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.

Why current user retention matters

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.

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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.

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.

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.

What you'll learn in this chapter

  • Get new users to form habits and become current users

  • Get current users to become core users, and core users to become power users

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:

  • The different behavioral personas of your current users

  • Finding the actions that drive someone to become a current user

  • Methods to nudge new or resurrected users to create a habit of using your product and become current users

Topics and methods we'll cover in this chapter

  • Current User Retention (worksheet)

5.1 | Current users diagnostic

active in the current period that you’re measuring.

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.

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.

5.2 | Find behavioral personas of your current users

—each persona represents a distinct way of interacting with your product.

The goals of finding the personas of your current users are to understand:

  • The value current users get from using your product

  • Whether there are distinct use cases

  • Any patterns of behavior that might impact retention (positively or negatively) later on

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:

  • High social only: Users who heavily use social features, but don’t play many games.

  • High gameplay only: Users who mainly play games, but don’t use social features.

  • High gameplay + high social: Users who both play games and use the social features actively.

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.

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:

  • Users who primarily listen to and swipe through different scenes.

  • Users who completed more meditations than average.

  • Users who turned on a feature that sends a Daily Reminder to meditate. These users also completed several meditations per week on average.

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.

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.

5.2.1 | Dig deeper into your personas—Product Analysis Toolkit

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.

5.2.2 | Critical event stickiness

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.

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.

5.3 | Discovering the drivers of Habit Formation

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:

  1. Onboarding

  2. Value Discovery

  3. Habit Formation

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.

5.3.1 | Studying current user retention is about understanding the factors that encourage people to form a habit

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.

, 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.

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:

  • Most users who complete the action(s) form a habit and become current users

  • Most users who do not complete the action(s) churn before becoming a current user

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.

5.3.2 | How to find your drivers of Habit Formation

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 IntervalsHabit Formation Period
DailyDays 4-6
WeeklyDays 8-14
BiweeklyDays 15-28
MonthlyDays 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.

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:

  • Brainstorming some actions that you think might be important drivers and measuring the percentage of users in your retained and dormant cohorts who did those actions.

  • Talking to users from both groups to get qualitative data.

  • Watching user replays or looking at user activity sequences from both cohorts.

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.

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5.3.3 | Apply what you've learned: Get more users to form habits & become current users

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.

  • Sending push notifications when a user’s social connection is active in the game, encouraging them to join that person

  • Presenting users with some kind of reward, like a badge or in-app currency, once they’ve used the social feature a certain number of times

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.

5.4 | Discover drivers from passive to core to power personas

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.

5.5 | Take Action

Now that you’ve completed current user retention analysis, summarize what you’ve found and form some hypotheses to test.

  • What are the key action(s) that you identified as drivers of Habit Formation

  • What are some methods you can test to get more new users to cross those thresholds?

  • Who are your passive, core, and power users? How are they different? How can you get core users to become power users?

  • Did your behavioral persona analysis reveal any use cases you didn’t expect, or didn’t think were very important? How might you improve or tailor the experience for those users?

  • Are some of your personas more important for your main business objective, like revenue?

  • How can you get more users to convert into one of your core or power user personas? The biggest improvements can come from targeting users who are not well-retained and getting them to perform actions of your power behavioral personas.

5.5.1 | Track improvement over time

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
  • Get new users to form habits and become current users

  • Get current users to become core users, and core users to become power users

We suggest tracking these metrics over time to measure your progress
  • The size (in absolute numbers) and percentage of your total active users that is made up of your current users (as calculated via Lifecycle or manual analysis).

  • Retention over time of all current users and of each behavioral persona.

  • Size and percentage breakdown of your important behavioral personas. Are you getting more people into important personas?

  • Stickiness over time for critical events. This will show you any changes in how active current users are in the product.

  • Conversion rate over time through your critical path funnel.

5.6 | Current User Retention (worksheet)

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.

5.6.1 | Current user diagnostic checklist

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.

  • Create a cohort of your current users

  • Plot the baseline retention curve of current users

  • Segment the retention curve by user properties

  • Measure conversion through your critical path funnel

  • Identify common user flows

  • Measure stickiness for your critical event

  • Measure session metrics

5.6.2 | Current user behavioral personas

Identify any behavioral personas within your current users and list them here.

5.6.3 | Drivers of habit formation

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.

5.6.4 | Drivers from passive to core, and core to power personas

Repeat the same exercise, looking for any drivers that shift passive users to become core users, or core to power.

5.6.5 | Take action: hypotheses & next steps

Ask yourself these questions as you form hypotheses and come up with experiment ideas.

  • What are the key action(s) that you identified as drivers of Habit Formation? What are some methods you can test to get more new users to cross those thresholds?

  • Who are your passive, core, and power users? How are they different? How can you get core users to become power users?

  • Did your behavioral persona analysis reveal any use cases you didn’t expect, or didn’t think were very important? How might you improve or tailor the experience for those users?

  • Are some of your personas more important for your main business objective, like revenue?

  • How can you get more users to convert into one of your core or power user personas? The biggest improvements can come from targeting users who are not well-retained, and getting them to perform actions of your power behavioral personas.

5.6.6 | Metrics for tracking improvement over time

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.

  • Get new users to form habits and become current users

  • Get current users to become core users, and core users to become power users

  • The size (in absolute numbers) and percentage of your total active users that is made up of your current users (as calculated via Lifecycle or manual analysis).

  • Retention over time of all current users and of each behavioral persona.

  • Size and percentage breakdown of your important behavioral personas. Are you getting more people into important personas?

  • Stickiness over time for critical events. This will show you any changes in how active current users are in the product.

  • Conversion rate over time through your critical path funnel.


Further Reading

The Amplitude Team

Alicia Shiu, Amplitude Blog

Kendrick Wang, Apptimize Blog