This post is part of a series in which we look at customer research projects that can make a difference in the way you understand your customers. In this opportunity we will explore how to go about understanding why people leave your service or product.

This post will cover what datasets can be used for qualitative analysis, what type of customer segmentation you may need and the outcomes for each project.

I hope you find them helpful. Let’s get started!

Your revenue churn makes it impossible to grow. You’re bringing in new customers, but the money they pay is being swallowed up by your off-the-charts revenue churn. Your growth is just an illusion. In reality, the company is shrinking.- Steli Efti

Understanding why customers decide to go can help your team build more effective retention campaigns, it can help you redefine what engagement or active users mean to your business and inform your product strategy. Ultimately, discovering the top reasons for churn can unlock your growth.

Most likely you already have all the data you need to fully understand why customer are churning, for example, if you are running NPS surveys, cross referencing feedback from NPS detractors with other sources of feedback like support tickets from churn users can help you identify any commonalities between people who already churned and people who may be likely to churn.

Most SaaS companies will also have automated emails or protocols to request feedback from churn users. Maybe you have replies from an automated email that gets sent as soon as they cancel, maybe you have a survey they need to fill in before they can unsubscribe. Those responses will come in handy here.

Identifying key themes across all these data points will enable you to design better hypothesis that can be further validated by arranging calls with churn customers.

Churned customers may not be very likely to jump on a call but even if it is just a few per week/month you will be able to get the context you need behind their feedback. Another important step for discovering why customers churn is to look at your retention rate. Your retention rate is basically how many users keep coming back to your product to perform any action in a given period of time.

“Increasing user retention and preventing churn is by far the most important thing you can do to build a sustainable user base and drive growth. It trumps other factors like new user acquisition and virality in how much it can impact your monthly active users.” - Archana Madhavan of Amplitude

Most analytics products provide out of the box calculations on retention rate. Looking at this data you can identify where and when you user drop off and try to understand is there is anything you could do to bring them back.

Having said that, trying to bring people back without an understanding of why that would be valuable to them it is a wasted effort. This is where customer feedback and in-depth interviews can provide the context needed to make sense of customer behaviour and metrics like retention rate.

Now it’s time for segmentation. As you know, not all churn is equal. Some customer segments can have a bigger impact on your revenue and strategic goals so when it comes to analysing churn, being able to segment your data us crucial. Especially if you want to focus your research on segments like enterprise customers or any other group that may represent your most valuable customers.

Analysing reasons for churn across all your segments may be tempting but if you focus on your key segments first you will be able to take action on the outcomes of the research much more easily and faster. The shorter the feedback loop the better.

“I don’t care how you do it… use a 3rd party tool, roll your own, do it by hand… but you need to be able to separate the “active” (whatever that means to your company) from the in-active customers. This way you can take those who are active and add some grease to the engagement wheel while you work harder on engaging those that have slipped off the radar… or never quite made it on in the first place” Lincoln Murphy of Sixteen Ventures

Once you have gathered enough data from churned customers from a specific segment you can start identifying pattern across multiple sources. As you discover themes across your data you can start building a taxonomy of reasons for churn that can be use in future research projects.Churn taxonomy example: Voluntary Churn:

  • Missing features
  • Bad customer support
  • Competition - Pricing
  • Internal champion left the business
  • UX issues
  • Performance
  • Reliability
  • Lack of product education

Involuntary Churn:

  • Customer was acquired
  • Customer went out of business
  • Payment failure
  • Fraud issues

Once you have identified the key drivers for churn for a specific customer segment it is time to start experimenting with solutions and building systems that can allow you monitor changes over time. This may mean rethinking your engagement loops, improving customer education, feature development, pricing strategy etc.

Customers change so being able to consistently understand why they are churning and more importantly finding ways to prevent churn in the future can make a big impact on your business.