Contents

• Conversions in GA4
• Conversion Rate in GA4
• Session vs. User Level CR%
• Conversion Rate and Standard Reports
• Conversion Rate and Explorations
• Conversion Rate and Specific Events
• Conversion Rate Summary Cards
• Concluding Thoughts

There is a lot to cover, let’s dive right in!

Conversions in GA4

A conversion is an important action that a user completes on your website or app. In 2014 I already wrote about the importance of setting macro and micro goals.

For Ecommerce websites, the primary goal or conversion is a sale, aka ‘purchase’ event in GA4. Google marks this event automatically as a conversion.In addition, there can be many more relevant events to track as a conversion.

• Submission of a (lead) form
• Account sign up
• Webinar sign up
• Newsletter sign up
• Add to cart action

This is just a starting point; think about your business and (user) goals when setting up conversions in GA4.

Main point here is that having a purchase conversion or setting up other / additional conversions is a prerequisite for working with conversion rate metrics.

“Conversion rate optimization (CRO) is the practice of increasing the percentage of users who perform a desired action on a website. Desired actions can include purchasing a product, clicking ‘add to cart’, signing up for a service, filling out a form, or clicking on a link.”

Conversion Rate in GA4

In Universal Analytics the conversion rate was measured at the session level: (number of conversions) / (number of sessions) x 100%.

You had the option to create a calculate metric at the user level and integrate the user conversion rate in a custom report (or do an analysis outside of the reporting UI).

Based on scope, Google Analytics 4 allows you to analyze two different conversion rates:

• Session conversion rate
• User conversion rate

This can be very helpful as analyzing not one, but both metrics gives a much better indication of performance.

Session vs. User Level CR%

Session or user level CR%, which one is better? Well, it depends!

The user conversion rate is the percentage of users that successfully performed a specific action. Whereas the session conversion rate refers to the percentage of sessions in which any conversion event (or a specific one) took place.

Personally, I think combining insights from both metrics is most powerful for better understanding your audience, traffic sources and conversion actions.

Also, the type of report or analysis where you include one or both metrics is crucial. For example, it makes sense to include a session conversion rate metric if all the other metrics and dimensions primarily relate to session level performance.

Important note: the session conversion rate won’t match if you divide the number of conversions by sessions. This is because of how both metrics are calculated.

• The number of ‘Conversions’ accounts for all conversions
• The conversion rate formula is based on the number of sessions with conversions

For example, a user has triggered four conversion events in one session. This will be counted as four conversions, but it will only be a single converted session (aka session with conversion). This means that the more different conversions you define, the greater the ‘discrepancy’ might be in this report.

Conversion Rate and Standard Reports

On default, the conversion rate is not available in standard reports in Google Analytics 4. Happily, you can customize the reports and add additional metrics.

Here is how to add it to a standard report, i.e. ‘Traffic acquisition’.

Step 1: navigate to ‘Traffic acquisition’ within the reporting UI.

Note: I recommend minimizing the navigation bar to have a better view on the data that is reported.

Step 2: click on ‘Customize report’ in the top right corner.Step 3: click on ‘Metrics’ under report data.

Step 4: click on ‘Add metric’ link.

Step 5: search for and add ‘Session conversion rate’; click on Save.

Step 6: save changes to current report.

Step 7: review the new metric in your report.

Good to know:

• Edit or administrator access is required for making these changes.
• You need to add this metric to every report where you want it to appear.
• You can change the order of the metrics; i.e. you can list it next to the ‘Conversions’ metric.
• In addition, the ‘user conversion rate’ metric is also available for you to include in a report of your choice.

Conversion Rate and Explorations

In addition to the standard reports, you can also include conversion rate metrics in Explorations.

I will use the Google Demo account for this example.

Step 1: navigate to the Explore section and create a blank report.

Step 2: add a name and select the metrics and dimensions to include in the report.

• Dimensions: Session default channel group.
• Metrics: Sessions, Conversions, Session conversion rate.

In this example I have only included ‘Session Conversion Rate’. You can review session and user-level performance side-by-side by additionally including user-level metrics and dimensions.

Step 3: review the report in all detail.

• Session conversion rate can be between 0 and 100% (aggregated and per dimension value, i.e. Organic Shopping).
• Session level conversion rate is 100% for a specific session if one or more conversions occur.
• Session level conversion rate on Google’s website is inflated due to the ‘page_view’ event defined as a conversion (below).

Defining a specific ‘page_view’ as a conversion is OK, but marking every ‘page_view’ event as a conversion will greatly skew your data.

Unfortunately, in Explore, there is no accurate way to narrow down the conversion rate to one specific conversion. However, this is possible in the standard GA4 reporting UI or when doing custom work outside the GA4 UI.

Conversion Rate and Specific Events

As mentioned, you cannot properly analyze the conversion rate of specific events in the Explore section.

Luckily, this is easy to do in the standard reports (after including the session and/or user conversion rate metric).

Step 1: open a standard report that includes a conversion rate metric.

Step 2: click on ‘All events’ and select the event of your choice.

Step 3: sort channels on conversion rate ‘purchase’.

This is a quick and easy way to review and analyze conversion data for one specific event. For better context, I recommend adding the same event under Conversions.

Step 4: select ‘purchase’ under Conversions.

Your report is ready now! The conversion count provides more, relative context to the ‘session conversion rate’ per channel.

Conversion Rate Summary Cards

In my previous blogpost about custom channel groupings in GA4 you can find how to include a specific dimension or metric in a summary card (example #5).

This is a very handy way if you want to include a conversion rate summary in certain report sections in GA4. For demonstrating purposes I have included this report card in the ‘Acquisition overview’ section.

Here you go! You can customize GA4 and the reporting interface to your needs.

Concluding Thoughts

Not everything in GA4 is straightforward or logical.

• Why was conversion rate not available in the beginning?
• Why does Google not automatically integrate it in related standard reports once the metrics was (re)introduced?

Well, it is a fact that not all things go as expected. The great thing is that Google responds to users’ needs (to a certain extent at least).

This blogpost provides clear instructions on how to report on session and/or user level conversion rate within the GA4 UI.

Use this guide for integrating any other metric or dimension that is available, but not included by default.

This is it from my side, hopefully you’ve learned something new!

What is your experience with customizing reports in GA4 and changing the default setup? Do you have any tips to share? Thanks!

The post How to Analyze and Optimize on the Conversion Rate Metric in GA4 appeared first on Online Metrics.

Funnels and customer journey analysis was a missed component for a very long time in the standard GA4 reporting UI.

That’s why a deep-dive into Explore, Looker Studio, Google Sheets or BigQuery (just to name a few) was always required for gaining insights at this level.

The great thing is that you can now review user purchase journey data directly in the standard GA4 UI. And even more powerful is the option to directly embed custom funnel reports. These new reports are accessible to anyone with access to the GA4 property.

Every business is unique and has different needs. And that’s exactly why the custom funnels feature is so powerful.

In this blogpost I will first explain about the new user purchase journey report. After, you will learn how to embed custom funnels within the standard GA4 reporting UI.

Table of Contents

• User Purchase Journey Report in GA4
• Refresher on Funnel Tracking
• Use Case: Custom Purchase Funnel
• Use Case: Custom Lead Generation Funnel
• Custom Funnels and the Standard GA4 UI
• How to Customize Custom Funnels
• Concluding Thoughts

User Purchase Journey Report in GA4

In Google Analytics 4, it takes two easy steps to access the new purchase journey report.

Step 1: navigate to the ‘Reports’ section in GA4.

Step 2: find and select the ‘User purchase journey’ report (under Monetisation).

And below you can find the brand new report in Google Analytics 4.

Here you can find all details on this report, but there are a few important things to note:

• Google applies data thresholds in GA4 because of low numbers for the dimension values ‘mobile’ and ‘tablet’ in combination with reporting identity ‘Blended’ (Google Signals is enabled and in scope).
  • Changing the date range to a longer time period will – in general – reduce the impact of thresholding.
  • Websites or apps with larger funnel numbers ‘suffer’ less.
  • Temporarily’ switching the reporting identity to ‘device-based’ will mitigate the data thresholds issue.
• You cannot customize this particular report.
• You can change the primary dimension for a more granular user journey purchase analysis.
• There is no way to include a secondary dimension in the report.
• You can work with comparisons or filters to analyze and/or compare subsets of data.

Are you interested in creating and sharing your own, unique funnels?

Great news, custom funnels are the solution to customize the funnel reporting experience in the standard GA4 reporting UI. And to match it with your specific reporting needs.

Refresher on Funnel Tracking

Last year I wrote an extensive post on how to best track and report on funnels via GA4 Explore / Advanced analysis.

Unsure about how to define funnels? Make sure to read that article before proceeding to the next chapter.

In short, here are five recommended steps when setting up custom (purchase) funnels:

1. Define all funnel steps.
2. Implement the funnel steps with GA4 events (if possible) or use page views.
3. Align the GA4 UI configuration (if required).
4. Create and double check the Ecommerce funnel in Explore / Advanced analysis.
5. Analyze the custom (Ecommerce) funnel data and take action.

Use Case: Custom Purchase Funnel

Here is an example of a custom purchase funnel set up on behalf of the Google Merchandise Store.

The default ‘user purchase journey’ report only contains five steps. In contrast, the custom funnel above contains eight different user journey steps:

1. PLP
2. PDP
3. Cart
4. Begin checkout
5. Shipping
6. Billing
7. Review
8. Purchase

Note: the button for including a custom funnel in the GA4 standard reporting UI is only visible to Editors and Administrators. This is why I am not able to add the above report via the Library to the standard reports section.

Use Case: Custom Lead Generation Funnel

I will now show an example of a custom lead generation funnel where I have the necessary access rights to include the funnel in the standard reporting UI of GA4.

Step 1: create the funnel in Explore.

Step 2: review the custom funnel report in the Explore section.

In the top right corner you can now see a button with the text ‘Save as a report in the Library’. This is great as it allows me to transfer this report to the standard reporting UI.

I will show how this works in the next section.

Custom Funnels and the Standard GA4 UI

Embedding a custom funnel in the standard reporting UI is very easy and requires just a couple of clicks.

Step 1: click on the link shown above and save the report in the Library.Step 2: navigate to the Library section in GA4 and find this new report.

Step 3: click on ‘Edit collection’ under a Collection (i.e. Custom – link above) and add the report to the selected collection (save changes to this collection).

Step 4: find the report in the new navigation structure.

There it is! Your first custom funnel in the GA4 standard reporting UI!

For extra context, you can include additional dimensions (i.e. device category) when creating your custom funnel report.

You can create many different custom funnels using this technique and add them to an existing or custom collection. By doing this you can fully customize the UI and everybody with access to the same GA4 property can easily see and analyze them.

The great thing is that you can create funnels for any custom journeys on your website, regardless of whether you are an ecommerce business or not.

Any business can benefit from this new type of report available in the standard reporting interface of GA4 (via Explore).

How to Customize Custom Funnels

There is one issue that I spotted when creating these reports.

You cannot edit custom funnels in the Library or in the reporting navigation structure after importing them from Explore.

Reporting navigation Structure

Library

Solution: click on ‘Rename’ in the Library and then ‘Save’. You don’t need to make any changes, just clicking ‘Save’ will do.

And now you are good to go:

You can also edit it directly in the reporting navigation structure via the new link ‘Customise report’.

This is great, but be aware of these current limitations:

• You cannot add, change or remove funnel steps.
• You cannot create funnel summary cards for embedding in other reports.
• You cannot change the funnel from a ‘closed’ to ‘open’ funnel or vice versa.
• You cannot integrate additional features like ‘elapsed time’ or ‘next action’.
• You cannot change the funnel report type (‘standard’ <-> ‘trended’ funnel).
• You cannot change the chart back to ‘funnel’ if you change it into ‘bar’, ‘scatter’ or ‘line’ chart.
• You cannot add ‘secondary dimensions‘ to this specific report.
• You cannot directly import your new funnel reports via the ‘reports overview’ section in Explore.

Don’t get me wrong, this is a great new feature. I just like you to know limitations when working with this new set of reports after initially creating them!

Concluding Thoughts

The standard user journey purchase report is just the start. It can already reveal user journey and funnel insights, but you want to get one step further.

There are some limitations with the new custom funnel feature, but in general it is a great, new addition to the GA4 Analytics package.

Here is what I recommend:

• As a starting point, define all relevant funnels you would like to explore.
• Map out all funnel steps and additional reporting requirements per funnel.
• Define which funnels are suitable to set up in the GA4 reporting UI and which ones you want to Explore elsewhere (i.e. Looker Studio, Google Sheets, BigQuery).
• Configure all relevant funnels in GA4 Explore, including additional requirements (i.e. extra ‘breakdown dimensions’).
• Import all funnels in the Library section of GA4.
• Decide where to embed them in the GA4 reporting navigation structure.
• Rename all funnel reports to enable ‘Edit’ mode for required future changes.
• Inform the internal/external teams about these new funnels.
• Analyze, test and improve your user purchase journey and other relevant funnels.

This is it from my side!  What are you waiting for? Go check out this new GA4 feature and let me know your thoughts!

The post From Basic User Purchase Journey Reporting to Pro Custom Funnels in GA4 appeared first on Online Metrics.

The long awaited feature of custom channel groups is finally there.

GA4’s custom channel groups are based on manually defined traffic ‘rules’.

The difference with the default channel grouping is that you are completely flexible on how to attribute site sessions to different channels within a custom channel group.

The default channel grouping in GA4 follows a rigid set of predefined rules and cannot be modified.

This new custom channels feature can be leveraged in a variety of ways in the GA4 UI. We will explore this and many more related topics throughout this article.

Let’s dive right in!

Table of Content

• Quick Introduction
• Why to Use Custom Channel Groupings
• How to Set Up Custom Channels
• Custom Channels in GA4: Setup Options
• Six Ways to Use Them in the GA4 UI
• Concluding Thoughts

Quick Introduction

Custom channel grouping is known as a popular feature in the previous version of Google Analytics, Universal Analytics. Google Analytics 4 is now finally supporting you in setting up custom channels groups in GA4 as well.

You can leverage them in several ways and places in the GA4 UI as we will soon explore together.

The predefined, default channel group in Google Analytics 4 is a great start, but using both the custom and default channel groups is what works best for most companies.

Why to Use Custom Channel Groupings

There are a ton of reasons why you would want to set up a custom channel grouping in GA4 (or two, current maximum) in addition.

Let’s look into three key reasons for doing so.

1. The default channel group is not customizable
2. There is no need to work with the API
3. Retroactive, custom reporting needs

1. The Default Channel Group is Not Customizable

This is what you will see when opening the Google Analytics predefined channel group in Google Analytics.

At this moment, the predefined GA4 channel group contains 18 channels, but what if you have different or additional needs?

• You can’t add new channels
• You can’t remove existing channels
• You can’t modify existing channels
• You can’t change the channel processing order

Doing some custom work outside of Google’s rigid scope might be exactly what you need for a more granular, detailed analysis.

Of course, you could leverage BigQuery, but what if you don’t have the knowledge or capacity to do so? Or you simply want to work in the GA4 UI? Custom channels are a great solution here!

2. There is No Need to Work With the API

The Google Analytics 4 API allows you to do many cool things:

• Create dashboards outside of Google Analytics
• Transform raw data via Google Sheets and/or BigQuery
• Build automated tools like the GA4 audit tool

However, not every user – and maybe you are one of them – has a need to (solely) do reporting or analysis outside of the GA4 UI.

Custom channel groups are a great fit if that’s the case for you.

3. Retroactive, Custom Reporting Needs

It’s important to mention that custom channel groups in GA4 work retroactively.

In other words, based on a set of rules for each channel you can rearrange all traffic data (including historical data) in specific channels which is very powerful.

Again, it doesn’t change your data, but it simply let’s you re-group your available data in new traffic buckets.

How to Set up Custom Channels

Setting up (advanced) custom channels in GA4 requires several steps and good knowledge on how your traffic sources are tracked (UTM parameters).

For simplicity, I will base the example on the standard Google Analytics channel setup and explain about other options in the next chapter.

Step 1: navigate to the GA4 admin section and select Channel Groups.

Step 2: click on Create new channel group.

Step 3: provide a descriptive name, i.e. custom channel group and description (optional).

In this case I will re-group all defined channels in aggregated high-level traffic buckets.

Step 4: define the new channel names and rules.

Paid

• Default channel group – matches regex: ^(Cross-network|Paid Shopping|Paid Search|Paid Social|Paid Video|Paid Other|Display|Affiliates|SMS|Mobile Push Notifications)$

Organic

• Default channel group – matches regex: ^(Organic Shopping|Organic Social|Organic Video|Organic Search|Referral|Audio)$

Email

• Default channel group – matches exactly: Email

Direct

• Default channel group – matches exactly: Direct

This is what it looks like in GA4 (‘Paid’ example).

* Please note that the channel definitions above are just for demonstrating purposes and they might vary in your case.

Step 5: save the new custom channel group in GA4.

Step 6: double check that it is correctly saved in the GA4 UI.

At the time of writing, Google allows you to create two custom channel groups in GA4 in addition to the default one.

Custom Channels in GA4: Setup Options

You have these options when creating a custom channel group in GA4:

• The setup always starts with the 18 base definitions from Google and you need to take it from there.
• You can edit, add and/or remove channels within the new custom channel grouping.
• You can reorder the channels: Traffic is included in the first channel whose definition it matches given the current order of channels in the group.
• The following dimensions are currently available for creating channel definitions:
  • Default channel group
  • Medium
  • Source
  • Source platform
  • Campaign ID
  • Campaign name
• You can combine one or more statements when setting up channels.
• Matching rules can contain any of the below expressions:
  • matches exactly
  • contains
  • begins with
  • ends with
  • matches regex
  • partially matches regex
  • doesn’t match exactly
  • doesn’t contain
  • doesn’t begin with
  • doesn’t end with
  • doesn’t match regex
  • doesn’t partially match regex

In short, the dimension options might sound a bit limited but they include the most important dimensions.

Further, you have a lot of flexibility when creating the exact rules for how to group your traffic sources into channels.

Six Ways to Use Them in the GA4 UI

The new custom channel group is accessible through two different dimensions:

• First user custom channel group
• Session custom channel group

This is similar to how you can access the default channel group in GA4.

In the next part of this article I will share six different ways to leverage the defined custom channel group in Google Analytics 4.

1. Primary Dimension in Standard Report

The first way to access the custom channel group dimension in GA4 is as a primary dimension.

However, in order to do so you first need to add it as a new dimension to the related report.

Step 1: navigate to a report (i.e. Traffic Acquisition) and click on Customise report.

Step 2: add ‘Session custom channel group’ as a new dimension.

Note: you have the option to select it as ‘default’ meaning it is selected by default when opening the report.

Step 3: save changes to current report.

Step 4: access the newly added custom channel group dimension.

Step 5: analyze performance based on ‘new’ report.

2. Secondary Dimension in Standard Report

The next option is to use one of the custom channel group dimensions as a secondary dimension.

In this case, you don’t need to add it to the report first.

Step 1: click the ‘+’ sign in any report of your choice.

Step 2: search for ‘custom channel group’ and add one of the dimensions to the report.

Step 3: analyze performance based on ‘new’ report.

3. Dimension in Comparison

You can also leverage the custom channel group through the comparison feature in Google Analytics.

4. Dimension in Report Filter

In a similar way you can set up a filter including a rule based on one of the custom channel group dimensions.

You have two options here:

1. Include the dimension in a temporary report filter.
2. Include the dimension in a permanent report filter.

Option 1 – Temporary Filter

Add the filter to any detailed report of your choice. The filter disappears if you close the session and come back.

Option 2 – Permanent Filter

Add the filter to any detailed report of your choice. The filter doesn’t disappear if you close the session and come back after saving the report. You can remove it at any time though, but it impacts any user that has access to the same GA4 property.

5. Dimension in Report Card

Another useful way to leverage these new dimensions is by adding them to a summary, report card.

Step 1: navigate to a report (i.e. Traffic Acquisition) and click on Customise report.

Step 2: modify the default summary card and/or add a new one (we will modify the current report card).

Step 3: click on Edit summary card.

Step 4: Add ‘Session custom channel group’ and save the new setup (click on Apply in top right corner).

Step 5: save changes to current report.

Step 6: review the new dimension in one of overview reports (which consist of report/summary cards).

Note: you have the option to preselect it as the default dimension.

6. Dimension in Exploration

There is one more option to discuss: adding this dimension to an Exploration report.

Step 1: navigate to the Explore section, create a Free-form report and search for ‘custom channel’.

Step 2: add ‘Session custom channel group’ as a dimension and ‘Sessions’ as a metric.

Note: you have the option to use one of the custom channel group dimensions in the report of your choice and modify it to your needs.

Concluding Thoughts

Using both the default channel grouping as well as one or two custom channel grouping(s) is a great way to leverage the insights through the GA4 reporting and exploration UI.

There are several reasons why to use the custom channel grouping feature in GA4. This post elaborates on six ways to effectively use them.

By now you should be able to set up a custom channel group and data analysis via the GA4 reporting interface.

An integration with BigQuery gives you even more flexibility in analyzing channel data, but it is not that easy for everyone to get this all implemented.

This is it from my side. Are you already using the custom channel grouping in GA4 and do you have tips to share?

The post Expert Guide to Using Custom Channel Grouping in GA4 appeared first on Online Metrics.

The original release date of GA4 was 31 July, 2019. The beta version of the product came out, under its previous name ‘App + Web‘.

It’s almost four years ago now and a lot of companies are still very concerned about Universal Analytics sunsetting very soon.

There are many different reasons for this and saying Google hasn’t delivered a complete product yet, isn’t the full story.

In contrast, many clients I am supporting are getting very enthusiastic after learning how to use this new GA4 version to its fullest potential!

It comes with so many benefits when compared to Universal Analytics and other Analytics tools.

In no particular order I have listed 22 benefits of using Google Analytics 4.

Table of Contents

There is a lot to cover, let’s get started!

1. GA4 Measurement Model

Universal Analytics is quite rigid in a sense. Different hit types (i.e. pageviews, events, social, transaction) and the taxonomy for events always follow this structure: ‘event category’, ‘event action ‘and ‘event label’. The measurement model focuses on sessions and pageviews.

Google Analytics (GA4) is really different.

One of the greatest benefits (at the same time a challenge) is the flexible data model. It is based on events and parameters; basically, you track every interaction with an event and how you define the details is up to you.

2. Integration of Web and App

Everything is evolving and there is clear need for insights across multiple devices and platforms. This is where Google Analytics 4 comes in.

You can set up a GA4 property for measuring your website(s), app (instances) or a combination of both. This is very powerful and isn’t possible in Universal Analytics.

3. Automated Tracking

Automated tracking aka Enhanced Measurement allows you to automatically measure certain types of interactions in addition to the ‘page_view’ event.

It’s your call in terms of whether or not to use it and for which feature.

In my experience, some of the automated tracking is powerful, but you need to figure out whether it works for your needs.

Read more:

• In-Depth Guide on Enhanced Measurement in Google Analytics 4 (GA4).
• [GA4] Enhanced event measurement.

4. Reporting UI Customisation

You can complain about the default reporting UI in GA4, but you can transform it into a much more powerful UI.

• In Universal Analytics you can create custom reports, but they sit in a different environment.
• In Google Analytics 4 you can create custom reports via Explore and you can fully customize the default reporting UI (below).

Here are just a few things you can do in GA4 which you can’t do in Universal Analytics:

• Change the structure of the reporting UI and its reports.
• Add, remove or modify reports.
• Add permanent report filters to allow for a more customized experience.

You can enhance the experience for everyone who has access to the same GA4 property.

Ok, Universal Analytics includes many more reports on default, but how many of them do/did you actively use? More is not always better.

Create a default reporting UI in GA4 that actually supports the data insights you are looking for and you are much better off than before!

5. Ease of Defining Conversions

“Toggle the switch on to mark an event as a conversion.”

It’s as simple as that in Google Analytics 4. You can measure up to 30 events as a conversion.

This allows you to define a conversion based on previously collected event data. But what if you want to set up the conversion in advance?

In that case you need to navigate to ‘Conversions’ and define the Conversion event upfront.

Please note that conversions are only counted going forward (after marking an event as a conversion).

Counting method (up to 12 April, 2023)

• GA4 counts every time an event conversion occurs – even if it occurs multiple times during the same session. You can create up to 20 goals per GA view. In GA4, that limit is 30 conversions per property.

Counting method (from 12 April, 2023 onwards)

• GA4 allows you to choose between ‘once per event’ and ‘once per session’ as the counting method for each individual conversion.

Step 1: navigate to conversions.

Step 2: click on the three bullets for the conversion setting you like to review or update.

Step 3: change the counting method if needed.

Google’s comment:

“Once per session is how Universal Analytics properties count goals. Select this option if it’s important for your GA4 conversion count to closely match your UA conversion count. Otherwise, select Once per event.”

I don’t agree with this statement as there can be a other reasons for choosing ‘once per session’ over ‘once per event’. This depends on your business and conversion type(s).

6. Audience Triggers

Audience triggers belong to my favorite features in Google Analytics. It allows you to do many powerful things and you can even use them to define conversions.

Also, you can decide whether or not to tick the checkbox “Log an additional event when audience membership refreshes”.

If a visitor initiates multiple sessions and during each session, completes a conversion, the audience trigger will dispatch the event once again (up to one per day).

You can ‘control’ how often conversions are measured by properly setting this up and connecting the event to a conversion.

Read more: Audience Triggers & Conversions in GA4.

7. Debugging

Google Analytics 4 comes with a great improvement in terms of debugging. The new GA4 debugging feature allows you to check the data within seconds. No need to wait hours to see the incoming data appearing in standard reports.I have seen a couple of cases where the data was correctly captured (based on DebugView), but didn’t show up in the reporting UI and related reports.

So it’s a best practice to evaluate the data in your reports as well when testing your GA4 setup and implementation.

Last note: keep in mind that your specific debugging data won’t appear in the reports when you filter out developer traffic.

8. BigQuery Integration

In other blogposts you can read a lot about BigQuery and why you should set up a (free!) integration with Google Analytics 4.

Five benefits of linking GA4 with BigQuery:

• No data sampling
• Unlimited number of dimensions
• Correct data errors on past data
• Combine Google Analytics data with third party data sources
• Integrate BigQuery connectors with visualizations tools (i.e. Looker Studio)

This was never available for you in Universal Analytics unless you had a GA360 license!

9. Explorations

Also, in Universal Analytics, the powerful feature ‘Explorations’ aka ‘Advanced Analysis’ was only available for paying users.

The great news is that you can access this module in GA4 as part of the built-in features, regardless of whether you are a GA360 customer or not.

Explorations allow you to perform a more granular analysis based on any business question you have.

In terms of Analysis depth, it falls in between the regular reports and advanced, data analysis via BigQuery.

You can build the exact reports (and share them) that provide you with the insights you are looking for.

There are various analysis methods and it’s great section to explore beyond the basics of the default reporting UI.

You can create a custom report from scratch or use one of the predefined templates (get started):

• Free-form
• Funnel exploration
• Path exploration
• Segment overlap
• User explorer
• Cohort exploration
• User lifetime

10. Predictive Metrics

Machine learning is an integral part of Google Analytics 4. You can use predictive metrics to identify users and their actions that likely lead to a purchase or conversion.

• Purchase probability
• Churn probability
• Predicted revenue

Please note that currently only ‘purchase’, ‘ecommerce_purchase’ and ‘in_app_purchase’ events are supported for the Purchase probability and Revenue prediction metrics.

You can use probability metrics in the User lifetime explorations of GA4.

Please note that to train predictive models successfully, Analytics requires that several criteria are met (traffic, conversion, churn).

Read more: [GA4] Predictive metrics.

11. Predictive Audiences

In addition, you can build predictive audiences if your website or apps receives a high volume of traffic and purchases.

Currently available suggested predictive audiences (if applicable and you are meeting the prerequisites):

• Likely seven-day purchasers
• Likely seven-day churning users
• Likely first-time seven-day purchasers
• Likely seven-day churning purchasers
• Predicted 28-day top spenders

There are a variety of ways in how you can leverage these powerful audiences:

• Advertising campaigns (Google Ads, Display & Video 360, Search Ads 360).
• Remarketing audiences.
• Re-engagement campaigns.

Last note, GA4 power users and analysts might want to edit suggested predictive audiences and/or create a custom predictive audience from scratch.

12. Free Product

Google Analytics 4 is free to use. In the last 15 years, I have used many different web analytics tools.

In terms of features and easy of use, most other tools don’t even come close to Google Analytics. And those that do, are often very expensive and/or difficult to use.

Keep in mind that the entire Analytics market faces the same browser and privacy restrictions as free Analytics tools like GA4.

The free version of GA4 is what is sufficient for 95%+ of the companies that want to use Google Analytics. GA360 (paid version) has its advantages, but I believe only a few of you really need it.

13. Better User Privacy Options

Compared to Universal Analytics, Google Analytics 4 comes with a lot of more privacy options.

These include, but are not limited to:

• IP address are not longer logged or stored.
• Disable collection of Google signals data.
• Set the data retention to 2 or 14 months.
• Mark particular events under no personalized ads.
  • The individual events and custom dimensions excluded from ads personalization are available for export to advertising products, though they cannot be used by those products to personalize ads.
• Easily delete the data of a particular user including a higher level of accuracy when doing so.

Read more: Privacy controls in Google Analytics.

14. Focus on the User Journey

Google Analytics 4 allows you to stitch together sessions from multiple browsers and devices (i.e. desktop and mobile apps). As well as provide you with accurate reporting in the GA4 UI and/or BigQuery.

You need to leverage the User ID feature in GA4 to set this up.

In general, GA4 is much more user-centric compared to Universal Analytics.

You can also see this in the standard reports in GA4.

• Strong focus on ‘user’ metrics and dimensions.
• Optionally you can now add user conversion rate metrics to standard reports.

15. User ID Integration

As emphasized in my previous point, GA4 can create an holistic view of user behavior and performance across multiple devices and browsers.

In Universal Analytics most User (ID) related analysis are in a separate reporting view (below).

GA4 brings all relevant data together within a single GA4 property which is much more efficient when analyzing and optimizing on the user journey and performance.

16. Flexible Reporting Identity

You can define and update the reporting identity that is applied when Google ‘calculates’ or prepares your data and reports.

• The most basic identification method is by device (device-based). This is how Google identifies your users if you haven’t enabled Google Signals or the User ID.
• You can define additional methods for user identification to make the user count and insights even more meaningful:
  • User ID
  • Google Signals
  • Modelled data

Changing the setup to one option or the other doesn’t affect the data on Google Analytics servers. It impacts the user count and related statistics in your GA4 property for all users.

This was never possible in Universal Analytics and is baked in Google Analytics 4.

17. Anomaly Detection

GA4 does a great job in anomaly detection. Setting up custom insights helps you keep track of anomalies in your data.

In addition, it marks noticeable changes as potential anomalies in some of your standard reports.

Every now and then this results in a false positive, but getting this automated feedback from Google can be very useful for many of us.

18. Easy Cross-Domain Tracking

Even basic cross-domain tracking in Universal Analytics led often to confusion. It requires to amend the GTM setup and/or hardcoded tagging.

In Google Analytics 4 this is much more easy if you have ‘basic’ cross-domain tracking needs.

I have found this to work pretty smoothly in many cases, but not always.

There are definitely edge cases that require extra attention:

• Cross-domain linking to an iFrame (they still exist in 2023 :-( )
• Redirect to a dynamically redirected destination page

Google’s default solution for measuring the user journey across multiple domains won’t work in these cases. It can get quite tricky and technical to fix this.

Read more: Cross-Domain Tracking on Google Analytics 4 (GA4).

19. Holistic Engagement Tracking

In Universal Analytics many marketers and analysts set up ‘engagement’ goals in terms of duration and pageviews.

I don’t believe these goals are very useful, unless you have a type of site where performance is directly correlated to these goals (i.e. certain advertising sites).

These micro goals can be used in context when analyzing macro performance goals. In general. Universal Analytics doesn’t do a very good job in terms on engagement tracking.

Now, with GA4, engagement tracking is much better.

“When a user begins a new session, Google Analytics starts to record the amount of time in the session (in milliseconds).”

The amount of time is sent to Analytics when any of the following events happen (with every event dispatched to GA4):

• The user moves the app screen to the background or focuses focuses away from your web page
• The user navigates away from the app screen or web page
• The site or app crashes

The amount of time is sent in an engagement_time_msec parameter and added to the next collected event.

Here is an example provided by Google.

20. Higher Data Collection Limits

Similar to Universal Analytics, Google Analytics 4 comes with data collection limits.

• Limits in Universal Analytics
• Limits in Google Analytics 4

Here are a few benefits of Google Analytics 4 (compared to Universal Analytics) in terms of collection/configuration limits:

• User sessions (web data stream) can collect an unlimited event number vs 500 per session in UA.
• 30 conversion in GA4 (property) vs 20 goals in UA (view).
• 50 custom dimensions in GA4 vs 20 in UA.
• 50 custom metrics in GA4 vs 20 in UA.
• Additionally, 25 user-scoped custom dimensions can be registered.
• Currently, there is no limit in terms of number of events that can be tracked in a GA4 property.
• 100 audiences per GA4 property vs 20 audiences per UA property.

These are great new quotas for those of you with an advanced, more complex GA4 setup.

21. Flexible Attribution Model

You can change the attribution model in GA4 if you have the Editor role for a Google Analytics 4 property.

Not all reports are impacted if you change the attribution model:

• Conversion details and Explorations facilitate on custom, i.e. data-driven attribution model.
• User- and session-scoped traffic dimensions are unaffected by changes to the reporting attribution model.

Report with user- and sessions-scoped traffic dimensions report on the same model as Universal Analytics: last-non direct click.

Read more:

• [GA4] Select attribution settings.
• LinkedIn discussion on GA4 and attribution model.
• First-click, linear, time-decay and position-based attribution models are going away.

22. Superior Funnel Analysis

Funnels in Universal Analytics were rigid and in my opinion useless.

GA4, with different types of Explorations, is incredibly powerful in this respect.

In this GA4 funnel blogpost you can learn how to build funnels on the fly in Google Analytics.

The example above illustrates how powerful funnels in Google Analytics 4 are and how you can customize them to your specific needs.

This is it from my side and here you go, 22 benefits of using Google Analytics 4! What important feature did I miss? Happy to hear your thoughts!

The post Top 22 Key Benefits of Google Analytics 4 (GA4) appeared first on Online Metrics.

Google Analytics 4 has – as far as I can tell – an unspecified cardinality limit on the underlying data tables accessed via the GA4 UI or data API. This to reduce the data processing cost on their end.

When you use a high-cardinality dimension (a dimension with many potential different values) or multiple dimensions, the underlying data table can hit the unspecified row limit. This causes any data past the row limit to be reported under the (other) row in GA4 reports.

Over 50% of the pageviews are grouped into the unknown bucket (other); can you imagine the negative impact on your data quality?

The rest of this article contains more background information on (other) and cardinality as well as actional steps to reduce the impact on your data.

Table of Contents

• GA4 and Three Data Tables
• (other) in GA4 Reports
• Cardinality in Google Analytics 4
• Eight Ways to Minimize (other)
• Concluding Thoughts

Let’s first start with some background notes on data tables in GA4.

GA4 and Three Data Tables

The first data table is the one you are most familiar with. This one is visible in the reports in the GA4 UI or via the data API.

The next or second data table contains processed and aggregated data instead of raw event and user-level data. This underlying data table you can’t see and it produces the data table that you see in reports in the GA4 UI or via the data API. In general, this second data table also ‘feeds’ many other dimensions that are not visible in your report.

And then we have the third type of data table. It is the one containing raw event and user-level data. You can review this type of data table via the Exploration reports in the GA4 UI or via BigQuery.

Later in this post we will discuss the value of BigQuery in this respect.

(other) in GA4 Reports

In the introductory report there is a dimension where over 50% of the ‘page path’ values are grouped into the (other) row. This can be higher or lower, depending on many factors.

The impact varies based on dimension type, the amount of data collected and the total number of unique values. Also, one report can affect the outcome of another report.

Here is an example of a landing page report where you see a relatively low amount of views grouped into the unknown (other) bucket.

To recap, this is what it means when you see a dimension with value ‘(other)’:

• A lot of unique values are grouped together and GA4 doesn’t report them as individual rows, but under the same ‘(other)’ value instead.
• You lose the ability to analyze the values under the (other) row.

This is a serious issue as it prevents you from accurately analyzing your full data set.

Let’s discuss ‘cardinality’ now as it helps you understand why this happens in Google Analytics 4.

Cardinality in Google Analytics 4

Cardinality refers to the number of unique values assigned to a dimension.

There are dimensions with a fixed number of unique values:

• Device dimension can have three values – desktop, tablet, mobile. In this example, the cardinality equals three.
• Logged-in (custom) dimension might have two values (boolean) – true or false. In this example, the cardinality equals two.

The two dimensions above will never cause any issues.

But what about Page path, Page location or (custom) User ID? These dimensions can have thousands (or many more) unique values.

“In Google Analytics 4, a high-cardinality dimension refers to every dimension that records more than 500 unique values per day.”

Many factors determine whether or not the (other) row appears in your reports:

• Analytics 360 properties have higher limits when compared to the limits for standard properties. The exact limits vary depending on the following other factors.
  • The individual report: i.e. the Pages and screens report have higher-cardinality dimensions, and therefore have higher limits.
  • The report and complexity: a standard report with one dimension is rarely affected by an (other) row. Reports with secondary dimensions, filters, or comparisons are more likely to include an (other) row. These reports need database tables with many dimensions causing properties with large and complex data more likely to exceed the cardinality limits.

Eight Ways to Minimize (other)

There are several things you can do to reduce (other) and there is only one real solution to this issue.

1. Use the BigQuery Export
2. Exclude URL Query Parameters
3. Avoid High-Cardinality Dimensions
4. Use the Standard GA4 UI
5. Use GA4 Explorations
6. Avoid Data Sampling
7. Upgrade to GA4 360
8. Use Epanded Datasets

1. Use the BigQuery Export

The best way to avoid having ‘(other)’ and to analyze large datasets (past the sampling limits of GA4 Explorer) is to use the BigQuery export.

The BigQuery export provides access to the raw event and user-level data. BigQuery data tables do not suffer from cardinality or data sampling issues.

A couple of related advantages:

• Avoid data retention issues and keep your user-specific data as long as you need.
• Access unsampled raw event and user-level data.
• Take ownership of your data.
• Manipulate GA4 data in anyway you need.
• Integrate GA4 data with many other data sources.

2. Exclude URL Query Parameters

Excluding unnecessary query parameters greatly reduces the cardinality of dimensions related to page measurements in Google Analytics 4.

It doesn’t completely resolve the issue (in some cases), but it will be a great help.

The video below clearly outlines how to get this done via Google Tag Manager.

In general, I recommend to not exclude the following parameters:

• Site search query parameters
• UTM parameters in GA4
• gclid and other advertising click IDs
• Query parameters that are added to ‘confirmation’ page URLs

3. Avoid High-Cardinality Dimensions

It’s a best practice in relation to (other) and cardinality to avoid using high-cardinality dimensions in your report.

Registering custom dimensions as Event ID, Event Timestamp, Session ID or User ID can easily lead to cardinality issues. This will most probably lead to (other) row issues in your GA4 reports.

Google is already warning you about potential issues when you are about to create a new custom dimension.

4. Use the Standard GA4 UI

Use standard reports whenever possible.

Standard reports have aggregate tables that decrease the likelihood of condensing the data under the (other) row.

Unfortunately, applying several comparisons, filters and secondary dimensions to a standard report easily results in cardinality issues.

It’s a better option to create complex reports either via the Exploration reports and templates or via a BigQuery integration and report.

5. Use GA4 Explorations

As a rule of thumb, recreate your standard report via an Exploration if your standard report contains the (other) row.

But, a great downside is that you might suffer from sampling issues (example below).

Step 1: review the standard report in GA4.

Step 2: at the top of the standard report, click on ‘Create an exploration’.

Step 3: review the Exploration and whether it is a solution in this case.

Think twice before using a similar report as above. The (other) row is gone, but the report might not be very accurate (based on 10% of available data).

In this case it is clearly not a solution to mitigate cardinality issues.

6. Avoid Data Sampling

Potential issues depend on the type of report you are using:

• GA4 UI standard reports: not subject to data sampling, but the (other) row is often visible when GA4 samples the data (in the background).
• GA4 Exploration reports: not subject to cardinality / (other) rows, but data sampling can be an issue. The reported data is greatly skewed in that case (as in the last example).

High-traffic websites can greatly benefit from migrating a GA4 property to GA360. This will help in minimizing or eliminating data sampling.

Read more: How Sampling Works in Google Analytics 4 (GA4).

7. Upgrade to GA4 360

Upgrading to GA4 360 isn’t for everyone, but it grants you with much higher sampling and cardinality limits if compared to the free version of GA4.

Data sampling

“The quota limit is 10 million events for users of the free Google Analytics product and up to 1 billion events for Google Analytics 360 users.”

Cardinality / (other) issue

“In GA4 most reports come with automatic custom tables enabled which means it is much less likely that you will suffer from the (other) row.”

Note:
Automatic custom tables don’t support all dimensions. Analytics 360 won’t use an automatic custom table and you will still see the (other) row in these cases:

• Attribution dimensions (including Default channel group)
• Session Conversion Rate (for one specific Conversion)
• User Conversion Rate (for one specific Conversion)

8. Use Expanded Datasets

GA4 360 properties have an extra option when they encounter the (other) row in the GA4 UI.

The ‘Expand this data’ option only appear for eligible reports.

Read more: [GA4] About expanded data sets for Google Analytics 360.

Concluding Thoughts

The integration of GA4 with BigQuery is your most powerful option. This is especially true if you are greatly ‘suffering’ from cardinality and (other) row issues.

But, as you have seen, there are several other strategies to mitigate the impact of cardinality on your data set.

At the end it comes down to your expertise, the volume of traffic and measurement requirements. These together will guide you in choosing your best option. Setting up and leveraging an integration between Google Analytics 4 and BigQuery is highly recommended, but not (yet) achievable for everyone.

Now it’s your turn. What is your experience with high-cardinality dimensions and data being aggregated under the (other) row in GA4? Happy to hear your thoughts!

The post How to Avoid (other) in Your GA4 Reports (Cardinality) appeared first on Online Metrics.

There are multiple ways on how users find your content or products. In some cases they immediately find what they are looking for, i.e. through SEO or PPC.

But, very often they arrive on your website and use the main navigation or internal site search functionality to narrow down before they exactly find what they need.

Site search can come in many flavors, but this search box is always implemented to facilitate the user in finding what she needs.

At the end of this post you will know in great detail how to implement and report on site search in Google Analytics 4.

Table of Contents

• Five Reasons to Track Site Search
• Site Search Tracking Requirements
• Site Search Tracking Event
• How to Implement Site Search Tracking
• How to Report on GA4 Site Search
• Why Some Site Search Terms Might be Missing
• Concluding Thoughts

Ok, let’s dive right in!

Five Reasons to Track Site Search

There are many reasons why tracking site search in Google Analytics 4 is key for any website with a site search feature.

Here are five reasons why you need to track site search in GA4.

1. Demystify great keywords for SEO
2. Demystify great keywords for PPC
3. Discover ideas for expanding your product assortment
4. Optimize site search performance
5. Optimize your website UX

#1 Demystify Great Keywords for SEO

Do you stumble upon many site search keywords or phrases you are not targeting via SEO? And even better, do they facilitate conversions on your website?

If yes, consider adding these keywords and long tail variations to:

• Your SEO content strategy
• Keyword focused and search query solving content pages
• Elements of your navigation structure

#2 Demystify Great Keywords for PPC

New keywords are relevant to add to your SEO strategy, but what about PPC? In many cases, using a combined keyword strategy can be fruitful.

Setting up a short targeted PPC campaign is very helpful for determining the actual value of keywords for your business.

#3 Discover New Product Ideas

Here is a specialized sunglasses company in the US: Costa Del Mar.

Their navigation bar shows the ‘Men’ and ‘Women’ categories.

But, what if many users search for ‘sunglasses for children’?

This is a potential opportunity for developing (smaller) sunglasses, specifically for children. And changing the navigation structure accordingly.

#4 Optimize Site Search Performance

In general, users who use the site search functionality tend to perform (convert) better. This is mainly because they are closer to the bottom of the conversion (purchase) funnel.

Setting up site search tracking in GA4 will support you in finding out about the performance of site search users vs non-site search users.

Embedding specific A/B tests can be beneficial to find out more here.

#5 Optimize Your Website UX

I recommend having a closer look at the popular search terms for your business and connected search result pages. Is there a high exit percentage on (any of) these pages?

Try to optimize the search result pages at least for the most popular queries. And even better, optimize the site search as a whole.

Very often a high number ‘no results pages’ indicates that there is something wrong:

• User intent and search queries are not in line with your website and content pages
• You site search functionality is lacking

Are you convinced site search tracking is valuable for your business?

Site Search Tracking Requirements

It is a requirement to have a site search feature on your website or app to get site search in GA4 to work.

In most cases, Google Analytics 4 is able to automatically track site search activity via Enhanced Measurement.

You can use Enhanced Measurement for tracking site searches in GA4 if:

• You have a site search feature on your website
• The site search keyword or phrase is exposed in the URL via a query parameter

This is what the second definition is referring to:

The site search query parameter is the part of the URL that comes after ‘?’:

• Query parameter -> q=site+search
• Search term -> q=site+search

Two alternative scenarios:

• Site search feature is available on your website, but the query parameter is not exposed in the URL -> you need to leverage GTM to send the site search data to GA4.
• Query parameter is not exposed in the URL and GTM doesn’t do the trick -> work with your developer to either 1) expose the query parameter in the URL or 2) push the search term to the data layer every time a visitor completes a search.

Note: for the purpose of this article, we don’t further explore these two scenarios, but focus on the most common scenario and implementation instead.

Site Search Tracking Event

Site search in GA4 tracks by default the ‘view_search_results‘ event (if enabled via Enhanced Measurement, which we will discuss in a bit).

You can use the ‘debugView’ to see this in real-time or simply use GA4 real-time.

Several parameters are captured in addition to the ‘view_search_results‘ event:

• search_term -> actual search term exposed via the URL query parameter
• unique_search_term -> not documented by Google, but it shows a ‘1’ if this is the first search on this particular term
• page_location -> full page URL

And a few more parameters that provide context to the site search data you are capturing are visible here.

How to Implement Site Search Tracking

The process of setting up site search tracking in GA4 is quite straightforward.

I have divided the setup in two parts.

Part 1: Enhanced Measurement

Step 1: navigate to the website for which you want to implement site search tracking in Google Analytics 4 (i.e. Amazon.com).

Step 2: use the site search feature on the website and make a note on the query parameter in the URL (‘k’ for Amazon.com).

Step 3: go to your GA4 property (Admin section).

Step 4: click on the ‘Data Stream’ in the ‘Property’ column where you want to implement site search tracking.

Step 5: if not yet done, enable Enhanced Measurement.

Step 6: click on the ‘gear’ icon under the toggle, enable site search and ‘show advanced settings’.

Step 7: review the list of the query parameters that are captured by default.

Step 8: add ‘k’ to the ‘Search Term Query Parameter’ field as it is not captured automatically.

Step 9: decide if you need to add additional query parameters.

Here is the Amazon.com URL:

• s?k=ga4&crid=363AXH4RRK9IT&sprefix=ga%2Caps%2C191&ref=nb_sb_noss_2

In this case we assume that ‘ref’ is providing extra context for site search and we want to integrate it in our tracking.

In general, defining additional query parameters can be useful when users can filter and further narrow down on the site search results.

Step 10: save all changes before closing the screen.

Part 2: Custom Dimension

You need to register a custom dimension in order to use the ‘search_term’ parameter properly in the GA4 UI and Explore section (not required for BigQuery analysis).

I will use a test property here (no custom dimensions implemented yet).

Step 1: click on ‘Custom definitions’ under the GA4 property.

Step 2: click on ‘Create custom dimensions’ button.

Step 3: create a new custom dimension (event-scoped).

Step 4: double check if everything is set up correctly.

Step 5: review if all data is collected properly.

We will explore this topic in more detail in the next section.

Last note:

“Google is continuously updating the list of parameters that are automatically available for further analysis (without registering). It seems now the ‘Search term’ is also added, which is great news as you might be able to save one slot here. You can freely use it in the Explore section, but still need to register it if you want to use it as a secondary dimension.”

How to Report on GA4 Site Search

In the GA4 UI there are several ways on how you can report on this data:

1. View the ‘Events’ report in the default reporting UI.
2. Create a free-from report in the ‘Explore’ section of GA4.
3. Bonus: replicate the aggregated Site Search effectiveness report of GA3/UA.

#1 Events report in GA4 default reporting UI

There are a few steps you need to take to review this data.

Step 1: navigate to ‘Reports’ and open the ‘Events’ report.

Step 2: search for the ‘view_search_results’ event and click on the link.

Step 3: find the ‘search_term’ custom parameter card.

• This is where you can get basic information about the top search terms. It is not an ideal overview.
• Please note that Google Analytics has applied thresholding to this card and will only display the data when the data meets the minimum aggregation thresholds.
• You need to register ‘search_term’ as a custom dimension to see this card here.

#2 Explore Report in GA4

I recommend using the Explore section of GA4 when you want to evaluate site search performance in GA4.

Step 1: navigate to the ‘Explore’ section and click on ‘Blank’ (Create a new exploration).

Step 2: name your report ‘Site Search Terms’.

Step 3: add ‘Search term’ to this free-form report.

Step 4: add ‘Event name’ to the selection.

Step 5: click on the ‘Import’ button in the top right corner.

Step 6: you will see both dimensions on the left now.

Step 7: repeat the same steps, but now for the metrics ‘Event count’ and ‘Event count per user’.

Step 8: add ‘Search term’ and both metrics to the report (to do this: double click on each element).

Step 9: add a filter on the ‘view_search_results’ event to remove the empty row.

Step 10: review the data in the new report.

In the next chapter I will explain why the Site Search terms list might not be complete here.

As promised, here is the bonus section that explains a more in-depth analysis of site search (based on aggregation of all search terms).

#3 Site Search Effectiveness in GA4

This is a bit more complex, but it will yield greater insights from a commercial insights perspective.

Step 1: review the site search conversion report in GA3.

Step 2: make a note of the requirements for this report in GA4.

Two segments

#1 – Segment that includes all site search sessions

#2 – Segment that excludes all site search sessions

Six metrics

1. Sessions
2. Transactions
3. Ecommerce revenue
4. Average purchase revenue
5. Session conversion rate
6. Per session value

You could work with ‘ARPU’ as ‘per session value’ is not available in GA4.

I haven’t used it as the site search report in GA4 is session-scoped.

Step 3: create the required report in GA4.

Add segments to free-form report

Add metrics to free-form report

Step 4: review the data in the new report.

Last note: be mindful about the ‘session conversion rate’ as in addition it might include other conversions than ‘purchase’.

Why Your Report Doesn’t Show All Site Search Terms

The number one reason why some dimension values are hidden (i.e. in the Site Search Terms report) is because thresholding is applied if you enable Google Signals.

How to avoid thresholding in GA4

• Don’t enable Google Signals if you are not interested in using Demographic reports and you don’t plan using GA4 Audiences for Remarketing via Google Ads.
• There is no 100% solution if you need one or both of these features – there is a workaround though.

Workaround to show all Site Search terms in GA4

• In most cases, iIf you use ‘Observed’ or ‘Blended’ reporting identity, thresholding is applied (and you don’t see dimension values with low values).
• But, switching to Device-based will remove thresholding AND Google will not use Google Signals to calculate users.

The set up of reporting identity is flexible and you can change the setup as many times as you want.

• The data stored is not affected and reporting identity is applied retroactively.
• Also, keep in mind that when you use device-based, the User ID calculations are not take into account (if implemented). This results in less accurate user counts.

Again, reporting identity does not affect the data collection, so data is only temporarily hidden or changed based on the reporting identity that you choose.

Be mindful when changing this setting as it affects the reporting experience of all users that have access to the related GA4 property.

Concluding Thoughts

Tracking site search in GA4 is straightforward (in most cases) because of the Enhanced Measurement integration.

However, reporting on it and make a conversion analysis can be tricky. You need to know how to explore your data outside of the default GA4 UI.

In this blogpost you have learned how to effectively use the default GA4 reporting UI as well as the ‘Explore’ section for analyzing site search performance.

In addition, think about the possibilities of analyzing site search data via an integration with Looker Studio and/or BigQuery. There are endless possibilities if you have the required skillset to dive deeper.

This is it from my side. Please let me know your thoughts and suggestions in the comments!

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