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 itby 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.
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.
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.
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.
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.
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.
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.
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.
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
YouTube video metrics configured for your website set up
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:
Log in to your Google Analytics account and click Admin.
Ensure that you are in the correct account and Universal Analytics property.
In the Property column, click GA4 Setup Assistant.
Scroll to the bottom of the page and turn off the Automatically set up a basic Google Analytics 4 property toggle.
Alternatively, click Manage GA4 migration in the yellow informational banner at the top of your Analytics account, and turn off the same toggle.
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.
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:
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Use a selected dimension (e.g. device category) to show the absolute number of users accounted by each subgroup of the dimension.
Choose the appropriate metric for the user journey (e.g. Total users or event count).
Exclude or include certain users by via a dimension or metric.
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.
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:
Open the Channel Groups section.
Click on “Create new channel group”.
Choose to copy the default group to create a new group.
Enter a name and description for the new group.
Edit the channels by adding new ones, removing existing ones, or modifying their definitions.
Reorder the channels if necessary.
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.
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.
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.
Click on the drop down menu for ‘My first project’ and then select ‘New Project’.
Now click on ‘Create project’ and a ‘New Project’ screen will open where you can name your project.
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.
You will now see the Notifications screen where you need to click ‘Select Project’.
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. Link Google Analytics 4 to Big Query
Now login to your Google Analytics 4 property and navigate to the ‘Admin’ area.
Go to the Product Linking section of the admin console and click on ‘BigQuery Linking’.
Click on the ‘Link’ button and this will open a screen which allows you to select your BigQuery project.
Select the ‘Choose a BigQuery project’ button and this will show you all your existing project.
Select the project ID that you have already created to send the data from the GA4 property. Then click ‘Confirm’ to continue.
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.
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.
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.
Fantastic, you have now successfully linked your GA4 property to a BigQuery project. This should be confirmed in the screen below.
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’.
In the dashboard you need to click on ‘+ Enable APIs and Services’.
Here you need to search for ‘BigQuery’ in the search input field. Select the ‘BigQuery API’ as shown below.
You will now see the BigQuery API and click on the ‘Manage’ button.
Here you will also need to select ‘Credentials’ to add a service account for the API.
Select ‘+ Create Credentials’ and this will open a drop-down menu for you to select a ‘Service account’.
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’
We are now on the ‘Create service account’ screen. Click ‘Done’ to complete setting up your service account to export data from GA4.
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.
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 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.
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.
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.
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.
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.
A Step-By-Step Guide to Create an Exploration Report in Google Analytics 4
Google Analytics 4 offers users a powerful suit of insight tools in the form of the Analysis Hub. I have previously covered how to create a funnel report in Google Analytics 4, and this should be one of your go to reports for optimising your site or app. In this post, I will take you through how to create an exploration report in Google Analytics 4.
The exploration report in Google Analytics 4 displays your data in a dynamic table format and provides advanced functionality which is not available in Universal Analytics (UA). This includes the ability to apply multiple custom segments and filters to uncover new insights to help you optimise your digital experience. The exploration report replaces custom reports in the UA version of Google Analytics.
The Analysis Hub in Google Analytics 4 makes it worthwhile upgrading to GA4 and complements what you already get from Universal Analytics. If you want to know more about how GA4 compares to Universal Analytics checkout my post on how to upgrade to Google Analytics 4 with GTM.
Go to your GA4 property and go to Analysis > Analysis Hub and click into the Exploration report.
3. Exploration Report in Google Analytics 4:
This will open up the Exploration interface which containers three tabs, Variables, Settings and the output tab. The first two allow you to tailor and configure the exploration report to your specific needs. However, this requires some planning and preparation to ensure you have access to the correct segments, dimensions and metrics for your report.
The variables tab is where you configure the data you want to use in your exploration report in Google Analytics 4. This requires some planning as you can only access dimensions and metrics that are being sent to your GA4 property. This tab covers:
4.1 Report Name:
Give your report name a descriptive title and bear in mind that in the Analysis Hub listing it won’t display the type of report (e.g., funnel or exploration) it is.
4.2 Date Range:
Set the period you want the report to cover, but consider when you first created your GA4 events, as this may limit the date range you can use. You can use a pre-set date range (e.g., Last week) or set a custom date range and compare data to the previous period.
The segments allow you to compare your exploration report results against different cohorts of interest. The default segments may not be relevant and are often incomplete. Delete the segments that you won’t need and create new segments that you are interested in. There are four types:
User segment: Visitors who meet a set criterion. For example, visitors who click on a paid advert in Google (Paid traffic) or are from an individual country.
Session segment: When sessions meet defined criteria. For example, all sessions where users were acquired from a set campaign or are in a set age group.
Event segment: These segments are defined by specific events, such as newsletter sign up, add to cart or purchase.
Suggested segments: These include three types of segments:
General segments such as recently active users and non-purchasers.
Templates cover such segments demographics (e.g., age or gender), acquisition and technology.
Predictive segments allow you to build predictive audiences based on purchasing and churning events. To be eligible to use predictive segments your site or app will need to meet these criteria:
Over a seven-day period at least 1,000 returning users who meet the predictive criteria (i.e., purchase or churn) over the previous 28 days.
Over a seven-day period at least 1,000 users who did not purchase or churn over the previous 28 days.
This must be sustained over a seven-day period, and the eligible data must be sent to the property as the purchase or app_purchase events.
Predictive metrics are then generated for each active user once per day. This data can then be used to build an audience for remarketing to these users via Google Ads.
Once you have decided which segments are important to your digital experience, go about creating these by using the appropriate type. For example, I wanted to compare mobile traffic to desktop users. Here I created the segment for desktop traffic by searching for Device category and selecting contains or equal to ‘desktop’.
You will also see a list of default dimensions. Just like with segments, you can create new dimensions by clicking on the ‘+’ button and delete dimensions that are not relevant. Dimensions can be used to create filters to exclude users who can never convert and to breakdown your exploration table for more detailed analysis.
You can have up to five dimensions in rows and two in columns for your GA4 exploration report.
Metrics define the nature of the analysis by transforming your report into actionable data. You may want to add metrics such as transactions, conversions, and user engagement to go along side more generic metrics like active users.
5. Tab settings:
This column allows you to create your exploration report and configure what it looks like. We are concentrating on the exploration report, but you can change this in the Technique drop-down menu where you can select other reports such as Cohort analysis, path analysis and Funnel Analysis.
Below the Technique drop-down menu, is the visualization selector where you can choose between:
Depending upon which type of visualization you choose, you will have different choices in terms of customisation of the report. I will concentrate on the table here as this gets most use and is where you may begin to identify new insights.
5.2 Line Chart:
However, the line chart is also worth briefly mentioning because this is useful to see if metrics are changing over time as a result of your optimisation activity.
The line chart allows you to set different levels of granularity to reflect the volume of data and how frequently you want to measure your key metrics.
The line graph also has an anomaly detector enabled by default. This allows GA to automatically indicate when something is causing a problem and so can save you valuable time during the analysis process. The training period determines how much data is collected to calculate the expected value. This means the longer the training period the more likely any anomaly will be real and so set the higher value here.
Sensitivity determines the sizes of the anomaly required to trigger the indicator. The higher the setting, the narrower the area of the expected value will be able to signify an anomaly.
5.3 Table Visualisation:
Going back to the table visualization, the next setting to consider is the segment Comparison. You can include up to 4 segments in your data table to compare your performance. Double click each segment or click and drag into the Segment Comparison widget.
The exploration report example here has the following configuration:
Two segments – purchasers and non-purchasers
Browser dimension in the row section
Device category in the column section
Active user as the chosen metric
5.4 Pivot Table Options:
The exploration reports gives you four pivot table options to choose from. The default option is ‘First column’, which means the segments is shown in the first column. This is what is looks like.
The next option is ‘First row’. This means the segment appears first in every row and looks like this:
The third option is ‘Last row’. This means the segment is displayed last in every row and it looks like this.
Finally, ‘Last column. This means the segment shows after all column dimensions.
This is where you select the dimension for the rows in your report. Here I have selected browser for my table, and so each row displays a different browser in the table.
You can have multiple dimensions in rows in your report. Here I have added browser version to go alongside browser. You can also decide from which row to start and set a maximum number of rows to display.
However, if you want to have more than a single row you may want to toggle to nested so that the second row is grouped according to the first row. Here the second row is the browser version and so all Safari browser versions are shown first before other browsers are displayed.
This allows you to add dimensions as columns. Click and drag dimensions into the row section. Here I have added Device category as the column. Again, you can decide from which column to begin displaying data and the maximum number of columns per single dimension.
Simply click and drag the metrics that you want to display in your table into the Values section. You can add up to 10 metrics in a single exploration report. The cell type allows you to choose how to display the metric cell based on its value and the ratio to other rows in the same column. The options here are:
Bar chart – this will display horizontal bar charts in every metric cell.
Plan text – no visual enhancement is shown if this is selected.
Heat map – the colour of cells are darker if their value is higher compared to other rows of the same dimension column.
This allows you to exclude certain users or events to reduce noise from the analysis. For example, here I use City to exclude users from outside London as the website only delivers within the boundaries of Greater London. You might want to exclude users who are logged in or visitors from certain countries, depending upon what kind of analysis you wish to undertake.
6. The Exploration Report:
Now that you have configured your exploration report in Google Analytics 4, you can now duplicate it to change the type of technique (e.g. make it a line graph) or add a new tab to begin creating a totally new exploration report. You can also download the report into various formats, including Google Sheets and a PDF.
By default, all your exploration reports are only visible to you. This means that you will need to share the report with other users of the GA4 account if you want other users to be able to access the report. If other users wish to edit the report, including changing the date range, they will have to duplicate the report so that they become the report owner.
If you right-click a cell in your report this give you the following options:
Create segment from selection
Include only selection. This adds an include filter to the table based upon the cell you clicked and so will enable you to narrow down the report accordingly.
Exclude selection. Adds an exclude filter to the table based upon the cell you clicked into.
Create segment selection. This will automatically open the segment creation interface with some conditions prefilled based upon the cell you selected.
View users. This will open an explorer report with users who make up the same selection.
The exploration report in Google Analytics 4 offers an extensive range of features to deep dive into your data. However, preparation is the key to success. Ensure you have created the events needed for your analysis using the GA4 interface and GTM.
Similarly, consider what segments you want to compare against in your report. If you have the required volume of purchasers you could create predictive segments and use these for remarketing. Next, set up the dimensions you want to breakdown your exploration report by as you will need these to be in the variables tab to add them to your report.
Ensure you also have your metrics defined as you can add up to ten to your exploration report. You can then decide which segments, dimensions and metrics to create your report. Remember to set appropriate filters to reduce noise and narrow your audience as required.
Google Analytics 4 not only offers advanced web analytics, but its predictive audiences can be used to identify which users are most likely to convert or churn. This allows marketers to create more effective campaigns in Google Ads for both remarketing and re-engagement.
GA4 could also allow marketers to make higher bids for keywords if they are triggered by a predictive audience that has a higher propensity to convert. If you haven’t already upgraded to Google Analytics 4 this may be a reason not to delay anymore. Predictive audiences deliver a valuable tool for conversion rate optimisation to help grow the business and improve conversions.
1. What are predictive audiences?
GA4 automatically enriches data using machine-learning to predict the future behaviour of your visitors. GA4 currently has three predictive metrics for building predictive audiences.
Purchase probability: The propensity that a user who has been active in the last 28 days will convert in the next 7 days. This is only for purchase and in_app_purchase.
Churn probability: The propensity that a user who has been active within the last 7 days will not be active in the next 7 days.
Revenue prediction: The estimated revenue from all purchase conversions in the next 28 days from a user who has been active in the past 28 days.
Given predictive metrics rely on machine-learning, GA4 requires a certain number of conversions to activate these metrics. To successfully train the predictive models, over a 7-day period GA4 needs a minimum of 1,000 returning users who had previously converted (purchases or churned users) in the past 28 days, and also 1,000 non-converters over the same period.
These volumes of conversions must be over a period of time for a GA4 property to be eligible for predictive metrics. If your property meets the criteria, the predictive metrics will be generated for each active user once per day.
3. Create predictive audiences:
To build predictive audiences go to your GA4 property and navigate to ‘Audiences’ and click on ‘New audience’.
This takes you to the ‘Build a new audience’ interface where you will have the option to create an audience from scratch or use ‘Suggested audiences’. Select the ‘PREDICTIVE’ tab on the Suggested audiences section.
Depending upon the volumes of data from your GA4 property, you will get an eligible or not eligible to use message below each audience.
There are currently 5 suggestions for predictive audiences:
Likely 7-day purchases. Users with a high propensity to purchase in the next 7 days.
Predicted 28-day top spenders. Users who are generating the most revenue in the next 28 days.
Likely 7-day churning users. Active users who are also unlikely to visit your site or app in the next 7-days.
You cannot modify the predictive condition for each audience, but you can add new non-predictive conditions. Such as Device category equals mobile.
4. How to use predictive audiences?
When you create predictive audiences, make sure you use a Google account that has permissions for your Google Ads account. Predictive audiences will then be automatically shared with all Google Ads accounts linked to your GA4 property. Google suggests two ways of using predictive audiences in remarketing campaigns and re-engagement campaigns.
4.1 Remarketing audiences:
Predictive audiences are ideal for remarketing campaigns because GA4 uses machine learning to identify deep patterns of behaviour that are unique to your site or app which indicate the user is likely to convert. The ‘Likely 7-day purchasers’ are the ideal audience for a remarketing campaign. A persuasive follow-up message delivered to these users could also be the trigger they need to complete a purchase.
4.2 Re-engagement campaigns:
Re-engagement campaigns can help maintain engagement with your business among users who are showing waning interest in your products or services. The ‘Likely 7-day churning users’ are a cohort of users who need to be re-engaged and would benefit from a strategic message or special offer to reverse a decline in engagement.
4.3 Strategic bid adjustments:
You could also use predictive audiences that are likely to purchase to trigger keyword bids. This should allow you to place higher bids on keywords because these users will have a higher propensity than normal to purchase.
4.4 New customer campaigns:
Predictive audiences can also grow your customer base. Try using the ‘Likely first-time 7-day purchasers’ audience as a means of attracting new customers across the Google Display Network, Gmail, YouTube and the Search Network.
Predictive audiences offer marketers an opportunity to improve targeting and conversions in the digital space. Testing of predictive audiences require building up the data to prove their effectiveness. But as the machine learning processes more data it should become more accurate at predicting outcomes.
The funnel analysis report in Google Analytics 4 is an awesome report which anyone involved in conversion rate optimisation will find invaluable. The report can help you instantly identify areas with the greatest potential for optimisation. It allows you to automate and compare completion rates for each step in a journey by key customer segments and breakdown the report by important dimensions, such as device category. The funnel analysis report is an exponential improvement on existing goal funnel reports provided in Google Analytics 3 (Universal Analytics).
Google Analytics 4 offers you a powerful new funnel visualisation tool that was previously only available in GA360. This allows you to create a funnel visualisation with any kind of event, including impressions, clicks and pageviews. In addition, you can build a funnel analysis report in Google Analytics 4 to include:
A comparison of completion rates by up to four customer segments (e.g. mobile, desktop traffic and tablet users),
Breakdown each step in your funnel by dimensions (e.g. source of traffic),
Elapsed time between each step of the funnel,
Identify the next event immediately after each step in the funnel,
Set a filter for the funnel to exclude users who could never convert (e.g. use country or region).
This allows you to immediately identify any differences in drop off rates at each step in the funnel for these important dimensions and segments. It also allows you to use filters to exclude traffic which will never convert or to create separate funnels for different markets or geographic locations.
In this blog post, I will show you how to create and fully configure a funnel analysis report in Google Analytics 4. This will ensure you fully benefit from the functionality of the Google Analytics 4 funnel analysis report.
Watch my video of how to plan and create a funnel exploration report or read the instructions below:
1. Planning Events for your Funnel Analysis Report in Google Analytics 4:
Firstly, to create a funnel visualisation in Google Analytics 4 you will have first need to configure GA4 events for each step in the user journey. Check out my post how to track events in GA4 using Google Tag Manager. This explains how to create most of the events you will need, including click and element visibility tags.
GA4 automatically records all pageviews under a single event name. For this reason, you may need to configure events for individual pages before creating your funnel analysis report. This can be done by setting up events in the GA4 interface as explained here.
It is also worth consider which dimensions and segments you will want to analyse your funnel report by. This is one of the most powerful elements of the funnel analysis and will save you from having to undertake custom analysis.
2. Create a New Funnel:
Secondly, in GA4 go to ‘Analysis’ > ‘Analysis hub’ and select ‘Create a new funnel’.
3. Funnel Console:
You will be taken to the funnel console which has a Variables, Tab Settings and Exploration tab.
Variables tab: This is where you enter your funnel name, set the time-period, and configure segments and dimensions for use in your GA4 funnel.
Tab Settings: Is where you define the nature of the analysis, including the type of visualisation, segment comparisons and funnel breakdown. It is also where you configure the individual steps in the funnel.
Exploration tab. Here you will see the funnel visualisation and the corresponding data table of each step of the funnel you created. What you see here is determined by your settings in Tab Settings. But you can also create other analysis here using all the features of the Analysis hub.
4. Name Your Funnel:
Choose a short, but descriptive name for your funnel as this is what is displayed when someone goes to the Analysis hub.
5. Date Range:
Now set a suitable date range and if you have only recently created events in GA4 you may have to use the custom range. This allows you to set a suitable date range based upon when you first started collecting data on the funnel steps.
5. Default Funnel:
You will now be able to see a default funnel which is automatically set up when you first create a funnel in Google Analytics 4.
6. Type of Funnel Visualisation:
Google Analytics gives you two types of funnel visualisation to choose from. You can select a standard funnel which gives you a snapshot in time or a trended funnel.
The later allows you see how the funnel is changing over time which can be very useful when you make changes to the user journey. We will select the Standard funnel for this example, but you can switch between the two if you so wish.
7. Delete Default Funnel Steps:
To create a new funnel Google Analytics 4, you first need to delete the default steps in the console. Remove each of the existing steps until there are no steps remaining.
8. Create First Step in GA4 Funnel:
Select the edit icon and this will open the first step in your funnel. Give the step a short, but descriptive name as this will appear in the funnel visualisation. Click on ‘Add new condition’ and begin typing the event name. Select the event name you wish to use.
You have the option to add a parameter if you need to set conditions based upon events or dimension that are configured in GA4. You can for example set the page path as ‘/’ OR ‘about-us’ to allow users to land on different pages before proceeding to the next step in the funnel. There is also an ‘AND’ condition if you want users to comply with two conditions.
On the right-hand side of the console there is a display which shows how many users comply with any conditions you set for your funnel. Click ‘Add step’ to create a second step.
9. Create Second Step in GA Funnel:
Here I want to create a step which records if the site can deliver to the customer once they have entered details of their location. Select the event name by clicking on ‘Add new condition’ and search for the name.
I now need to set a parameter value which indicates the customer’s location is valid. Click ‘ADD PARAMETER’ and search for the parameter’s name which is related to the event being used. In this case it’s ‘delivery_response’.
Now select the parameter condition, which in this case is ‘exactly matches (=)’.
Finally, select the parameter value, which in this case is ‘accept’. This was set by getting a developer to add a data layer push to the site to record if the user’s address was valid for delivery.
GA4 allows you to set conditions so that only users who proceed directly from the previous step are shown in your funnel. This often occurs for secure, logged pages which limit navigation options.
Select ‘is indirectly followed by’ and this will give you the option to choose ‘is directly followed by’ to set this condition in your funnel.
You can also set a time limit for the period between a previous step. This may be useful if your site has strict time-out conditions for users to complete certain tasks. This is common for ticket purchases on event sites. Check the box shown below.
This will allow you to choose the appropriate time-period from seconds, minutes, hours, or days. I would avoid using days because increasingly browsers are deleting cookies after 24 hours and so it is unlikely to be a reliable metric.
If you forget a step or get the order of events wrong, click on the three dots on the right-hand side of each step. This allows you to copy, remove or add a step (above or below).
10. Continue with Remaining Steps:
The GA4 analysis funnel allows you to have up to ten steps and so you may need to plan your funnel to ensure you don’t run out of steps. Let’s hope Google increases this as ten steps is quite limiting for many sites.
Once you have created your final step in the funnel, remember to ‘Apply’ as this will save your report. If you don’t ‘Apply’ you will lose the steps you have created.
11. Set Dimensions to Refine the Funnel:
Dimensions are useful because they allow you to set filters, next steps or to breakdown your funnel by important categories. The default dimensions in the funnel analysis report are limited and so you will probably need to remove some default dimensions and add more useful ones to the report.
Click on the plus sign and search for relevant dimensions. Here I want to use city because the site only delivers to certain locations in the UK. Select the dimension and click ‘Apply’.
Continue with this process until you have added all the dimensions you need for funnel breakdown, next steps, and filters.
12. Create a Filter:
Consider using a filter to remove visitors who could never convert. In this example, I will use ‘City’ because the site won’t deliver to users outside of a certain geographic location. You may want to use country or region depending upon the nature of the site. Click and drag the City dimension into the Filter box.
Select the match type, in this case ‘exactly matches’, and then search for the expression.
Select the expression you are looking for, I’ve chosen ‘London’ for the example here and ‘Apply’. The filter is now set, and it should avoid you including users who have no chance of converting.
13. Open or Closed Funnel:
This tab setting allows you to choose between a closed and open funnel. An open funnel shows users who enter the funnel at any point rather than only following the sequential approach of a closed funnel. A closed funnel won’t include users who join the funnel after the first step, and so may give a false impression of your conversion rate if users can join it on any step. The default setting is closed.
14. Segment Comparisons:
The Funnel analysis allows you to compare up to four segments that are shown in the Segments section of the Variables tab. You can drag and drop or just double click the segments.
You may need to add the relevant segments before you can do this. Click on the ‘+’ to open the Segments interface. You will be presented with a choice between creating a custom segment or using a suggested one.
There are three types of custom segments:
User segment: Where all users meet certain criteria. For example, desktop visitors or users from specific countries.
Session segment: Where all sessions meet set criteria. For example, all sessions where users originated from an individual campaign or visited a certain page.
Event segment: Where you only include certain events. For example, all visitors who have previously made a purchase or added to basket.
In this example, I want to compare mobile users to desktop visitors. As desktop traffic is not a default segment in the Variables Tab it is necessary to create this first. Give the segment a suitable name and add a description.
Search for the condition, select the match type and the expression (desktop). You also have the option to check ‘At any point’ to include users who meet the matching condition at any point in the user journey. Now click ‘Save and Apply’ to add the segment to your funnel.
In the report, each segment is shown as a separate bar chart above the funnel and you can hover over it to display the raw data. Your segments are also shown as the second column in the funnel table and so you will see segment A followed by each parameter for your breakout dimension.
This allows you to breakdown your funnel by a single dimension (e.g. Device category). Where the dimension has more than a few possible values you can specify the number of rows per dimension. The default is 5. Click and drag the dimension you want to breakdown the funnel by as shown below.
16. Elapsed Time:
This displays the average time between each step in the user journey. It’s great for understanding how long it takes users to proceed through a user journey and often highlights how users on different devices behave.
17. Next Action:
To see what event occurs immediately after each step in the funnel you can add ‘Event name’ or ‘Screen name’ for apps to the ‘Next Action’ box. Scroll over the bar chart to see the top 5 next events.
18. Share Your Funnel:
By default, your funnel report is only available to you and so you need to share it with other users of the property if you want other people to be able to view it. Click on the icon shown below and click ‘Share’ to give read only access to other users of the GA4 property.
19. Download the Funnel:
You can download your funnel analysis report in:
PDF (all tabs)
20. Funnel Visualisation:
You should now have a fully configured analysis funnel. This will allow you to instantly investigate each step of the user journey by defined segments and breakdown by individual dimensions. This is a huge improvement on goal funnels you may have used in Universal Analytics.
Spend time analysing each step and summarising insights that you identify. The funnel analysis report should save you from having to create many custom reports and time manipulating data to compare completion rates by important segments and dimensions.
21. Replicate Funnel Analysis Report:
You may want to replicate the funnel for different journeys or users with different needs. This can be easily done by coming out of the report and navigating to the Analysis hub. Click on the three dots as shown below and select ‘Duplicate’.
22. Don’t Navigate to Another Property When in a Funnel Report:
When you are in a funnel report, avoid changing to another property. If you navigate directly from the Analysis funnel report to another GA4 property, the funnel report will move to the second GA4 property. This is a bug that may be fixed by Google, but at present it can cause you problems. If this happens to you, navigate directly back to the original GA4 property.
Summary of how to create a funnel Visualisation in Google Analytics 4:
The funnel analysis report in GA4 report is one area without doubt where GA4 outshines Universal Analytics. However, it does require some planning and preparation to optimise your funnel report.
Create events in GTM, and if necessary, configure pageviews for individual pages in the GA4 interface. Consider which segments and dimensions you will need to analysis your funnel by and which might allow you to exclude visitors who could never convert.
Decide what type of funnel you want, either a standard funnel or a trended funnel. Set a time range which relates to when you began collecting your events in GA4.
The funnel analysis report allows you to create up to ten individual steps for a user journey. Remember to use parameters when you need to restrict the nature of the funnel, such as how users respond to a question. Use the indirectly or directly followed settings as needed.
If you have secure pages and strict time-out periods, you can consider setting time limits between steps. Once your steps have been saved, consider setting a filter to exclude users who could not possibly convert on the site.
Decide whether to use a closed or open funnel. If users can join the funnel at almost any step, I would strongly recommend using an open funnel.
Use the dimensions you have configured to apply up to four segments for comparison. Set a single dimension, such as device category to breakdown the funnel.
Enable the ‘Elapsed time’ to display how long it takes users to go from one step to another.
Set the ‘Next Action’ to show the event which occurs immediately after each step in the journey.
Finally, remember to share your funnel analysis with other users of the GA4 property.