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 you determine 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 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 (LTV).
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)
A return metric (the thing you want to know about them)
SaaS cohort analysis – example 1
For a SaaS cohort analysis example, let’s say you are a mobile game developer, and you want to determine if users on iOS devices have been more or less profitable than users on Android devices over the last quarter. Since equal resources have been used to promote the app on both platforms up to this point, you decide to measure how valuable users are on each platform by comparing the average revenue per user (ARPU) between users on iOS devices and Android devices.
In this case, the characteristics of the cohorts are defined by the mobile operating system each user has (iOS or Android). The inclusion metric for both would be being an active user over the last quarter. And the return metric for both would be ARPU.
Let’s say the inclusion metric tells you that the iOS cohort has 400,000 users and the Android cohort has 500,000 users. The inclusion metric indicates that the iOS cohort has 200,000 active users over the last quarter while the Android cohort has 250,000. The return metric indicates the iOS cohort has an ARPU of $3 while the Android cohort has an ARPU of $2.
From this analysis, you might conclude that iOS users are less likely to download the game but slightly more profitable on a per user basis than Android users; and, therefore, you may choose to appropriate a greater portion of the company’s marketing budget towards promoting the iOS version of the app for the upcoming quarter.
SaaS cohort analysis – example 2
So, for example, you have a cloud-based time tracking app. Let’s say it’s December and you want to compare the retention rates of the customers you acquire from two distinct marketing campaigns: those who signed up from a Mailchimp drip email campaign in April, and those who signed up from a Google Adwords campaign in May.
The characteristics of your cohorts are defined by the marketing campaign attributed to the new customer (Email or Adwords). The inclusion metric for both would be taking the action of signing up. And the return metric for both would be customer status (current or lapsed) in December.
Say the inclusion metric tells you that the email cohort has 200 customers while the Adwords cohort has 300. The return metric indicates that the email cohort has 100 remaining current customers come December, while the Adwords cohort has 250. The retention rates are 50% for those who signed up in from the email campaign, and 83% for those who signed up from the Adwords campaign.
From this analysis, you can conclude that the retention rates for customers who signed up from the Adwords campaign are significantly higher than those who signed up from email marketing. Therefore, you might choose to focus future marketing campaigns on Adwords or even test some other combination of search engine marketing (SEM) and display marketing strategies for future analysis.
Dive deeper into customer cohort analysis
With this information, you can now cross-reference this analysis with other data to try to figure out why the difference between the groups is so large. You may want to run the same analysis on February, March, and June sign-up cohorts, for example, or to look at how the customers in these cohorts converted into customers.
Insights for future growth
Cohort analysis can provide all manner of useful insight into what works best to engage, convert, and retain customers. It’s something savvy business owners should return to, frequently, as you seek to answer both basic and complex questions about your company’s progress and growth.
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