It’s critical to understand why your customers churn, but if you’re relying solely on the reason the customer gives you for that churn, you’re definitely missing the actual reason.
‘Churn Reason’ analysis, like most things in Customer Success, is more complex and requires more deliberate effort to get right than it might seem on the surface.
It’s easy to just ask a customer why they’re cancelling or not renewing and to leave it at that.
It’s easy, but it’s not going to get you the answer you need to keep future churn from happening.
This guide will help you analyze the “churn reasons” in a way that will get you much closer to the truth and help you take appropriate action to keep that churn from happening in the first place.
Let’s go!
My (now) standard disclaimer: As with everything I publish, this is just an article. It is necessarily incomplete and generic. I cannot tell you exactly what you should do and if someone who doesn’t know your business and your customers tells you exactly what your processes should look like, run away. Run far and fast.
Churn: Syntax and Nomenclature
“Also, dude, churn is not the preferred nomenclature.” – Walter Sobchak
For the purposes of this guide, when I say “churn,” it simply means that a customer has stopped doing business with us because they:
- actively cancel their subscription.
- choose not to renew their contract.
- buy one thing from us and never do so again.
Churn is also a behind-the-scenes term that I would encourage you not to use with your customers. Be more specific with them:
- “Why are you canceling your subscription?”
- “Why did you choose not to renew your contract?”
- Why did you only buy one thing from us and never come back?”
With that out of the way, let’s get into this…
‘Churn Reason’ Analysis: Internal vs. External Churn Reasons
Ideally, every former customer will have two churn reasons associated with their customer record: an internal reason – one derived from our understanding of why they churned (finance data, support tickets, pre-cancel usage data, etc.) – and an external reason that comes from the customer directly.
This “external” information can be from a conversation with the customer, a survey, third-party interview, etc,
We do need to be realistic that you’ll not always get a response from a churned customer – though you should always try – but you can at the very least posit a theory (your “internal” reason) of your own based on their behavior – in-app and beyond – as well as customer satisfaction surveys, financial data, support tickets, training consumption, etc.
When your internal reasons do not align with the reasons given by the customer, you know your ability to predict churn is low because your signaling is off.
However, if Internal and External churn reasons are aligned, you know you’re relying on the appropriate Success Vector inputs.
But you can’t do that sort of comparative analysis unless you’re capturing and recording both internal and customer-sourced reasons for churn.
So add two fields to your customer record and from now on, when a customer churns, make sure you have 100% coverage on internal reasons and as many customer-derived reasons as possible.
Whatever your coverage is on External reasons, make it part of your OKRs to drive that number up over time.
And if you can, go back, at least, 30-days for self-service, month-to-month “contracts” or a quarter or two for annual contracts to get customer-derived reasons for churn.
Asking customers that churned further back than those time frames may not yield enough to warrant wasting cycles, but you could – and should – go back as far as you can (don’t worry about your early adopters, pre-pivot customers, etc.) to fill in the internal reason.
When you ask your customers why they churned, ensure you ask about their experience and not “so you can help other customers better” or to help yourself. Keep it about them.
Which brings us to…
When to Ask for a Churn Reason
Do you ask them why they’re churning during the cancellation process or do you wait until after it’s done?
If you want a real, honest answer that will help you to prevent future churn, you’ll ask AFTER the cancellation process is complete.
If you want rushed, path-of-least-resistance answers that will do little to help you down the road and will likely just add negativity to the customer’s experience, then ask DURING the cancellation process.
The psychology is simple; people want out, they’ve already made their decision, and by asking them a question before they’re out, you’re putting up a barrier to that exit (or maybe even trying to save them!), so they’ll tell you whatever they think you need to hear to just let them out (and keep that save from happening).
They’ll pick the first option on the list.
They’ll tell you it’s them, not you.
They’ll say, “I don’t have the budget,” and it’s hard to argue with that, so customers will choose this “reason” a lot.
If it really is a budget issue, that actually means they didn’t get value, but this is typically a decoy answer to just make you go away and let them leave.
They’ll basically tell you anything to expedite this process.
But if you let them cancel – or handle the cancellation / non-renewal for them – and then ask them, the psychology is totally different.
They’re free!
They got what they wanted.
Sweet relief.
And now, they’re open to giving you an honest answer.
It’s wild what happens when you think about how humans operate.
What about Saving Customers?
The amount of energy and resources companies put into “saving” customers from canceling – often using exit surveys and interviews, and complex cancel flows to facilitate this process – would have a FAR greater return on that investment if it were spent making customers successful and keeping them from wanting to cancel in the first place.
There are lots of gimmicks, hacks, apps, and just plain BS nonsense you can apply to “saving” customers, but the reality is that if they get to the point where they want out, that decision is hard to reverse, and the process of trying to do that just makes the overall experience of the customer that much worse.
Anecdotally from my experience with companies that put in repeatable processes to “save” customers (before they engaged me to not let customers get to this point in the first place), I see an average save rate of about 12%.
That means 88% of the customers looking to cancel didn’t get saved and were subjected to a poor experience on the way out.
In several instances, we were able to find negative reviews left by customers that went through the “save” process but opted-out. Would they have left a negative review anyway? Maybe. But I am fairly confident this didn’t help. Especially when they mentioned it IN THE REVIEW!.
Even better, of the 12% of customers that were “saved,” within 3 months, 80% of those customers are gone. And in several instances, just like above but so much worse, we were able to show that upwards of 50% of those also left a negative review. Ugh.
So… is it worth “saving” customers?
Probably not, especially if you have finite resources to invest. Invest those resources in making the customer successful to begin with.
But the negative market sentiment that comes with disgruntled customers leaving bad reviews and, among other things, how that will affect Customer Acquisition Cost (CAC) efficiency, is just not worth the extra couple of months of revenue.
Unless it is.
Do your own math, but actually do it and don’t just assume it’s worth saving customers this way.
I will say this. If you choose to “save” a customer when they exhibit cancel intent, just understand that you’ve simply kept them from canceling. They are NOT successful yet. They have realized ZERO additional value. They are very much still a churn threat.
Your job now is to get them back (or for the first time) on a path to achieving their Desired Outcome… otherwise you’re just prolonging the inevitable, only when it happens, it’ll be that much worse (negative sentiment).
Okay, I digress. Let’s get to the analysis, shall we?
Applied ‘Churn Reason’ Analysis for Customer Success
Okay, so now that we have a better understanding of what’s going on here and we have collected the data, it’s time to analyze it.
Starting with our list of churned customers, we need to answer the following questions:
Are they a Good- or Bad-fit Customers?
I like to start by going through and tagging customers that churned as Good or Bad-fit. If you aren’t sure why this matters, check out my post on Quantifying the Cost of Bad-fit Customers.
If they’re a Bad-fit customer, no further analysis is required. You can always keep going and look for patterns to back-up why Bad-fit customers are bad for business, but ultimately, you can stop at this designation.
If they’re a Good-fit customer and churned out, that’s a problem.
Did they have Success Potential?
When we say a customer is a Bad-fit, we’re saying that, based on their characteristics, they are not likely to be successful.
But we can have customers that characteristically are a Good-fit, but for other reasons, they do not have Success Potential.
This “Churn Reason” analysis will focus on Good-fit customers that had Success Potential.
If a customer was a Good-fit but lacked Success Potential, there are actions to be taken, such as looking at the lack of Success Potential to see if it’s tied to customer characteristics. If so, we probably need to adjust our Bad-fit Customer profile so we can ensure we’re only bringing-in customers that have the potential to be successful with us.
It’s also important to recognize whether a customer came in with Success Potential but, due to some change on either their end or ours, the bit got flipped from “Has Success Potential” to “Doesn’t.”
Ultimately, Good-fit customers with Success Potential shouldn’t have churned, so clearly something happened, either with them or with us.
Different “Churn Reason” Sources
We’ve already talked about Internal vs. External churn reasons, and this is where we put that to good use. But there is also a third churn reason data source that I liked to include.
So you’ll want to look at three things when doing churn reason analysis:
- The reason they gave (External churn reason)
- The reason we think they canceled/didn’t renew (Internal churn reason)
- The reason their comments and actions indicate (Context Clues)
Context Clues and Critical Thinking
In addition to the Internal and External churn reasons, we can look at what the customer actually says and does to figure out what’s really going on.
When they choose “Cost” as the reason for canceling or not renewing, but tell you that they weren’t getting value because the product is broken and they could never even get started on a project, this is clearly not a “cost” issue. It’s a value realization issue caused by product instability or something.
Sure, they don’t want to keep paying for a product they’re not getting value from, but this is not truly a “cost” issue.
So we might have a couple of reasons to apply to this loss that actually have nothing to do with “cost.”
But they may have just said “Cost” to try to move the uncomfortable conversation along.
It’s so much easier to just tell you they can’t afford it than to tell you your product sucks, your service sucks, or you suck… and people like to avoid conversations like that, even if having that uncomfortable conversation would actually make you or your product or service suck less.
If we just go with “Cost” as the reason, though, we can incorrectly write them off as a “bad-fit customer” who just didn’t want to pay our fee instead of seeing what’s really going on and perhaps fixing it.
Performing the Analysis and Taking Action
We now have a list of Good-fit customers that had Success Potential, the reason we thought they churned, the reason they told us, and the reason we were able to glean from their statements and behavior. Awesome.
But what do we do with this information?
You can do a lot of different things, but what you’ll end up doing will entirely depend on what your data show.
But here’s an incomplete list of 5 things to get your brain moving in the right direction:
- By comparing Internal, External, and Context reasons, is our signaling (what we get from our customer data systems – CRM, our app, support, etc. – on point or are we missing things? If the latter, fix it.
- Are there missing / broken features, workflows, or other UX issues that are causing the customer to not be able to realize value? If so, this could be used to reprioritize your product development roadmap.
- Are there service issues – Customer Success, Support, Professional Services, Integrations, etc. – that are lacking or broken that are leading to churn? If so, this should be the catalyst to examine those services and fix whatever glitches are present.
- Are there pricing, positioning, or other marketing things that are either leading to mismanaged expectations or simply attracting customers with misaligned Use Cases? This should be the catalyst for aligning Customer Success with Sales, Marketing, and Product to ensure this does not happen again.
- Did something change that turned otherwise Good-fit customers with Success Potential into customers that were not able to realize value from their relationship with us? For things that happened on our side to cause the switch, this is an internal alignment issue where Customer Success and Product need to work together closer. For things that happened on the customer’s side, this may be a signaling or engagement process issue. Either way, both are fixable now that we know it’s happening.
If you want to take this analysis to the next level, combine it with my Churn Classification Framework.
