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Churn rate is a tough realization for any business. Get ready for some sobering stats:
In 2020, the average mobile app lost 89% of its DAUs within the first 7 days after install. Within 30 days, that number jumps to nearly 95%.*
For your app to succeed, you can’t let these stats be your reality. It’s easy to look at your app’s growth metrics and see what you want to see. A million downloads? Woohoo!
But downloads and installs don’t fuel your business. Those downloads may boost your acquisition numbers, but with these app churn rates you can’t call that a win. 2021 global average
Cost Per Install is $2.44 across devices and platforms.* You simply can’t afford to lose hard-won users you’ve invested time, energy, and money in acquiring.
Reducing your churn rate needs to be your #1 priority. And you can’t reduce it without first measuring it.
By the end of this post, you’ll know how to calculate churn and understand key app churn rate benchmarks. And you’ll have some practical strategies to reduce uninstalls for your mobile app.
Churn rate, also known as the rate of attrition, is the percentage of users who stop using an app within a given period.
For an app to grow, the number of retained users must be higher than the number of users who leave.
Say you start January with 600 users, and at the end of the month, you have 400 users.
Here’s how you would calculate your app churn rate:
(600-400)/600 = 33.33% churn rate.
This may seem pretty straightforward, but churn can be tricky.
Different factors can influence this formula’s outcome, like how you define an active vs inactive user or the period of time you’re looking at. But this equation is a good starting point to get a baseline for your app.
Why should you care about churn? Because it suppresses growth.
It’s the leaky bucket analogy: as uninstalled users drip out, you’re struggling to refill your bucket by adding new users. And that gets expensive fast.
But that’s not all. Churn rate also impacts key business metrics like customer lifetime value and retention rate.
Together, these numbers can help you build more accurate forecasts for growth, revenue, and scaling your business. They show how your app is performing now and what to expect in the future.
Every lost user has a different reason for uninstalling your app: not enough device space, got frustrated with bugs or a confusing UI, or they just lost interest in your app.
We surveyed 2,000 app users to find out the most common reasons people uninstall an app:
The good news? You can do something about it.
Find out the main reasons leading your users to uninstall by asking for user feedback and using mobile analytics features like funnels and flows to understand how users navigate your app and where the friction points lie. Uninstall tracking is another must-have analytics tool that will help you understand why people delete your app — and even predict user churn.
All apps will experience some churn, even the most successful ones. So you may be asking yourself:
What’s an average churn rate? What’s normal?
Let’s start with some quick facts and benchmarks for mobile apps:
Across the board, the average churn rate for app users is around 95.5% within 90 days.*
So yeah. There’s a lot of room for improvement.
It’s true that retention is hard to master and can be an uphill battle. But it’s also true that even small gains can have a significant impact on your success.
Now let’s zoom in a bit and look at average rates based on app industry.
Source: Statista, August 2020
Churn rate varies by app type, but it’s clear that no single industry has cracked the code when it comes to retaining new users. Rates are high across the board.
Mobile apps must get better at attracting the right users to their apps, providing value to newly acquired users early and often, and delivering a memorable experience that keeps users coming back over the long term.
With these benchmarks in mind, it’s time to take a hard look at your app. Step one is to calculate your baseline churn rate, so you know where you stand.
You can calculate churn a few different ways, depending on what you need to know.
In some cases, you may want to find your monthly rates to get a closer look at monthly growth and retention. Other times, you may want to calculate your annual rates to see how growth is evolving year over year.
Let’s look at a couple of monthly and annual calculations in action.
Users at start of month: 2,000
New users added that month: 400
Users lost at the end of month: 366
Monthly churn rate: 366/2,400 = 15.2%
Here’s an Annual Churn Rate Example:
Users at start of year: 50,501
New users added during year: 16,765
Users lost at the end of year: 27,890
Annual churn rate: 27,890/67,266 = 41.5%
These two calculations are a good starting point for some entry-level figures. 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.
Keep in mind, though, that rapid growth can make these simple equations less accurate. This is especially important for new apps to remember.
In this case, consider using a different formula like probability calculations.
Here’s an example of when it makes sense to use the probability calculation:
If your app is adding new users at a fast enough pace, both churn and the number of new users can go up. If the number of new users is increasing more than your churn rate, the rate will decrease.
The problem?
The way the calculation is set up means all those new users can skew the numbers and make it seem like you have a lower churn rate than you do. It might look like you’re improving when in reality it’s getting worse.
In this case, consider using a different formula like probability calculations. Stephen Noble of Shopify came up with this solution for calculating churn as a probability.
Each day a user keeps an app is one time when they didn’t churn. So, over the course of ten days, there were ten opportunities for that user to leave forever. A user day is defined as one day that a user remained active.
To calculate your probable monthly churn, start with the number of users who churn that month. Then divide by the total number of user days 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.
Here’s an example equation:
Users at the start of the month: 1,000
Users at the end of the month: 1,322
Net new user gain: 322
Days in month: 30
User days in month: (1,000 x 30) + (0.5 x 322 x 30) = 34,830
Total churns in month: 366
Churns per day: 366/34,830 = 0.01%
Monthly churn rate: 30 x 0.01% = 0.3%
Notice how this method factors in probabilities?
This approach helps account for rapid growth that can skew monthly and annual formulas.
Once you’ve established your baseline rates, you can use cohort analysis to find out where to focus your retention efforts.
Instead of looking at all your users as a whole, cohort analysis breaks them down into related groups.
By comparing and trending cohorts, you can uncover the specific reasons users leave and which actions drive retention. You might look at:
Cohort data helps you discover trends and patterns, so you can pinpoint what hooks new users and keeps them coming back.
You can study this visually using a cohort graph like this:
The y-axis shows a series of groups representing new users who first downloaded the app on that specific day. The x-axis indicates the number of users who launched the app that same day, one day after, two days after, etc.
With this graph, you can see exactly where the most significant drops in user retention occur.
So how does cohort analysis help identify causes of churn? It shows you the most important moments in your user journey.
Say you have a food delivery app. For the past few months, new users have been engaged, typically submitting an order within the first three days of installing the app.
But in week two, they’re spending less time in the app and placing fewer orders. By the end of the first month, they’ve stopped opening your app altogether.
What gives?
By breaking your user base down into cohorts, you see that most of your retained users who are placing regular orders launch the app between 10 and 11 am. Users who don’t open the app until after noon either close the app within 90 seconds or abandon their cart. 98% of these users become inactive or leave within the first month.
So what can you do to boost engagement? Send a personalized push notification to these users between 10 and 11 am reminding them to order lunch, along with a promo code.
You can only spot those patterns by breaking your user base down into smaller groups.
Now that you know a few ways to calculate app churn let’s look at some more ways to decrease it.
Here are a few strategies to get you started:
Can you fix a high churn rate?
Absolutely.
But only with a strategy based on the right data.
By using data to understand why users leave, you can optimize your app’s user experience and get more users to count your app in their list of favorites.
