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User Lifetime report was highly improved in Google Analytics 4 compared to Universal Analytics, and taking into account that the majority of us never used UA report because it wasn’t sufficient, we can see User Lifetime report as a new excellent feature that should help us to understand and learn more about users.
Before we dive deeper into this report, I would like to inform you that this article is an episode of the series about Google Analytics 4 Explore. The place where you can build reports by drag&drop interface.
If you want to improve your knowledge of the rest reports available in GA4 Explore, don’t hesitate to read the articles below:
- Free form Exploration
- Funnel Exploration
- Path Exploration
- Cohort Exploration
- User Explorer
- Segment Overlap
So, let’s get started with what’s a User Lifetime report and when you should use it.
What is User Lifetime report, and when should you use it?
User Lifetime report helps us understand how much time someone stays with the connection to our business and how much money or other events that person generates over this time. The time is the user’s lifetime, while the monetary value is the user’s lifetime value.
Let me give you an example of why lifetime value (LTV) is so significant. Imagine there are two users. The first user visits your website and makes an order for $200, then in a month, revisits your website and makes an order for $500. The second user visits your website once and makes an order with a total value of $200.
In the first case, the user generates a lifetime value of $700, and the lifetime equals 2 months. In the second case, the lifetime is 1 month, and the lifetime value is $200.
Therefore analysts are interested in two metrics, and Google Analytics 4 can answer them and even give you more.
If you implemented GA4 right and your business is eligible for predictive metrics (audiences), you can use these metrics in User Lifetime report to see the purchase probability and the revenue you can potentially receive in future.
Of course, just looking at these two metrics is helpful. Still, much more valuable is to break down users into cohorts and see, for instance, how much LTV your different paid ads campaigns generate or how different referrals perform. As Avinash Kaushik says: “Let me segment or die”.
If you know that, you can allocate paid ads budgets accordingly and spend more on those that bring greater LTV, you can overcome your competitors and grow your business to a lucrative level.
In theory, everything sounds familiar and easy, but how to achieve it in Google Analytics 4? So, let’s build our first User Lifetime report.
GA4 User Lifetime Report Interface
First, in order to create the User Lifetime report in Google Analytics 4, you should take a few actions:
- Open GA4 and go to Explore tab
- Click on Template Gallery and Select “User Lifetime”
After these steps, you should modify the settings to see what you need. For this article, I will use the publicly available GA4 property that stores data about ecommerce. If you are not an Ecommerce business, you can change the events. All instructions below will apply to any company.
For our case, I will check how much lifetime value paid ad campaigns (Google CPC) generate that acquire US traffic.
So, as you can see in the screenshot above, the report consists of three parts. The first part is called “Variables”, the second “Tab Settings”, and the last one “Report View”.
Report part #1 – Variables
Let’s start with the first one. It consists of the following elements:
- Exploration Name
- Dates range
- Segments
- Dimensions
- Metrics
1.1 Exploration Name
Exploration Name allows you to specify the report’s name and save it in Google Analytics 4 to access later. You don’t need to click on any button. GA4 saves the name and the report automatically whenever you exit the report.
1.2. Date ranges
You can use date ranges to extend the dates of your report or limit it to one date. GA4 Exploration allows you to see the data up to yesterday. If you are interested in real-time (today’s data), you should enable BigQuery integration.
1.3 Segments
The segments feature allows you to select a group of users for the analysis.
For instance, you can be interested in analysing only traffic of your specific Paid Ads campaigns or Referral traffic. In our case, we create the custom segment with “Country ID” equals “US” since we want to check only those campaigns that drive US traffic. You can use (custom) dimensions and metrics in Google Analytics 4 to create more advanced segments.
1.4 Dimensions
This section allows you to add as many dimensions as you want. You can use the dimensions to break down your data afterwards (by adding more rows and columns) or create filters.
For instance, you can be interested to see how LTV changes based on “Last audience name”.
1.5 Metrics
Apart from importing and using dimensions, you can also use metrics. The most appropriate metric for this report will be “Total users”, “LTV:average”, “Lifetime engagement duration:average”, and others. When you import metrics, you can see the whole number of metrics available for this report.
After understanding the first part of the report, let’s look at the second.
Report part #2 – Tab Settings
The second section of the segment exploration report is “Tab Settings”. It consists of the following sections:
- Technique
- Visualization
- Segment
- Rows
- Columns
- Values
- Filters
Let me explain each of them to you.
2.1 Technique
This feature could be more helpful and can be used to jump from one report to another of Google Analytics 4 Explore functionality. Although I rarely use it, I will use this to remind you that you can learn about other GA4 explore reports in these articles:
- Free form Exploration
- Funnel Exploration
- Path Exploration
- Cohort Exploration
- User Explorer
- Segment Overlap
2.2 Visualization
Another report – free table – allows selecting the type of visualisation. User Lifetime report has a table format by default; you can’t change it.
2.3 Segment
This is where you apply the segment you created in section 1.3. To apply the segment, you should drag and drop it from section 1.3 to section 2.3. Remember that segments and filters in this report are the most crucial aspect. You can use segments to define the best-performing channel or geo-location.
2.4 Rows
Every table has rows and columns. You can add as many rows as you need to break down the data. Since we are interested in Paid Ads campaigns, we can add “First user campaign” into rows.
There are a few more options in this section that you can change. “Start row” allows you to specify from which row you want to start. For instance, you can want to miss first 5 rows and start from 6.
You can also change the number of rows to show from 10 (default) to 500. If your data includes more rows, export the report as .CSV or Google Sheets and get all rows there.
If you use a few dimensions in rows, you can find “nested rows” helpful. It groups the rows of one dimension in one. For instance, you can see the US, all its cities, the UK, and all its cities, instead of UK and US cities mixed together.
2.5 Columns
You can also add columns to your report. In order to do that, you should drag and drop dimension from section 1.4 to section 2.5. The navigation also allows you to select from which column you want to start and how many of them you want to see. So if you are not interested to look at the first column value, you can set “start column group” to 2 and the first column will be removed from the visualisation.
2.6 Values
This section allows you to add metrics as values to the report. You can use metrics such as “Total users”, “LTV:average”, “Lifetime engagement duration:average”, “Churn probability”, “Predicted revenue” and others.
Cell type allows you to change the display of the metrics in the table: bar chart, plain text or heatmap.
2.7 Filters
If you want to see the users that you acquired on a specific date, you can use segments or apply filters. For this, you should use the “First visit date” dimension. However, you can’t use some dimensions in filters, for instance, we created a segment to see only US traffic, it’s impossible to use “country” dimension in User Lifetime report.
For our analysis, we should apply “First user medium” equals “cpc” and “First user source” equals “google”.
Report part #3 – Report View
There are a few things left to discuss. The third part of the report is “Report View”. This window presents the final data after applying filters, segments, rows, columns, and other settings.
3.1 Right-side menu – Plus icon
This icon allows you to create and include more reports in your exploration report.
3.2 Left-side menu – Undone, Re-do, Share
You can use the left-side menu to share a report with your peers. The share functionality allows you to share the report in read-only mode. You can also export your report in CSV, PDF and as an image. Apart from that, you can undo or redo the latest changes you made.
3.3 Report Navigation
When you click on the right button of your mouse, you can include or exclude selections from the report. Once you click on any of these options, GA4 creates the filter and applies it automatically.
At the beginning of this section, our goal was to find the LTV of the Google CPC campaigns that acquire US traffic. Below, you can see the screenshot that shows the relevant data.
GA4 User Lifetime limitations
There are at least a few limitations of User Lifetime report that are worth sharing.
- User Lifetime report was realised on August 15th 2020 therefore, this report is available only for users who visited your website after that date. If you installed GA4 after that date, you shouldn’t worry about that too much.
- The second limitation is that you can’t use many dimensions to create filters available in other reports, for instance, “country” in User Lifetime report. However, you can overcome it it by using segments instead.
- The third limitation is the sequence of the second is that you can’t use “default channel grouping” at all. You can use medium and source, export the report in CSV, and build “default channel grouping” using Google Sheets but not GA4.
One more thing you should be aware of is that User Lifetime uses user_id whenever it is assigned to a user, and if you don’t send user_id or it wasn’t assigned to the user, it will use device_id to aggregate users’ data for this report.
Conclusions
User Lifetime report is a new excellent report available in Google Analytics 4. You can improve your paid ads campaigns and other channels’ performance by finding answers to users’ lifetime value and prediction metrics.
This article covers all the necessary elements of this report. Please let me know in the comment section below if you are still looking for an answer to your question.
People also ask questions
User Lifetime report helps us understand how much time someone stays with the connection to our business and how much money or other events that person generates over this time. The time is the user’s lifetime, while the monetary value is the user’s lifetime value.
User Lifetime report was realised on August 15th 2020. Therefore, this report is available only for users who visited your website after that date. If you installed GA4 after that date, you shouldn’t worry about that too much.
There are a few limitations that you should be aware of: this report shows data only after August 15th, 2020, and there is no possibility to use many dimensions, including “default channel grouping”, but you can use segments to overcome it.
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Written By
Ihar Vakulski
With over 8 years of experience working with SaaS, iGaming, and eCommerce companies, Ihar shares expert insights on building and scaling businesses for sustainable growth and success.
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