The Product Manager’s Guide to Mobile User Behavior

Building a sustained active user base helps a product stay alive and thrive.

November 8, 2016
Image of Archana Madhavan
Archana Madhavan
Instructional Designer
The Product Manager’s Guide to Mobile User Behavior

You’re seeing those download counts for your app start to rise. More and more people are signing up, logging in, and taking first steps. Your app looks like it’s on its way to success.

But it may not be time to celebrate just yet. While downloads and first-time visits are exciting, they only tell a fraction of your app’s story; it’s important for any mobile product manager to measure user behavior and how many users actually find long-term value in your app.

By analyzing long-term metrics, you’ll get a better idea of exactly how valuable users consider your app for continued use weeks and months after launch. The most important of these metrics are:

  1. Retention rate: The rate at which users continue to use your app over time.
  2. Engagement: How many times a user performs a specific action.
  3. Usage interval: The frequency with which you expect people to naturally use your app.

Here we’ll show why measuring this mobile user data helps you track your own progress and see how you stack up against other successful apps. You’ll learn how to plan the long-term trajectory of your app’s development, and ensure that it’s a long trajectory for users too.

1. Understand how to measure your retention

Many products measure the long-term success of their product with retention, the change in active users over time.

The tech industry measures retention in a number of different ways, each of which makes sense in different contexts.

As we’ve mentioned before, N day retention measures the proportion of active users that are active on exactly the Nth day after performing some start event; typically this “Day 0” is the first day the user uses the product.

Analogous to N day retention, weekly or monthly retention looks at the proportion of users that are active any time during the Nth week or Nth month after first use.

We’ve also talked about unbounded retention and bracket retention, two other ways to calculate retention.

Different ways of measuring retention rate make sense in different contexts.

Pokémon GO, for example, found that trying to measure retention by looking at their DAU/MAU ratio for the first month after the app’s launch was misleading. The hype surrounding the game’s launch drove up the initial downloads. This led to a very high MAU. However, the DAU on subsequent days as hype leveled off was low. This made the DAU/MAU ratio on any given day much lower than for other apps.

Measuring retention on a week-by-week basis was a better indicator for them for determining how many players were actually coming back week after week.

Take action on your retention

A successful growth team will investigate retention of their users as they move through different phases of being an active user. Among these phases, retention of your new users is incredibly important because it’s your app’s first impression.

Most users decide whether they want to keep using an app within the first 3-7 days after download. This means that measuring 7-day retention after the date of downloading will help you determine if your app is succeeding at getting users to return within that critical window.

N-day retention of iOS vs Android

The benchmark for the upper limit of Day 7 retention is 22% for both Android and iOS apps. Aim for this retention rate seven days after download to shift this curve up and have more sustained active users at 30 days, 60 days, and later after downloading.

Take action and get more users to return within this critical window by making sure the user experience in the first few days is the best it can be:

  • Reward small actions with an encouraging message or a virtual prize—even small intangible rewards can incentivize people to return and get that warm fuzzy feeling of accomplishment again.
  • Encourage friend referrals to help users build a social group that uses the app and have the benefit of having friends who use the game. After all, the future of apps is moving to the hive and social apps compel users to return if they provide meaningful ways to interact with other users.
  • Make features crystal clear and easy to use. If you bombard users with information about features that they don’t yet need or understand, you’ll risk losing customers to confusion, frustration, or disinterest.

2. Measure engagement to find your “aha moment”

Engagement measures how many times a user performs a specific action in your app. These events may include opening the app, using a certain feature, reaching a new level, or making in-app purchases.

When a company finds the event that best engages users, they say that a user taking that action is having an “aha moment.” They are realizing the value of the product so that they will want to return and keep using it.

Here’s the rub: when users perform an “aha moment” action they are very likely to stay engaged, and when users don’t perform the action they are very unlikely to stay engaged.

The events that cause “aha moments” are often startlingly simple. Twitter found that users who followed at least 30 people were likely to continue to be active, while users who didn’t follow 30 people were unlikely to remain active.

Take action on your engagement

Your aim is to maximize engagement by finding the event that is most successful at getting users to return.

You can measure engagement for any event by measuring the percentage of people who perform an action out of the population of people that use the app. You can then section off this population as a behavioral cohort.

Creating behavioral cohorts allows you to group users based on actions they perform and learn more about how those particular users interact with the app. You’ll gain insight on the engagement impact of different actions by measuring which event is most closely correlated with returning users.

This success of an action in getting a user to return is called the positive predictive value. The likelihood that a user who misses out on this action will not return is called the negative predictive value.

Here’s how to calculate the positive predictive value: divide the number of people who returned after performing a certain action by the total number of all returning and non-returning people who performed that action.


Then calculate the negative predictive value for that action—divide the number of people who didn’t return and didn’t perform that action by the total number of returning and non-returning people who didn’t perform that action.


The positive predictive value for a Facebook user to add 7 friends in 10 days (their “aha moment”) was **99% **and the negative predictive value for this same event was 95%. Take action to maximize engagement with your most engaging event:

  • Create behavioral cohorts by grouping together users who perform the same actions.
  • Measure positive and negative predictive values for user actions, which describe the correlation between that action and the return of those customers.
  • Find the event with the highest positive and negative predictive values—whether it’s Facebook’s value exactly or somewhere close.
  • Update your app so that this action is more accessible and noticeable to users. This could mean highlighting the feature in an introductory tutorial or placing a button for the action on the landing screen.

3. Be aware of your product usage interval

Some apps are meant to be used daily, while others might serve purposes that only make sense every other day or weekly. The product usage interval for your app is the frequency with which you expect people to naturally use your product.

Understanding your app’s usage interval will allow you to better understand metrics like retention rate and engagement. If your product usage interval isn’t daily but is instead weekly, measuring N-day retention on an arbitrary day might not capture an accurate sample of your returning players. In that case, something like this graph would look very disheartening.


Meditation app Calm, for example, realized that their optimal product usage interval was about once a week .

Take action on your product usage interval

Your product usage interval is a product metric that characterizes your product, and it should be one that you monitor and pay attention to, though you may not necessarily set any goals against it.

While you may have an intuition already about how often users come back to use your app, you can systematically determine your product usage interval by:

  1. Determining your critical event.
  2. Setting a conversion window, or an amount of time in which users are likely to complete two critical events, for example, 90 days.
  3. Creating a cohort of users who complete this funnel — who complete both events within the conversion window.
  4. Within this cohort, measure the percentages of users who completed the critical events within various shorter conversion windows, for example 60 days, 30 days, 15 days, 3 days.

We usually define the product usage interval as the time it takes 80% of users to complete the two critical events.

Take action to get your users to repeat the critical event within your product’s usage interval. Make that second critical event easier for users to complete:

  • Change the copy within your app to emphasize frequent use. Condition users into thinking of using the app as a habit with words like “daily” or “when you return.”
  • Provide incentives for users to complete your second critical event. These incentives could be virtual currency in a gaming app or the ability to unlock features in a utility app.
  • Give users the option to set a reminder to perform an action within the app, like Calm did with its daily mindfulness reminder.

Short-sighted success is self-sabotage

Focusing on short-term success is a great way to fuel your ego. But building a sustained active user base helps a product stay alive and thrive.

Strive for these benchmarks when you’re aiming for long-term success:

  • Retain over 22% of users on Day 7 after download.
  • Find your most engaging event that has the highest positive predictive value and negative predictive value of all the events you test — benchmarks for prime stickiness are to see 99% of users who complete the action return and 95% of users who don’t complete the action stop returning.
  • Determine the product usage interval by figuring out how long it takes for 80% of your users to complete two critical events.

When you see users retaining, engaging with your app, and completing important actions sooner — then you can think about the champagne.

Check our Ultimate Guide to Mobile Analytics for even more information with over 50 more resources for app makers.

About the Author
Image of Archana Madhavan
Archana Madhavan
Instructional Designer
Archana is an Instructional Designer on the Customer Education team at Amplitude. She develops educational content and courses to help Amplitude users better analyze their customer data to build better products.