Ultimate Guide to Churn Rate: Definition & 4 Churn Rate Formulas for Calculating Churn
Patrick Campbell May 27 2020
At its core, customer churn rate is a super simple concept: Your churn rate is the percentage of your customers that leave your service over a given time period.
Yet, in looking at hundreds of different SaaS companies, we've discovered that there's a wealth of complexity behind this seemingly simple calculation. Some necessary  breaking down your churn into segments, cohorts, etc. Some invented  counting trialers in your churn, not properly counting episodic/seasonal customers, etc. In fact, retention rates have become so complicated, at last count there were 43 different ways public SaaS companies were accounting for the metric.
Unfortunately, all of this complexity ends up putting us down a rabbit hole of wasted time and hidden opportunity, because you end up spending more time trying to understand and qualify churn rate, rather than actually using the metric to build your business and drive revenue growth.
Above all else, you need to understand the foundation of your customer churn rate and the axes through which you and your team can impact that number. To do this, you can't make your churn rate calculations overly complicated or they'll lose their impact. Let's explore this concept by first walking through the elements of churn, including what churn is specifically used for, before walking through a number of ways to calculate churn rate, and why you should fundamentally just keep it simple.
Table of Contents:
2. Why churn rate is so hard to understand
3. How does user churn rate affect other SaaS metrics?
4. What you use churn rate for
5. 4 ways to calculate your churn rate
What is churn rate?
Churn rate is a business metric that calculates the number of customers who leave a product over a given period of time, divided by the total remaining customers. Customer churn is vital to understand for the health and stickiness of a business, but actually calculating churn rate can be unnecessarily complex.
Churn Rate Formula
The Churn Rate Formula can be calculated as the number of churned divided by the total number of customers:
number of churned customers / total number of customers
Where number of churned customers is how many people have left your service over the period out of the total number of customers you had during the period.
Skip to The Four Different Ways to Calculate Churn
Why churn rate is so hard to understand
Calculating churn rate looks pretty straightforward, but (1) how exactly we define those two numbers can greatly affect the output and (2) businessdependent external factors may wreak havoc on our understanding of what number comes out.
Counting customers is complicated
The total number of customers for a period, say, 1 month, isn’t a welldefined concept because that number will change during the month due to new signups and cancellations.
For any given month, you have three kinds of customers:

Those that signed up prior to the month. These customers will come up for renewal in the current month.

New customers during the month.

Newly churned customers during the month.
If, for instance, your group of new signups is large in proportion to the existing customer base, that can distort your churn rate calculation in a number of ways:

The number you use for the “total number of customers” in the denominator will be much different on day 1 than it is on the last day of the month. This will mean that no matter what number you use for the “total number of customers”, it will either be pretty distant from the day 1 number, the day 30 number, or both.

New customers typically churn at a higher rate than customers that have stuck around for a bit. That means that if your company is growing, your churn rate will skew higher than it really is.
The moment of churn has multiple definitions
People define the moment of churn in two ways:

At the moment, the subscription ends and renewal doesn’t happen, or

At the moment of the cancellation.
We’ve written about how when a customer cancels, they haven’t churned yet. Customers don’t churn until the end of their subscription period arrives and they don’t renew, because they’ve already paid up until the end of their subscription period. If they’ve only canceled, you still have a chance to win them back before their subscription ends.
By that definition, it’s not actually possible for new signups to churn in their first month, and this does away with the issue of how new customer growth can distort the churn rate calculation. New signups wouldn’t be included in churn or the total number of customers for their first month.
Some call the moment of cancellation churn so that they can have the most current churn number as possible. The issue is that churn is fundamentally a lagging indicator and it shouldn’t necessarily be looked at in realtime.
Sample sizes can be misleading
When we look at a churn rate of, say, 10%, we’re implying that a churn rate of 1/10 is equivalent to a churn of 1,000/10,000.
Early on and under conditions of hyper growth, our calculated churn rate is just as much a product of our small sample size as it is a number that’s representative or predictive of how well our service retains customers. We don’t have much data in terms of the number of cohorts and how the cohorts behave over time.
This alone can cause wild fluctuations that make it difficult to compare churn rates on a monthly basis. Under these conditions, it’s important to recognize the limitations of the inferences you can draw from your churn rate.
Time frames might paint different pictures
You may be looking at customer churn rate over the period of a week, month, quarter, or year.
Also, you want your churn rate calculation to be robust with respect to the time frame chosen. You don’t want your calculation to go from generally correct to wildly incorrect when you move from a monthly frame to a quarterly frame.
Customer segments churn differently
You may have a consumer plan and then an enterprise plan. They’re going to have wildly different churn rates, perhaps with a difference as great as 15% churn monthly vs. 0%.
An aggregated number dissolves the differences between your customers, and that can lead you to a misunderstanding of your churn number if you just take it at face value. For instance, growth in a higher churn customer segment could be mistaken for an increased churn rate overall, and that could lead you down the wrong path of trying to fix a nonexistent churn problem.
Seasonality impacts churn rate
If your business varies based on the season, your churn rate may show changes that correspond with the seasonality of your business that might be hard to understand until you’ve gone through several cycles.
And more
There are a number of other external factors that are likely to be businessspecific. What’s important for how you calculate churn rate is that you apply the calculation consistently so that you can compare not only from month to month but year to year as you grow.
How does user churn rate affect other SaaS metrics?
Yor churn rate is a direct reflection of the value of the product and features that you're offering to customers. ProfitWell helps SaaS companies visualize types of churn as well as the revenue impact over time to give your team realtime insight on how to adapt your retention strategy.
Your company should constantly optimize your product to reduce user churn rate. When the product is great and aligned with a value metric, the rate of cancellations should get down to zero monthly.
Additionally, user churn directly affects your financial metrics (MRR/LTV/CAC). Churn affects recurring revenue, lifetime value, and acquisition costs:
 Monthly recurring revenue: If customers leave, so does the revenue. In SaaS, monthly recurring revenue (MRR) is not only the lifeblood of a company, it's also an indicator of longterm viability. User churn directly decreases revenue, so it is vital to keep it at bay.
 Customer lifetime value: The lifetime value (LTV) of a customer also indicates the profitability and longevity of a SaaS company. Churn directly lowers LTV because when users leave, the value or revenue that could have been earned decreases.
 Customer acquisition costs: If you are spending to acquire customers and they churn before you make back those costs, then you are running a tough deficit. Churn increases your average CAC. If you are reducing churn rate at every chance you get, then you can secure your CAC back from your users quicker.
 Net Negative MRR Churn: One of the most powerful ways to build a growth engine in SaaS is through net negative MRR churn rate. When you have net negative churn, the additional revenue you generate from your existing customers month over month is outpacing the revenue you're losing through cancellations and downgrades. The lower your user churn rate, the easier it is to achieve net negative MRR churn.
What you use churn rate for
Churn rate is used in a number of different ways:

as a measure of the company’s health and longterm prospects

understanding whether we’re improving customer retention on a monthtomonth basis

identifying changes that had an adverse effect on customer retention

figuring out which customers are most successful with your product

forecasting your company’s performance

and more
Cramming all of those different use cases into one number is impossible. That’s why your churn rate is a starting point, not an endpoint, for your analysis.
Know what problem you’re trying to solve, take a deepdive into your data, and do cohort analysis and customer segmentation as needed.
The how, why, and who of churn that your headline number leads you to is what’s important about your overall churn rate. When calculating churn rate you’re doing so in order to achieve a greater understanding of your customers, and why they leave your product. Improve that, and you’ll improve retention rates to strengthen your business for the longterm.
4 ways to calculate your churn rate
To calculate your churn rate, divide churned customers over a period of time by the number of customers you had at the start of that period. While overly simplistic, this allows you to focus on churn by cohort and analyze the cause — instead of debating between overly complex methods to analyze churn.
But that’s just the start.
Steve Noble, a data specialist at Shopify, outlined 4 basic ways to calculate churn rate: (1) Simple, (2) Adjusted, (3) Predictive and (4) the method he ultimately settled on. We’ll walk you through these increasingly complex churn rate calculations.
You’ll see the same constants throughout these examples:

ChurnedCustomers
is the number of customers that churned in the time window. 
n
is the number of days in your chosen time frame. When calculating over a month,n
=28,
29,
30
or31
. 
Customers
is a list of the numbers of customers on any given dayi
, 1 throughn
. For example,Customers_1
is the total number of customers you had on the first day of the window.
1. The Simple Way
The simplest way to calculate churn rate is:
You’re dividing the total number of churned customers over the period by the number of customers you had on the first day of the period.
The Good & The Bad
The main pro' of the simple version of calculating churn rate is its simplicity. The churn rate formula is easily understandable and quickly calculable. You only need to know 2 quick numbers to figure out your churn rate for the month, and all you need is those two numbers for each month to be able to compare monthtomonth churn.
The problem with this simple churn rate calculation though is that it has a hard time dealing with significant growth. When you have a lot of growth, both your churn and total customers can go up. If your total number of customers goes up more, your churn rate will go down, even when you have more customers churning out of your product than the previous month.
If you’re an established company with a significant customer base and stable growth month on month, this isn’t an issue. But if you’re a new company with substantial new customers each month, this can lead to a strange interpretation where you can lose more customers per month, but your rate will get better.
Example
Here is an example from the Shopify post illustrating the shortcomings of the Simple Way:
To calculate churn rate, begin with the number of customers at the beginning of August (10,000). In this example, you lose 500 (5%) of these customers, but acquire 5,000 new customers throughout the month, of which 125 (2.5%) churn out. This gives you a churn rate of 6.25% for August.625 / 10,000 = 0.0625
You're then starting September with 14,375 customers. You see exactly the same behavior this month, with 5% (719) of existing users churning, 5,000 new customers joining, and 2.5% (125) of those customers churning. Your simple churn rate for September comes in as 5.87%. 844 / 14,375 = 0.0587
Wait, what happened? You’ve seen the same behavior, 5% of existing customers and 2.5% of new customers churning, in both months, but the outcome is two completely different churn rates. It looks like your churn rate has gone down, but the underlying behavior has remained the same.
Your high growth has distorted your calculation. In August, 125 churned customers are added to the numerator, but the 5,000 new customers that join in August didn't get added to the denominator—which means that the churn rate is artificially high. In the following months, the growth is less proportionally to the existing customer count, so the effect is lessened.
2. The Adjusted Way
To account for significant monthly growth, we can take the midpoint of the customer count for the month, rather than using its value on the 1st of the month.
Here we’re dividing the number of churned customers by an adjusted average of the number of customers throughout the window.
The Good & The Bad
This approach manages to deal with the growth issue by normalizing changes in total customers over the time window. Now you have a more stable platform to base your churn rate on, with the time window for your total customers the same as your time window for churn.
However, though this approach to churn rate calculation does deal with the growth issue, it fails to scale with different time windows. Using the same calculation and the same data, you’d get very different answers for daily, weekly, monthly, and quarterly churn.
Example
Using the above data again, now with added October:
Now we see the churn rate as the same, even with a different number of customers at the start of the month.
August becomes: 625 / 12,187.5 = 0.0513
September becomes: 844 / 16,453 = 0.0513
October is: 1052 / 20,505 = 0.0513
Quarter: 2521 / 16,239.5 = 0.1552
Bingo! Problem solved. We can all go home for tea and medals.
Not quite so fast. The main problem with this approach to churn rate calculation is that it makes assumptions about the data. If you calculate this over the course of 3 months you come out with a churn rate of 15.52%. Divide this across the 3 months and you get 5.17%, very close to the individual monthly customer churn rates. So far so good.
But what if you don’t have exactly the same numbers across each month? Let’s make August a bad month for our imaginary B2B SaaS company. This time, it only gets 100 new customers, 2 of which churn out.
The behavior is the same in terms of churn (5% of existing customers and ~2.5% of new customers), and when calculated individually each month shows the same churn rate of 5.13%.
But when calculated as a quarter, you get a 3month churn rate of 13.72%, which, when divided across each month, is 4.57%.
August: 502 / 9799 = 0.0513
September: 605 / 11,795.5 = 0.0513
October: 825 / 16,080.5 = 0.0513
Quarter: 1932 / 14,084 = 0.1371
Now our monthly churn rates no longer tally with our quarterly churn rate, even though they use the exact same data. This is because we’ve changed the time window we’re calculating. This approach assumes that churn is spread evenly within the time period, with a linear distribution. But churn is never this helpful. A good churn rate ratio should be able to expand or contract well with the length of time it measures, and still deliver comparable results.
3. The Predictive Way
Any good churn rate calculation should give some actionable advice. In this example, Shopify has tried to incorporate a predictive element into the equation. They’re trying to determine a weighted average churn rate, so that rate*customers
will predict the likely churn rate on any given day.
InactiveCustomers
is an array of how many customers active on day i
are inactive on day i+n, i.e. one month later. If you have 1000 customers on September 1, you then look forward in time to see how many of those 1000 have churned on October 1. You sum that up, then divide by the sum of total customers in September.
The Good & The Bad
It seems awesome to be able to predict churn. Having a weight that you can multiply with customers to get predicted churn would be great for planning your finances. Who doesn’t want to do that?
Well, you have probably noticed a critical problem with this approach: “...you then look forward in time...”
This requires two months of data to run one month’s calculation. In order to determine your churn rate for this month, you have to wait until the end of next month. That isn’t good for a metric that is supposed to keep you uptodate on your company’s success. If you have a number of accounts cancel in September, you won’t have this information until October.
The flip of this is that when you do get to the end of October and have a churn rate, it’s now from a month ago. It’s not current. You can no longer report churn rates to your employees for the prior month, you are instead telling them what happened a month ago.
This approach has all the same problems as rolling metrics, and you know you should stay away from those.
Calculations in SaaS metrics are supposed to take all your data and transform it into easily understandable, actionable numbers. This churn rate calculation makes your numbers more complicated and less actionable.
4. The Shopify Way
Instead of roughly taking the average of the first day and last day of the month as we do with the Adjusted Way, we can take the average of every day in the month to get a more accurate churn rate calculation.
You divide your number churned by the average of your customer count between days 1 and n.
The Good & The Bad
This deals with the issues that plague the other variations. You can use it in periods of high growth, and it scales nicely across different time windows. You can also use it in a timely manner, getting an uptodate churn rate.
But there are always going to be variations in your numbers that a single calculation can’t account for. Newer customers churning at a higher rate the older customers, differences in cohorts, in plans, in size of accounts. None of these are captured in this churn rate formula, and by using it, companies could have a false sense of security that the number they get each day, week, month, or quarter is the whole story of their churn.
Why you should simplify churn rate calculation
As Noah Lorang at Basecamp points out, SaaS analytics shouldn’t be rocket science. One of his “three secrets” is to “make it easy.”
When you reduce complexity on your churn rate calculation, you get the following benefits, which can’t be underestimated.

It’s easily understandable — anyone in your organization can understand that number. This is absolutely critical for a key metric. If no one understands your number, they can’t act on it.

It’s easily comparable — the more complexity you add and the more cases you attempt to account for, the harder it will be to compare your churn rate calculation across different periods of time. You create consistency by taking the simple and straightforward path.

It serves as a starting point for deeper analysis — you’re able to easily comprehend what your number accounts for, what it doesn’t, and where you need to dig in to learn more. With more complex calculations, your first step will be reminding yourself how to calculate churn rate.
That’s why, at ProfitWell, we use the Simple Way with a monthly time window.
We keep the churn rate formula simple so that you can spend your time taking a deeper dive on the number, analyzing churn by cohort, and so on—not spending it trying to calculate how we arrived at our number. This churn rate calculation method has worked for thousands of our customers, and it can work for your B2B SaaS company (or any other subscription business) as well.
All of your topline metrics are just headlines. They’re not the story. The story is buried deep within the numbers. You need to be looking indepth at the how and why of your churn rather than trying to account for every variable within your churn rate calculation.
Your deep dive into the numbers is where you’ll actually find out about your business, and how you’ll be able to make actionable decisions to improve customer retention rate.
Churn rate FAQs
Churn is a tricky subject, but we get many of the same questions on churn rate again and again. Here are some of the most asked questions, and our answers for them.
What is a good churn rate?
Average churn rates are everywhere from 2%  8% of MRR, per our churn studies. Therefore, a churn rate at the low end (2%) would be considered “good”. By company age, 10+ year old companies have a 24% churn, whereas younger companies range from 4%  24%.
What is negative churn rate?
Negative churn rate occurs when added revenue from new customers (expansion revenue) surpasses lost revenue from churned customers. Negative churn rate is usually caused by activities such as = upgrades, service options, addons, etc.
Does churn rate affect retention?
Yes, churn rate is in inverse of retention. When customers are not retained, they churn by default. Understand your customers better using this cohort analysis excel template.
How can I track churn?
Services like ProfitWell Metrics and ProfitWell Retain help you track churn and deploy means to manage and reduce it.
By Patrick Campbell
Founder & CEO of ProfitWell, the software for helping subscription companies with their monetization and retention strategies, as well as providing free turnkey subscription financial metrics for over 20,000 companies. Prior to ProfitWell Patrick led Strategic Initiatives for Bostonbased Gemvara and was an Economist at Google and the US Intelligence community.