As a business owner, one of the most important things you can do is track your customer churn. Churn, or the rate at which customers stop using your product or service, can have a major impact on your bottom line. That’s why it’s so important to understand what causes customers to leave and take steps to prevent it. One way to get insights into customer churn is through SaaS cohort analysis. This type of analysis allows you to see how different groups of customers interact with your product over time. By understanding these patterns, you can make changes that will keep more customers coming back.
SaaS Cohort Analysis: Evaluating Customer Behavior and Engagement Over Time
SaaS cohort analysis is a method of evaluating customer behavior and engagement over time. This analysis can be used to improve customer retention or to identify areas where customers are struggling with your app.
Cohort analysis can be complex, but with practice, it becomes easier to extract value from it.
By analyzing user activity, you can better understand how customers interact with your product. This, in turn, can help you optimize marketing initiatives, influence different points in the customer journey, and reduce churn.
What is a Cohort?
A cohort analysis is a method of studying the behavior of a subset of users called a cohort. We typically use this method for software-as-a-service (SaaS) companies to determine why users are churning (i.e. canceling their subscription).
There are two types of cohort analysis: behavioral and acquisition. Using cohort analyses, you can answer questions like:
- How long do users keep returning to my app?
- When do users churn the most?
- Which segment churns the most?
You need a table like this to conduct cohort analysis.
Recurring revenue from your subscribers is essential to your growth. User cohort analyses let you determine exactly how many users are sticking around every month of their customer journey.
A cohort is a group of users who sign up during a specific time frame. By tracking the retention of these cohorts, you can measure how successful your onboarding process is.
So, why is knowing your user retention important?
If your users don’t ever change their habits, your product doesn’t evolve, and there are no new entrants to the market, you only need to track the monthly retention.
The cohort analysis table, despite being simple, provides more information than you could gather on your own.
Why is Cohort Analysis Important for SaaS?
Cohort analysis allows you to just collect the data you need for actionable insight. While vanity metrics are a great way to gather a lot of information, they can lead to you being lost in a rabbit hole.
By looking at when customers sign up and when they decide to cancel, cohort analysis can help you identify which marketing strategies are most effective.
Cohort Analysis can help you:
- Check if your product is sticky or is considered a “one-off” service.
- Find out how long it takes for a customer to lose interest and leave your product.
- Improve user retention goals.
- Measure the impact of changes you made to your product, onboarding, or marketing strategy.
- Create an actionable retention strategy like targeting sensitive months in a customer journey.
Tracking your cohorts is important, especially for a Saas company, because it allows you to see how well your software is doing and helps you improve your retention rates.
Types of Cohort Analysis
Acquisition cohorts are groups of users who share a common characteristic, such as sign-up date, that can be used to measure user behavior over time.
Behavioral cohorts are groups of users who share common behavior, such as making a purchase, that can be used to measure how that behavior changes over time.
Acquisition cohorts are a type of cohort analysis that gathers data based on when users first sign up. This allows you to track and compare the behavior of different groups of users over time.
People who have subscribed to your email newsletter in October will be grouped together in the same cohort.
Cohort analysis allows you to see how changes to your products affect a pool of users. It also helps you see if your strategy is improving or worsening.
Additionally, cohort analysis can help you figure out when in the customer journey users are most likely to churn.
A customer’s acquisition date, however, is not enough to understand their reasons for churning. To truly understand why they left, you need to analyze their behavior.
A behavior-based analysis allows you to look at different types of customers, such as those who only signed up once, those who used your product for 6 months, and those who used it for 12.
Behavioral cohort analysis goes beyond acquisition dates to analyze different types of user behavior.
You can group users by:
- Buyer persona
- Subscription plan
- App features they engaged with
- Channels through which customers were acquired
- Actions they have taken within the app
Behavioral cohort analysis has many benefits for software-as-a-service (SaaS) companies.
Cohort analyses can help you understand which types of users are the most valuable to your business. This knowledge can be used to determine which marketing personas to target, which products you should build, and which plans you should offer.
It can be difficult to determine the exact behavior that will drive users to engage with your app. It’s often a combination of different behaviors that drive them.
One way to group users with shared behaviors is through cohort analysis. This type of analysis can help you understand which users are leaving your app and why. By grouping together users with similar behaviors, you can identify patterns and trends that may be causing them to leave.
How to Perform a Cohort Analysis to Track Customer Retention Rate
Measuring customer retention rates can be done by running a cohort analysis.
The idea is to group customers by their signup dates (such as on a daily basis, on a weekly basis, or on a monthly basis, depending on your business needs and pricing structure).
To track the retention rate of your customers, you’ll need three pieces of information:
- Who you are tracking
- Sign up date
- Metric value on all subsequent dates after sign up
All of this information can be found in a table with a user ID and event timestamp. We will bring this together in an SQL query and add it to the data source as a custom table.
The assumption is that your source data has the ability to create Common Table Expressions (CTE). If not, this will need to be done as a sub-query.
To form a query, you’ll need to answer two important questions: “who are we targeting?” and “when did they initially sign up?”.
If you don’t have a user’s table and aren’t sure how to figure out their sign-up dates, you can look at their events. Usually, the MIN or minimum event is their sign-up.
Next, we combine the two query results and then calculate the difference between their signup date and the event. This will be done using a common table expression.
How to Use Cohort Analysis to Combat Churn
Not every prospect is going to be interested in your product. There will always be some amount of attrition.
If your users are canceling their subscription, it may be because of one of the following:
- Users’ expectations don’t match the product’s features.
- App has activation issues.
- Onboarding is poor.
Cohort analysis can help you identify why your users are churning and what you can do to combat it. By tracking cohorts of users over time, you can see how they interact with your product and identify patterns in their behavior.
You can fight customer attrition in software as a service (SaaS) by using these 5 steps to analyze your cohorts.
1. Define Your Business Goals
What are your business goals? How long are you willing to wait to reach them?
Having clearly defined and attainable business goals and objectives allow you to track how successful you are at meeting them.
For example, you might want to increase the one-month retention by 15% of a social media scheduling tool that is built for agencies and freelancers.
2. Analyze Data
This is where you analyze the behavior of different cohorts.
- What stage of the customer lifecycle did my customers leave?
- What kind of customer segments is churning?
- What is the retention rate in a specific behavioral cohort?
- When does the retention curve start to flatten out?
This will let you see which characteristics are affecting your retention or attrition rates.
3. Build a Hypothesis
Build your hypothesis about what can help you improve your customer retention rates based on the results of your data analysis.
For example, a social media marketing tool could add additional productivity features like being able to assign tasks to team members or assigning multiple social accounts at once.
Additional features could include adding team members and grouping accounts.
Another potential is mid-term churn. Figure out ways to keep users engaged after they’ve signed up and started using your product.
4. Test and Implement
Test your hypotheses with different cohorts.
For example, create and deploy an onboarding experience that tests your hypothesis. With no coding required, you can be up and running in minutes.
5. Review and Repeat
Did you find that your hypothesis was correct, or did you disprove it?
To ensure your results are reliable, you should run your test multiple times.
If you want to keep your customers coming back, it’s important to understand what causes them to leave in the first place. SaaS cohort analysis is a great way to get insights into customer churn. By understanding how different groups of customers interact with your product over time, you can make changes that will keep more people using your service. So if you’re looking to improve your customer retention, be sure to give SaaS cohort analysis a try.