Cohort Exploration report is one of the new reports available in Google Analytics 4. It allows you to check your product’s daily, weekly, and monthly retention metrics.
This is one of the features that allow me to compare GA4 with such tools as Mixpanel, Amplitude and Tableau. However, it also has considerable limitations that I will talk about in this article.
This article is one of the articles dedicated to going through GA4 Exploration functionality. If you are interested to learn more about GA4 Explore, please consider reading all articles from this series:
- Free form Exploration
- Funnel Exploration
- Path Exploration
- Segment Overlap
- User Explorer
- User Lifetime
So, let’s get started.
What is Cohort Analysis, and what questions can it answer?
Cohort analysis is an essential part of every business intelligence. In this analysis, you take segments of users and analyse their usage patterns based on their shared traits to learn more about their behaviour.
SaaS companies use cohort analysis more often than ecommerce companies because they need to track and improve metrics such as retention and churn to push their users to extend their subscriptions to the next billing period.
Apart from tracking retention and churn metrics between groups, they also measure how these metrics improve over time. The primary retention metrics are day #1, week #1 and month #1. They allow you to see how sticky your product is and if your recent adjustments help convert more users into paid customers.
Of course, the first step of every cohort analysis is understanding how this report works and, in our case, understanding the GA4 interface. Therefore, let’s overview all critical sections.
GA4 Cohort Exploration Report Interface
First, in order to create a cohort exploration in Google Analytics 4, you should take a few actions:
- Open GA4 and go to Explore tab
- Click on Template Gallery and Select “Cohort Exploration”
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 you use. For instance, I can recommend two events that you can use if you have a SaaS products and mobile apps: “signup and “login” events.
For our case, I will check how many first-purchasers come and make the second purchase in the second month, third month and so on.
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 “Path exploration”.
Report part #1 – Variables
Let’s start with the first one. It consists of the following elements:
- Exploration Name
- Dates range
1.1 Exploration Name
Exploration Name allows you to specify the report’s name and save it in Google Analytics 4 to access it later. You don’t need to click on any button. GA4 saves it automatically when 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.
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 in analysing your clients’ first product session (relevant for SaaS and mobile apps). You can use (custom) dimensions and metrics in Google Analytics 4.
This section allows you to add as many dimensions as you want. You can use the dimensions to break down your data afterwards or create filters.
For instance, you can be interested to see how mobile and desktop users’ experience differs by applying “device category” dimension as a breakdown.
Apart from importing and using dimensions, you can also use metrics. The most appropriate metrics for the cohort exploration will be “Active users”, and “Total users”. It doesn’t mean you can’t use others, but it’s used across many tools. For instance, you can use “revenue” and see how much revenue you receive from your users in the first, second and other months.
After understanding the first part of the report, let’s look at the second.
Report part #2 – Tab Settings
The second section of the cohort exploration report is “Tab Settings”. It consists of the following sections:
- Cohort inclusion
- Return criteria
- Cohort granularity
- Rows per dimension
- Metric type
Let me explain each of them to you.
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
- Segment Overlap
- User Explorer
- Cohort exploration
- User Lifetime
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 2.2.
2.3 Cohort Inclusion
This is the event (action) users need to perform to be added to the cohort. In our case, it’s the first transaction. You don’t need to specify “first”. GA4 defines it automatically.
2.4 Return criteria
The return criteria are the event that users need to perform after the cohort inclusion event. In our case, it’s the purchase in cohort spans. Therefore, cohort inclusion and return cretiria are the same for our case.
But, as I mentioned early, for instance, SaaS companies can use “signup” as cohort inclusion and “login” as return criteria.
2.5 Cohort granularity
This section allows you to specify the span (period) between cohort columns. There are three available values: daily, monthly and quarterly.
If you consider having yearly or custom spans, you can achieve it by working with GA4 data in BigQuery and visualising the data in Looker Studio.
This section is the most important in the GA4 Cohort interface and can make you wonder what it stands for. First, I always recommend looking at the official documentation, but, unfortunately, even for myself, it wasn’t clear enough. Therefore, let me explain to you each of them and then give you an example.
Standard means that each cell includes all cohort users that meet the return criteria in precisely this period, periods before and after don’t matter.
Rolling means that each cell includes all cohort users meeting the return criteria in that exact period and the previous periods.
Cumulative means that each cell includes all cohort users meeting the return criteria in any exploration period.
So, imagine we have a cohort with cohort inclusion “purchase” and return criteria “purchase” as well. And we have a user that made the first purchase in January 2023 (Month #0) and purchased in February (Month #1), and the third purchase was in April (Month #3). This way, we have the following stats.
|Calculation||Month 0||Month 1||Month 2||Month 3||Month 4||Month 5|
As mentioned in 1.3, you can add dimensions to break down your cohort exploration data.
For instance, you can break it down by device category. In order to do that, you can drag “device category’ from the dimensions and drop it in the breakdown.
It’s also worth mentioning that when you include a breakdown dimension, users are only attributed to the first instance of the breakdown value that applies to them. For example, say User A first appears as a mobile user, then returns the same day as a desktop user. User A only appears in the mobile breakdown for that cohort.
2.7. Rows per dimension
If your breakdown dimension has more than 5 variants (for instance, Default Channel Grouping), it means that GA4 will cut and show you only 5 first rows, if you want to see more, you should adjust the number of rows per dimension. Remember that you can’t see more than 15 rows per dimension. It’s a GA4 limitation.
This section allows you to select metrics. You can use multiple ones. I can recommend these ones: Total users, Active users or Event count.
2.9. Metric type
Metric type allows you to switch from absolute numbers (#) to relative numbers (%). “Sum” is absolute numbers, while “Sum per user” is relative.
However, whatever option you select, you will always have a pop-up with two metrics. So there is no right or wrong way here. It’s up to you.
Now, after we configured our report, we received this view.
Report part #3 – Report View
There are a few things left to discuss. The report view doesn’t have as many features that you can work with as other exploration reports have. But let’s go through them anyway.
The third part of the report is “Report View”. This window presents the final data after you apply filters, segments, breakdowns and change other settings.
3.1 Right-side menu – Plus icon
This icon allows you to create more reports and include them in your exploration report.
3.2 Left-side menu – Undone, Re-do, Share, Download
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.
GA4 Cohort Exploration limitations
Although Cohort exploration is an excellent new feature, it has limitations that you should be aware of. Some of them are critical and can let you consider using BigQuery instead.
Firstly, the cohort report doesn’t use user_id to calculate cohort statistics but uses device id, what can skew your product data significantly. Therefore, if your users use multiple devices or your product is for business clients where many people can log in to the same account, I strongly advise creating the cohort report using SQL in BigQuery.
Secondly, when you apply the breakdown dimension, it can’t include more than 15 values. If it’s a dimension with multiple values, it will be cut to 15 values.
Thirdly, the cohort exploration doesn’t allow you to see more than 60 cohorts. In our case, it means the breakdown dimension and period you use (daily, weekly, monthly).
Fourthly, if your GA4 property doesn’t have much data and you use demographic data to create cohorts or segments, GA can apply a threshold to anonymise users.
How to use Cohort Exploration report for SaaS?
As mentioned above, the GA4 cohort exploration report can help you understand core SaaS metrics such as retention and churn. For instance, you can see how the onboarding process works and what other techniques you can use to improve product stickiness.
However, GA4 functionality could be better, and the fact that it uses a device id instead of a user id can skew your data significantly.
In this case, you should build the cohort exploration in BigQuery or Mixpanel or Amplitude.
I hope that Google fixes it over time and that we, product owners, receive a fully functional tool that we can use daily.
Cohort exploration report is a new excellent feature that Google Analytics 4 offers us that we didn’t have in Universal Analytics. We can use it to understand our users better and improve their retention.
The report has advantages and disadvantages, and the core limitation of that is that it uses device id instead of user id.
Frequently Asked Questions
Cohort exploration in GA4 is a report that allows you to take segments of users and analyse their usage patterns based on their shared traits to learn more about their behaviour.
You can track W1 and M1 retention metrics by using Cohort Exploration report available in GA4 Explore.
You should go to GA4 Explore and select “Cohort Exploration” as a template. After that, you should adjust the settings.