The Churn Doctor's Ultimate
Guide to Customer Churn
Nearly everything you've heard about churn is wrong. This guide is your secret weapon to the truth about churn, and what to do about it.
It Ain’t What You Don’t Know That Gets You Into Trouble.
It’s What You Know for Sure That Just Ain’t So.
Mark Twain
Is this you? ↓
You've read the "definitive" churn blog posts, and listened to the "expert" webinars, and you've gone to the customer success events. And what you've heard are the same ideas repeated over and over again.
If everybody's saying the same thing, then these must be the right ideas for solving customer churn! Right?
The problem is... they don't work!
What is going on here?
THE GOOD NEWS: Customer satisfaction has been steadily improving. Our data shows customer satisfaction increased by more than 12% over the last three years.
THE BAD NEWS: Customer retention has actually gotten worse for most companies! Our data shows retention decreased overall by ↓22% over the at the same time satisfaction was going up!
What's your experience?
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You’ve worked hard to improve your customer experience
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And your imperfect product is better than it used to be
→ So, then why is churn still getting worse?
It’s frustrating, but I can assure you that you are not alone.
The problem is that the conventional way of doing things simply isn't working.
It's time for a new approach.
The only way out of this trap is to START OVER. We need new ideas based on real data about what works rather than the same old conventional approaches that obviously don’t work.
This guide dismantles all the popular churn myths, fallacies, and delusions. We use real data, and our extensive experience solving churn in many companies, to identify what is true and what works.
Topics we'll cover in this guide to churn:
What Is Churn?
Churn is when customers leave.
Retention is when customers stay.
That's all. It's simple. Where we go wrong is when we make it more complicated than that.
CHURN ≠ METRIC
The most popular definition of churn is actually a metric: "Churn Rate". But churn is NOT a metric, it's a thing that happens in reality. "Churn Rate" is just one way of measuring churn (and it's not a particularly good way).
Defining churn as a metric is the source of a lot of our problems because it focuses our efforts on solving the "churn rate" rather than solving actual customer churn.
These are very different because the churn rate is not just a measure of churn but also depends on the sales growth rate. The "denominator effect" means that even if customer churn goes DOWN↓, the churn rate might still go UP↑!
CHURN ≠ UNHAPPY
Another mistake is conflating churn with unhappy customers. People often use the word "churn" interchangeably with "dissatisfaction".
But customers leave for several reasons. And research shows that low customer satisfaction is NOT even one of the main reasons. https://lnkd.in/g7GWjbVt
CHURN ≠ DOWNSIZING
Probably the worst mistake is to use the word "churn" when customers downsize their accounts to adjust to their changing needs.
This can really mess things up because what we do to keep DOLLARS is not the same as what we do to keep CUSTOMERS. In fact, efforts to reduce "dollar churn" can actually INCREASE customer churn!
The lesson is clear: Overcomplicating the definition of churn makes it much more complicated to solve churn.
Why Churn Matters
Churn is the speed limit of growth
Churn is the most important factor in the ultimate success of any subscription business. Investors and acquirers use churn to judge what a business is worth. CEO's know churn is the key to profitability. But, above all, churn matters because the business can't grow when too many customers are leaving. It's that simple.
You can spend time breaking down how it costs more to acquire a new customer than to keep and expand an existing customer. And that's true. But it really misses the point. Nobody wants to have anything to do with a business that isn't growing. Period. And churn stops companies from being able to grow.
But there's another way to look at why churn is so important...
Churn is the truth
No matter what you believe about your business:
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what you think you do and how well you think you do it,
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who you think your ideal customers are,
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what you think is the value you provide...
Churn is always there as a loud signal that what you believe is often DEAD WRONG.
Because churn is the ultimate pain signal in any business. Like a hand on a hot stove, it's there to alert you to danger. So if churn is only the signal, then what is the danger?
The real danger is that our ideas about what we do and who we do it for never really get challenged. The failure to correctly understand what and who we are FOR is the true existential threat to a business.
And churn is here always pointing us to the truth about these matters, if we will listen.
But in order to know what churn is telling you, it is essential to understand how churn really works.
How Churn Works
Churn is nearly universally misunderstood because there has been effectively zero good data and research on churn. Until now.
The problem starts with the fact that churn has always been represented as a simple rate (eg. Churn Rate = 10% per year), so we think of churn in a simplistic way.
But there are 2 things about churn that make it more complicated...
1. Churn is Delayed
This simply means that some time passes between when customers join and when they leave.
The problem is that when something (like churn) happens our brains are naturally wired to look for the cause close by. So when there is an increase in churn our instinct is to try to find a cause right around the time it happened.
But churn is delayed. That means the churn you are seeing today was caused by something that happened in the past. This introduces a key element of complexity for understanding churn.
For example, one reason for an increase in churn is that, in the past, there was an increase in sales.
The bump in new sales is followed after a some time passes by a matching increase in churn. The amount of time it took for the additional churn to arrive is the "Churn Lag".
The increase in churn does not mean that the rate of churn increased (though that is certainly possible). This increase in churn was inevitable because more customers arrived and some of them will eventually churn.
The difficulty is that we often cannot clearly connect churn to the events in the past that caused it. This is made more difficult because every customer's churn lag is not the same. This leads us to the next thing that makes churn complex...
2. Churn is Not Linear
In the real world, customers don't line up to exit to leave in an orderly fashion.
But when we use churn rates, we can't see this reality. For example: If you have a churn rate of 10% per year, this seems to indicate that you are steadily losing 10% of your customers in each period.
If you were to look at a group (cohort) of customers, here's how 10% churn rate would be simplistically represented on a graph:
But churn is never this simple, and often looks very different. Here are more churn lines with the same average rate:
All of these have the same average rate, but they obviously represent very different kinds of churn.
The 3 Types of Churn
In the real world, there are only 3 ways that churn happens. We have studied hundreds of churn lines (or "curves") like these, and have found that there are three consistent shapes.
1. Decelerating Churn
It is when more customers leave early and then fewer as time goes on. The shape looks like this:
This is the most common type of churn and indicates that the primary causes of churn happen early in the customers' engagement.
An example of an early cause of churn is selling to customers who are a bad fit. These customers can't properly use or benefit from the solution. Most of these customers will discover this and leave quickly.
Another example of an early churn driver is ineffective new customer onboarding.
2. Accelerating Churn
A different type of churn is when most customers stay for a while, but eventually start to leave at an accelerated rate. It looks like this:
This is the least common churn type and indicates that the primary causes of churn happen later in the customers' engagement.
An example of a late churn driver is when customers subscribe to the solution to meet a limited purpose, after which they no longer need the solution.
3. Constant Churn
The final type of churn is when customers leave at a steady pace. It looks like this:
Constant churn indicates that churn is not impacted significantly by early or late factors, but is primarily the result of the failure to continually increase customer results over time.
How to:
Find Your Churn Type
Conducting your own churn analysis is one of the most important steps to gaining control over your churn. Here we explain how and provide a downloadable spreadsheet template to help you below.
The key to determining your churn type is to track customer churn by "cohort" which is the group of customers who started in the same month. Each month you calculate the percent of the cohort that remains. Older cohorts will have more months of data. The table looks like this:
After that, you simply make a line graph for your cohorts, or for the average of all the cohorts as below:
In this example, the churn shows a mild decelerating pattern.
Note that you'll need more months of data if you have annual subscription contracts where most churn is visible only beyond the first 12 months.
How Churn Impacts Growth
Churn is a problem because before you can grow you have to replace all the customers that churn. Too much churn and it becomes impossible to sustain robust growth. That's why I say that:
-- Churn is the speed limit of growth. --
Few people understand churn's true relationship to growth. For example, there are hundreds of churn articles and "Ultimate Guides" that purport to tell you what "benchmark" churn ranges are "good" or "bad" for your business. This actually makes no sense when you understand growth because the impact of churn on overall growth depends on what is happening with sales growth.
The Growth Trap
There's an iron-clad law in subscription businesses that I refer to as The Growth Trap. It refers to the fact that churn will eventually stop your growth. How can that be? It's based on this mathematical principle:
The Growth Trap Rule: Unless sales continually increase (you sell more customers each period than the previous period), any amount of churn (greater than 0%) will eventually prevent further growth.
This is simply because as your customer base grows eventually you will be churning the same number of customers as you are bringing in. It looks like this:
But this isn't realistic, because obviously plenty of companies increase their sales. In that case, the inverse of The Law of the Growth Trap applies: as long as sales are increasing, no amount of churn (less than 100%) will prevent growth.
But this isn't completely realistic either because it's not reasonable to expect that any business will experience increasing sales forever. Sooner or later there comes a point where it is not feasible to add more subscribers in each successive period.
The Growth "S Curve"
What happens in reality is that companies experience increasing sales for some period of time followed by slowing sales. The outcome is a combination of the dynamics above to form the classic growth "S Curve". It looks like this...
One easy way to understand this chart is to notice that Growth happens when the sales and churn curves are getting further apart. When the sales and churn curves start getting closer together, growth slows.
The Death Spiral
This slowing process can lead to "flat" growth where the active subscribers don't go up or down. But that's not what usually happens. The process of slowing sales often combines with increases in real churn to lead to a point where the sales and churn curves cross.
This happens when churn overtakes sales and active subscribers actually start to decrease. This is called the "death spiral" because it tends to happen rapidly and is notoriously difficult to reverse.
One of the reasons it is difficult to get out of the death spiral is that attempts to reignite sales nearly always lead to MORE increases in churn. This is because the most effective methods for rapidly increasing sales are actually among the biggest causes of churn.
Because churn is delayed, few companies will understand how their own response is driving their business into the ground.
How to create Long-Term Growth
So how can businesses grow if sales in the face of the inevitable Growth Trap?
The answer is:
You can't always sell to more customers,
but you can always sell more to your customers.
In other words, the subscription model is primarily an expansion-based growth engine.
The act of closing a new subscriber should be correctly viewed in the same way as someone entering a shopping mall. The ultimate value of a customer comes from how long they stay in the mall and how much they buy.
What that means is that the way out of the Growth Trap is to shift the burden of growth from new sales to customer expansion. You can see that means that growth is not dependent on continually finding new customers.
It looks something like this...
The implication is simple but almost impossible to overstate:
The subscription model is fundamentally a customer expansion business model.
Any subscription business that depends on net new sales for growth will eventually collapse.
What does this have to do with churn?
1. Churn is the speed limit of growth in the expansion business model because...
→ YOU CAN'T EXPAND A CUSTOMER YOU NO LONGER HAVE ←
2. The primary driver of long-term churn is a lack of expansion. It's not as simple as: first retain your customer, and then expand your customer. The fact is that long-term retention depends on expansion.
It's just as accurate to say that you first must expand your customer, and then they will renew.
How to:
Chart Your Growth Dynamics
Visualizing how your sales and churn combine to produce your growth is extremely valuable. Here we explain how to build a simple growth curve and offer a downloadable spreadsheet template to help you below.
There are two building blocks of a simple growth curve analysis:
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New subscribers (logos not dollars) by period (year or quarter)
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Churned subscribers (logos not dollars) by period (year or quarter)
It can look something like this, where "Active Subscribers" is calculated as the cumulative subscribers minus those that have churned...
Growth Dynamics Data
After that, you simply make a line graph for your each series as below:
Now you will be able to see how the relationship between your sales and churn curves is driving your subscriber growth. This is a very important insight.
Measuring Churn
Measuring churn is notoriously fraught. The main reason for this is the mistaken idea that churn is best viewed as a rate. A huge amount of problems are downstream of this fallacy. Let me explain...
Churn Rate Is A Meaningless Metric
It is extremely easy to get caught up in arcane discussions of the various ways to calculate churn rates and their pros and cons. But this is a waste of energy because churn rate is a meaningless metric, and there's actually a very simple way to see why. Think of it this way...
Q: What does a company's churn rate actually reveal about their churn?
The answer is virtually nothing!
For example, most people view churn rate as a measure of the severity of churn. But that's wrong.
The severity of churn only matters for how it impacts growth. And we have shown how the growth impact of churn is a function of its relationship to sales. So, if someone tells me they have 30% annual churn I simply cannot know if that is "good" or "bad" on the basis of that metric alone.
As we learned above, a very low churn rate can be associated with slow or declining growth, and a high rate of churn can exist simultaneously with rapid exponential growth.
Churn Rate Benchmarks Are Bunk
This also reveals why there is no valid way to compare churn rates between companies, verticals, or business models. In fact, it's not valid even to compare a company's own churn rate over time!
Nearly every "Guide to Churn" contains some set of basic churn rate range "benchmarks", usually broken out by the type of business, customer size, and/or vertical. Never mind the extremely dodgy origins of the benchmarks themselves, the entire idea of comparing rates in this way is fallacious.
You Can't Action Churn Rate
The churn rate doesn't reveal any insight into what is causing churn or what to do about it.
No matter what your churn rate is, it is impossible to glean even the tiniest insight as to what the causes of churn may be or what actions might improve it.
For example, the most common way of interpreting churn rates is as a measure of "customer satisfaction", which turns out to be completely false (see What Causes Churn).
I would argue that any business metric or KPI is worthless unless it points to the action you can take to improve it. Churn rate fails this requirement.
This means that there is ultimately no valid case for measuring and using churn rates as a key business metric. And the almost universal reliance on them is a major reason why so many companies fail to ever gain traction in solving their churn.
Calculating Churn Rate is a Mess
I'd love to leave the topic of churn rates, but unfortunately, there's more you need to know...
Because let's face it, there's no chance you are going to be able to eliminate churn rates from your life. Even if you could convince your peers, it's much more difficult to convince your leaders and basically impossible to convince your board, investors, banks, acquirers, market analysts, etc.
So if we are going to be effective, we must know what we are dealing with when it comes to churn rates. And this turns out to be very complicated. I will focus on two really big problems with commonly used churn rates.
The Denominator Problem
The churn rate is a measure of the churn as a share of something. It's an equation in which churn is the numerator, and that means there must be a denominator. The problem is that the denominator ultimately determines the outcome. The most common way of calculating churn uses all of the active subscribers as the denominator.
But using total or active subscribers as the denominator makes the metric highly sensitive to changes in the growth of sales, and the churn delay. The result is that the churn rate is almost always distorted to look higher or lower than it really is.
Churn Rate Can Be Distorted to Appear to be too LOW
For example, if sales is growing at an increasing rate, then the delay in churn means that in every period the denominator is growing ahead of the arrival of the relevant churn. The result is that the churn rate is distorted to appear to be lower than it really is. The more rapidly sales is growing, the greater the distortion is in the churn rate metric.
Churn Rate Can Be Distorted to Appear to be too HIGH
On the other hand, if sales growth is slowing, then the churn rate can suddenly jump up to appear very high. This is the reason so many companies come to me in a panic when, after a long period of satisfyingly low churn, the situation suddenly appears to reverse with unexpectedly skyrocketing churn.
Any attempt to act on these distorted metrics will be literally misguided. The all too common pattern is to underinvest in solving churn while the churn rate metric is distorted too low, and to panic and pursue irrelevant and unproductive churn mitigation strategies when it suddenly spikes.
Churn Rates Fluctuate Constantly
Perhaps you've also noticed that churn rates tend to fluctuate more than you would expect over time. This is also due to the denominator problem, the churn delay, and the relationship to sales growth. The oscillations that most companies experience in their churn rate do not reflect real changes in churn.
The worst thing you can do is to attempt to react to the churn rate and its fluctuations on the mistaken notion that the fluctuations are a meaningful signal. This is a recipe for disaster as each reaction causes its own delayed effects which combine to make the oscillation even worse as time goes on.
Churn Is Nonlinear
We learned above that churn is not linear, and this is another serious flaw with churn rates because they conceal this nonlinearity. Recall that the same churn rate might represent three very different patterns of churn.
Relying on a churn rate makes it impossible to understand and react to your churn effectively.
Churn Rate Calculation Madness
Books could be written about the problems and pitfalls of churn rates. As has been made clear already, there's no reason to dive into all of the endless permutations of different churn rate calculation methods because the result can never be useful no matter how you calculate it.
It's enough to say that the purported benefits of so-called "improvements" represented in virtually any of the various calculation techniques are dubious at best, and the costs in terms of confusion and legibility are high.
Churn Rate Manipulation
Because of the complexity and variation in churn rate calculations, they are extremely vulnerable to many forms of manipulation. This is way more common than you might think, because it is almost impossible for operators to resist the temptation to manipulate churn rates, especially when their job, their company, and a lot of money are on the line.
Below we cover several of the most common and egregious churn rate cheats and manipulations.
How to:
Calculate Churn Rate Correctly
If you must use a churn rate (and this is unavoidable for most) it should be the simplest method that most literally represents the phenomenon without coloration or confusion.
There are two key rules to accomplish this:
1. Measure subscribers not dollars
The clearest and most essential churn is the loss of a customer (eg. subscriber, account, logo, etc.). It is much less obvious what it means for a dollar to "churn". Note that there are myriad problems with measuring dollar churn (eg. MRR, ARR) which we won't cover here.
The key issue is that dollar churn is always lower than subscriber churn because smaller and lower-paying subscribers always churn at higher rates. The result is that dollar churn always looks better than customer churn, which means it is actually concealing real churn. The bigger the difference between dollar churn and subscriber churn, the more churn it is concealing.
→ But isn't dollar churn a more accurate financial metric?
The answer is: only if you are looking only at one moment frozen in time.
Remember that growth for subscription businesses is ultimately built on account expansion. When there's a difference between subscriber churn rate and dollar churn rate, long-term growth is unlikely because it is an indication that the company cannot expand smaller accounts.
In the world of SaaS, what matters for your valuation are your prospects for long-term growth. Using dollar churn essentially obscures the most important growth signal: long-term subscriber retention.
The same holds true for measuring the "user" churn rate. This is analogous to dollar churn and has all the same problems - as well as a few more.
If you are measuring subscriber churn, then you are already on the right track with the numerator in the churn rate equation.
2. Get the denominator right
The simple rule for the denominator is only to include customers that could have churned or renewed in that period. This is the number of accounts "Available to Renew" (ATR).
This method works for any period. For example, the annual churn rate would be calculated as the total number of subscribers churned during the year, divided by the total number of subscriber ATRs for the year, which is simply how many available subscriber renewals occurred during the year.
For example, if a subscriber joined halfway into the year, and they have a monthly renewal cycle, and did not churn, then that customer would have represented 6 subscriber available renewals for the year.
This simple approach partly addresses the distortion problem related to sales growth described above because we are only including customers that are available to renew. However, some distortion may always remain if there is significant sales acceleration or deceleration.
Cohort Half-life:
The Powerful Alternative Churn Metric
There is a powerful alternative churn metric that addresses most of the problems with churn rates.
In particular, it accounts for the reality of nonlinear churn and is much more robust to comparisons across time and even between companies.
The idea of Half-Life was created by scientists as a consistent way to measure and compare nonlinear dynamics. You may remember from school chemistry, that the decay of a radioactive isotope has a swooping curve that looks like this...
This looks exactly like most of the cohort churn curves that I see every day in my churn analyses. Applying this concept to cohort customer churn, the half-life is simply the amount of time it takes to lose half (50%) of the customers in the cohort.
This is what I call "Real Churn" because the curve accurately shows how many and when the cohort's customers are churning. The curve reveals the true story of what is actually going on.
Churn half-life is magic because it represents a simple and undistorted metric of the severity of nonlinear churn, that is comparable between cohorts. Cohorts of different sizes are comparable because churn decay curves are calculated as percentages of the cohort remaining in each period. What matters is the slope and shape of the curve.
For example, two different cohorts can be clearly compared to determine if churn is getting better or worse by measuring the time it takes for 50% of the cohort to churn.
In the example above, Cohort 2 clearly is experiencing higher churn. The half-life decreased from 2 years for Cohort 1, to 1 year for Cohort 2.
The intuition for cohort churn half-life is simple: longer is better. A longer half-life represents lower churn, and a shorter half-life represents higher churn.
Using half-life as the metric is powerful because it makes the two cohorts clearly comparable even though they have a complex, nonlinear shape.
What About Contracts?
The smooth curves in the chart above are typical of monthly subscriptions or any scenario where the customer may leave at any time. However, when the customer cannot churn at any point, such as in the case of annual contracts, then you will see curves that look more like "steps". Here's what it looks like...
Notice that although the subscribers can only leave on the annual renewal dates, the steps still reveal the underlying churn shape. More customers from the cohort are leaving early, and fewer are leaving as time goes on.
The underlying curve represents the actual rate of customer failure. Half-life is still the best way to characterize the cohort's churn even with contracts.
Benchmarking Churn Using Half-life
Comparing churn between companies is problematic for reasons most people intuitively understand. Important factors influencing churn are different between companies such as customer size, sales model, pricing, renewal cycle, and even the product use case (we describe many of the biggest churn factors below).
But these factors are under the company's control, and therefore are all valid targets for improving churn. The fact that two companies have made different choices does not mean their results cannot be compared. Quite the contrary, this is exactly why the comparison is useful. Half-life provides a valid method for making such comparisons.
Most importantly, churn half-life is a comparable measure of the "speed limit" churn places on growth. Ultimately growth is a function of the combination of sales and churn, and churn half-life is a universal metric for assessing the churn side of the growth equation.
Half-life Findings From Our Research
In our work, we create cohort churn curves and measure the cohort half-life for many companies. Here are just a few of the findings:
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Most subscription companies have Decelerating churn curves (like the previous two charts above).
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Churn half-life is a key component of overall growth.
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Most companies have experienced increases in real churn over the past several years.
How to:
Calculate Your Churn Half-life
Cohort churn half-life is the result of Conducting a Cohort Churn Analysis which we describe and provide a downloadable spreadsheet template above.
Once you have completed your cohort churn curves, for a specific cohort (or the average of all cohorts) simply:
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find the point in the curve where it crosses below 50% remaining, and
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use the bottom axis to identify the time (usually in months).
For example, in the chart below the cohort half-life is 24 months. This means that it took 2 years to lose half (50%) of the cohort.
This can be repeated for multiple cohorts (or annual averages) to identify the trend in real churn, whether it is getting better or worse.
What Causes Churn?
When it comes to our understanding of churn, we have been living in a dark age.
A dark age after all is just a time when people are no longer curious because they believe they know everything worth knowing. As a result, learning stops, opportunities for improvement remain untapped, and progress grinds to a halt - or even goes in reverse.
How does this apply to churn? The world of business has for many years been astonishingly uncurious about what causes churn. The reason is that we think we already know because we have a theory that everyone assumes is an established, unassailable fact.
Everyone believes that happy customers stay, and unhappy customers leave.
That's it. That's the entire theory. And everyone just "knows" that this is true.
But it's not true. Not even a little bit!
The first red flag about this should be just how shockingly simplistic this thinking is. Really? Just one single factor that explains virtually everything in business? No.
Let me be clear: I am NOT saying that customer satisfaction is one of many factors. I'm saying...
CUSTOMER SATISFACTION IS NOT A FACTOR IN CHURN.
And I can prove it...
Churn is NOT about Customer Satisfaction
Our company has compiled probably the biggest set of data on customer churn and retention in the world. And we have been able to test all kinds of factors to see what drives churn - and what doesn't.
Using our huge customer data set we have repeatedly tested the relationship between customer satisfaction and loyalty, and the results totally demolish the universal business theory of our time.
For example, using the most popular measure of customer satisfaction - Net Promoter Score (NPS) - our data consistently shows that:
There's literally NO correlation between customer satisfaction and customer retention.
And this has been the same result every time we run this test across companies and different customer satisfaction scoring methods.
This finding is nothing short of revolutionary because it calls into question virtually everything we do in business. For many years the entire world of business has been in the grip of a single-minded focus on improving the "Customer Experience" as the sole strategy for success.
Nearly everything we do is based on this idea which has become so ubiquitous that we've essentially forgotten it's a theory at all.
The other red flag about this theory is that in spite of investments of untold billions in improving the customer experience, customer retention has not kept pace. Actually, for the vast majority of companies customer retention has been steadily going down for years.
How can this be?
The answer is profoundly uncomfortable to most companies: satisfaction has nothing to do with customer retention.
Churn is NOT the result of Bad Customer Experiences
Using our data we have also debunked another universal fallacy, that negative customer experiences lead to more churn.
Actually, it's just the opposite. The data consistently shows that:
Customers who've had bad experiences stay LONGER than customers who haven't had negative experiences.
Our research reveals that customers with negative experiences have roughly twice the average lifespan as those who have not had bad experiences!
For example, customer trouble tickets are a strong positive predictor of customer retention. This contradicts the popular misconception that tickets are a predictor of customer churn.
This phenomenon was first discovered way back in the 1990s when Sun Microsystems looked closely at their own customers and found their customers who'd had trouble stayed longer than those who hadn't.
So, what's going on here? It doesn't matter if customers are satisfied or unsatisfied? And, customers with positive experiences do worse than those with negative experiences?
WHAT GIVES??
HOLD ON: l am NOT suggesting that providing a good customer experience isn't important, or that we should not satisfy our customers. Of course, we should do both of these! I am simply suggesting that these will not reduce churn or lead to higher retention.
Asking the WRONG Question
One of the reasons we don't understand what's going on is that we start with the wrong question. Nearly every serious effort to solve churn starts by asking:
"Why do customers leave?"
It makes intuitive sense: if we can figure out why they leave then we could remove the reason and fewer customers would leave. Right?
The problem with this approach is that these are the customers who have the least to teach us precisely because they failed to achieve results. When you think about it, all you can learn from churned customers are all sorts of things about customers who did NOT succeed.
I've personally conducted or been involved in many of these customer exit surveys and similar efforts. And, it NEVER works to reduce churn.
Here'sThe RIGHT Question
It's much more productive to ask: "Why do customers stay?"
We do this by seeking out customers who've achieved good results and are staying. When you start with the right question, the answer finds you.
It's All About Results
We have tested dozens of factors against our huge customer data set, and by far the most predictive factor for customer retention is: customers achieving measurable results.
Nothing is more correlated with long-term customer retention than this, which is why it is the basis for the First Law of Customer Retention:
First Law of Customer Retention:
Customers stay to get results.
This means that effective customer churn and retention efforts are those that focus on ensuring customers achieve measurable results.
This explains the surprising fact that customer satisfaction has nothing to do with customer retention. Customers who achieve results stay, and those who don't leave.
There is nothing you can do to more significantly impact your churn than driving measurable customer results. Period.
But there are a LOT of things that potentially entails. So, how do we focus on the actions that will have the greatest impact?
This leads to another key question...
"Why do customers get results?"
This question reveals why it's a waste of time to survey churned customers. These are the customers who didn't do the things that lead to good results, so they have nothing to teach us about success.
The winning approach is to seek your most successful customers. Not your "happy" customers, or your favorite customers. Your successful customers.
These are the customers that can actually inform our actions to drive customer results. We need to investigate them closely. This is what we call a "Success Analysis".
How to:
Conduct a Success Analysis
The key to solving churn is driving customer results. In order to do that we have to truly understand why customers succeed. This is the reason for conducting a Success Analysis.
The Success Analysis relies on two key elements:
1. First you have to identify the successful customers.
Be careful because the instinct is going to be to tap customers who are happy or who have a good relationship. But neither one of these is relevant. Remember that happy customers churn at the same rate as unhappy customers.
What we are looking for are customers that have achieved measurable results. Some of them might actually be in the "unhappy" column.
Look for customers that:
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Have a renewal history
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Have achieved measurable results that are important to them
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Have successfully leveraged the core product capabilities
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Regularly rely on the product for important business functions
Spend time vetting a list of at least 10 of these successful customers. The objective is to schedule 5 customer meetings.
2. Second, investigate their success deeply.
Here's what we are looking to learn:
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What key results are most important to them?
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How do they measure their results?
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How good are their results?
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What did they do to get results?
Conducting a Success Analysis interview involves more than simply asking the right questions. It is necessary to prove and explore the customer's answers to uncover the full picture of their path to success. This is because most interviewees don't instinctively think of some of the most important elements in their success.
The Success Analysis will reveal key insights into what it takes to win with your solution and in your customers' businesses.
But these insights always point to the fact that what actually leads to customer results is their own behavior. And in particular, to key changes in how they work that specifically drive improvements.
This is the Second Law:
Second Law of Customer Retention:
Customers get results because they change their behavior.
It's intuitive to attribute the value customers get directly to the product. But, that's an oversimplification.
Think of it this way... If customers get your product but don't change ANYTHING about how they are working, will they get any benefits?
The answer is obviously, no. At the very least they need to use the product, and almost certainly it's a lot more than that.
Think of it this way... If your product produced results, then all your customers would get results. But they don't. In reality, customer results are all over the place! Some customers get phenomenal results, and many others get no results at all, and everything in between.
Technology doesn't produce results. Behavior change produces results and technology makes it possible and scalable.