Cohort Analysis – The Ultimate Guide for SaaS
A cohort is a group of users with common characteristics or experiences over a defined period. Cohort analysis is a type of behavioral analytics where you group user data into different cohorts to better track and understand user behavior.
It is also called customer churn cohort analysis or customer retention cohort analysis.
Cohort analysis helps you detect emerging patterns across a user’s life cycle and tailor your decisions to the needs of specific cohorts. This lets you reduce churn and boost user retention.
So, let’s dig deeper to learn how to use cohort analysis to combat churn and grow your product!
- Cohort analysis is a type of behavioral analytics that helps you see what a segment of your users (a cohort) is doing within your product.
- Cohort analysis will give you actionable insights to make informed decisions to combat churn and grow your SaaS business.
- Acquisition cohorts and behavioral cohorts are two types of cohort analyses. Acquisition cohorts group users based on when you acquire them, while behavioral cohorts allow you to track events that make users churn.
- The best cohort analysis tools include Baremetrics, Google Analytics, and ProfitWell.
- To combat churn using cohort analysis, you need to define clear goals, explore and analyze data, and build a hypothesis centered around the data. Then, test and implement the hypothesis, and review and repeat the results.
What is cohort analysis?
Cohort analysis is a type of behavioral analytics that helps you see what a sub-section of your users (a “cohort”) is doing within your tool. We usually use cohort analysis for SaaS to measure user churn, i.e. how many customers stop using your product and leave.
Cohort analysis helps you answer the following questions based on the type of cohort analysis (behavioral or acquisition cohort analysis) you perform:
- How long do my users keep returning to my product on average, and when do they churn the most?
- What segment of my users (e.g., by persona, demographic, plan, etc.) churns the most?
You need a cohort table to conduct cohort analysis:
Here’s what you can decipher from the cohort analysis table above:
- In the rows (going from top to bottom), you can see the start dates (months) for each cohort, e.g., a cohort of users who signed up in October 2017 vs. a cohort of users who signed up in May 2018.
- In the columns (going from left to right), you can see the number of months any particular cohort has been using your product.
- From the cells, you can know how many (in %) users have churned within a specific month of subscribing.
Since SaaS is a subscription-based business model, user retention is vital for achieving recurring revenue. Cohort analysis lets you learn the exact number of users you’re retaining in each month of their user journey, based on when they started it.
Still wondering why it’s important to know the user retention rate in every month of signup?
Well, let’s say if your product never changed, your users didn’t change, and everything around you was standing still, with no competitors breaking through the horizon – you would be okay with tracking only the overall month-on-month retention rate.
Despite being very simple, the cohort analysis table brings much more important information than that at your disposal.
Why is cohort analysis important for SaaS?
Vanity metrics can pile up a lot of unnecessary information that will keep you searching in the dark. On the other hand, cohort analysis will allow you to just gather enough user data for actionable insights.
It will reduce the time it takes to sift through endless data and help you make informed decisions about your business.
With cohort analysis, you can look at when your users churn and how the churn rate changes based on when the new cohort of users signed up for your tool. This provides valuable insights into the effectiveness of your product strategy and marketing strategy.
You can use cohort analysis to get help with the following:
- Check if your product is sticky or is considered a “one-off” service.
- Learn the time it takes for an average customer to lose interest and leave your product.
- Improve your user retention goals.
- Check the effects (good or bad) of any changes you made to your product, UX, or marketing strategy had on your customer retention rates.
- Know the impact of the changes on your user onboarding on your first-month customer retention rates.
- Creating an actionable retention strategy, such as targeting the ‘sensitive’ months in the user journey.
What types of cohort analysis are there?
There are two types of cohorts in cohort analysis:
- Acquisition cohorts
- Behavioral cohorts
Acquisition cohorts gather cohort data based on the acquisition date -the time when all new users sign up.
For instance, people who signed up in October will belong to the same cohort.
Some benefits of acquisition cohorts are:
- Shows the impact of changes in your product over time on the entire pool of users.
- Allows you to check if your product strategy is headed in the proper direction (decreasing Time to Value and boosting customer retention plus product stickiness).
Furthermore, acquisition cohorts can also help you work out the moment in the customer lifecycle your users are inclined to leave.
However, an acquisition cohort cannot support your SaaS business on its own. To dive deeper into the reason your customers churn, you need a behavioral cohort.
Behavioral cohorts go past the acquisition date to look at specific types of users.
You can segment users by:
- A specific user persona (e.g., by company size, role)
- The subscription plan they signed up to
- Features they engaged with inside your app
- Channels through which they were acquired
- The actions they have taken (e.g., whether they’re using certain features)
Behavioral cohorts can benefit your SaaS business in a number of ways:
- Helps you understanding the type of users who get the most value from your product.
- Helps you understand what product features you should inspire users to adopt. This is because the adoption of these value-driving features translates into customer retention via user onboarding, in-app experiences, or personalized calls.
- Guides your marketing campaigns (what kind of personas you should target and what channels give you the most qualified leads).
- Helps you decide which plan would provide the highest value for users’ money.
It can be challenging to find a clear link between user behavior and user retention. Often, it’s a combination of user behaviors that drives user engagement inside your app.
You need to group the shared behaviors of the most highly engaging users into the same cohort. This way, you can use cohort analysis to find out the users who are leaving.
How to read cohort analysis tables
You might find it intimidating to navigate through cohort charts if you’re just starting out. But it’s actually quite easy to read a cohort table once you have a basic idea of its components.
Below, you can take a look at what our cohort charts appear like in Baremetrics.
First, let’s check out a single cohort.
The table from left to right goes like this:
- April 2019: This is the cohort. It consists of users who signed up for our product in April as of 2019.
- 24: This is the cohort size – the number of users in this cohort
- 92%: This is the percentage of active users who remained within the first month after the signup. Thus, in April 2019, 8% of the initial 24 customers left at the end of month 0.
After that, every column gives the percentage of users who remain from that cohort at the end of each subsequent month. Hence, under column ‘6’, we can see that 71% of the beginning 24 customers remain at the end of the 7th month after initial signup.
If you don’t prefer percentages, you can obtain the customer retention data in absolute numbers. This will give you the exact number of users retained every month.
You can also check revenues instead of users.
For your cohort table, you can segment users in even more ways, such as:
- Plan type
- Acquisition channels
- Business size
- Key actions completed (e.g., Userpilot – created triggers)
- Within a particular time frame (e.g., created triggers in the first week)
- The frequency of triggers needed within the time frame (e.g., created 3 triggers in the first week)
Having the data and knowing how to read a cohort analysis table is good, but it’s still futile unless you put the data and knowledge into action.
Here are some business analytics tools best suited to perform cohort analysis.
Best cohort analysis tools for SaaS
The Google Analytics cohort analysis report lets you concentrate your user data to a specific cohort and analyze its user behavior and the impact of your marketing campaigns on it.
You can alter the settings for cohort size, cohort type, metric, and date range at the top of the cohort analysis report.
- Cohort size: This is based on the number of users who signed up in a specific month. You can define this by month, week, or day. For example, if you acquire a cohort of users in September, another in November, and another in December, the cohort size of each month will be the number of users acquired in that month.
- Cohort type: This is a limitation of Google Analytics since the only option present is the acquisition date – the time when you acquired new users.
- Metric: You can segment users based on the metric you wish to analyze them. The default metric in Google Analytics is user retention – the percentage of users who come back to your product each month.
- Date range: The date range lets you classify users based on a particular window of time, e.g. (the last 6 months).
Additionally, Google Analytics lets you add more segments, such as web/mobile traffic, to compare cohorts, like any other report.
However, the cohort analysis report has a real gem – the heat map. The heat map in Google Analytics enables you to easily determine the lowest and highest performing metrics by week after acquisition date and cohort.
Google Analytics can not only help you with your websites but also with your products.
And – it’s completely free!
ProfitWell’s main objective is to boost subscription revenue. It helps you to concentrate on your users and products.
It allows you to carry out cohort analysis. Nonetheless, ProfitWell’s advanced algorithms and AI set it apart from Google Analytics.
- Audit-proof revenue recognition: ProfitWell gives you the exact source of your revenue and variations, like downgrades and upgrades.
- Churn reduction and customer recovery: You can gain more in-depth insights on churn even when it’s not associated with your product. On the flip side, you need to understand much of the data by yourself with Google Analytics.
- Price optimization: ProfitWell lets you build a powerful pricing strategy. It feeds you real-world data on your market and industry to come up with competitive prices for your SaaS products.
If you are just embarking on a business, you may find it hard to understand and analyze all the metrics. It would take some time to make the best use of reports.
Moreover, you will further lose time while integrating ProfitWell with some other platforms or tools.
ProfitWell is free to use, even for cohort analysis, with the option to include an unlimited number of users. If you need extended functionalities, you can opt for its premium plans that start at $1000 per month.
Baremetrics is an analytics tool for subscription businesses and SaaS. Data augmentation and user segmentation help you get a better understanding of user behavior and make informed decisions.
Although it has similarities with other cohort analysis platforms, it is set apart by its ability to connect with Stripe and other data sources, such as your marketing automation tool and CRM.
It also tracks ‘traditional’ metrics, such as churn, MRR, and lifetime value. If a marketing plan is driving greater churn than the rest, you can dig deeper into the reason.
Baremetrics collects data directly from Stripe and accumulates them on one dashboard. But, it has fewer features when compared to ProfitWell.
Unlike Google Analytics and ProfitWell, Baremetrics is not permanently free. The paid version starts at $350 per month, with extra features, such as cancellation insights and payment recovery.
How to use cohort analysis to combat churn in SaaS
Your product is not going to be appealing to every user out there. There’s always going to be some natural churn.
However, if your users are churning when they shouldn’t or churning frequently, it’s a matter of grave concern. It could be due to at least one of the following reasons:
- Your users’ expectations don’t match your product’s features.
- There’s a problem with the activation.
- Your onboarding experiences require revision.
You can use cohort analysis to fight churn in SaaS by following these 5 steps:
1. Define your business goals
What are the objectives of your business? How long do you want to take to achieve those goals?
Your goals need to be clearly defined, attainable, and measurable. This helps to prioritize business opportunities.
For example, you might want to increase the one-month retention by 15% in the ‘Pro’ plan of a social media scheduling tool that is built for agencies.
2. Explore data and analyze them
This is where you compare different cohorts and analyze their behavior. It helps you answer questions like:
- When in the customer lifecycle do my customers leave?
- What kind of customer segments is churning?
- What is the current user retention rate in a certain behavioral cohort?
- When does the retention curve start to flatten out?
This will let you know which common characteristics of a particular cohort are positively or negatively affecting your churn or retention rate.
3. Create a hypothesis
Build a hypothesis about what can enhance your retention rate in terms of the outcome of data exploration and analysis.
For instance, a social media scheduler for agencies could add certain key productive features for teams instead of only using an onboarding experience to point customers to only 2 core activation points (add social media accounts plus schedule the first post).
The additional features could be:
- Where to add team members.
- How to group accounts, such as by clients.
Another example could be mid-term churn. The resulting hypothesis could be figuring out how you can drive engagement once users have onboarded and activated.
4. Test and implement the hypothesis
Create tests to examine the validity of your hypothesis for different cohorts.
For example, create an onboarding experience and implement the hypothesis. Userpilot helps you achieve this without any coding knowledge and is deployable in minutes.
5. Review and repeat
Did you confirm or nullify your hypothesis?
Whatever the test result, you should repeat the tests to ensure reliability.
In the example above, you can use the test results to improve the onboarding experience and maybe even plan further.
Cohort analysis might seem complex at first glance. But the more you get used to it, the easier it becomes to extract value from it.
Cohort analysis enables a SaaS company to segment users into cohorts to analyze their behavior from multiple dimensions. You can leverage behavioral analytics to improve marketing efforts and influence different customer lifecycle stages to combat churn.
Want to boost your retention rate and cut churn? Use a free Userpilot demo and get brilliant results for your SaaS product!