Mastering Customer Retention Strategy
You've invested time and money into acquiring new users—but are you keeping them? With a deliberate focus on customer retention, you can improve retention at all stages of the user lifecycle to reduce churn and boost lifetime value.
The Retention Lifecycle Framework
Now that you've investigated the state of your analytics, your current metrics, and your product's usage, it's time to explore the Retention Lifecycle Framework, our in-depth approach to improving retention based on how users interact with your product.
Coming up with a retention framework
We developed our retention framework by working with eight customers at different stages of growth and across different verticals. By diving into their user behavior analytics, we validated the principles and methods of our framework and found real insights and recommendations for improving their retention.
Across the following chapters, you’ll see how different companies—from utility apps to ecommerce products and mobile games—have used the Retention Lifecycle Framework to understand their users better, implement strategies to improve long-term retention and accelerate their growth.
Ways to measure user retention
There are different ways to calculate retention, so it's essential to understand how your company or analytics platform defines it and whether it suits your product. If you're unsure how your analytics platform defines retention, get clarity before proceeding further.
The retention calculation you should use depends on your business goals and how users naturally use your product. Your critical event and usage interval should give you a good understanding of this.
Before we help you evaluate which type of retention is right for you, let’s define a couple of helpful terms.
N-Day and Day 0 retention
When most people discuss retention metrics, they’re talking about N-Day retention.
“Day 0” is the first day a user is active in your product. It could be the day they first download, register, or perform any action within your app—play their first song, add their first friend, etc.
For all the users who first become active on a particular Day 0, you can calculate their Day-N retention over the following days, looking at what proportion of users were active on Day 1, Day 2, Day 7, and so on.
- Day-1 retention looks at how many users returned exactly one day after they were first active;
- Day-3 retention looks at how many users returned on exactly the third day after they were first active, Day-7 on the seventh day, and so on.
If you were looking at Day-7 retention, it doesn't matter how many times users came back between Day 1 and Day 6 or if they returned on Day 8. If they aren't active on Day 7, they are not considered Day-7 retained.
What do we mean by an “active” user?
In Chapter 2, we introduced the concept of a critical event—an action that you want a user to perform in order to be counted as truly active or retained. We recommend measuring retention based on whether users return to your app and perform your critical event, not just whether they open it. This will give you a more accurate view of how many users are getting value out of your product.
Now, let's look at each type of retention to determine which is best for your product:
- N-Day Retention - Amplitude calls this “Return On”
- Unbounded Retention - Amplitude calls this “Return On or After”
- Bracketed Retention - Amplitude calls this “Return On (Custom)”
- Measures the percentage of users who are regularly active in your product on a specific day, week, or month after first use.
- The most common and typical type of retention calculation.
- Well-suited for any product in which your goal is to get users to exhibit regular, repeat behavior, such as gaming, social apps or weekly exercise app.
- Day 7 retention = percentage of users who came back exactly on Day 7.
The Return On retention curve above shows the weighted averages of all the Return On retention numbers in a Day 0 to Day 30 time frame.
What if you expect users to use your product regularly, every week, or every month instead of daily? In this case, 'N-Week retention' or 'N-Month retention' would be more appropriate. Conceptually these are the same as Return On retention.
Third-week retention, for example, reflects the proportion of active users during the third week after they were first active (Week 0). Similarly, third-month retention reflects the proportion of users who are active any time during the third month after they were first active (Month 0).
One of the App Store’s most popular mobile social gaming apps measures Return On retention because their most engaged users return daily to challenge other players worldwide. In fact, for this app, completing a certain number of games within the first day is a key promoter of long-term retention.
Another Amplitude customer, the maker of the mindfulness app Calm, measures Return On weekly retention. Engaged users of this app come back on a weekly cadence to complete a meditation session, so their retention metric measures how many users continue to stay active week after week from when they first downloaded the app.
Return On or After
- The percentage of users that come back on a specific day or any time after.
- You can think of Return On or After retention as the opposite of churn rate. By measuring the inverse of your Return On or After retention, you can see how many users engaged with your product on Day 0 and never returned.
- Well suited for products where a user doesn’t have a consistent usage pattern—for example, a food delivery app where people place orders sporadically. Unbounded retention may provide a more accurate measure of business health.
- Day 7 retention = percentage of users who came back on Day 7 or any subsequent day.
This Return On or After retention curve depicts the same data points as the Return On retention graph above, but in the context of Return On or After numbers. Notice our Return On or After retention rate is 73.5% compared to only 55% for Return On. This indicates 73.5% of new users on Day 0 were active at any time on or after Day 1, as opposed to Return On, which covers only those users who engaged Day 1.
A grocery delivery service does not expect people to use their product daily; they might not even use their service with a predictable cadence. Instead of looking at whether someone comes back exactly on Day 7 or Day 30, which is what N-Day retention would indicate, this company would get more value out of looking at their unbounded Day 7 retention—that is, how many new users return to buy groceries after their first week.
Return On (Custom)
- A more nuanced version of Return On retention that enables you to split your retention analysis into custom periods that a user is active instead of limiting it to daily, weekly, or monthly periods. With this retention type, you define time brackets where any time a user is active they are considered retained. These can be from a single day, week, month or to multiple days, weeks, or months based on your user’s expected usage patterns.
- Well suited for products where usage isn’t clean cut to a week, month or day—for example, a product that people use every three weeks.
- You could set your 1st bracket as Day 0, your second bracket as Day 1-7, and your third bracket as day 8-14, and measure the percentage of users that return during each custom period.
Each multiple-day Return On (Custom) is like a bucket; if a user is active anytime inside that bucket, you consider them retained. You space each bucket in time based on the pattern you want to see your users exhibit.
For example, let’s say you sell a product that people use every three weeks to stock up on home goods. You might create buckets that span three weeks, plus or minus a week. In this example, you don’t care whether people return on Day 1 or 3 or whether their second order is after two weeks or a month—you just want to see a rhythm that proves they’re getting value.
In this scenario, let’s define an active user as someone who returns to the app and performs any activity. Then, you could consider a user fully retained by:
- Registering for the app on Day 0
- Returning on Day 1, Day 2, or Day 3
- Returning on Day 4, Day 5, or Day 6
- Returning on Day 7, Day 8, Day 9, Day 10, or Day 11
Pinterest uses one type of retention metric, which they call “1d7,” to measure how many of their new users come back and discover value in their product. This metric looks at the proportion of new users who visited Pinterest at any time between Day 1 and Day 7. The growth team also looks at retention of these users in the Day 28-to-Day 35 bracket to understand what percentage of new users are still active one month after signup.
Which retention method suits you?
To summarize the key differences between Return On, Return On or After, and Return On (Custom) retention:
- Return On retention: The percentage of users that return on a specific day.
- Return On or After retention: The percentage of users who return on a specific day or after.
- Return On (Custom) retention: A flexible version of Return On retention using custom timeframes.
Your retention method heavily depends on how frequently you expect people to use your product. You can start by measuring retention using different methods to see which gives you the most meaningful improvement and accurate view of your business health.
The Retention Lifecycle Framework
By now, you're likely used to seeing a typical retention curve like the one below. The fundamental flaw of this retention approach is that it lumps many different types of active users into a single curve. But not all active users are created equal. To make meaningful, long-term improvements to retention, you need to understand your active users as they flow through different retention stages.
The Retention Lifecycle Framework can help you do so.
What is the Retention Lifecycle Framework?
Our analysis and approach to improving retention should vary depending on a user’s stage in their product journey.
Users undergo four different retention stages:
- New user retention: Active users using your product for the first time
- Current user retention: Active users who have been engaging with your product consistently for a period of time
- Resurrected user retention: Active users who were once actively using your product and then become inactive
- Dormant users: Were once actively using your product and then became inactive
The first three groups encompass your total active users at any given time. If you're a daily usage product, this means that on any given day, an active user will be in the new, current, or resurrected user stage.
The image depicts the User Lifecycle flow of users between these different stages:
To achieve retention that rivals the likes of today’s powerhouse products, you have to engage differently with new and current users, implement strategies to resurrect inactive users, and boost overall engagement across users.
Chapters 5-7 will cover each user stage in more detail, but here's a quick high-level overview:
New User Retention
Most content about user retention focuses on retaining new users—with ideas to revamp your onboarding flow or send new user drip campaigns. This makes sense, given so many users churn within the first seven days.
- Why it matters: Your new user experience is your product’s first impression.
- How to improve: Determine which behaviors or features return new users.
Current User Retention
Don’t take your current users for granted. Every current user has the opportunity to become a highly-engaged power user. Your goal for current users is continuously providing value and ensuring they keep returning.
- Why it matters: Understanding and improving your active user experience is critical for long-term growth.
- How to improve: Identify key behaviors of certain groups of current users.
In the next chapter, we’ll cover how to group your users into different behavioral personas, which can help you better understand and capitalize on the value current users derive from your product.
Resurrected User Retention
Resurrected users are typically your biggest potential user pool. Many of these users probably use a competitor’s product, so they’re also high-value. Numerous studies show it's cheaper to resurrect a dormant user than to acquire a new one.
- Why it matters: Untapped potential for more active users.
- How to improve: Analyze why users are returning.
When more dormant users return to your product, figuring out why is crucial. Did they respond to a particular win-back campaign or push notification? Have they returned to current user status, or did they drop off again?
Reminder: Be careful not to over-optimize for just your power users. Understanding power usage is important, but you can’t convert everyone into a power user overnight. As you iterate on your product, we recommend you optimize to retain the most number of users, not just the best users.
Why do you need the Retention Lifecycle Framework?
Too many products artificially increase their active user count through new user acquisition. And although the top of the funnel is important, growing your current and resurrected user base drives actual growth.
Here is a bar graph showing the total number of active users for a particular product over 12 weeks. The product grew from having 6 million active users to just over 8 million active users by week 12—things seem to be going great, right?
However, it's a different story if you break down these users into new, current, and resurrected users.
New users (teal) are increasing over time, but current users (blue) are decreasing. If you take this one step further and graph the number of users who become dormant every week (red), you'll see that this population is getting larger and larger over time.
The bottom line? Although you may be gaining more new users, you're not experiencing real growth if many users are churning and you don't have a sustainable, growing base of current users.
Creating your lifecycle cohorts
In product analytics, the broadest definition of a cohort is a group of users with common characteristics. You must first create these cohorts to analyze your new, current, and resurrected users in the following chapters.
Depending on your analytics software, you can define these cohorts within the platform or in your raw data.
As we alluded to in Chapter 2, your product's usage interval is vital to accurately measuring retention across all Retention Lifecycle Framework stages. This interval determines how you define your timeframes for new, current, and resurrected users, as well as your dormant users:
- New user: In their first interval of using the product.
- Current user: Used the product in previous and current intervals.
- Resurrected user: Active in the current interval but inactive in the previous interval. Also, they were active before the previous interval (i.e., they are not new). Note that a user can only be “resurrected” once they've become dormant, which is why this definition requires one interval of inactivity.
- Dormant user: A user who did not use the product in the current interval but was active in the previous interval.
In Amplitude, you can use the Lifecycle feature to see your retention lifecycle breakdown and easily create cohorts. Lifecycle breaks out your active users into new, resurrected, and current buckets and shows your churn in each period. In this 5-minute Amplitude Academy video, you can learn how to track the growth of your user base with Lifecycle.
Using lifecycle cohorts to get a pulse on growth
You can calculate a pulse ratio to understand how many active users you gain for each user that churns after splitting your user base into the lifecycle cohorts.
The Pulse ratio is calculated as
(# of new users + # of resurrected users) / (# of dormant users)
Note that since newly acquired users and resurrected users increase the pool of active users, they contribute to your total user influx.
At a high level:
- Pulse ratio > 1 indicates you're gaining more users than you’re losing. Your product is experiencing real growth.
- Pulse ratio < 1 indicates you’re losing more users than you’re gaining. Your product is not experiencing real growth.
In Amplitude, Pulse is a chart view in Lifecycle that lets you get a quick pulse on your product growth, automating the calculation above. It depicts the ratio of incoming users to outgoing users for a particular day, week, or month and enables you to see how many active users you gain for each user that churns.
Whichever retention analysis you choose, the framework remains the same:
- Understand how users retain at each stage of their lifecycle.
- Implement strategies to convert active users into highly engaged current users.
The following chapters will explain the deeper nuances of new, current, and resurrected user retention. To prepare yourself, make sure you:
- Determine which type of retention analysis makes sense for you (Return On, Return On or After, or Return On (Custom)
- Complete the worksheet “Your Retention Lifecycle” to define your lifecycle cohorts, measure your retention lifecycle split, and calculate your Pulse ratio.