Churn Analysis: How to Measure, Predict, and Prevent Customer Churn

Last updated on August 27, 2020

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Churn Analysis: How to Measure, Predict, and Prevent Customer Churn

As a customer retention platform, we spend a lot of time thinking and talking about mobile churn. In fact, we have a whole section of our blog dedicated to it. And the reason is pretty simple: it’s impossible to ignore if you want to build a sustainable business.

You need to see how many customers stop using your app, where they drop off, and why in order to fix the leak in the bucket.

So if churn is so important to your growth, the big questions are: how do you analyze churn rate? How do you conduct churn analysis? Is there a way to successfully predict and prevent customer churn? We’ll answer these questions and go through a churn analysis example process to help you conquer customer attrition. 

Churn Analysis Basics

Let’s start by defining what customer churn means. Churn is a metric that quantifies how many users have uninstalled your app over a specific time period. Why churn analysis is important is because it helps you glean actionable insights from those numbers so that you can work toward better user retention.

How do you get actionable insights, exactly?

  • Monitor your churn numbers regularly – anything that isn’t measured can’t be improved
  • Look for the dropoff points – where in your app users begin to disengage
  • Identify the causes of churn so you can improve the user experience and keep users coming back

Step 1: Monitor Your Churn Numbers

Churn rate is the percentage of users who stop using an app within a given time period. For an app to be healthy, the number of new users must be larger than the number of users who uninstall.

What is the formula for churn?

But there are other numbers to track as well, including your probable or predicted churn. 

To calculate your probable monthly churn, start with the number of users who churned that month. Then divide by the total number of user days (days a user remained active) that month to get the number of churns per user day. Then multiply by the number of days in the month to get your resulting probable monthly churn rate.

Or, if you want to skip the math, you can fill out your own customer churn analysis Excel spreadsheet and our free template will calculate your churn and retention rates for you.

Once you know your internal churn rate, compare it with industry benchmarks. For the average mobile app, the worldwide app retention rate in 2019 was just 32%, and 25% of users abandoned an app after just one launch.* How does your app compare? 

Step 2: Find Where They Drop Off

If you want to figure out where in your app users begin becoming inactive, or eventually uninstalling, you’ll need to use funnel analysis.

Funnels allow you to identify how users navigate your app and where they drop off before reaching a conversion step. You’ll get answers to questions like:

  • Where are we losing our users after onboarding?
  • What do they do prior to becoming inactive?

The data from funnel reports help you plot the actions performed by each user so you can figure out which behaviors lead to greater retention, how to move users away from churn, and encourage them back on a path toward conversion. And it helps you improve the overall user experience.

Step 3: Find the Reasons for Churn

Once the actual dropoff points are found, you can begin to pinpoint why your users uninstall. Or rather, come up with hypotheses that can be verified via A/B tests or other methods.

We conducted a survey of 2,000 app users and found that the top 3 reasons for uninstalling are:

  • no longer using the app
  • lack of device storage space 
  • too much advertising

Just keep in mind that app uninstalls aren’t forever. Often, depending on your industry or app category, your users may only be taking a temporary break from your app.

The point of this all being: once you know why your users churn, you can go about optimizing the user experience.

Predicting Churn: Challenges and Solutions

So the thing about churn analysis is you can only get so far with calculating probable monthly churn.

There are existing predictive models that use statistics to guess at outcomes. With a lot of them, however, there is a reliance on quantifying risk using static data about the customer as they are at this moment. Or even as they were in the past. Many traditional statistical and data-mining methods are rooted in older data that only shows past user behavior. 

Predictability of future actions has only recently become available to marketers as big data and AI tools have improved.

The problem with churn is that it’s based on human behavior. And we all know that humans behave in random, unpredictable ways. One day they’re spending hours on your app, the next, they’ve uninstalled it and moved on.

While these older approaches still offer value and can successfully identify a low percentage of at-risk customers, they aren’t always accurate. This means your marketing dollars are being wasted chasing after the wrong segments.

Now, with advancements in big data and AI tools, predicting what users will do in the future has recently become available to marketers.

Go From Churn Analysis to Action

Our Intent Based Segmentation tells mobile marketers which users are most likely to meet any goal, like making a purchase — or uninstalling the app. 

Our data science engine creates an intent model for any goal by considering millions of different data points as inputs:

  • How frequently does a user visit?
  • Which users clicked on a specific campaign?
  • When was a user’s most recent visit?
  • How many times has a user transacted in the past?

This allows mobile marketers to create more engaging long-term strategies that stop users from churning and improve overall business performance.

For more on intent based segmentation, read:

Use RFM Analysis to Turn Churn Analysis Into Immediate Campaigns

But more than simply identifying potential user behavior such as churn, our tools also provide mobile marketers with the capability to take immediate action on those insights.

For example, our RFM analysis tool automatically breaks down your entire user base into specific segments based on their scores for recency, frequency, and monetary value – i.e., how recently they last used the app, how often they use your app, and how much they’ve bought from you already.Churn Analysis - Screenshot of the RFM Analysis tool in CleverTap

On the left side of the grid you see how CleverTap has identified your hibernating, at-risk, and can’t lose them user segments. And best of all, you can initiate engagement campaigns directly from this view.

For more on RFM analysis, read:

Churn analysis is a critical piece of the customer retention puzzle. But to successfully build retention and grow your business, you can’t stop there.

With the right analytics, marketers get clear insights into the causes of churn and can even predict which users are at risk of uninstalling. This deeper understanding is the foundation for successful engagement campaigns that deepen customer loyalty and help them fall in love with your brand. 

See how our industry-leading customer retention platform can uncover the secret to improved retention for your business.

See how today’s top brands use CleverTap to drive long-term growth and retention

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