Who are your app’s users? They’re people just like you and me. They have busy lives, friends, daily commutes, and when confronted with challenges, they prefer to avoid confrontation. It’s only natural. According to research by thinkJar, that’s why 25 out of 26 customers churn silently without complaining.
As a product manager, this is awful news. With each of these departing users, you lose a treasure trove of unvoiced feedback. Why don’t they speak up? Because complaining carries a cost. They would have to reach out to you, wait for a response, and hope that your support team is unlike most support teams they’re used to. Their other option—to search for alternatives—is both faster and easier.
For product teams, fighting this silent user churn without any insight is like filling a leaky bucket. How can you patch yours up? Product analytics.
Reporting on churn
To understand churn, product managers need a way to report on user behavior. They can either build out a system themselves to warehouse and analyze their data or they can invest in product analytics. Either way, the goal is the same: determine what users are thinking and feeling at each stage of their journey, in order to drive the right behaviors. Just as, according to The Verge, Facebook can predict that couples will break up before the couples themselves know, you too can know if your users are planning on breaking up with you so that you can try to work it out first.
And of course, you’ll need to get details. Simply knowing that a breakup is imminent doesn’t tell you how to prevent it. And just knowing which door someone exited through doesn’t help either. Your analytics system needs to allow you to go deep and track individual user journeys and ask specific questions related to your core business goal, be that purchases, usage, or more users.
Questions product leaders commonly ask of their analytics:
- What events are highly correlated with my business goal?
- What events are highly correlated with users not reaching my business goal?
- Are my subjective hypotheses supported or disproved by the objective data?
Next, you’ll need a way to act on what you know. This can mean automating in-app notifications, email, push notifications, personalization, SMS messages, A/B tests, and more. Whatever you use for analytics, it needs to hook into or provide this messaging system.
With the ability to see and instantly react, you can start to uncover insights and plug leaky holes.
Here we cover the four typical times users churn, and what you can do about each:
1. Users churn if they don’t complete activation
Users are in danger of churning if they don’t ‘get it’ right away. How can you spot if this is happening? You need the product analytics to see what they’ve done, step by step. Sometimes it’s obvious, and the drop-offs occur at a login page, or after users add items to their shopping cart. Other times, it’s harder to tell. For example, what if users drop off after reading a few articles? Or disappear in the middle of filling out their profile? Or what if there’s no clear pattern? Did these users leave because they didn’t find what they were looking for, or did they leave because they found it?
There’s nothing to do here but begin asking questions and tracking the answers. Asking why users churn in general isn’t possible, but asking questions about what leads to churns is. For example, if you A/B test a simpler onboarding process, does it cause them to stick? If you add a walk-through, does it improve time spent in-app? If users leave, can they be brought back? Does the user’s acquisition source matter, and do some sources produce more valuable users than others? All of these questions deserve consideration.
One sports gaming company and Mixpanel customer increased their conversion rates by answering these questions. Before implementing analytics they could see that users were converting at a low rate, but didn’t know why. With analytics and the ability to see user journeys, they noticed that the greatest drop off occurred just after users created their sports lineups but before they submitted a cash bet. The analytics also showed that users who reached this point but didn’t progress were highly unlikely to return. The pay wall, it appeared, was causing the churn. The solution? The team there automated push notifications offering a discount whenever users created lineups but then didn’t submit bets. Their conversions promptly ticked up.
2. Users churn when retention efforts fail
Users can churn when the product doesn’t show them enough value early on. Facebook knew this and famously discovered that their users were less likely to stay if they made fewer than seven friends in ten days. This so-called magic number became one of many things Facebook optimized for. This led them to create such features as friend recommendations, a prompt to import contacts, and the ability to invite new friends.
Your own app’s goals and KPIs will be unique, but product analytics should allow you to identify the key characteristics of both long and short-term users. Typically, the difference between them is whether they found enough value in your app.
Value comes in many forms. For some users, it’s utility: if ordering taxis through their phone is more convenient, they’ll keep doing it. For others, it’s an experience, such as a video that makes them laugh, or just feeling well-informed.
How do you detect value? Again, product analytics. Look at where users spend their time. It’s our most precious resource in today’s attention economy and in most cases a great bellwether for value:
- Where do users spend most of their time?
- Where do they spend more/less time than expected?
- What’s the first place they navigate to when they open the app?
- Do they spend time here by choice, or out of frustration?
3. Users churn when they become frustrated
Users churn when they get frustrated, and most frustrations can be avoided with better design. To improve your design, watch your users closely and see where they get stuck. They’ll often show you the solution that they want. This is how the University of California Berkeley is rumored to have designed its quad: instead of placing paths, they planted a lawn and watched where students walked. Wherever areas of grass were trampled and natural trails emerged, they placed cement walkways.
Talk of this ‘desire path’ method is common among designers but rare in practice. Many product teams become attached to their pre-determined user paths and then are disappointed when actual user behaviors diverge. But, as experience teaches us, the surest way to become disappointed with users (and to disappoint them) is to have inflexible expectations.
“Never blame the user,” says Matt Aronoff, co-founder of the app design firm Logical Animal. “There are only two reasons users get lost: either the app does something you didn’t expect, like the user’s network connection goes away, or the user doesn’t do something that you expect. Both are your fault.”
It’s easy for product teams to blame users when they lacks insights, and even mistake user frustration for success. Irzana Golding, a data scientist at Cisco, provides the example of a chatbot team celebrating after seeing the number of messages spike while failing to realize that those messages were coming from irate users who didn’t have their questions answered sufficiently.
How can product teams detect user frustrations and ease their pain? Delve into deeper product insights to find:
- Repeated actions
- Excessive ‘undo’ actions
- Abnormally frequent visits to the menu
- Long click-strings before users arrive at value
- Drop-offs mid-action
4. Users churn when the product team confuses correlation with causation
Finally, users churn when product teams incorrectly assume that they know what makes users stay. Without the granular data that analytics provide, product teams only have intuition to work with, and while they may be expert guessers, we’re all human. Everyone is subject to logical fallacies. Sometimes they confuse correlation with causation and assume that just because two events occur together, they are related. Take cargo cults for example.
During WWII, U.S. troops built and then abandoned runways on Pacific islands where natives had previously had no other contact with the outside world. When the war ended and the planes full of precious cargo disappeared, the natives built their own runways and crude signal towers hoping to lure them back. They confused causation with correlation.
Most of us do this all the time, from wearing our team’s jersey on gameday to building towers without the thirteenth floor or, say, assuming that more time in-app leads to more purchases. With the entire company often relying on the business-critical decisions the product team makes, it’s important not to be building needless signal towers and runways.
How can product teams avoid the causation trap? By making sure that they have both qualitative and quantitative data to back their assumptions. The qualitative data comes from their experience and user interviews, and the quantitative data comes from product analytics where they can ask the question, ‘Are these factors causal or simply correlated?’ and get a firm answer.
Most users churn silently, but with adequate product analytics, their stories won’t go untold. Product teams need deep, granular, user-level insights to distill narratives from the data. When they achieve that, they can finally address their churn and free their users to grow and flourish within the app.