the thrill-a-minute world of cohort analysis

Information you never knew you needed to know

The Thrill-a-Minute Worldof Cohort Analysis

‘REGULAR’ BUSINESS

When it comes to generating revenue, some businesses have a straightforward approach.

They invest resources into acquiring new customers, and once a sale is made, that's it: the money is in their pocket, and they can focus on acquiring new customers.

SAAS BUSINESS

In contrast, SaaS businesses operate on a recurring revenue model. Every month, it’s possible to retain their customers, and generate revenue – and it’s possible to lose customers, and lose out on revenue.

CUSTOMER CHURN

The rate at which existing customers cancel their subscription to your service is known as customer churn, a simple variant of which is shown in the formula.

Customer churn is hugely important, because it directly affects the growth of MRR.Unfortunately, there’s a serious problem with this calculation.

OLD VS NEW CUSTOMERSIn doing so, new customers (those that signed-up during the month) and existing customers (those that were already subscribers before the month started) are bundled together.

This is problematic because new customers and old customers will likely have different reasons for churning; and churn rates will vary between the groups as a result.

Simple churn calculations are typically done with a monthly measurement of churn across the entire customer-base.

THE HIDDEN PROBLEMWith customer churn having such a big impact on growth, we need to understand the reasons behind it.

Unfortunately, simple ‘blended’ churn calculations won’t reveal the hidden problems that are causing different customers to churn.

EXAMPLE

For example, you may have a fantastic onboarding process, but lousy customer support.

Happy new customers will contribute to low churn in the first few months, but mounting frustrations further down the line will lead to high churn later in life.

Unfortunately, if we used a simple average measurement of churn each month, the low churn rates of new customers would offset the high churn rates of existing customers.

We wouldn't gain any insight and, we couldn't fix the problem.

THE SOLUTION

Instead of calculating churn across all of our customers, we can divide them into groups that share similar experiences or characteristics – known as cohorts.

In this example, customers are grouped together by conversion month – the month they became paying customers. Each month, customer churn is then calculated separately for each cohort.

THE SOLUTION

This reveals two very important pieces of insight:

1. We can easily see if churn varies by conversion month (orange). It might be that we notice a statistical outlier, and discover that customers that signed-up in January were more likely to churn than those from other months. Digging further, we might be able to attribute it to a surge in outbound advertising, and an influx of less-than-best-fit customers.

THE SOLUTION

2. We can see how churn varies according to lifetime month (blue). Instead of comparing monthly customer churn across customers of differing ages, we can easily compare churn rates across customers one-month into their commitment, two months, three months and so on – irrespective of when they signed-up.

We might see that churn rates are exceptionally low during the first 2 months, but start to increase sharply from 3 months onwards. A few customer surveys later, and we've heard praise for our onboarding tutorial and disappointment at our ongoing support. 

USING COHORTSCohorts can also be used to easily visualise changes in customer churn over time…

USING COHORTS…visualise MRR by monthly cohorts (in this instance, showing strong negative churn)…

USING COHORTS…and visualising customer churn across customers using different devices.

IN CLOSING• Simple metrics hide the story behind changes to your growth and

health.• To identify and solve the root problems limiting your growth, it’s

necessary to go beyond simple KPIs.• Cohort analysis allows you to add qualitative context to

quantitative data – calculating crucial metrics separately across similar groups of customers.

• Cohorts can (and should) be used in a wide range of capacities, from calculating MRR and CLTV, through to sign-ups and engagement levels.

LEARN MORE• Want to learn more about using cohort analysis to improve

your SaaS growth? Check out my blog post: http://www.cobloom.com/blog/one-big-reason-your-saas-business-needs-cohort-analysis