Aug 18, 2023

Read Time 5 min

Churn analysis: 3 approaches for getting to the bottom of churn


When a customer cancels their software subscription with a solution provider, the logical next step for the provider is to analyze why the customer churned. However, you’d be surprised at how often this doesn’t happen.

Often, in place of a proper churn analysis companies operate and make decisions based on opinions, hunches and best guesses. As a result, the root cause of churn problems goes undiagnosed, and the cycle continues.

Kristen Hayer found herself in a similar predicament. In a past role as a customer success leader, she worked for a company that was churning over customers at a seemingly high rate. She proposed performing a customer churn analysis to find out why.

Despite the fact this was a growing area of concern, the company’s leadership pushed back at her suggestion. They believed they already knew why those customers were churning so there was no need to go asking. Secondly, by virtue of asking, the leadership feared this would stir up even more churn.

Fortunately, Kristen was able to overcome these objections. Her analysis showed the segment of customers that were churning were the smallest and lowest paying accounts. Further, it was a segment the company was moving away from serving.

These were early customers dating back to when the company was first founded. Since then, its product had grown, pricing had risen and the ideal customer profile (ICP) had evolved.

The findings of her churn analysis proved to be a great relief to the leadership team. They could now explain to potential investors that while the logo churn rate seemed high, it was from a lower-paying tier of customers. More importantly, it had little effect on the churn rate by revenue and no impact on their growth plans.

Today, Kristen is the founder and CEO of The Success League, a customer success consulting firm. She says this is exactly why a formal churn analysis is necessary: “Without it, companies make broad assumptions about churn that probably aren’t true.”

What is a churn analysis?

Churn analysis sometimes gets confused with the churn rate. A churn rate is a metric that tells you how many customers are leaving. By contrast, churn analysis is an investigation to learn why they are leaving. The analysis is the explanation behind the metric.

By way of analogy, it’s like getting your blood pressure taken at an annual exam. The numbers are only an indicator, not the root cause. Your blood pressure could be high from a stressful commute to the doctor’s office. Or maybe it’s symptomatic of a more serious health concern.

In the same way your doctor will use other symptoms and tests to make a diagnosis, SaaS companies need to do the same with a customer churn analysis. This is because there are lots of different reasons why a customer may churn.

Some of the common churn reasons we hear include:

  • Weak onboarding process;
  • Failed implementation;
  • Customer is a “bad fit;”
  • Falling product utilization;
  • Missing features;
  • Outgrown the product; and
  • Customer champion loss.

Of course, there are many other reasons too—like pricing—which just underscores why a churn analysis is so important. Anything less is merely a guess.

A churn analysis should strive to answer four key questions:

  • Which customers are leaving by segment?
  • What is the root cause of their departure?
  • Based on the findings, which customers are likely to churn in the future?
  • What can the company do to reduce customer churn?

Churn analysis approaches

Experts inside and outside of ChurnZero agree there is no “one size fits all” solution to churn analysis. The process you follow should be tailored to your company—and yet flexible enough to be modified as the organization grows or changes. Below are three approaches from customer success experts to conduct a customer churn analysis.

1. Form a hypothesis and then test it

Kristen from The Success League recommends writing down all your hypotheses about why customers are churning. Then design tests to see if any of those hypotheses are true.

For example, one hypothesis might be that customers who are unable to get the product fully implemented are mostly likely to churn. Another is that some customers that churned don’t use a certain product feature. Product usage data can validate or invalidate these theories.

Next, you can segment the data based on demographics. That could be company size, vertical or subscription tier, for example. Segmentation will likely show that different segments churn for different reasons.

At this point, you have enough information to interview some of the customers you lost and ask them why they churned first-hand. Kristen recommends waiting a month or so before requesting interviews and having an executive or outside consultant conduct them—rather than tasking customer success managers (CSMs).

Waiting provides a cooling-off period so you can have a conversation on the merits. Similarly, CSMs tend to be too close to the customer, which means customers will make up an excuse, rather than provide a real reason they didn’t renew, to avoid any bad feelings.

An executive doesn’t have to interview every single lost customer. The segmented data will drive who should be interviewed so that you can get a representative sample for every root cause discovered.

2. Quantitative analysis first, then qualitative

Justin Garlock, a senior customer success analyst at ESG, prefers to dive into the metrics first. Metrics are agnostic and provide insight into “true customer behavior” he says.

The caveat is that there isn’t a standardized list of metrics to use. The right metrics to evaluate will vary based on a company’s unique needs. It’s also highly dependent on what data and metrics the company has captured to date.

Some of the metrics one might look at include product usage data, customer satisfaction (CSAT), engagement rates and net promoter scores (NPS). The goal is to create an outline to see if any of the variables are correlated.

Correlation means doing some math, like a regression analysis, to find what’s statistically significant. You might find, for example, that a segment of customers that churned had a high NPS score but low product usage. In such a case, you need to find out the root cause of churn, and also “why you have fake advocates.”

The data analysis sets the stage for qualitative input based on customer interviews. You’ll be able to prioritize which customers to speak with and how many based on the quantitative findings so far.

Justin says one common mistake that’s made when performing a churn analysis is measuring churn based on a company’s internal timelines. For example, a company might start looking at why customers churned in the second quarter of the year, but this is misguided. Customers have their own timelines and the decision to churn may have nothing to do with that period at all.

“You’ve got to think about churn from the customer’s perspective,” he advises.

3. Top 10 causes of churn mapped to revenue

Paul Piazza, vice president of customer success at Webex Events, has a simple but efficient method of churn analysis. He articulated his approach in a short video interview (embedded below), and subsequent article, with Irit Eizips, CEO of the consultancy CSM Practice.

He consults with his CSMs and asks the team to identify the top 10 reasons customers churn. He’ll then take the final list and map it to lost customers by revenue and at-risk customers by revenue. These two things never match, he says. In other words, what CSMs think are the top reason that customers churn is rarely the top reason by revenue.

This just goes to show why quantifying the impact of churn is important—even for companies that think they already know why customers churn. It also provides customer success leaders with the ability to propose a solution that is backed by numbers. You can say, for example, “Here’s $10 million in recurring revenue that you can affect by fixing this one problem.”

A churn analysis is a chance to get ahead of churn

Whether you use one of the approaches or one of your own, they key is to operationalize the process so you can get ahead of the problem. When you understand why customers are churning, you can take steps to address the root cause before it takes effect.

Part of your strategy for reducing customer churn is to benchmark it. Explore customer retention benchmarks for SaaS in 2023 to find out where you stand.


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