Mastering Customer Retention Strategy

Before analyzing metrics, you must first ensure you've instrumented your analytics correctly. It's tempting to rush through this part, but this would be a mistake. Sending optimal event data to your analytics platform is the most important step toward understanding how your users engage with your product. It's worth the upfront time investment to get your instrumentation correct.

Organize your event taxonomy

In event-based analytics, the term event describes any action performed by the user or any activity associated with the user. Opening an app, making a payment, and adding songs to a playlist—all of these are examples of events a user can perform.

In contrast, things like receiving or interacting with push notifications are examples of activities associated with the user.

It's critical that your event taxonomy reflects your business objectives. That’s why understanding your company type (e.g., the vertical you're in, your business model), what success criteria you care about, and the metrics that are important to you.

Quickly check your event taxonomy by asking yourself:

• Are the events you're tracking aligned with your analytics goals? Think about your analytics goals in terms of business objectives. How will you use analytics to measure the value you deliver to your users and vice versa? Are you able to track revenue, retention, and conversion? What experiments and funnels do you want to run in the future? Are you tracking the relevant events to run those experiments?
• Can everyone understand what each event is and why it's being tracked? Ensure you understand the context around all the events you’re currently tracking and when you expect them to fire. Having an organized event taxonomy document listing each event currently being tracked and its corresponding name and properties in a central location is critical to everyone being able to derive data insights. Check out Amplitude's Event Taxonomy training or our Taxonomy Playbook for more information.
• Are you tracking events aligned with your critical path funnel? You've probably envisioned an ideal path to conversion that your users flow through—one that perfectly syncs with the product's core value. Make sure you're tracking all events along this path. For example, if you're an ecommerce product, you should be tracking all events leading up to the user clicking 'checkout' and completing a transaction. We discuss how to determine your critical event in Chapter 2 and how to set up your critical path funnel in Chapter 4.
• How are you defining an active user? Do users simply need to open an app to be considered “active,” or are there specific actions they need to take?

Validate your data

To analyze your user behavior, you need to have a deep understanding of how their actions reflect in your analytics platform. The easiest way to check your instrumentation is to be your own user.

• Check your onboarding: Download your app and simulate your first-time user experience. Go through the onboarding process. Identify yourself as a new user in your analytics platform, which should be possible with a unique user ID. Then check to see that events are firing correctly and all behaviors are being captured properly as you complete each step of the onboarding process.
• Check your critical paths: Simulate an “ideal” user flow through your app, from start through conversion, and make sure those events are being captured correctly.
• Complete rigorous error testing: Bugs and crashes can be major retention detractors and should be resolved before making product optimizations (more on this in Chapter 6). Try “breaking” your app and forcing it to throw errors and track events that correspond to those errors and crashes.

Taking the time to do a comprehensive audit of your event taxonomy and data quality will ensure you have a solid foundation for more granular analyses. Doing this legwork upfront is critical to being able to trust your data.

This “toolkit” includes critical concepts and methods to help you understand user behavior and retention at all stages of the retention lifecycle. Reference this sheet as you work through Chapters 5-7 of this playbook to remind yourself of all the methods available.

Behavioral personas

A behavioral persona describes a distinct way of using your product. Identifying your product’s personas will inform your product development for different types of users.

• Qualitative: User interviews and testing can provide more context for trends observed in your product analytics data.
• Quantitative: Segment by different user & event properties. Bucket users based on the frequency at which they perform key events or use a clustering algorithm to group users based on similar behaviors automatically.

Compare baseline retention for each cohort and persona

Comparing the retention curves of different behavioral personas will help you decide which personas to focus on. For example, should you commit resources to convert more users to specific “power” personas?

Make sure you choose the best retention method for your product: Return On, Return On or After, or Return On (Custom) retention.

Return On or After retention

• Return On or After retention shows what percentage of users return on a specific day or later. You can also think of Return On or After retention as the opposite of your churn rate.
• Example: Day 7 retention = percentage of users who returned on Day 7, or any day after that.

Return On retention

• Return On retention tells you what percentage of users return on a specific day.
• Example: Day 7 retention = percentage of users who returned exactly on Day 7.

Return On (Custom) retention

• Return On (Custom) retention allows you to define custom time Return On (Custom), from a single day/week/month to multiple days/weeks/ months.
• Example: You could set your 1st Return On (Custom) as Day 0, your 2nd Return On (Custom) as Day 1-7, and your 3rd Return On (Custom) as Day 8-14. Amplitude will measure the percentage of users that return during each Return On (Custom).

Segment by user properties

Measure the breakdown of key user properties to identify trends and groups of users you should study more closely. Some common examples are country, language, platform, and paying vs. non-paying. Make sure you segment by the properties most relevant to your business.

Segment your baseline retention curve by different properties to identify factors that could positively or negatively impact retention.

Behavioral cohort analysis

A behavioral cohort is a group of users who did or didn’t perform specific actions within a defined period. Create cohorts for specific behaviors and then measure the retention of those users to see how well that behavior correlates with retention.

You can also apply behavioral cohorts to funnel conversion rates and other analyses in this toolkit.

Critical path funnel

A critical path funnel is the series of actions you anticipate users taking to complete your critical event. Comparing conversion rates for your behavioral personas and by different user properties will help you identify areas for improvement.

Common user flows

Funnels are great for measuring well-defined sequences, but user behavior isn’t usually so linear. Observe the most common paths to or from important actions, and compare the paths of your behavioral personas and cohorts.

Stickiness

Stickiness refers to the frequency at which people use your product. Specifically, stickiness measures the number of days out of a given period that a user was active or did a specific event, like your critical event.

Session metrics

Key session metrics you can measure are:

• Length distribution: the distribution of session lengths of all users, shown as a histogram.
• Average length: the average session length per user.
• Average per user: the average number of sessions per user per day.

Here are some definitions of terms used throughout this book. To learn more about these topics, check out our Help Center at amplitude.com.

Acquisition cohort: A group of users who started using your product during the same period.

Active user: A user who has done some action in your product during a given period.

Behavioral cohort: A group of users who did or didn’t perform certain actions within a defined period in your product.

Behavioral persona: A group of users who have a distinct way of using your product. Understanding the behavioral personas within your product will inform how you design the experience to meet the needs and habits of different types of users.

Churn rate: The percentage of users who used your product on Day 0 but did not return; the inverse of your Return On or After retention rate.

Cohort: A group of users who share some common characteristics. See acquisition date cohorts and behavioral cohorts.

Compass: A feature in amplitude that identifies the user behaviors that best predict retention.

Conversion window: The amount of time a user has to complete a funnel from the time they enter it.

Core user: People who are using your app at a regular frequency and in the “expected” way. This can describe one of your behavioral personas.

Critical event: An action users take within your product that aligns closely with your core value proposition. This is the action you want users to perform to be counted as active or retained.

Critical path funnel: The series of actions you anticipate users taking to complete your critical event.

Current user: Someone who has been using your product consistently for a defined period. In Amplitude, this is defined as a user who used the product during the last interval and the current interval.

Dormant user: Users who once actively used your product and then became inactive. In Amplitude, this is defined as a user who did not use the product in the current interval but was active in the previous interval. You can think of dormant users as people who you have the potential to resurrect.

Event: An action a user takes in your product. This could be anything from pushing a button, completing a level, or making a payment.

Event property: An attribute that provides more detail about that event. These are up to you to track and depend on the information necessary to understand a particular event. For example, if you had a 'Check-out' event, some event properties might include 'total amount,' 'number of items,' and 'payment method.'

Habit Formation phase: Follows the Onboarding and Value Discovery phases of new user retention. Once a user has discovered value in your product, you must make sure they develop a habit to keep returning over time. Users who successfully pass through the Habit Formation phase become current users of your product.

Journey: A feature in Amplitude that enables you to explore users' actions to or from any point in your product (i.e., path analysis). Journeys aggregates users' paths to see the percentage of users or sessions that followed each sequence.

Lifecycle: A feature in Amplitude that breaks out your active user base into new, current, resurrected, and dormant users during any time interval. Lifecycle helps you measure the health of your product and can identify imbalances, for example, if your churn is outpacing new user acquisition.

New user: Someone who is using your product for the first time. In Amplitude, this user is in their first interval of using the product.

Onboarding phase: This is the first phase of new user retention and is the first day of use for this playbook. During this phase, a new user of your product completes the onboarding experience and uses the product for the first time. You must get users to experience your product's core value quickly.

Passive user: People who might not be contributing or using your app in the core way you designed but are still returning at a regular frequency to do something. This can describe one of your behavioral personas.

Path analysis: Measures the most common sequences of events users take in your product.

Personas: In Amplitude, Personas automatically groups users into clusters based on similarities in behavior. This is one way to identify behavioral personas in your product.

Power-user: People who use your product with a very high frequency or use a “power” feature that most users don't take advantage of. This can describe one of your behavioral personas.

Product usage interval: How often (daily, weekly, monthly, etc.) users naturally use your product. When determining your product usage interval using the framework in Chapter 2, this is the time interval at which 80% of users complete the critical event a second time.

Pulse: A chart view in Ampliude's Lifecycle feature that depicts the incoming and outgoing user ratio for a particular period. This ratio is calculated as follows: (# of new users + # of resurrected users) / (# of dormant users).

Resurrected user: Someone who was once actively using your product, then became dormant for a period of time, and then became active again. In Amplitude, this is defined as a user who used the product sometime before the previous interval but not in the previous interval and is now active in the current interval.

Retention: Measures how many users return to your product over time after some initial event (usually first use). For different methods of retention, see Return On (Custom) retention, Return On retention, and Return On or After retention.

Retention curve: A line graph depicting user retention over time. It shows the percentage of users that returned to the product during a specified time period after acquisition.

Retention lifecycle: The flow of active users between the different stages of user retention: new, current, and resurrected user retention.

Return On (Custom) retention: A flexible version of Return On retention where you can look at the proportion of users who return during custom time frames you define.

Return On or After retention: Measures the proportion of users returning to your product on a specific day or later. For example, Day 30 Return On or After retention would give you the percentage of users who returned on Day 30 or any day after Day 30. This is the inverse of your churn rate.

Return On retention: Measures the proportion of active users in your product on a specific Nth day after an initial event.

Segment/Segmentation: A subset of users with a common characteristic, like a user property. Segmentation involves dividing a chart by this characteristic; for example, graphing a retention curve by country.

Stickiness: Measures the frequency with which people are using your product. Specifically, stickiness measures the number of days out of a week or month a user was active or did a specific event.

User Composition: A view in Amplitude that lets you quickly visualize the breakdown of different user properties for a specific group of users.

User property: Any characteristic tied to an individual user. User properties include country, device type, age, gender, referral source, plan type, number of photos uploaded, number of units of in-game currency, and current level in a game.

Value Discovery phase: Follows the Onboarding phase of new user retention and precedes the Habit Formation phase. During this time, it's essential to show your product's core value as often as possible.