How to Redact Email Addresses and Query Parameters in Google Analytics 4

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Today, we’re diving into a nifty GA4 feature that helps you redact sensitive data like email addresses and query parameters.

Why Redact Data?

Redacting data is crucial for privacy and compliance reasons. You don’t want to accidentally collect sensitive user info.

Step-by-Step Guide

Head to Admin Area

  1. Go to Admin: Open your GA4 property and head to the Admin area.
  2. Data Streams: Click on ‘Data Streams’ and select your web data stream.

Redact Email Addresses

  1. Redact Data: Scroll down to find the ‘Redact Data’ option.
  2. Enable Radio Button: Simply enable the radio button next to ‘Email Addresses’.

Redact Query Parameters

  1. Check Your Data: Before redacting query parameters, check which ones are actually in your data. Neil recommends creating a free-form report in GA4.
  2. Add Query Parameters: In the ‘Redact Data’ section, you can add up to 30 query parameters. Separate them with commas.

For example:

  • gtm_debug,
  • fbclid
  1. Save: Don’t forget to hit save!

Verify

After a day or two, revisit your free-form reports to make sure the redacted data is no longer appearing.

Popular Query Parameters to Consider

Check out our list of popular query parameters like gtm_debug and fbclid.

Final Thoughts

That’s it! A simple yet effective way to keep your GA4 data clean and compliant. Check back in a day or two to make sure it’s all working as it should.

You can also check out more of our blogs here.

How to Use Google Analytics 4 for Form Analytics: A Step-by-Step Guide

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Introduction

Are you looking to get the most out of your website forms but don’t want to invest in expensive form analytics software? Good news: Google Analytics 4 (GA4) has got you covered! With GA4, you can track user interactions with your forms, right down to individual fields. This enables you to identify drop-off points, measure the time elapsed between completing each field, and more.

In this blog post, we’ll walk you through how to set up GA4 as a powerful form analytics tool. We’ll cover everything from setting up events and triggers in Google Tag Manager (GTM) to creating a funnel in GA4 to visualise your data. By the end of this, you’ll be able to track each field interaction, see drop-off rates, and even measure the time between each field.

Bonus: If you’re more of a visual learner, you can watch our detailed video tutorial on the same topic. Or, continue reading the blog post below for all the insights.

Setting Up Events and Triggers in GTM

Step 1: GTM Preview Mode

First, head over to your form (we’re using a ‘Contact Us’ form for this example) and enable GTM’s preview mode. This allows you to track events as they fire.

Step 2: Create Events for Each Field

For each form field, create a separate event. For instance, when a user starts filling out the ‘Name’ field, an event called form_fill_started should fire, containing a parameter called field_value.

Note: You’ll need a developer to implement a script for this. The script should fire the event after a user enters at least three characters, indicating their intent to complete the field.

Step 3: Monitor Events in GTM Tag Assistant

Use GTM Tag Assistant to monitor these events. For example, filling in the email address should fire an event called form_fill_email.

Step 4: Create a Trigger in GTM

  1. In GTM, navigate to ‘Triggers’ and click ‘New’.
  2. Name the trigger (e.g., “Form Fill Trigger”).
  3. Choose the trigger type as ‘Custom Event’.
  4. Use the RegEx pattern ^form_fill_ to match events that start with form_fill.
  5. Save the trigger.

Step 5: Create an Event Tag in GTM

  1. Go to ‘Tags’ and click ‘New’.
  2. Name the tag “GA4 Event – Form Field Interaction”.
  3. Choose ‘GA4 Event’ as the tag type.
  4. Configure the tag to pick up the event name automatically and add the field_value using a data layer variable.
  5. Assign the trigger you created in Step 4 to this tag.
  6. Save and publish the changes.
Conversion Uplift Form

Creating a Funnel in GA4

Step 1: Navigate to GA4’s Explore Section

Go to the ‘Explore’ section in GA4 and create a funnel. Name it something like ‘Form Analytics’.

Step 2: Add Steps to the Funnel

Start by removing the pre-set steps in the funnel that GA4 adds when you create a new report. Now begin adding the first event, which could be a visit to the ‘Contact Us’ form. Then, add each form field step, starting with the Event “form_fill_start” and continue for each form field you had your developer include a data layer push for.

Step 3: Save and Apply

After adding all the steps, save and apply the funnel. You can also extend the date range for more comprehensive data. You can also add a breakdown, such as by Device Category.

Step 4: Enable ‘Show Elapsed Time’

This feature shows the time elapsed between each step, helping you understand user behaviour.

Analysing the Data

Once your funnel is set up, you can analyse the data to see user drop-off rates for each field, giving you insights into potential issues.

Conclusion

GA4 offers a detailed level of form analytics. With some help from a developer, you can set up a comprehensive system using GA4 and GTM.

For those who prefer a visual guide, our video tutorial covers the same steps. You can also check out more of our blogs here.

How to Set Up a GA4 Roll-Up Property

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Introduction

Tracking user behaviour across multiple websites is crucial for businesses, Google Analytics 4 (GA4) offers a solution through its roll-up property feature. This blog post aims to guide you through the process of setting up a roll-up property in GA4 using Google Tag Manager (GTM). You can either watch the video tutorial below or continue reading for a step-by-step guide.

Creating a GA4 Roll-Up Property

  1. Navigate to GA4: Open your GA4 account and create a new property. This will be your roll-up property.
  2. Name the Property: Give it a descriptive name, such as Roll-up Property, so that users can easily identify it.
  3. Copy Measurement ID: Once the property is set up, go to ‘Data Streams’ and copy the ‘Measurement ID’.

Creating a Duplicate Google Tag in GTM

  1. Open GTM: Go to your Google Tag Manager account and create a new workspace.
  2. Duplicate the Tag: Click the three dots next to the Google Tag and select ‘Duplicate’.
  3. Rename the Tag: Give the duplicated tag a new name to indicate it’s for the roll-up property.
  4. Update Measurement ID: Change the ‘Measurement ID’ in the duplicated tag to the one you copied from your GA4 roll-up property by creating a new variable. If the site only has one environment, you can create a Constant variable and past the Measurement ID into the variable.

Setting Up Cookies

  1. Add Cookie Prefix: In the duplicated tag, add a new parameter called ‘cookie_prefix’.
  2. Set Prefix Value: Assign the value ‘roll-up’ to the ‘cookie_prefix’. This ensures that the cookies for the roll-up property are distinct from your main property.

Duplicating Event Tags in GTM

  1. Find Event Tag: In the same GTM workspace, locate the event tag you want to duplicate.
  2. Duplicate the Tag: Click the three dots next to the event tag and select ‘Duplicate’.
  3. Rename the Tag: Give the event tag a new name to indicate it’s for the roll-up property.
  4. Update Measurement ID: Change the ‘Measurement ID’ to the one you copied from your GA4 roll-up property.

Configuring Cross-Domain Tracking

  1. Go to GA4 Roll-Up Property: Navigate back to your GA4 roll-up property.
  2. Configure Domains: Go to ‘Data Streams’ and click on ‘Configure Domains’. Add all the domains you want to track.

Testing Your Setup: A Deep Dive

GTM Preview Mode

  1. Activate Preview Mode: In GTM, activate the preview mode to test your setup before publishing.
  2. Check Events: Make sure that both the normal and roll-up page view events are firing. You should see these events in the GTM preview pane at the bottom of your website.
GA4 Roll-Up property

Chrome Developer Tools for Cookie Inspection

  1. Open Developer Tools: Right-click anywhere on your website and select ‘Inspect’ to open Chrome Developer Tools.
  2. Navigate to Application Tab: In the Developer Tools, go to the ‘Application’ tab.
  3. Check Cookies: Here, you’ll see a list of cookies. Look for cookies with the ‘roll-up’ prefix to ensure that your cookie prefix setup is working.

Chrome Developer Tools for Event Inspection

  1. Go to Network Tab: Still in Chrome Developer Tools, switch to the ‘Network’ tab.
  2. Refresh the Page: Refresh your website page to trigger the events.
  3. Check Measurement IDs: In the ‘Network’ tab, you should see network requests with different measurement IDs. These IDs should match the ones you’ve set up for your main and roll-up properties.
  4. Inspect Payloads: Click on these network requests to inspect their payloads. You should see ‘page view’ events for both measurement IDs, confirming that events are being sent to both properties.

By following these steps, you’ll ensure that your roll-up property is not just set up correctly but is also capturing data. This comprehensive testing using GTM Preview Mode and Chrome Developer Tools is crucial for confirming that your setup is flawless.

And there you have it! A comprehensive guide to setting up a roll-up property in GA4 using GTM. This allows you to aggregate data from multiple sites into one property, making it easier to analyse user behaviour across platforms.

You can read more of our blogs here.

ChatGPT+ Avian GA4 Plugin

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Introduction:

I am going to introduce you to ChatGPT+ via the plugin – Avian.

Overview of Video Dialogue:

Create an account with Avian. They do have a free trial on ChatGPT+ which is the paid version.

Go to the bottom left-hand corner and the 3 dots and go to settings. Make sure you click on Beta features, then enable the plugins and code interpreters. Click on the plugins and then the down arrow and go to the plugin store. Search for the Avian plugin and install it. There is also free plugin called Daig.ram which is also worth downloading.

You can see that we’ve got property name, because I’ve connected several different GA4 properties within the Avian app. Indicate the time period, the metrics, break down by dimensions, table rows and table columns to tell it what type of table you want and what the format is. We’ve copied that as it’s better to do it outside of ChatGPT just in case you type it in, and delete it by mistake.

It can take a few minutes, for the Avian plugin to retrieve, and process the data. It does have some limits, for GA4. It’s what they called 8,000 tokens, which is, I believe, around 6,000 words. When you’re querying page paths, URLs, page titles, that can be used up fairly quickly. Try and avoid those sorts of queries or do it in a series of small queries, say a month or a week at a time, depending how many page paths you have.

There’s no filter on the prompts because they don’t have filters, which is I think is a major limitation. That’s something they need to add in as otherwise you can’t refine your query.

It’s come up with the data, over the months, showing it by device category. Now, what I would do if I had filters is set desktop and mobile only, but I haven’t. We’ve got, Smart TV showing up by 2 users by looks of things or 4 users in total.

It’s asking if I want to visualise the chart. Here you have to carefully specify what to visualise. Say, I want to create a time-series chart using the user conversion rate data only, otherwise it will try and combine the two sets of data and it won’t be very useful.

Let’s put that in to prevent adding to chart the users alongside the conversion rate, which is very different metrics. It will take a few seconds to process. It’s using the Daigr.am plugin now so it will be interesting to see how that visualises the data.

Sometimes it’s faster to do the tables yourself because you know exactly what you want. Sometimes I upload data from GA4 in a spreadsheet, and that works well. Supposedly this plugin is designed for people who don’t know how to use GA4, but I don’t see how that will work. You still need to know things like the event and parameter names. You would need to specify exactly what you want and the type of analysis or chart. I think you are still going to need people who know what they’re talking about to put in the prompts.

They have got, some data now, desktop and mobile primarily. It took a while because it’s not as fast as they sometimes make out but it did get there.

Summary:

I’ve explained how to connect to ChatGPT+ using a plugin named Avian. To begin, users need an Avian account and can access the plugin on the paid version of ChatGPT+. After enabling Beta features in the settings, you can install the Avian plugin by going through specific steps. There’s also mention of another free plugin called Daig.ram.

For using the Avian plugin, we suggest a specific prompt format that includes property names, time periods, metrics, dimensions, table rows, and table columns. This prompt is better formatted outside of ChatGPT to prevent mistakes, and then it’s pasted into the chat to retrieve data.

The Avian plugin has some limitations like token limits and lack of filtering options. We suggest avoiding queries that consume tokens quickly or breaking down queries into smaller periods. The absence of filters also makes refining queries challenging.

The Avian plugin fetches data, and we mention visualising the data using the Daig.ram plugin, which takes time to process. We find that manual data manipulation sometimes works better and questions the plugin’s suitability for those unfamiliar with Google Analytics (GA4) concepts.

Ultimately, We demonstrate how to access data and create charts using these plugins but also acknowledge limitations in speed and functionality.

For more videos, check out our YouTube channel: conversion-uplift-ltd or view all of our blog posts: conversion-uplift.co.uk/post.

Code Interpreter Data Visualisations

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Introduction:

I am going to introduce you to a powerful tool for data exploration, which is Chat GPT’s Code Interpreter with Google Analytics 4.

Overview of Video Dialogue:

I’m going to show you how to use chat GPTs Code Interpreter to analyse data from Google Analytics 4.

Go to the settings, click on Beta features and enable Code Interpreter. Go to Chat GPT 4 and click on Code Interpreter. This allows you to upload data files such as event data from GA4.

Ask it to provide a set number of visualizations. We are going to ask it to provide 4 data visualizations. Sometimes it will ask questions.

It’s worked out that there are 2 sets of data, 2 date ranges. You sometimes need to clean up data when you download it from GA4. Make sure everything’s a number, rather than text.

It’s going to do visualisations now, in the form of a bar chart. You can give it more instructions and once it’s provided the visualisations you can also ask it to redo them. This explains what the bar charts are doing:

  • Event Counts and Event Counts for the First Data Range
  • Event Counts for the Second Data Range
  • Total Users vs Event Count for the First Data Range
  • Total Users vs Event Count for the Second Data Range.

It’s done two charts for each date range and you can also ask it to do more. That’s how you start using Chat GPT code interpreter for data visualisations. Hope you found that useful and you will try it out yourself.

Summary:

In this video, we introduce the use of Chat GPT’s Code Interpreter for analysing Google Analytics 4 data. We guide viewers through the process, which involves enabling the Code Interpreter in Chat GPT settings, uploading event data from GA4, and requesting visualisations.

The model processes the data, identifying two sets of data ranges. We highlight the importance of data clean-up and numeric formatting.

Visualisations, including bar charts, are generated, explaining event counts and total users against event counts for each data range.

For more videos, check out our YouTube channel: conversion-uplift-ltd or view all of our blog posts: conversion-uplift.co.uk/post.

Google Analytics 4 Audit

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Our Google Analytics 4 (GA4) audit checklist is the most comprehensive checklist you will find and is divided into 16 sections, each with a particular focus area.

You can access our free Google Analytics 4 checklist here.

Google Analytics 4 Audit Checklist

Here’s a brief summary of the sections:

For more details on how to set up Google Analytics 4, go to our blog post how to create a GA4 property.

1. Planning and Performance Marketing:

  • This part is all about defining clear plans for measurement and tag implementation. It also involves the documentation of the GA4 event structure and evaluating how GA4 has been installed.

2. Data Privacy & Controls:

  • This section checks if privacy-related tools like Google consent mode and Google Signals are enabled. It also involves configuring advanced Ad Personalisation settings, data retention periods, and blocking personalized advertising for certain events/audiences.

3. Property Settings:

  • This involves evaluating if the property structure meets the business needs, considering the use of sub-properties, and verifying time-zone and currency settings.

4. Data Stream Settings:

  • The focus here is to review data streams, ensure data is being received by the GA4 property, and configuring specific events in Enhanced Measurement.

5. Configure Tag Settings:

  • This section includes setting up domain and cross-domain tracking, filtering internal and developer traffic, configuring unwanted referrals, and also reviewing session-timeout settings.

6. Data Settings:

  • Involves enabling data collection, activating internal traffic and developer filters, creating custom channel groups, and ensuring proper use of UTM parameters.

7. Google Tag Manager (GTM):

  • This section focuses on the correct implementation of GTM, including configuration of custom dimensions and metrics, setup of marketing Pixels (e.g., Google Ads, Facebook), and proper data layer configurations.

8. Events and Conversions:

  • This section reviews the most important events being received correctly in the GA4 console, implementation of user ID where users can login, and setting up guardrail custom events or metrics.

9. Ecommerce Implementation:

  • Here, you check which ecommerce events have been implemented. Such as product impressions, product clicks, add to cart, remove from cart, start checkout, and make a purchase among others.

10. Integrations:

  • This section covers linking Google Analytics with other Google tools and third-party applications, like Google Ads, BigQuery, Google Search Console, Google Ad Manager, and A/B testing tool.

Other Items:

  • This involves considering reporting identity, creating audiences for re-targeting or for events for alerts, and excluding query parameters from URLs.

GA4 Console and Prebuilt reports:

  • This section focuses on customising prebuilt reports, creating new collections for important user segments, hiding unused reports, and creating exploration reports.

Data Quality:

  • Lastly, this section checks for signs of cardinality, sampling, and thresholding, ensures custom definitions aren’t using reserved parameter names, and validates ecommerce data with back-end data. It also checks for duplicate events and bot protection measures.

14.0 Importing Data:

  • This section discusses the process and advantages of importing data into Google Analytics 4, especially data obtained from advertising on non-Google platforms. It explores the possible benefits of using the Measurement Protocol, a tool that allows developers to make HTTP requests to send raw user interaction data directly to Google Analytics servers. This helps in understanding the impact of ads run on platforms like Facebook or Twitter.

15.0 Roll-Up Property:

  • This portion of the audit focuses on the creation and configuration of a roll-up property. A type of property in Google Analytics that aggregates data from multiple websites. It highlights the need for such a property when tracking multiple websites and discusses the conversion event limit. This section also discusses how to set up a Google Tag Manager (GTM) container for the roll-up property and the necessary steps to duplicate all events and parameters.

16.0 Server-Side Tagging:

  • This section considers the implementation of server-side tagging to improve website performance and data security. It acknowledges that this may necessitate a separate project due to the technical complexities involved. Server-side tagging moves some of the tag execution from the client’s browser to a cloud server. Potentially enhancing website load times and providing more robust data privacy controls.

How to opt out of automatic GA4 migration

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Opting out of Automatic GA4 Migration

To assist you in opting out of the automatic GA4 migration, we have produced a video tutorial that shows you how to do it. You can follow the steps outlined in this YouTube video by Conversion Uplift to prevent a new GA4 property from automatically being created for you and to retain control over the migration process.

Google Analytics 4 is replacing Universal Analytics. On July 1, 2023, Universal Analytics properties will stop collecting data, and only GA4 properties will be collecting data after that date. Google strongly recommends that you manually migrate your Universal Analytics settings to GA4. Not all UA configurations have an obvious GA4 counterpart, and also the automated process might not make the same choices as you would. Therefore, it’s important to review and adjust your settings before the migration occurs.

Opting for a manual migration over an automatic process can provide a range of benefits. By using a web analytics expert, you can ensure that your GA4 property is set up according to your specific needs and preferences.

For example here are some items you will miss out on if you rely on automatic migration:

  • Form submission success event
  • Transform GA3 Enhanced Ecommerce tracking for GA4
  • Configure content groupings in GA4
  • Create element visibility events (e.g. banner or button is visible in browser)
  • Change data retention period from 2 to 14 months
  • Remove unwanted query parameters from landing page URLs
  • Create events for important pageviews or event clicks in GA4 console
  • Enable conversion events in GA4 console
  • Capture download file name and type
  • Time on page event tracking via GTM
  • Capture button and navigation text on clicks
  • Redacting personal identifiable information to prevent breaching GDPR regulations
  • Page load speed metrics via GTM
  • Migrating all non-destination goals from GA3
  • Adding new metrics and dimensions, such as Session Conversion Rate and landing page, to standard reports
  • Register custom dimensions & metrics in GA4 console (e.g. browser language)
  • YouTube video metrics configured for your website set up
  • JavaScript error events to track as an important indicator of site health
  • Link Google Ads and create audiences for remarketing via Google Ads
  • Audience trigger events in GA4
  • Set domains for cross-domain tracking
  • Filtering out internal traffic
  • Capture browser language
  • Remove unwanted referrals from payment methods used by your users
  • Track affiliate visitors and sales via a 1st party cookie
  • Capture and categorize content groupings
  • Remove trailing slash from page paths to prevent duplication
  • Trigger events based upon source, medium & campaign UTM parameters
  • Configure data from different environments to be sent to separate GA4 Properties
  • Create and configure new marketing pixels (e.g. Tiktok or Google Ads)
  • Set up a GA4 test property for other environments
  • Test your events to validate they work

By opting for a manual migration with the help of a web analytics expert, you can ensure that you don’t miss out on any of these valuable features and functionalities. This can also help you make the most of your GA4 property and get the insights you need to make informed decisions about your website and business.

To opt out of automatic GA4 migration, you will need to have the Editor role on your Universal Analytics property.

Here are the steps to follow:

  1. Log in to your Google Analytics account and click Admin.
  2. Ensure that you are in the correct account and Universal Analytics property.
  3. In the Property column, click GA4 Setup Assistant.
  4. Scroll to the bottom of the page and turn off the Automatically set up a basic Google Analytics 4 property toggle.
  5. Alternatively, click Manage GA4 migration in the yellow informational banner at the top of your Analytics account, and turn off the same toggle.
  6. If you have multiple properties that you want to opt out of, you can use the GA4 Migrator for Google Analytics add-on for Google Sheets. After installing it, select Set the automated GA4 setup opt-out status, and then follow the on-screen directions.

In conclusion, while Google will automatically create a GA4 property for you based on your Universal Analytics settings, it’s important to manually review and adjust your configurations to ensure a smooth transition. Opting out of the automatic migration is also an option for those who prefer to handle the migration manually.

How to Create a GA4 Path Exploration Report

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What is a GA4 Path Exploration Report?

A GA4 path exploration report allows you to analyse user paths and the sequences of interactions they take across your website, mobile app, and other digital properties. This report helps you identify the different pages and events which users complete before and after they reach a specific page or event, and the different paths that they take.

The GA4 path exploration report can help you uncover insights such as:

  1. User behaviour: You can analyse the paths users take to complete a particular action or conversion goal. This can help you understand user behaviour and optimize your website or app for better user experience.
  2. Popular paths: You can identify the most common paths that users take on your website or app. This can help you optimize your website or app’s navigation, user flows, and content to improve engagement and retention.
  3. Conversion paths: You can identify the paths that users take before they convert on your website or app. This can help you optimize your conversion funnel and increase conversions.
  4. Exit points: You can identify the pages where users tend to drop off and exit your website or app. This can help you identify potential issues with these pages and improve their performance.
  5. Channel performance: You can analyse the paths users take from different channels, such as organic search, paid search, or social media, and identify the most effective channels and touchpoints for driving conversions.

Overall, the GA4 path exploration report is a powerful tool that can help you understand user behaviour, optimize user flows, and improve conversions and engagement on your website or app.

To create a new GA4 path exploration report, watch my short video or follow the instructions below.

When you log into the GA4 console, click on “Explore” in the left-hand menu.

Then select “Path Exploration” from the template gallery.

You will now see the Path Exploration report. Here, you can explore the different paths that users take on your website. Before starting your analysis, consider the nature of the path you want to investigate and how you want to configure the analysis.

For example, the time frame, a segment to focus on (e.g. PPC campaign name or first time users), dimensions to breakdown the analysis by (e.g. device category or channel groups), and the metric (e.g. Total users or Event Count).

Once you have planned your analysis, click on “Start over” in the top right of the console. You can then choose to either begin at the start of the journey or the end of the user path.

Starting point:

This allows you to select the page or event where you want the analysis to begin. You can choose any page or event in your property, including custom events that you have set up.

Ending point:

This allows you to select the page or event where you want the analysis to end. You can choose any page or event in your property, including custom events that you have set up.

Filters:

This allows you to filter the data in the report based on specific criteria. You can filter by traffic source, device type, user demographics, and more.

Segment:

Use a default segment (.e.g. mobile traffic) or a custom segment you have created (e.g. campaign name) to limit your analysis to an important group of users.

Node type:

For each step in a user journey, you can choose between an event name, page title and screen name, page title and screen class and a page path and screen class.

Breakdown:

Use a selected dimension (e.g. device category) to show the absolute number of users accounted by each subgroup of the dimension.

Values:

Choose the appropriate metric for the user journey (e.g. Total users or event count).

Filter:

Exclude or include certain users by via a dimension or metric.

Node Filters:

Right click on a node to exclude it for the “Selected only” path or “From all paths”. Use this to exclude non-interactive events, such as page load speed or session replay tool events.

By adjusting these settings, you can customize your path exploration report to analyse the data that is most relevant to your business and your goals.

Overall, the GA4 path exploration report is a powerful tool that can help you understand user behaviour, optimize user flows, and improve conversions and engagement on your website or app.

How to Create Custom Channel Groups in GA4

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Creating Custom Channel Groups in GA4:

Creating custom channel groups in GA4 can provide several benefits, including:

1. Better Understanding of Traffic Sources:

By creating custom channel groups, you can group together different sources of traffic based on your business goals and marketing strategy. This will give you a more accurate picture of where your website traffic is coming from and which channels are driving the most conversions.

2. Improved Campaign Performance Tracking:

They allow you to track the performance of specific marketing campaigns more effectively. You can group together all the traffic from a particular campaign, ( e.g. pay per click) which will give you more detailed insights into how well that campaign is performing.

3. Enhanced Reporting:

Custom channel groups can help you to create more meaningful and insightful reports. You can easily filter your reports by custom channel group to get a more detailed view of your website’s performance.

4. More Accurate Attribution:

Custom channel groups can help you to accurately attribute website conversions to the appropriate marketing channels. This can help you to make more informed decisions about where to invest your marketing budget and resources to optimise your conversion rate.

5. Customization and Flexibility:

It gives you the flexibility to define channels that are unique to your business. This means that you can create channel groups that are tailored to your specific business needs, which can help you to get a more accurate view of your website’s performance.

Watch this video or following the instructions below to create a new custom channel group in GA4.

Creating A New Channel Group:

  1. Open the Channel Groups section.
  2. Click on “Create new channel group”.
  3. Choose to copy the default group to create a new group.
  4. Enter a name and description for the new group.
  5. Edit the channels by adding new ones, removing existing ones, or modifying their definitions.
  6. Reorder the channels if necessary.
  7. Click “Apply” and then “Save group” when you’re done.

Remember that traffic will be included in the first channel whose definition it matches, based on the order of channels in the group. For more details of custom channel groups in GA4, check out Google’s documentation.

Currently, GA4 does no allow you to add your new custom channel groups to the pre-defined reports in the console. You will need to add it as a dimension to a custom report in the Explore section of the console. Hopefully this will change in time.

How to connect Google Analytics 4 to BigQuery

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Connecting Google Analytics 4 to BigQuery – A Step-by-Step Guide

Google Analytics 4 brings data science to the mass market by allowing you to export data for free to Google BigQuery, Google’s powerful cloud based data warehouse platform. Google Analytics 4 has many innovative features which makes it a valuable complement to Universal Analytics. One of these benefits is the ability to export raw and unsampled data from Google Analytics 4 to BigQuery for free. You can also use a free version of BigQuery, called BigQuery Sandbox.

If your website has a high volume of traffic or you try to analyse data from a long date range, there is a risk of sampling of data in GA4 if you run a non pre-existing standard report. Sampling will occur in GA4 when a non pre-existing report exceeds 10 million events, and it is also prone to sampling when you analyse data over more than 60 days.

If you are not yet using GA4, you can read my step-by-step guide to upgrading to Google Analytics 4 here.

You can view the video or read the blog below.

1. What is BigQuery?

BigQuery is an enterprise multi-cloud data warehouse platform which can process high volumes of data in a few seconds. It allows you to conduct real-time analysis of data and use SQL to process it within a few seconds. Because it’s part of the Google suite of solutions it easily integrates with other Google products like Data Studio and Google Sheets.

The real power of BigQuery comes though comes from integrations with many third-party CRM and other marketing tools. This includes HubSpot, Slack, Facebook Leads, and Salesforce.

2. Why Link Google Analytics 4 to BigQuery?

As with any data warehouse you need a high level of security and BigQuery offers two-factor authentication and gives you secure by design infrastructure from Google.

No sampling of data. Sampling of data is common is many Google Analytics reports when you have a website with high volumes of visitors or you are using time series data. But sampling reduces data reliability because it can distort reporting and lead to misinterpretation of results. BigQuery allows you to export raw data without any sampling and so you can conduct much more granular analysis with confidence.

Affordability. BigQuery allows you to just pay for what data is collected and processed.

  • A scalable solution which can easily and quickly adjust to large volumes of data.
  • Export custom event parameters and dimensions.
  • Connect GA4 data with third-party API’s.
  • Connect data from BigQuery data with popular data visualisation tools such as Data Studio, Power BI and Tableau.

3. How to connect Google Analytics 4 with BigQuery:

New BigQuery customers are often offered free credits to use for the Google Cloud in the first 90 days. Customers also receive 10 GB storage and up to 1 TB for queries per month for free.

4. Create a BigQuery Project:

Go to your BigQuery account here: https://console.cloud.google.com/bigquery?

Click on the drop down menu for ‘My first project’ and then select ‘New Project’.

1. New Project in BigQuery
Source: BigQuery

Now click on ‘Create project’ and a ‘New Project’ screen will open where you can name your project.

2. Select Create Project
Source: BigQuery

Your project name will automatically create a project ID which cannot be changed once it has been set. Click ‘CREATE’ to continue. With your free account you can have up to 25 projects.

3 Create Project in Big Query
Source: BigQuery

You will now see the Notifications screen where you need to click ‘Select Project’.

4. Select Project
Source: BigQuery

Well done, you have now created your Google BigQuery project. You should be able to see your project name at the top of the screen. On the right of the screen you should also see the details of your project, such as the project name and ID.

5. GA4 Project Created in BigQuery
Source: BigQuery

5. Link Google Analytics 4 to Big Query

Now login to your Google Analytics 4 property and navigate to the ‘Admin’ area.

6. Google Analytics 4 Admin
Source: BigQuery

Go to the Product Linking section of the admin console and click on ‘BigQuery Linking’.

7. BigQuery Linking in GA4
Source: BigQuery

Click on the ‘Link’ button and this will open a screen which allows you to select your BigQuery project.

8. Link GA4 to BigQuery

Select the ‘Choose a BigQuery project’ button and this will show you all your existing project.

9 BigQuery Link
Source: BigQuery

Select the project ID that you have already created to send the data from the GA4 property. Then click ‘Confirm’ to continue.

10. Select a BigQuery Project for GA4
Source: BigQuery

Edit the data location for the cloud region where your data is stored. As I am based in the UK I select London. You can then click on the ‘Next’ button.

11. BigQuery Location and Next in GA4
Source: BigQuery

You can now adjust your configure settings. This allows you to edit your data streams if necessary. Select the checkbox to ‘Include advertising identifiers for mobile app streams’ if you are sending data from a mobile app and want to export advertiser identifiers to BigQuery.

12. GA4 advertising identifiers and frequency of data import to BigQuery
Source: BigQuery

Choose the frequency of your data import to BigQuery by selecting by ‘Daily’ and ‘Steaming’ options on the screen. You can now click ‘Next’ to continue.

You should now be able to review your link to a BigQuery project and if you are happy with it you can ‘Submit’ to complete the process.

13. GA4 BigQuery Link Review and Submit
Source: BigQuery

Fantastic, you have now successfully linked your GA4 property to a BigQuery project. This should be confirmed in the screen below.

14. GA4 BigQuery Link Confirmation
Source: BigQuery

6. GA4 Data in BigQuery:

Check that your GA4 project is selected in the top menu. From the left-hand navigation select ‘APIs & Services’ and then ‘Dashboard’.

15. BigQuery APIs
Source: BigQuery

In the dashboard you need to click on ‘+ Enable APIs and Services’.

16. Enable APIs
Source: BigQuery

Here you need to search for ‘BigQuery’ in the search input field. Select the ‘BigQuery API’ as shown below.

Source: BigQuery
17. BigQuery API
Source: BigQuery

You will now see the BigQuery API and click on the ‘Manage’ button.

18. Manage BigQuery API
Source: BigQuery

Here you will also need to select ‘Credentials’ to add a service account for the API.

19. Credentials
Source: BigQuery

Select ‘+ Create Credentials’ and this will open a drop-down menu for you to select a ‘Service account’.

20. Create Credentials in BigQuery
Source: BigQuery

You will now see a screen to set a service account name. Use the account ID and add ‘.gserviceaccount.com’ to the end of it. The service account ID will then be generated automatically. Give you service account a suitable name to reflect the Google Analytics 4 data it will be exporting to BigQuery. You can now click ‘Create’

21. BigQuery Service Account Details
Source: BigQuery

We are now on the ‘Create service account’ screen. Click ‘Done’ to complete setting up your service account to export data from GA4.

22. BigQuery Service Account Done
Source: BigQuery

Congratulations you have now finally created your API account and begin exporting GA4 data to BigQuery. You should also see your service account name under your BigQuery project as shown below. You may have to wait up to 24 hours for the first of your GA4 data to be exported to BigQuery.

23. Login to BigQuery and Select Service Account
Source: BigQuery

7. Access GA4 Tables in BigQuery:

Once you have waited 24 hours you can go back to BigQuery and you should be able to see your GA4 project under pinned projects.

Below your project name, you should see a data set with your GA4 property ID appended to the name as shown here “analytics_property_ID”. The analytics data set contains two tables which hold your Google Analytics 4 data.

  • Events_(number of days)
  • Events_intraday_<current date>
24. Select BigQuery Project
Source: BigQuery

Events Data Table:

Your GA4 data from the previous day will be automatically exported from the property to BigQuery every day. You will notice this as the number appended to the events data set will reflect the number of days imported into BigQuery.

Click on events_(number of days) and this will display the structure of the table schema. Above the table you will see the last date when data was imported. If you click on the date below ‘Events’ you will open a drop down which shows the individual dates you have data for. You can also select an individual date to view the data for that particular date.

25. BigQuery Analytics Project - Events
Source: BigQuery

Select the ‘Details’ tab if you want to see the size of the table, number of rows and when the table was made. If you click on ‘Query’ you can begin to run analysis using SQL.

26. BigQuery Project Details and Query
Source: BigQuery

However, if you select the ‘Preview’ tab you can inspect your data without having to run a query. This is good practice as it allows you to view the data you have imported and check it as you expected for your analysis.

27. BigQuery Analytics Project - Events - Preview
Source: BigQuery

Events Intraday Table:

Data from today will be imported into the events_intraday table. The data is automatically imported throughout the day and this will correspond with the ‘streaming’ frequency setting in Google Analytics 4.

As with the events_(number of days) data table, you have separate tabs for schema, details and preview.

28. BIgQuery events_intraday Table for GA4
Source: BigQuery

8. Conclusion:

BigQuery is a powerful cloud-based data warehouse that can automatically import your raw and unsampled GA4 data into. This avoids distorting your reporting by using unsampled data and allows you to undertake deep analysis of metrics without any limits imposed by GA4. Other benefits of using BigQuery with GA4 data is that it allows you to:

  • Track the whole user journey by freeing yourself of the limits of analysis within the GA4 console.
  • Create reports without any limits on the amount of data or the dimensions you apply.
  • Connect BigQuery with many other third-party solutions such as Snowflake and many other data analysis platforms.
  • BigQuery also integrates with popular data visualisation tools such as Data Studio and Tableau.

Begin the process of taking your GA4 data analysis to the next level by connecting it to Google BigQuery.