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Churn is a killer for startups, no matter how many users you attract. If no one sticks around, growth stalls. This post shows you how to diagnose and ultimately fix churn at its roots, starting with cohort analysis and data-driven insights. Plus, you'll get our free cohort analysis template to diagnose and reduce churn.
Every startup wants explosive growth, but there’s a cruel irony to this: Getting new users means nothing if they don’t stick around.
Churn, the pace at which users leave your product, can silently kill a business, no matter how much potential it has.
Retaining is the new growing!
Product managers have done everything from overhauling user onboarding flows to offering custom nudges, but the first step to real improvement is understanding why users are leaving in the first place.
That's all about getting your hands dirty and digging deeper into your product data and figuring out exactly where and why the users have fallen off.
In this guide, we'll walk through how you can use cohort analysis and other actionable insights to isolate the root causes of churn, and instead, make targeted improvements that actually move the needle on retention.
In this guide on how to conduct cohort analysis, you’ll learn:
Cohort analysis is a type of behavioral analytics in which you're grouping users and analyzing user interactions based on their common characteristics to better track and understand their actions.
User retention is the new growth.
With costs of acquisition going up and product categories getting more and more crowded, user engagement impacts both your product and marketing strategies.
Effective cohort analysis gives you the transparency to understand in precise terms when and why users are falling off, for you to make the proper optimizations that actually move the needle on churn and revenue from the entire customer lifetime value.
Without it, you might just lump all users together (instead of in separate user segments). This means you'll fail to understand the true customer behavior triggers of why some groups leave or miss out on identifying the behaviors that lead to long-term loyalty from power users.
But here's what most people get wrong or don’t know when they compare cohorts:
The 2 most common types of cohorts are:
Acquisition cohorts help you understand when an action is taking place, but behavioral cohorts are best for discovering and understanding churn rates, as they tell you why a user has taken an action.
Cohort analysis is a valuable tool for anyone looking to gain a deeper understanding of their customers and why they make certain choices in your app. Here are some of the benefits of conducting cohort analysis:
Your users are the ones with mouths, but the timeline is going to tell you more about your churn problem than they ever will. If you find out when the churn happens, you can figure out what’s happening around that time to cause it.
But how do you establish the timeline in the first place? By performing an acquisition cohort analysis.
In this case, you need to create a cohort chart. You need your various cohorts, as well as the number of users for each and a column for each day of the period you’re analyzing.
Like this:
As you can see, the cells under each day show the portion of the original cohort for that row that you’ve retained on that day. Nice.
A couple of things to remember as you’re setting up your acquisition cohort analysis:
With your trusty acquisition cohort analysis and timeline in hand (who and what), the next step is the analysis (why).
Look for the big drop-offs and make a note of them. Ask yourself what happened on those drop-off days.
Imagine you’re seeing users drop off by 23% on day 3 (yikes). What happens on day 3? Are you asking them to sync their data (for example)?
If the answer is yes, you’ve found the problem. Maybe not the problem, but a problem you can solve nonetheless.
Your analyses will likely be more complex. In fact, you’ll probably need to apply this analysis to all of your app’s core features.
Here’s what you should not do: See how app engagement in the first 30 days correlates with churn.
Why? Because that information tells you nothing about what to change.
Here’s a better idea: How does the completion of an app onboarding checklist correlate with churn?
In other words, keep it specific. Which specific features are sticky for your users? That’s what you need to find out.
Wouldn’t it be great if the problem was always a single feature? Sure. But that’s almost never the case.
It’s usually a combination of features and behaviors that influences cohort churn. For example, those who complete the onboarding checklist in your app may be much less likely to churn when you ask them to sync their data than those who didn’t.
That’s just one extra layer, but remember—there are dozens of layers to consider.
How do you do that? By comparing your behavioral cohorts.
If you’re handy with pivot tables and conditional formatting and have a lot of time on your hands, you can do it in a spreadsheet.
Or you can use one of the many tools designed to streamline the churn cohort analysis process. Amplitude, for examplitude, is purpose-built for creating and comparing behavioral cohorts in a flash.
(Here’s a quick guide on how to use Amplitude for cohort analysis.)
As you get deep into the data, don’t forget your purpose. You’re trying to find the combinations of behaviors and features that are influencing retention—positively or negatively.
That means you need to be analyzing this stuff in a way that spits out hypotheses ripe for the testing.
ALERT: You’ve just found that users who don’t complete your onboarding checklist fall off by 67% by day 10.
Obviously, it’s time to change everything. Add dozens of reminders from day 1 to day 7 to complete the checklist.
We’re kidding. Please don’t do that.
You’re just as likely to increase churn with harsh pivots like that. Try this instead: Test, test and test some more.
Your gut feeling that you need to add some reminders about the checklist to promote the best onboarding experiences may be exactly right. That’s great, but test it so you can back it up with data.
And if you test a change to your app that improves retention, don’t stop there.
You should have at least a handful of other hypotheses to test. Test those, too.
Why? Because you may find that other changes reduce churn even more than the first one you tested.
Be thorough. Take your time. Iterate it, rinse it, repeat it until you’ve solved the problem you came to solve.
What do AdRoll’s 3X increase in upsells and GetResponse’s 60% user activation rate have in common? Us! Take advantage of Appcues suite of tools to improve your app and grow your adoption, conversion, and retention rates. Get started here.
To give you a clearer picture of a customer cohort analysis, let's do one on a fictional data set from a productivity app.
Starting with your data, ask yourself: where do users drop off?
Dig into your cohort data to identify the moment when the drop-off in user retention is steepest. Maybe it's a sudden drop of 3% between day 14 and day 15. Tackling the biggest and most impactful changes in your data ensures you’re focusing on the moments that matter the most, turning overwhelming data into clear yardsticks for further development.
You know what to do: Hypothesize about why users are leaving.
Start with the churn. Here's the average churn rate for the productivity app based on an acquisition cohort analysis:
Then, compare that average churn rate to the rate for users who engage with certain features or complete a key action.
For example, here's that same average churn compared to the churn of users who use one of the core features of the productivity app: the checklist feature.
Bingo!
Users who engaged with the core feature (the light blue line) churned at a very low rate. Most of the people who churned did not use this core feature.
The next step in this shockingly simple and tidy fictional example, of course, is to come up with ways to adjust the factor we’ve just identified. How could we improve user engagement with the checklist?
To complete the cohort analysis, we would come up with ideas and A/B test them until we found the winner.
Cohort analysis can move you from product momentum to retention.
Those that employ cohort analysis to target friction points and boost retention can drive focused improvements that lift value (in dollars and loyalty). Others will simply blindly fly by vanity metrics, one-size-fits-all solutions, and hunches.
On one end is raw data, at the other are actionable insights.
Transforming raw data into actionable insights involves turning raw data (like user clicks, page views, or survey answers) into meaningful conclusions that drive the next actions.
Raw data only lets you know what went wrong, but actionable insights tell you why and how to make things better. For example, it’s not actionable to know that users fall off during onboarding; an actionable insight would provide the specifics of where and why they fall off and suggest changes to mitigate friction.
So what should you do next?
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