Last editedNov 20202 min read
Customer churn and retention are vital concepts for SaaS businesses to understand. Looking at the raw data can be useful, but to really grasp why some customers churn while others stick around, you’re going to need a more sophisticated form of analysis. That’s where cohort analysis comes into play. Helping you to understand why your customers are churning, how they’re churning, and when they’re churning, cohort analysis for SaaS is an enormously beneficial tool that you should take advantage of. Find out more about the meaning of cohort analysis with our simple guide.
Cohort analysis meaning
The term “cohort” refers to a group of users who experience a common event within the same period. Cohort analysis refers to the analytical framework that allows you to derive insights from these users. Within a SaaS context, a cohort is a subsection of your customer base that shares a common characteristic. Generally, this characteristic is the date/month that they were acquired. However, additional characteristics, such as the channel that they were acquired on, may also be used to broaden the scope of your analysis.
Why is cohort analysis important?
Put simply, cohort analysis is a more meaningful way to separate your users. It gives you the opportunity to ask specific questions about your audience and make informed decisions that can have a dramatic impact on your bottom line. Let’s think about cohort analysis for churn. If you aren’t using some form of cohort analysis, you’re going to end up lumping all your users together in one large dataset. However, that’s going to skew your results, because new customers and existing customers are likely to have very different reasons for churning.
For example, if you offer an excellent onboarding process but limited customer support, you’ll see low rates of churn in the first few months of the customer lifecycle, but higher rates of churn a little further down the line. Simply measuring the average rate of churn won’t help, because the high churn rate of your existing customers is likely to be offset by the lower churn rate of your new customers. In other words, cohort analysis for SaaS can help you identify issues with your business that may otherwise have gone unnoticed.
How to perform cohort analysis
Implementing cohort analysis for SaaS can be a challenge, so let’s break it down into a few manageable steps. Remember, cohort analysis can be as complex or as simple as you’re willing to make it:
Identify the problem. For example, you may wish to look at why your customers are churning, or perhaps where the customers with the highest LTV are sourced from.
Formulate a hypothesis. Look at your internal data and come up with a hypothesis related to the problem you identified in the previous step.
Start collecting data. Record the new customers you acquire and the specific characteristics of each cohort. You can bucket customers according to acquisition month, as well as other important characteristics like acquisition channel. When you’re splitting the users into cohorts, ensure that the way you’re splitting them will help you answer the problem you identified in the first step.
Check the results. Finally, you need to work out if the hypothesis was correct or not. Then, you can use these results to improve your company’s long-term strategy.
Cohort analysis vs. segmentation
You may see cohort analysis and customer segmentation used almost interchangeably, but there’s a significant difference between these two analytic terms. Essentially, cohort analysis is time-bound, whereas segmentation isn’t. As such, customer segments tend to be specific subgroups of people within a cohort based around a specific characteristic. When it comes to cohort analysis vs. segmentation, it’s important to remember that it’s not an either/or situation. You should utilise both forms of analysis to gain richer insights into your customers.
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