Cohort Analysis in a SaaS business is a method of tracking and analysing the behaviour and performance of specific groups of customers over time. It involves grouping customers who share similar characteristics or joined the service during a specific period into cohorts and then examining their engagement, retention, and revenue patterns.
Cohort Analysis is not a metric as such, but rather a framework for evaluating customer retention and their lifetime value to a SaaS business. Cohort Analysis provides a deeper understand of the why, how, and when of our customers’ actions. It is measured via bookings (logs) and dollar retention.
In SaaS business, the most commonly used characteristic to perform Cohort Analysis is the date or month of customer acquisition. The purpose of the analysis is to study how bookings retention, monthly recurring revenue (MRR) retention and customer actions evolve over time. This ultimately leads us to understanding customer lifetime value, retention patters and payback periods.
Here is an example of a Cohort based on time when customers were acquired:
In the above example we can see the following pattern:
At the most basic level, we are looking in the Cohort Analysis for answers to the following questions:
Question 1: Are they still a customer of ours? With each monthly cohort, we can visualize logo retention, dollar retention, and any “break points” in the data.
Question 2: Can we recognize a pattern and identify the root-cause of it?
Cohort Analysis is not unique to SaaS business, and in fact its application is most widely spread in science. And the scientific method of applying Cohort Analysis is a 7-step process:
Step 1: Ask question: What is the “thing” we want to know about customer retention and monthly recurring revenue (MRR)?
Step 2: What information is already known? This is the existing internal knowledge and anecdotal evidence about customer retention.
Step 3: Set the hypothesis, or an educated guess or prediction of the outcome which you want to prove or disapprove. Put differently, if we take or implement action X will the result Y or not occur, and why? Making these educated guesses constantly happens in any SaaS business, but Cohort Analysis formalizes the process of making these guesses and gives structure to it.
Step 4: Carry out an experiment to test the hypothesis. Does the real world behave indeed as we predicted. Each month a SaaS business places the tactical components of the hypothesis into action, be it fine-tuning the customer acquisition channels, sales process, customer success, tech support, or even product features.
Step 5: Gather data generated during the experiment and summarize your observations about the experiment. Each month we record the new customers acquired and specific characteristics around each cohort that, we feel, are important in understanding customer behaviour and long-term success. This data can be summarized into:
Step 6: Reach conclusions about whether the hypothesis set in step 3 was true or not true. Validating or invalidating the hypothesis is done using data collected in step 5.
Step 7: In this final step the conclusions reached must be communicated by sharing the results along with recommended actions for improved customer retention and customer lifetime value.
The Cohort Analysis, as outlined above, is not a one-off exercise but continues being applied as a framework, over and over again.
It is helpful to compare the Cohort Analysis to a production line, as depicted on the image below.
When analysing the output of the Cohort Analysis we must ask the following questions about our customers and our business:
Question 1: Are they still a customer of ours? With each monthly cohort, we can visualize logo retention and any “break points” in the data.
Question 2: Can we recognize a pattern? For example,
Here is the formula for calculating customer retention, on a logo basis rather than a dollar basis:
Here is an example of how Cohort Analysis helps evaluation customer retention:
In addition to tracking customer retention, we track the total MRR associated with each cohort. By tracking MRR, we want to know if retained customers are expanding or downgrading. Is the customer adding users, modules, and
thus more MRR? Or are they downgrading by dropping seats, modules, etc.? And, of course, we track churned customers which removes MRR from the
cohort. As a result, we can track gross dollar retention (GDR) and net revenue retention (NRR).
We discussed GDR and NDR in Chapter 2 but let’s recall the meaning of these metrics.
Here is the Gross Dollar Retention formulae:
And here is an example:
And here is the Net Dollar Retention (NDR) formulae:
And here is the Net Dollar Retention (NDR) formulae:
We have mentioned earlier that there are basis and segment Cohort Analysis. What has been outlined above is the basis type. The segment Cohort Analysis follows the exact same logic and flow, except that we go deeper into the data on the basis that certain characteristics of the cohort (e.g. acquisition channel) are fundamentally different and require a separate analysis. This could look as follows:
Cohort Analysis is not simple, but it is powerful. It takes data discipline to track and analyse cohort data. It doesn’t magically appear in your monthly reports. The observations and analysis provides actionable insights into operational strategy and resource allocation. Again, the purpose of cohort analysis is to improve customer retention and increase lifetime value.
Here are some tricks and tips on performing Cohort Analysis:
Cohort analysis and SaaS financial metrics are closely related and provide valuable insights into the performance and growth of a SaaS business. Cohort analysis helps to understand customer behaviour and measure their value over time, while SaaS financial metrics provide a quantitative view of the company’s financial performance. Let’s explore the link between the two:
Overall, cohort analysis complements SaaS financial metrics by providing a more in-depth understanding of customer behaviour, which in turn influences these financial metrics. By analysing cohorts, SaaS businesses can make data-driven decisions to optimize customer acquisition, retention, and expansion strategies, thereby enhancing their financial performance and overall success.
In conclusion, cohort analysis enables data-driven decision-making by providing granular insights into customer behaviour, retention, and revenue. It helps SaaS businesses identify opportunities for growth, optimize customer experiences, and drive long-term success of the SaaS business.
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