Why Churn Analysis is the Key to Improving Customer Retention

Churn analysis measures the pace at which customers leave, downgrade, or stop paying for your product or service–resulting in revenue loss.

Customer acquisition is expensive, and the cost-per-click for digital ads is rising, driving more companies to focus on retention to grow revenue. To do so, you must first understand what makes customers retain or churn. A churn analysis can help you identify and address issues that trigger customer churn to improve customer retention.

What is churn analysis?

Churn analysis helps you discover where people stop using your product in the customer journey and why. Churn and retention have an inverse relationship: when churn goes down, retention goes up, and vice versa.

Churn analysis aims to determine how to fix the problems that cause people to leave. Retention analysis includes reducing churn and finding ways to add value to users and improve retention.

Types of churn analysis

There are two main types of churn analysis you should focus on to improve customer retention: customer and revenue.

Customer churn is the number of customers who stop paying for your product or service. Customer churn analysis helps you understand why you lost customers over a defined period. Churned customers cancel their subscriptions or leave your business for a competitor within a specific time frame.

Analyzing customer churn shows how people behave in your app and is essential to understanding the overall health of your product and customer base. Determining which experiences or friction points drive users away helps you effectively prioritize new features. The resulting product improvements should boost your overall retention rate and user experience.

Revenue churn is the revenue lost over a specific period and indicates the amount of money churned customers took with them, why they left, or why they downgraded their plan. You can incur revenue losses without losing customers, particularly when customers downgrade their subscriptions. An example of revenue churn is when a customer switches from a $99 subscription to a $49 plan.

You need both customer and revenue churn analyses to boost user retention and improve customer experience. It’s easy to overlook simple product fixes that affect all customers equally if you only focus on revenue churn.

Customers often downgrade or leave when they don’t see enough value in a product. Identify the features existing customers desire or churned customers wish they’d had and prioritize product enhancements accordingly. Combine revenue and customer churn analyses to get a holistic view of product and revenue best practices to increase customer retention.

Why churn analysis improves customer retention

Understanding where customers abandon your product enables you to identify and prioritize features that motivate them to stay and spend more money.

Here are four ways churn analysis enhances customer retention:

1. It shows where customers are leaving

Customers typically drop out of the funnel immediately, stick around for a while, or slowly disengage or quit when they encounter a specific roadblock. Breaking down your customer journey into phases and subphases during your churn analysis will reveal where customers leave and what caused them to abandon your product. You can use a churn analysis tool, like Amplitude Audiences, to discover where customers disengage with your product.

Like Calm, you should cut extraneous steps and get users to value quickly to reduce churn in the onboarding process. When they discovered users were dropping off at the breath exercise step of its onboarding flow, they promptly tackled the problem. But first, you have to run a churn analysis to discover which parts of the customer journey are the most affected by churn and then address the issues.

2. Uncover issues specific to particular users with churn analysis

Each user interacts with your product differently, so each experiences different roadblocks in their customer journey. Behavioral cohorting is a powerful way to identify causes of churn that you might overlook if only using the overall churn rate.

Let’s say the churn rate in a music streaming app is 10% month over month. That doesn’t sound bad until you realize that the churn rate is 80% for users who don’t share a song in the first week. With insight into this cohort behavior, you can explore how to get more users to share a song during their first week.

3. It reveals behaviors that boost retention

With churn analysis, you can identify and encourage the behaviors positively associated with retention and discourage the behaviors associated with churn. But correlation does not equal causation, so run tests to accurately determine what causes churn or drives retention.

Calm used behavioral cohorts to discover that its retention rate increased by 3x for users who set up daily reminders but couldn’t determine if the reminders drove this increase. The only way to find out was to run an experiment.

The test revealed that the daily reminder notification was critical to customer engagement and retention. Like Calm, churn analysis can help you unearth tactics to boost customer retention.

4. Churn analysis can reveal a lack of product-market fit

If people sign up for your app or SaaS platform but don’t stick around, it may indicate a poor product-market fit. At least 10-20% of your customers should keep paying for your product two months after onboarding. Anything less, and you have a product problem.

If you don’t have enough customers paying for your product, you won’t get statistically significant retention signals on which to base decisions. Let’s say your analysis indicates that only three of the one hundred customers you acquired in the past sixty days have continued paying for your product.

If you segment these three customers into cohorts, you might find they have different reasons for staying, but that’s not enough information to base product decisions on. Instead, it might be time to rethink your target market. When you start marketing to the right people—who want and need your product—you’ll see a marked improvement in retention.

How to perform churn analysis

Accurate churn analysis will improve your user retention, and you can unlock the power of churn analysis by following these steps:

1. Calculate your churn rate

There are several formulas for calculating customer and revenue churn.

The simplest one for calculating customer churn rate is:

If a software-as-a-service (SaaS) business lost 70 customers over a 30-day period and had 1,400 customers at the beginning of the 30 days, their churn rate is (70/1400) x 100% = 5%.

You can calculate the revenue churn rate with the following formula:

If a SaaS company had $1,500,000 in MRR at the start of the month, $1,350,000 in MRR at the end of the month, and an extra $100,000 in MRR from existing customer upgrades, the revenue churn rate would be:

((1,500,000 - 1,350,000) - (100,000))/1,500,000 = (150,000-100,000)/1,500,000

= (50,000/1,500,000) x 100% = 3%.

If you’re using Amplitude Analytics, you can perform your calculations seamlessly from your dashboard. Amplitude enables you to calculate customer churn by platform, region, and quarter.

You should strive to retain customers while simultaneously acquiring new ones. That will offset customer cancellations and propel you to a lower churn rate. You’ll also get better at churn prediction in the future and plan your customer acquisition better.

2. Perform cohort analysis

Cohorts are a group of customers that belong to specific demographics or exhibit the same behavioral traits. Sample cohorts include:

• Customers in your highest pricing tier.
• Customers who completed X milestones during their journey.
• Customers who read case studies before buying your product.

Break your customers into segments or cohorts to identify which have low or high churn rates, and then run tests to discover why they’re churning or staying.

Cohort analysis reveals your most profitable customers and gives insight into your ideal customer profile to target your messaging and marketing campaigns. Patterns will emerge in your cohort analysis, and you can run experiments to understand their significance. Remove any roadblocks to retention for your high churn-risk customers and watch your customer retention rate improve.

Leverage churn analysis to start improving your retention today

As valuable as churn analysis is, it’s only a means to an end—improved customer retention. The next step is proactively identifying churn predictors and strategies to forestall them before customers leave.