Mastering Customer Retention Strategy

As covered in the introduction of this playbook, there are two main ways to improve your retention curve: shift the curve up or flatten it. Improving new user retention shifts the curve up by decreasing the initial drop-off during a user's early experience.

Why new user retention matters

New user retention is the most commonly and closely analyzed type of retention. And for a good reason: A user's early experience is critical.

Leveraging Amplitude user data for iOS and Android apps on 500 million mobile devices, we benchmarked new user retention using both Return On and bracketed retention and found:

• 14% of users return to an app on day 7 after installation
• 66% of new users don’t return in the first week after installation

What this indicates is that the new user experience presents a huge improvement opportunity for retention and growth. If you don't successfully onboard and show value to new users as quickly as possible, there's a very high chance your new user will never return.

Think of it like dating: On the first date, you just want to make a good impression and get to the second date—not get married. In the same way, focus your early user experience on getting users to return for the next session. Analyzing new user retention will help you understand your user onboarding experience and value discovery.

Understanding your new users provides insight:

• The behaviors and factors contributing to whether a new user retains or churns
• How to effectively onboard new users
• How to quickly show value to new users early on

New users diagnostic

First, take the new user cohort you created in , and plot your baseline retention for new users.

Investigate user properties and segment your retention curve

Once you create your new user cohort, look at user properties to get a high-level understanding of these users. Measuring the breakdown of key user properties can help you identify trends and groups of users to study more closely.

You should also segment your retention curve by significant user properties to identify any differences to investigate; for example, platform, location, attribution source, etc.

The pie chart shows the breakdown of where new users are coming from. When we segment the new user retention curve by country, we see that although the overall retention curve looks similar across countries, there are some significant differences. For example, by Day 7 users in India have 9.6% higher retention than those in Brazil.

If you see differences like this, it's worth digging into the user experiences in these countries to find contributing factors. Are there ways that this company could get its retention in Brazil and other countries to match India?

Find behavioral personas of new users

Now it's time to look for who are new to your product. Remember, behavioral personas describe distinct ways people use your product. For more background on behavioral personas and their importance, flip back to .

Studying your new user personas can help you understand:

• Why users try your product
• Where new users come from
• Patterns of early behavior that might positively or negatively impact retention later

By measuring user retention for each persona, you can compare the best-retained clusters to those with lower retention. For personas with high retention, you can hypothesize that their behaviors as new users correlate to their retention. Further testing can confirm whether that's true.

Real-life example: New user personas of a B2B SaaS company

Amplitude works with a B2B SaaS company that enables marketers and other professionals to make professional animated videos easily. When this customer looked at new user personas on their website, they first established a critical way to group new users: those who created an account vs. those who did not.

Users who created an account were likely to be video “producers,” whereas users without accounts were likely video “consumers” using the platform to view video content. This company decided to separate account creators from those who didn’t create an account before looking for distinct personas within each group. This is an excellent example of using qualitative product knowledge to group users before using quantitative methods.

Discovering new user Personas in Amplitude

To start, the team created two behavioral cohorts in Amplitude: “New User - Account” and “New User - No Account.” Then, they ran each cohort through the Personas tool to find clusters of users who behave similarly (see for a refresher on how Personas works).

For the “New User - Account” cohort, they identified that most users were taking actions related to producing videos — “Producers.” Using the Personas feature, the SaaS company identified two main groups within “Producers:”

• Producers - Heavy Asset Users: Launched the video maker and added many assets to videos.
• Producers - Low Asset Users: Launched the video maker but did not use many assets.

The chart shows that retention of “Heavy” users is significantly better than that of “Low” Asset Users.

These findings highlight that using assets in a video early on is essential to understanding the platform's value, which increases the chances of user retention.

For the “New User - No Account,” they also found some interesting personas.

• Consumers: Mainly watched videos created on the platform.
• Potential Leads: Visited the company's website and performed actions consistent with potential platform customers.

The differentiating action was visiting the pricing page—most users in other personas were not doing this.

Overall, this company found that the product experience and messaging should be very different for these personas, which they easily identified via Personas in Amplitude. The company used to optimize the web experience for the “Potential Leads” persona.

New user lifecycle phases

After users start using your product, they progress through three phases: Onboarding ➔ Value Discovery ➔ Habit Formation. Users who don't make the transition across these phases are dormant.

Onboarding - During onboarding, a user engages with your product for the first time and completes the onboarding experience. Users must experience your product's core value in this phase quickly.

Value Discovery - After onboarding, there’s a limited window to continue showing your core value to new users. During this phase, ensure users experience the core value as often as possible so they understand how your product improves their current way of doing things.

Habit Formation - Once a user has discovered product value, you must ensure they develop a habit so they keep returning over time.

In this chapter, we want to understand the transitions during the Onboarding and Value Discovery phases. What behaviors push someone into the next phase? In the next few sections will deep-dive into each of these phases.

Understand your onboarding funnel

First impressions matter

Poor onboarding leads to poor new user retention. This is why one of the most important parts of understanding new user retention is analyzing your onboarding funnel. Onboarding is the first phase of new user retention, so increasing the number of successfully onboarded users will shift your entire retention curve.

Define your onboarding funnel

Many apps have a well-defined sequence new users move through. If that's the case for your product, defining your onboarding funnel is quite easy: Simply track an event for each step to define your funnel.

If your first-time user experience is more open-ended and flexible, think about the key events a user needs to complete before getting value out of your product (see for help identifying those event sequences).

Measure the retention impact of your onboarding flow

If you have a defined onboarding flow or tutorial, you should also measure the impact your onboarding has on retention. Do users who complete the onboarding flow have higher retention than those who don't? How important is it to get users to complete the tutorial?

Real-life example: Onboarding funnel for B2B SaaS

A B2B SaaS company customer’s onboarding includes a tutorial to introduce new users to the platform. They created a funnel to capture the significant steps in their onboarding process, from sign-up to tutorial completion. At each step, they measure the percentage of users who continue from the previous step and those who don’t continue.

Start with the most significant drop-offs

Any time you're diagnosing a funnel, it helps to start with the most significant drop-off to see what you can improve. Here, you can see that the most significant drop-off is between the 'Begin Tutorial' and 'Completed Tutorial' steps. Only about one-third of the users who start the tutorial complete it.

In this case, the company started tracking specific steps within the tutorial to learn where users were losing interest. Then they used that data to redesign the tutorial to make it more engaging.

Real-life example: Retention impact of an onboarding tutorial

In another Amplitude customer example, a marketplace app enables users to buy and sell items. The first time a user opens the app, they enter a tutorial that points out key features, but new users can exit the flow as desired.

As the funnel chart shows, only 26% of users finish the onboarding tutorial.

This company created behavioral cohorts of new users who completed and didn’t complete the tutorial. Remember that in Amplitude, you can use the Microscope feature to create cohorts straight from any chart.

When they compared the retention of the two cohorts over the first 30 days, they found that users who completed onboarding had around 5% higher Return On or After retention than users who did not.

Five percent might not seem like a huge difference, but over time it adds up. Incremental improvements like this can result in more users retained in your product over time. We recommended this company investigate the following:

• What value is the onboarding flow adding for users who complete it? Is there a specific feature that they're using earlier?
• Is there a way to resurface that value later in the user experience for users who skip the onboarding flow?
• Why are 74% of new users abandoning the onboarding flow? Could it be improved to convey the essential information but have a higher completion rate?

What do "dropped-off" users do instead?

can be instrumental in identifying what users do once they drop out of a funnel. Do these users abandon the app altogether, or are they doing something else instead?

For example, look at the Journeys chart for the aforementioned marketplace app, where around 74% of new users abandon the onboarding tutorial. What are those users doing instead?

The chart shows that of users who abandon the onboarding flow, around 5% leave the app after abandoning onboarding. The majority of the time, their next action is to view product listings, followed by viewing product details, other product listings, or searching for items. Some users even begin listing an item to sell.

Based on this data, most users who abandon the onboarding flow end up using the product as expected. This may be because the marketplace app is intuitive enough for users to understand without a structured onboarding tutorial.

We recommend the marketplace company try the following to see how they impact retention:

• Streamline the onboarding flow so it has a higher completion rate.
• Try eliminating the onboarding flow. Drop users straight into the app and provide contextual tooltips to explain features and encourage users to act as they encounter them.

The Phases of New User Retention: Onboarding and Value Discovery

The prevailing wisdom around new user retention has been to benchmark Return On retention on some arbitrarily appointed days: D1, D7, D30, and D90. This approach is problematic because products have very different expected usage patterns. For instance, you expect someone to play a mobile game or use a music streaming service at different frequencies than an ecommerce site or on-demand food delivery app.

Defining the timeframes for the onboarding and value discovery phases

For any product, we're setting the Onboarding phase to Day 0, which is the first day someone opens your app. The length of the Value Discovery phase, however, is determined based on your product's usage interval, which you calculated in .

The table above outlines the phases for products of different usage intervals.

Once you have the timeframes for your product, create cohorts of new users who return during those time frames. You'll use these cohorts to understand the transitions between the phases.

Identify the drivers of successful onboarding

To identify behaviors that drive a new user to successfully onboard, follow these basic steps:

• Step 1: Create a base cohort of new users during your usage interval.
• Step 2: Create a retained cohort who were in the base cohort and were retained in the Value Discovery period.
• Step 3: Create a dormant cohort who were in the base cohort and were not retained in the Value Discovery.
• Step 4: Compare your retained and dormant cohorts to identify behaviors present in the former but not the latter.
• Step 5: Once you've formed a hypothesis of trigger actions, measure the difference in retention between users who do and don't complete that action.

Alternatively, you can enter your new user and value discovery cohorts into to get a list of potential drivers. Compass will identify the user actions most correlated to successful onboarding. In the next chapter, we’ll look at these same steps in more detail but applied to discovering behaviors that lead to habit formation.

Real-life example: Retention impact of an onboarding tutorial

Below is an example report from a meditation app.

Let's look at the “Meditation Session Completed” event. According to Compass, completing this event at least one time within the first day of use is moderately predictive of successful onboarding.

The “Compare Retention Rates” graph also shows that users who complete a meditation session on their first day have better retention than new users overall.

Based on this Compass report, Calm learned that getting a new user to perform one meditation session in their first 24 hours indicates they will successfully complete the Onboarding phase. Accordingly, Calm tailored a first-session experience to encourage users to complete a meditation.

Identify the drivers of successful value discovery

Now, we'll repeat the same process for the transition from the Value Discovery into the Habit Formation phase.

Real-life example: Value discovery drivers for meditation app

Let's continue with the meditation app example. In Compass, we'll now set the “Value Discovery” cohort as the base cohort and the “Habit Formation” cohort as the target predicted cohort.

The report shows that completing meditation sessions in the first seven days is still the most correlated action for retention from the Value Discovery to the Habit Formation phases. The best predictor of successful Value Discovery is completing at least three meditation sessions in the first week.

Based on these findings, the company tested ways to encourage users to complete two additional meditation sessions between days 1-7, for three meditation sessions in the first week.

Apply what you've learned: Getting more users through the onboarding and value discovery phases

Once you've discovered your drivers for Onboarding and Value Discovery, you’ll understand the milestones new users need to achieve to increase their likelihood of retention.

Sometimes you'll find that the drivers are similar for both the Onboarding and Value Discovery phases; in this case, it may make sense to consolidate your approach to focus on a single period.

This was the case for an Amplitude customer whose app helps users find and book nearby fitness classes. The company’s highest correlated event for both phases was booking a class, so we recommended they focus on getting users to book one class during the Value Discovery Phase instead of limiting the focus to only the Onboarding period.

To implement these insights and improve overall retention, think of ways to get more users to achieve these milestones during their early experience. By experimenting with different methods, you can find the most effective ways to get users through the phases of new user retention.

To get your ideas flowing, here are additional examples and recommendations to get more users to complete those actions:

Don't forget retention detractors

This playbook mainly focuses on finding positive correlations with retention so you can get more users to complete milestones that improve their retention. However, it's also essential to consider factors that might hurt retention. Fixing retention detractors can provide massive wins.

Improve product quality first

The overall quality and performance of your product can have a big impact on retention. Common culprits include bugs, crashes, and slow load times. If your app performance is unreliable, it doesn't matter how much value your product can theoretically provide. Users will quickly give up and abandon your app. If you have any performance issues, spend engineering resources to fix them before implementing your retention strategy.

Here are some ways to find detractors via your analytics data:

• Find bugs or crash events if you log them in your user analytics.
• Find events that have a low correlation with retention. In Amplitude, you can find these low correlation events in Compass.
• Segment your dormant users by different user properties to identify any trends. For example, you might notice that retention on Android devices has taken a sudden dip and pinpoint a bug in your latest release.
• Use Amplitude's Personas feature, or a similar clustering algorithm, to identify groups of users with low retention and see what events or properties they have in common.

Take Action

To recap, these are the key components of new user retention analysis:

• Identify important user properties and behavioral personas of your new users.
• Diagnose your onboarding funnel.
• Identify the actions that drive users to complete the Onboarding and Value Discovery phases of new user retention.
• Identify and mitigate retention detractors.

Here are some key questions to ask yourself:

• Who are your new users, and what are their behavioral personas or significant user properties? Is there a specific persona that you should focus on?
• Why do some of your behavioral personas retain better than others? Are there particular behaviors that seem to positively or negatively impact retention?
• How does the first-time onboarding experience affect later retention? Are there steps that can be improved? Would a tutorial or structured onboarding flow be beneficial for your product?
• What key actions did you identify as drivers for successfully passing the Onboarding and Value Discovery phases of new user retention? What methods can you test to get more new users to cross those milestones?
• What are your retention detractors? How can you improve them?
• What experiments can you run to determine whether a certain action or sequence of actions is critical to your new users' retention?

Track improvement over time

As you start testing some of your hypotheses and trying new ways to improve your new user retention, it's important to keep track of your metrics to see what is and isn't working.

Remember, the overarching goal of new user retention analysis is to lay the foundation for new users to become current users. Remember this as you form your metrics and KPIs for new user retention.

KPIs to help measure your progress in converting new users to current users:

• The percentage of new users who become current users.
• Retention over time of new users and important behavioral personas.
• Set up a Return On (Custom) retention curve that follows your New ➔ Onboarding ➔ Value Discovery ➔ Habit Formation phases. Depending on where the most significant improvement opportunity is, you might choose to focus on improving one part of this curve first.
• Conversion rate over time through your onboarding funnel.