Discover the drivers of habit formation, find behavioral patterns in your user base, and take your users from “passive” to “core” to “power.”
By now, you’ve already accomplished a lot: you’ve determined your critical event and product’s usage interval (Chapter 2). You’ve learned the Retention Lifecycle Framework and created cohorts for each stage of the lifecycle (Chapter 3). You’ve also learned how to dig deeper into your retention by identifying behavioral personas and using various product analysis methods (Chapter 4).
Now it’s time to dive into each stage of the Retention Lifecycle. In this chapter, we’ll start with understanding current user retention.
Current user retention matters because it focuses on your most important users: those who are active right now and consistently use your product. Understanding and improving the experience for your active users is critical for long-term sustainability of your business.
TERMS TO KNOW:
A current user is someone who’s actively using your product with some level of consistency. For this playbook, we’re defining a current user as someone who was active in the previous time period and active in the current period that you’re measuring.For example, if you determined that you have a weekly usage interval based on the usage interval calculation in Chapter 2, your current people are users who were active last week and active this week.
Most articles and presentations about retention focus on new user retention, but as we discussed in Chapter 3 with the Retention Lifecycle Framework, the retention of your current and resurrected users can be equally important.
In fact, it’s best to start with analyzing your current user retention so that you understand what successful long-term usage looks like. Once you fully understand the value that these users are getting from your product, you can leverage that information to create better experiences for retaining new users or reengaging dormant users.
Once you understand what causes someone to become an engaged, repeat user of your product, you can use that knowledge to get more people to become current users.
Improving your current user retention is also critical to creating a sustainable business.
If your retention curve doesn’t flatten out at some point, it will become impossible to sustain true growth. At some point, even if you keep adding new users, your poor retention will cause your overall growth to stagnate, and even decrease.
Notice how the blue curve flattens off around day 7. Although there’s some initial drop off in the first 7 days, a steady user base remains—these are your current users. If your retention curve flattens off at some point, you have a base to work off for this Playbook.
The goal of current user retention analysis is to move this baseline up.
The green curve, however, keeps going down and never flattens off, meaning that the product is not attracting a steady base of people who keep using the product. If your retention curve looks more like the green curve and trends toward zero, that’s an indication that you haven’t reached product/ market fit yet. In that case, you should work on rethinking the core value of your product before optimizing for retention.
PRO TIP:
In Nir Eyal’s book “Hooked,” he outlines a process he calls ‘Habit Testing.’ Habit Testing has 3 steps, which correspond well with how we’re laying out the process in this Playbook:
Step 1 – Identify: find habitual users
Step 2 – Codify: understand what these users have in common
Step 3 – Modify: adapt user flow based on these learnings
In the context of this Playbook, habitual users = current users; these are people who are consistently using your product as you expect them to. Nir’s rule of thumb for how many habitual, or current, users you need before beginning to Habit Test is 5%.
This means that at least 5% of your users are getting enough value from your product to use it as intended. If you don’t have 5% yet, Nir recommends rethinking the vision and core value proposition of your product before diving into Habit Testing.
The overall goals of current user retention analysis are to:
By the end of this chapter, you will have learned how to identify distinct behaviors of your currently engaged users and understand the factors that contribute to their retention. This includes:
Topics and methods we’ll cover in this chapter
5.1 – Current users diagnostic
5.2 – Find behavioral personas of your current users
5.3 – Discover the drivers of Habit Formation
5.4 – Discover drivers from passive to core to power personas
5.5 – Take action
5.6 – Current User Retention (worksheet)
First, create your current user cohort, as we covered in Chapter 3.3, and plot your baseline retention for current users.
Remember, you can use either N-Day or unbounded retention, as discussed in Chapter 3.1. In the example below, you can see a retention curve for the cohort of current users.
Investigate user properties & segment your retention curve:
Once you create your current user cohort, look at user properties to get a high-level understanding of who these users are. Measuring the breakdowns of key user properties can help you identify trends and groups of users you should study more closely.
You should also segment your retention curve by your major user properties (e.g. platform, location, attribution source) to identify any differences to investigate. Refer back to Chapter 4.3.2 for a refresher on segmenting by user properties.
In Chapter 4, we introduced the concept of behavioral personas—each persona represents a distinct way of interacting with your product.
The goals of finding the personas of your current users are to understand:
In this section, we’ll discuss some examples of behavioral personas and principles for deciding which personas to focus on.
Example: Personas for a casual mobile game
One of our customers has a social casual game for mobile smartphones. The game matches players against each other in real-time and also includes a social component where users can chat with each other.
When this company analyzed their current users, they found three core personas who all had high retention despite distinctly different behavioral patterns:
As you can see in the retention chart below, the 3 personas have significantly higher retention than the baseline for all current users. In addition, the high gameplay + high social persona has the highest retention.
Takeaway: While this data indicates that users who both play games and use the social features will retain the best, it shows that users who actively play games or use the social features will still retain at a much higher rate than the baseline. This means that even if the company starts with focusing on just increasing engagement with one aspect of the product (social or gameplay), they’ll likely see some significant retention gains.
Example: Passive and core user personas for a mindfulness app
One of our customers’ products is a mindfulness app for mobile smartphones that provides meditation courses as well as ‘scenes’ with calming background sounds.
Using the Personas feature, the app’s product team identified three personas:
The ‘Alert Savers’ persona was particularly interesting: only a very small percentage of users, about 1%, were setting an alert. This feature was buried deep in the Settings screen of the app, so very few users were actually discovering it—but these users had very high retention compared to other groups.
Using the power/core/passive framework, the company classified Listeners as passive users, Meditators as core users, and Alert savers as power users because they were using a “power feature.”
Comparing retention curves of different personas
This company compared the retention curves of Listeners and Meditators. They found that both personas had similarly high Day-N retention for the next 30 days after the current period, although Listeners had slightly lower retention.
To see how these personas might differ in retention longer-term, they looked at weekly retention for the next 24 weeks. This helped to uncover some larger differences: Alert Savers have the highest long term retention, followed by Meditators, followed by Listeners.
Takeaway
Based on these retention graphs, Listeners are a fairly active Passive persona, but still have lower retention than Meditators long-term. In addition, Alert Savers who set a daily reminder to meditate have the highest long-term retention at 24 weeks. To increase overall retention, the company could think about trying to convert Listeners to become Meditators, and getting Meditators to set a daily reminder and become Alert Savers.
Once you identify your own current user personas, you should use some or all of the analyses in the Current User Worksheet at the bottom to get a fuller understanding of how these users behave. This will help you identify opportunities for improvement and more potential drivers of current user retention. If you need to review any of the methods, refer back to Chapter 4 – Product Analysis Toolkit.
It’s important to also look at stickiness for your critical event. In this case, the stickiness graph is measuring each day that a user did the critical event in your product.
For example, one of our customers has developed an app that helps users find and book fitness classes near them. This company’s critical event is when a user books a class through the app, so stickiness of bookings is a more meaningful metric than the stickiness of general app usage.
The charts show that stickiness for booking an appointment is significantly lower than general activity stickiness. So, while a high percentage of each of the three personas are opening the app and doing something, like browsing classes or checking class schedules, on at least 15 days out of a month, there’s a much lower percentage booking appointments that frequently.
Looking at stickiness for ‘Appointment Booked’ also helps to differentiate the personas better. Here we see that Personas 2 and 3 have significantly better stickiness than Persona 1: about 30% of users in Personas 2 & 3 book an appointment at least 3 days out of a month, compared to 19% for Persona 1 users.
Takeaway
Based on these results, the team realized that Personas 2 & 3 would be more valuable to focus on than Persona 1. They decided to focus on getting more users into Personas 2 and 3 and improving the product experience for these personas.
Current users have formed a habit of using your product
When a new user first starts using your product, they go through a few phases before becoming a retained, current user:
Once a user completes the Habit Formation phase, they’ve successfully transitioned from being a new user to a current user of your product.
Current users of your product have formed a habit. You have successfully onboarded them and shown them value while they were new users, and now they’re returning on a regular basis. In this chapter, we’re going to focus on the drivers that help get a user through the Habit Formation phase. The Onboarding and Value Discovery phases happen during the new user time period, which we’ll discuss in the next chapter.
By studying your current users, you’ll look for indicators of habit formation. You can then apply this knowledge to get more new or resurrected users to form habits. To help you make this a repeatable process, we’re going to show you how to look for behavioral drivers that tend to tip the scale for habit formation.
To understand what gets new users to become current users, you need to dig into the user behaviors that drive that transition.
The concept of a behavioral driver that helps a user transition from one phase to the next is essentially the same as the concept of the ‘aha’ moment, a commonly used concept in the field of product retention. Traditionally, the ‘aha’ moment is something that a user does early in their experience that makes them much more likely to retain.
The most famous example is when Facebook found that users who added at least 7 friends in their first 10 days retained better.
However, you can apply this concept of important behaviors to any stage of the user lifecycle, not just for the ‘a-ha’ moment of new users. To identify these drivers of habit formation, find an action or set of actions that separates users who successfully go through Habit Formation, from those who don’t. In other words, for action(s) to qualify as a driver:
To identify drivers of Habit Formation, find an action or set of actions that separates users who form a habit, from those who don’t.
In this section, we’ll go over how to find the behaviors that drive users to complete the habit formation phase.
You can use the following 5 steps to help you find your drivers (but after this we’ll show you a much easier and faster way to do it in Amplitude)
Step 1: Create a base cohort of users who were retained during the Habit Formation period.
The table and images below show the Habit Formation period that you should analyze based on the usage interval you calculated in Chapter 2.
| Product Usage Intervals | Habit Formation Period |
|---|---|
| Daily | Days 4-6 |
| Weekly | Days 8-14 |
| Biweekly | Days 15-28 |
| Monthly | Days 31-60 |
Step 2: Create a retained cohort of users who were retained in the next interval after the Habit Formation phase.
These are your current users who successfully formed a habit.
Step 3: Create a dormant cohort of users.
These are users who were in the base cohort and were not retained in the following time period.
Step 4: Compare your retained and dormant cohorts to look for behaviors that are present in the retained cohort, but not in the dormant cohort.
You can do this by:
For example, a music streaming product would hypothesize that some important actions would include: playing songs, creating playlists, favoriting songs, and so on. The team would then look at whether there are any differences between the retained and dormant cohorts in the number of times users perform these actions.
Step 5: Once you’ve formed some hypotheses of actions that might be drivers, measure the difference in retention between users who do that action, and users who don’t do that action.
This will help you confirm whether or not performing that action correlates with higher retention. In the image below, you can see that users who favorited at least 1 song have significantly higher retention than users who do not.
To get more details about this process of finding behavioral drivers or ‘a-ha’ moments, check out this article.
Once you’ve discovered your own drivers of habit formation, you know the milestones that you need to get new users through to increase their chances of continuing to use your product as current users.
To put these insights into action, think about ways you can get more users to pass these milestones during their early experience. For the gaming company that we discussed, these could include:
By experimenting with a few methods, you can find the most effective ways to get users across the habit formation threshold.
Note that the drivers we’re talking about for Habit Formation are different from the onboarding experience—we’ll focus on onboarding in the next chapter: New User Retention. Habit formation happens after a user has already been successfully onboarded and has started to discover value in your product or service.
As we discussed in Chapter 4 about behavioral personas, you can often classify personas as passive, core, or power users. From the personas of current users you identified earlier in this chapter, you should have identified some personas that are more active and valuable than others.
Remember that for the mindfulness app, they found a passive persona of Listeners and a core persona of Meditators. To increase core usage of their app, they should try to get more Listeners, who already use the app on a pretty regular basis, to become Meditators.
So: how do you get a passive user to become a core user, or a core user to become a power user? Just like we identified drivers of Habit Formation, you can identify behaviors that drive people to become a core user or a power user. Use the same process we just went over in Section 5.3 to do that.
Now that you’ve completed current user retention analysis, summarize what you’ve found and form some hypotheses to test.
Here are some key questions to ask yourself:
As you start testing some of your hypotheses and trying out new ways to improve your current user retention, it’s important to keep track of your metrics to see what is and isn’t working.
Keep the goals of current user retention in mind as you form your metrics
We suggest tracking these metrics over time to measure your progress
A current user is someone who was active in the previous time interval and active in the current interval that you’re measuring. Current user retention matters because it focuses on your most important users: those who are active right now and consistently use your product.
Understanding and improving the experience for your active users is critical for long-term sustainability of your business.
Run through the metrics below to get a baseline understanding of your current users. Refer back to Ch. 4 for a refresher on any of these methods.
Identify any behavioral personas within your current users and list them here.
Use the process in Section 5.3 to identify the behavioral drivers of habit formation. List those drivers here and some ideas you have for how to get more users to perform those actions.
Repeat the same exercise, looking for any drivers that shift passive users to become core users, or core to power.
Ask yourself these questions as you form hypotheses and come up with experiment ideas.
As you start testing some of your hypotheses and trying out new ways to improve your current user retention, it’s important to keep track of your metrics to see what is and isn’t working.
Keep the goals of current user retention in mind as you form your metrics:
We suggest tracking these metrics over time to measure your progress:
The Amplitude Guide to Customer Retention: 40+ Resources to Increase Retention
The Amplitude Team
The Amplitude Team
Hooking Users in 3 Steps: An Intro to Habit Testing
Nir Eyal, author of Hooked
Why You Need Cohorts to Improve Your Retention
Alicia Shiu, Amplitude Blog
This Is How You Find Your App’s Aha! Moment
Kendrick Wang, Apptimize Blog