It can be very useful to compare groups of customers in assessing your company’s progress. As you analyze how various groups behave during a standard time period, you can pick out patterns and use that information to better identify problems, satisfy customers’ needs, and design engagement strategies.
Do customers you acquired last month act differently than ones you signed up the month prior? Do users who responded to a discount or promotion behave differently than those who purchased at full price? Cohort analysis answers these questions and allows a company to identify clear patterns across different customer groups.
What is cohort analysis?
Cohort analysis is a type of behavioral analytics, which is primarily identified by breaking down customers into related groups in order to gain a better understanding of their behaviors. It’s an informative business analytics tool every business owner should have in their back pocket. Following is a run-down on how cohort analysis works and why it’s a useful strategy for gaining insight.
What is a cohort?
In cohort analysis, a cohort is the group of customers being analyzed. More specifically, a cohort is a group of people who have something in common during a specific time period. The parameters of this group are generally identified based on the question you want the analysis to answer and the metrics determined to be significant.
A cohort in a general sense could be anything as random as “people born in 1978 who are colorblind.” For the purposes of cohort analysis for your business, however, the groupings are usually made up of users who performed certain actions during a chosen time frame, such as downloading your app during a particular month or finding your product via social media in a given week.
The time-boundedness is key: Customers grouped by behavior but without a time parameter are called segments, not cohorts.
Why is cohort analysis useful?
This type of analysis is valuable due to the specificity of the information it provides. It allows companies to find answers to targeted questions by analyzing only the relevant data. Here are some things this process can help you do.
Know how user behaviors affect your business. Cohort analysis allows you to see how actions those in the cohort took or didn’t take translate into changes in business metrics, such as acquisition and retention.
Understand customer churn. You can marshal your data to assess your hypotheses regarding whether one customer action or attribute leads to another, such as whether sign-ups related to a specific promotions encourage greater churn.
Calculate customer lifetime value. Analyzing cohorts based on acquisition time period, such as grouping customers by the month they signed up, allows you to see how much customers are worth to the company over time. You can then further group these cohorts by time, segment, and size to assess which acquisition channels lead to the best customer lifetime value (CLV).
Optimize your conversion funnel. Comparing customers who engaged in various ways at given times with your sales process can allow you to see how user experience throughout the digital marketing funnel translates to value in your customers.
Create more effective customer engagement. As you see patterns in how various cohorts engage with your company, SaaS website, and product you can take steps that will encourage all customers to take various actions more efficiently.
How to do cohort analysis
How you go about performing cohort analysis depends on what question you’re trying to answer. You’ll need to select the following information from whichever data-management solution you use:
The characteristics of your cohort (what defines the group)
An inclusion metric (the action that precipitated inclusion in the group)