Picture this: Over the past six months, your churn rate has gone from a respectable 4%, to now, over 10%.
Confused (and probably a little worried), you scratch your head and wonder what in the world happened?
At this point, you could go into panic mode and make random, drastic changes to your business to stop the bleeding.
Or, you can pause, take a step back and analyze what went wrong by doing some churn analysis.
In this article, we’re going to teach you how to calculate customer churn and what to do with the results. At the end of this guide, you’ll be able to figure out:
- Which customers are churning
- Why they’re cancelling
- How to fix the problem
What is a customer churn analysis?
Churn analysis is the use of customer data to understand why they stopped using your service. The higher your churn rate, the more users, and revenue, you are losing. Churn rate is a crucial metric for calculating other metrics like Customer Lifetime Value (CLV).
Analyzing your churn doesn’t only mean knowing what your customer churn rate is. It’s about figuring out why customers are churning at the rate they are, and how to fix the problem.
While similar, churn analysis and churn prediction aren’t the same. Churn analysis helps you understand why customers are cancelling, so you can make a plan to reduce it. Churn prediction is forecasting the likelihood that a customer will churn based on feedback and historical data, so you can plan ahead. Learn more about churn prediction here.
Why you need to analyze churn
It’s one thing to know that you have a 13% churn rate. But unless you understand which customers are cancelling, why they’re cancelling, when they’re cancelling, and other data points, it’s really hard to improve.
That’s why churn analysis is so important for subscription businesses.
We’ve run into situations where a SaaS company will recognize that they have a problem with customer retention, but aren’t sure how to fix it. So they resort to making assumptions or randomly using tactics they’ve found online.
The problem with that, is unless you understand the “why” behind your churn, any changes you make could potentially make the problem worse.
And when you’re already dealing with high churn, the last thing you need is to lose more revenue by changing your pricing, features and processes with no rhyme or reason.
When you take the time to analyze why customers are churning, you have a clearer idea of what to change. Then, you can do some controlled tests to measure the impact.
All your metrics, in one place
You should regularly check your churn, but there’s usually no need to do a deep analysis every day, or even every week. Use Baremetrics to get dashboards to monitor your churn, and receive daily/weekly notifications of your churned customers.
How to calculate customer churn and analyze the results
The first thing you should ask is what problem are you trying to solve?
In this case, we want to understand how to reduce our churn. In order to do that, we’ll need to answer two main questions:
- Which customers are churning?
- Why are they churning?
With those two questions as our starting point, we have context and direction for how we look at our data. That’ll make it less likely to go down a wormhole of endless data with no end in sight.
Now that we know what we’re looking for, let’s dive into a step-by-step guide to churn analysis.
Step 1: Setup churn analytics tools
This isn’t even step number one. It’s more like step 0. Before you can do any type of churn analysis, you need to have data to analyze!
There are a ton of tools out there that could help, but let’s keep things simple for now.
You’ll need some type of subscription analytics tool. Obviously, I’m going to recommend Baremetrics. Not only because it’s our product, but because you can get enough insights with just Baremetrics alone to make informed decisions.
You can get all the info you need right from within our dashboards, and then export data into a spreadsheet if you want to play around with it even more.
Also, I’m going to be using Baremetrics for the rest of this guide, so it’ll help you follow along easier!
Another tool that’s helpful if you want to do some advanced churn analysis is a product analytics tool like Mixpanel or Amplitude. These tools let you dig into how people use and engage with your product.
For instance, you can see what features are the most “sticky”—the ones that your customers come back to use the most. On the flipside, you can see which features your users may use once or twice and then never return to.
Product engagement can be a big indicator of churn, so this data can really come in handy.
We won’t get much into that in this article, since we really want to focus on customer churn analysis, rather than user behavior. But if you’re interested, Mixpanel has a great series of videos on how to analyze user behavior.
Try not to get fixated on all the different tools out there though. Start with the basics, and if you aren’t able to get the insights you need, then branch out.
Step 2. Find out why customers are churning
I mentioned that one of the two questions we’re going to answer with our churn analysis is “why are people cancelling?”
In order to get those answers, you need to start asking! There’s a couple of ways to do this.
Option A, which I’ve seen a lot of smaller SaaS companies do, is to just send an email out after customers cancel. They’ll either ask customers why they cancelled directly in the email, or direct them to a questionnaire (you can make one through Typeform or Google Forms).
Here’s an example from Pat Walls, owner of Pigeon. He sends out an email to all customers that cancel, and asks for feedback.
Short, clear and simple.
This approach works well in the early stages of your SaaS company. But as your business grows, you’ll want a more scalable way to find out why customers are churning. That’s where Option B comes in.
Here’s what we do at Baremetrics. We use Cancellation Insights to create a questionnaire for customers to answer before they close their account (we also have an email option). It looks like this:
The form captures all the info and we’re able to track every response in our dashboard.
We also allow people to give more details into why they’re cancelling, which is particularly helpful for customers that chose “Other” as their cancellation reason.
We can do some basic churn analysis with this info alone.
For instance, we can take a look at the most common reasons people churn, and see exactly how much MRR we’re losing each month from each churn reason.
For cancellation reasons like “Too expensive”, you’ll need to do more digging. But don’t worry, we’ll dive into ways to analyze that a little later.
But looking at the data this way allows you to prioritize what to do next.
For instance, if customers choose “Switching to another product”, take note of which competitors they’re switching to.
Then, create a feature comparison matrix like this one from Crayon to see how your product stacks up against competitors customers are switching to:
Or maybe pricing comes up as a consistent cancellation reason. Then you’d dig deeper into your pricing, and possibly consider running some tests.
A word of caution though. If you notice pricing is a consistent cancellation reason, that doesn’t automatically mean you need to charge less!
It could mean that you need to provide a little more value to justify your current price, or the customers that cancelled were on the wrong plan.
Here’s a note from our head of growth on why lowering your price isn’t always the “fix” for churn.
Head of Growth @ Baremetrics
“Too expensive” is one of the most common cancellation reasons across every business. But before you assume your product is priced too high, consider what I like to call, the value:price ratio.
A 1:1 value:price ratio means customers are getting just as much value as they’re paying for, and while this seems reasonable, they’ll likely feel that they’re not getting much out of it and will try to find something at a lower price.
Whereas with a 10:1 value/price ratio, customers will practically feel obligated to tell others about it and stick around for a long, long time.
We ran a pricing experiment fairly recently where we doubled pricing and virtually saw no difference. So that was a good indicator that our price, in fact, was not too high.
Another interesting example is that we did see a lot of pushback from our $100/mo plan by customers upgrading from the $50/mo plan. And often a lot of responses from trialing users, who didn’t convert, that they just couldn’t justify $100/mo at their revenue.
So we introduced a new plan in between the $50/mo and $100/mo at $75/mo and have seen very little churn from that plan and virtually no pushback from customers upgrading to that plan now.
Another thing to consider is the type of churn you’re getting. Are your customers cancelling, or are they just not paying you?
In Baremetrics, you can see a breakout of your churn by cancellations vs. unpaid.
Step 3. Analyze customer churn rate by cohorts
Imagine you’re a SaaS company that sells budgeting software. Over the past three months, you’ve had 300 customers churn.
Those 300 customers were on different plan levels, signed up at different times, and are in different countries.
Do you think it’d be more effective to analyze all 300 customers at the same time, or group them into “buckets” based on plan level, subscription date and location?
If you analyze your churn the first way, you might be able to see some high level trends.
But in order to get more actionable insights, it’s much more beneficial to go the second route and break down your churned customers into smaller segments, or cohorts.
Two good cohorts to start with are plan level and subscription date.
First, let’s look at an example of how to do churn analysis by price-point or plan level.
To get a quick overview of which plans have the most churn in a certain month, you can go into Metrics > User Churn in Baremetrics. Scroll down slightly, and you’ll be able to see a list of all your plans, and the churn rate for each.
Once you identify which plans have the highest churn for any given month, you’ll want to zero in on why those specific customers are cancelling. We’re going to use a spreadsheet here.
Head over to Cancellation Insights and select your date range. Then go down to the list of customers, and Download Table.
In the spreadsheet, you can sort the data by plans, and see the specific cancellation reason for all the users who cancelled under that plan.
It’s not pictured in my screenshot, but you can even see the comments for each cancellation reason in the spreadsheet. From there, you’ll want to look for any trends in cancellation reasons by plan-level.
Another way to analyze churn by cohorts, is to look at customer retention by signup date. You’ll look at all the customers who signed up during a certain month, and see how many months they stay on afterwards.
Don’t worry, it’s simple to do.
In Baremetrics, just head over to your User Churn dashboard. Then, scroll down to the customer retention table. This shows you the month over month retention rate of your customers based on signup date.
One way to use this data is to compare churn trends based on when people signed up.
For instance, looking at the table above, one of the first things I noticed is the July 2019 and April 2019 cohorts have a much steeper dropoff after the first couple of months compared to the other cohorts. On the flip side, the March cohort had pretty solid numbers.
I’d be curious to dig a little deeper into this. So my next step would be to compare customers who signed up in July and April, to ones that joined in March.
To do that, I’ll go to Customers in Baremetrics. Then add a couple of filters to only show customers that have signed up in July of 2019, with an LTV of greater than zero. The LTV filter is to make sure I’m only seeing data for paid customers.
I did the same thing for the March and April cohorts as well.
Now, I want to get a little more insights into the two different cohorts to see if anything stands out. I’ll start by comparing the average revenue per user (ARPU) of the two cohorts.
This will give me an idea of the value of the customers in each cohort.
Customers with a lower ARPU might be more entry level, and are mainly interested in testing Baremetrics out. That’d help explain why customers in the July and April cohorts dropped off sooner than the March ones.
Here’s what I found from a six-month comparison.
The ARPU for our March cohort is more than double the July and April cohorts.
Based on this, one of my assumptions is that in March there were probably some “bigger fish” that signed up on higher priced plans or even had some add-ons.
So I went and double-checked what plans the March cohort customers signed up with, compared to April and July, and my assumption seems pretty accurate.
In April and July, we had quite a few customers sign up on lower-cost plans, which could explain why we didn’t retain as many of them.
On the flipside, in March we had more people sign up on higher tier plans, and they retained longer than average.
The takeaway I got from this quick churn analysis is that we have a higher chance of retaining customers who:
- Use some of our add-ons like Cancellation Insights and Recover
- Sign-up for or upgrade to our more advanced plans
Funny enough, this is actually spot on with what our head of growth (who’s done way more research into this than I have) found. And it’s one of the core philosophies of our Growth Manifesto, which he shared publicly.
Here’s another way to use the cohort analysis data to analyze your churn.
Look at months 0-2 in the chart:
If you see steep drop-offs within the first 90 days, it’s usually a sign that there’s a problem. In most cases, it can come down to a combination of:
- Misaligned expectations between the customer and your product
- Poor onboarding
- A bad activation model (freemium, free trial, paid trial, money-back guarantee, consultation, etc)
A good next step would be to look at the accounts that canceled within that 90 day period. To do that, you can head to Customers in Baremetrics, and apply a couple of filters.
For our example, I applied a filter to just show customers who signed up in March, and another to single out the customers who cancelled after ~90 days.
Note: One of the cancelled customers re-subscribed so their status is “Active”
Then, you could go into each account and see their cancellation reason. In Baremetrics, you can see a timeline of the customer’s activity from the time they signed up to when they cancelled.
Here’s an example from one of those churned customers in the screenshot.
We can see, the customer signed up and cancelled within days. And their cancellation reason was “Too expensive”. In this case, it could be a sign of misaligned expectations after they tried the product a bit.
If we wanted to get more insights into why this customer cancelled, we could email them to follow up.
If you have a lot of customers churning within their first 90 days, it wouldn’t be a bad idea to include exit interviews in your cancellations flow so you can find out exactly what’s going wrong.
In addition to analyzing the first 90 days, you can also use the cohort chart to find long-term retention trends. The same way we created segments for customers that churned within 90 days, you could repeat the process for 6, 12, 18 and 24 months.
Those were just a few examples of how to do churn analysis. There are a ton of ways you can segment your churned customers and further analyze them. But if you stick with the framework we outlined, you’ll be able to get most, if not all the insights you need to take the next step.
Reduce your customer churn rate with Baremetrics
Step one is complete. You know which customers are churning and why. The next question is: What do you do with everything you found?
Lucky for you, we wrote an entire article about it! I spent over a week talking to a bunch of founders and marketers to get a peek into how they’ve been able to keep their churn under control.
If you’re interested in learning more, I highly recommend reading it here: 6 Proven Strategies to Reduce Churn (With Real Examples). And if you’re ready to start analyzing your churn, grab a free trial of Baremetrics today.