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The 4 customer retention metrics you should be measuring now

The right customer metrics can tell you who is leaving your product, when they're churning, and (most importantly) why.
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Churn is a fire alarm. 

It's a solid indicator that something's gone wrong, but it won’t help you put out the fire. In order to diagnose and fix your customer retention problem, you need to look beyond the simple churn formula of customers out versus customers in. 

You need to get an understanding of the customer retention metrics that tell you who is churning, when they're churning, and why. 

Only then will you be able to use your time wisely, and make the adjustments that will have the biggest impact on the health and growth of your business. 

Below, we’ll take a closer look at how to find the source of that fire, and put it out before things burn down. Here are 4 important questions to ask about your churn rate—and 4 essential metrics to help you find the answers. 

1. What is the best way to calculate churn?

No one's denying that great acquisition is instrumental to the success of a company. But you can't let your success in acquisition mask a churn problem further along the flywheel.

Let's remind ourselves of the churn equation:

Churn = Number of users churned/total number of users

Here’s an example that illustrates the problem with that simple calculation:

churn simple calculation example chart
(Source)

The trouble with that chart is that the exact same behavior (adding 5,000 users) doesn't yield the same result—the churn rate for September is lower than that of August. Rapid growth artificially decreases churn since all the new customers added each month haven't yet had time to cancel.

When improving churn by just a few percentage can mean up to a 25% increase in revenue down the line, you don't want to risk any inaccuracies.

💡Calculate churn the Shopify way 

A churn number skewed by growth isn't going to accurately tell you when something has gone right or wrong. That's why Shopify came up with an adjusted formula that works for periods of high growth—and for startups that are growing fast. 

shopify customer churn equation adjusted formula
(Source)

Yes, we know that looks formula scary. So let's break it down.

  • Churn = # of users churned
  • ∑ = the sum of the number of users on every day(i=1) in the data set (n)
  • n = number of days in the period

If the users were added to the customer base gradually, then the average will increase, and it will affect your monthly churn number more. If the users were added to the customer base near the end, it wouldn't skew your churn rate and give you the impression that it's lower than it really is.

Basically, all this means is that you're balancing out your acquisition with the average. Big spikes in growth won't distort your number or mislead you into thinking you outperformed one month, and then underperformed the next.

2. Does your churn level off?

Inconsistency can be an even bigger red flag than high churn alone. If you can't get your retention curve to flatten, then your product has no traction.

So let's say that 100 users who start on January 1st and had a 40% retention rate by the end of the month—but the group hasn't yet stopped churning. It's still continually shrinking after the month ends; by the end of the second month, nothing will be left. If you have cohorts that are dwindling away into nothing and never leveling off, you have a huge problem. 

If you’re losing customers at essentially the same pace as you’re acquiring them, your cohorts are going to look something like this:

maus by monthly cohort bad retention curve looks like this
(Source)

Basically, your user base is just going to plateau and all that effort your teams put into acquiring new users never adds up over time. By the end of that time period, the new folks that you’re adding to your product each month barely make up for users lost.

Compare the curve of the graph above with the one below, in which we imagine that 50% of users stick around. Even when growth slows, your user base will grow significantly over time:

maus by monthly cohort example customer retention curve
(Source)

💡Measure customer retention per cohort

Separate users based on start date and perform a cohort analysis. What you want to look for is a flattening of the retention curve, or, that point in time in the cohort—whether it's day 2 or week 3—where users stop churning. Take a look at these 2 cohort analyses.

cohort analysis spreadhseet examples comparing good cohort retention and bad cohort retention

In the first, each cohort is slowly churning until there's nothing left. 

But in the second, the retention curve flattens around Day 12, so that each new cohort builds on the growth achieved through the previous one. 

If your analysis looks like the first, it means your should focus more attention on providing value to your users during onboarding and the early stages of retention. Look for ways to shorten your time to value to help users reach their aha moment as close to day 0 as possible.

3. How valuable are your users?

Even if you’re able to completely flatten that retention curve, some users are still going to fall off sooner or later. The goal is to decide how soon is too soon, and what we can do to keep them longer. 

Legend has it that as long as the lifetime value of a customer (LTV) is higher than the customer acquisition cost (CAC), then you're solid. 

Basically, as long as you spend less on marketing than your overall revenue from those acquired users, you're doing well.

LTV > CAC = 😁

Right? 

Not exactly. 

As the folks at OpenView Partners point out, this formula operates under 2 inaccurate assumptions:

  1. Churn rates are constant
  2. Everyone will eventually churn

As we showed in the previous section, churn rate isn't constant, nor do we want it to be. We work to constantly improve it. 

And assuming that everyone will eventually churn is a cop out—unless you operate in a truly transient market, you should be striving to have customers that never churn. Obviously, we’re not all going to reach Netflix-level retention. But that doesn’t mean your teams shouldn’t be sincerely trying to retain customers for life.

💡Calculate cumulative cohort revenue (CCR)

OpenView has come up with a better formula for understanding the value of your users. They recommend looking at something called cumulative cohort revenue (CCR) and comparing that against the CAC. The CCR is the total amount of revenue you made from a chunk of customers acquired within a certain time period (usually 12 or 24 months).

12 month ccr formula how to calculate cumulative cohort revenue

You'll notice that the above formula includes a span of time. This formula ensures that you're comparing the actual total revenue of any given cohort against the amount of money you spent to acquire them. No false assumptions for this one, and it gives clear insight into where you break even with your CAC.

cumulative cohort revenue (CCR) vs customer aquisition cost (CAC)
(Source)

Comparing your CCR versus CAC across different cohorts will show you whether you're improving over time and how quickly you can recoup the amount of money you spend acquiring customers.

4. Which users are on the brink of churn?

Your user retention numbers are helpful, but they don't tell the whole story

Thing is, a user might churn a minute, an hour, or even a week after they've made their mind up about your product. Maybe they keep meaning to cancel, but forget. Or maybe they keep thinking they'll give it one more shot, but never get around to it.

So let's say your customer retention graph has this concerning slope to it.

customer retention slope negative retention

Yikes! 

You might see that retention is plummeting but have no idea where things are going wrong. It looks like the curve steepens on day 14—but is that because something goes terribly wrong on day 14 or is that just when they remember to cancel?

💡DAU/WAU/MAU

Instead of looking at just retention, you should also be looking at behavioral analytics. This will give you a sense of who's active and who just hasn't gotten around to unsubscribing. 

For that, you need to look at your activity levels. Depending on your product, you need to keep close attention to one of these metrics:

  • Daily Active Users (DAU)
  • Weekly Active Users (WAU)
  • Monthly Active Users (MAU)

If your product’s core value hinges on daily use (a messaging app, a workflow organizer, etc), then you have to look at daily activity numbers. 

If, however, your product's core value hinges on infrequent check-ins, keep track of the WAU or even the MAU. 

Users don't just wake up one day and decide to leave your app. Churn is usually preceded by a decline in activity. Set activity benchmarks for your users—if they don't reach them, start re-engaging before it's too late.

Diagnose before treating symptoms

It's tempting to blindly apply any and all retention strategies to your user base—whether you have a churn problem or not. But this can result in you spreading yourself thin and achieving very little. 

These 4 metrics equip you with the insights necessary to pinpoint your churn problem, in order to make the most impact ASAP.

And once these metrics show you great results, you can buckle down and start focusing on getting even more value out of those users.

Author's picture
Ky Winborn
Customer Success Operations Specialist
Ky is the Customer Success Operations Specialist at Appcues. Ky is also an avid curler who loves rewatching Scrubs and discovering new Mexican joints in his spare time.
Skip to section:

Skip to section:

Churn is a fire alarm. 

It's a solid indicator that something's gone wrong, but it won’t help you put out the fire. In order to diagnose and fix your customer retention problem, you need to look beyond the simple churn formula of customers out versus customers in. 

You need to get an understanding of the customer retention metrics that tell you who is churning, when they're churning, and why. 

Only then will you be able to use your time wisely, and make the adjustments that will have the biggest impact on the health and growth of your business. 

Below, we’ll take a closer look at how to find the source of that fire, and put it out before things burn down. Here are 4 important questions to ask about your churn rate—and 4 essential metrics to help you find the answers. 

1. What is the best way to calculate churn?

No one's denying that great acquisition is instrumental to the success of a company. But you can't let your success in acquisition mask a churn problem further along the flywheel.

Let's remind ourselves of the churn equation:

Churn = Number of users churned/total number of users

Here’s an example that illustrates the problem with that simple calculation:

churn simple calculation example chart
(Source)

The trouble with that chart is that the exact same behavior (adding 5,000 users) doesn't yield the same result—the churn rate for September is lower than that of August. Rapid growth artificially decreases churn since all the new customers added each month haven't yet had time to cancel.

When improving churn by just a few percentage can mean up to a 25% increase in revenue down the line, you don't want to risk any inaccuracies.

💡Calculate churn the Shopify way 

A churn number skewed by growth isn't going to accurately tell you when something has gone right or wrong. That's why Shopify came up with an adjusted formula that works for periods of high growth—and for startups that are growing fast. 

shopify customer churn equation adjusted formula
(Source)

Yes, we know that looks formula scary. So let's break it down.

  • Churn = # of users churned
  • ∑ = the sum of the number of users on every day(i=1) in the data set (n)
  • n = number of days in the period

If the users were added to the customer base gradually, then the average will increase, and it will affect your monthly churn number more. If the users were added to the customer base near the end, it wouldn't skew your churn rate and give you the impression that it's lower than it really is.

Basically, all this means is that you're balancing out your acquisition with the average. Big spikes in growth won't distort your number or mislead you into thinking you outperformed one month, and then underperformed the next.

2. Does your churn level off?

Inconsistency can be an even bigger red flag than high churn alone. If you can't get your retention curve to flatten, then your product has no traction.

So let's say that 100 users who start on January 1st and had a 40% retention rate by the end of the month—but the group hasn't yet stopped churning. It's still continually shrinking after the month ends; by the end of the second month, nothing will be left. If you have cohorts that are dwindling away into nothing and never leveling off, you have a huge problem. 

If you’re losing customers at essentially the same pace as you’re acquiring them, your cohorts are going to look something like this:

maus by monthly cohort bad retention curve looks like this
(Source)

Basically, your user base is just going to plateau and all that effort your teams put into acquiring new users never adds up over time. By the end of that time period, the new folks that you’re adding to your product each month barely make up for users lost.

Compare the curve of the graph above with the one below, in which we imagine that 50% of users stick around. Even when growth slows, your user base will grow significantly over time:

maus by monthly cohort example customer retention curve
(Source)

💡Measure customer retention per cohort

Separate users based on start date and perform a cohort analysis. What you want to look for is a flattening of the retention curve, or, that point in time in the cohort—whether it's day 2 or week 3—where users stop churning. Take a look at these 2 cohort analyses.

cohort analysis spreadhseet examples comparing good cohort retention and bad cohort retention

In the first, each cohort is slowly churning until there's nothing left. 

But in the second, the retention curve flattens around Day 12, so that each new cohort builds on the growth achieved through the previous one. 

If your analysis looks like the first, it means your should focus more attention on providing value to your users during onboarding and the early stages of retention. Look for ways to shorten your time to value to help users reach their aha moment as close to day 0 as possible.

3. How valuable are your users?

Even if you’re able to completely flatten that retention curve, some users are still going to fall off sooner or later. The goal is to decide how soon is too soon, and what we can do to keep them longer. 

Legend has it that as long as the lifetime value of a customer (LTV) is higher than the customer acquisition cost (CAC), then you're solid. 

Basically, as long as you spend less on marketing than your overall revenue from those acquired users, you're doing well.

LTV > CAC = 😁

Right? 

Not exactly. 

As the folks at OpenView Partners point out, this formula operates under 2 inaccurate assumptions:

  1. Churn rates are constant
  2. Everyone will eventually churn

As we showed in the previous section, churn rate isn't constant, nor do we want it to be. We work to constantly improve it. 

And assuming that everyone will eventually churn is a cop out—unless you operate in a truly transient market, you should be striving to have customers that never churn. Obviously, we’re not all going to reach Netflix-level retention. But that doesn’t mean your teams shouldn’t be sincerely trying to retain customers for life.

💡Calculate cumulative cohort revenue (CCR)

OpenView has come up with a better formula for understanding the value of your users. They recommend looking at something called cumulative cohort revenue (CCR) and comparing that against the CAC. The CCR is the total amount of revenue you made from a chunk of customers acquired within a certain time period (usually 12 or 24 months).

12 month ccr formula how to calculate cumulative cohort revenue

You'll notice that the above formula includes a span of time. This formula ensures that you're comparing the actual total revenue of any given cohort against the amount of money you spent to acquire them. No false assumptions for this one, and it gives clear insight into where you break even with your CAC.

cumulative cohort revenue (CCR) vs customer aquisition cost (CAC)
(Source)

Comparing your CCR versus CAC across different cohorts will show you whether you're improving over time and how quickly you can recoup the amount of money you spend acquiring customers.

4. Which users are on the brink of churn?

Your user retention numbers are helpful, but they don't tell the whole story

Thing is, a user might churn a minute, an hour, or even a week after they've made their mind up about your product. Maybe they keep meaning to cancel, but forget. Or maybe they keep thinking they'll give it one more shot, but never get around to it.

So let's say your customer retention graph has this concerning slope to it.

customer retention slope negative retention

Yikes! 

You might see that retention is plummeting but have no idea where things are going wrong. It looks like the curve steepens on day 14—but is that because something goes terribly wrong on day 14 or is that just when they remember to cancel?

💡DAU/WAU/MAU

Instead of looking at just retention, you should also be looking at behavioral analytics. This will give you a sense of who's active and who just hasn't gotten around to unsubscribing. 

For that, you need to look at your activity levels. Depending on your product, you need to keep close attention to one of these metrics:

  • Daily Active Users (DAU)
  • Weekly Active Users (WAU)
  • Monthly Active Users (MAU)

If your product’s core value hinges on daily use (a messaging app, a workflow organizer, etc), then you have to look at daily activity numbers. 

If, however, your product's core value hinges on infrequent check-ins, keep track of the WAU or even the MAU. 

Users don't just wake up one day and decide to leave your app. Churn is usually preceded by a decline in activity. Set activity benchmarks for your users—if they don't reach them, start re-engaging before it's too late.

Diagnose before treating symptoms

It's tempting to blindly apply any and all retention strategies to your user base—whether you have a churn problem or not. But this can result in you spreading yourself thin and achieving very little. 

These 4 metrics equip you with the insights necessary to pinpoint your churn problem, in order to make the most impact ASAP.

And once these metrics show you great results, you can buckle down and start focusing on getting even more value out of those users.

Author's picture
Ky Winborn
Customer Success Operations Specialist
Ky is the Customer Success Operations Specialist at Appcues. Ky is also an avid curler who loves rewatching Scrubs and discovering new Mexican joints in his spare time.
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