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.
Product analysis toolkit
This chapter will introduce some key concepts and methods you'll use to analyze the behavior of each user group—new, current, and resurrected users. You should use this chapter as a reference as you work through Chapters 5-7.
An introduction to behavioral personas
Although demographic data can be informative, leveraging behavioral analytics to understand user behavior in your product is the best way to understand customer experience. The actions and behaviors of active users point to the value they derive from your product.
The concept of buyer and customer personas is common in marketing. A persona is a representation of a key target segment, and can include characteristics like demographics, habits, and goals. This information helps marketers develop the right messaging and marketing channels to reach buyers.
For this playbook, however, we will talk about behavioral personas. Each persona describes a distinct way of using your product. Much like in marketing, understanding these behavioral personas will inform your product design for different types of users. We'll look at behavioral personas for all three stages of the retention lifecycle: current, new, and resurrected.
It’s essential to remember that people can use and experience your product in various ways and may not all derive the same value. This brings us to the concept of behavioral personas, or user groups.
To illustrate, these are YouTube’s behavioral personas:
- Creators: The small percentage of people creating and posting videos.
- Viewers: The vast majority of YouTube's traffic watching videos.
- Viewers + Commenters: People who view videos and leave comments.
Each of these behavioral personas use YouTube in a distinct way and for a specific reason. Creators use YouTube as a platform to get their content on the web and establish an audience, while Viewers and Commenters use it for entertainment or to follow Creators they like.
Why you need to know your behavioral personas
Understanding the behavior of different user groups and how they derive value from your product helps you:
- Shape your product to provide the best possible user experience.
- Get a more nuanced understanding of your retention and identify areas for product improvement. Each persona may have drastically different retention rates, which you'll overlook if you only focus on retention for all current users lumped together.
If you're an early-stage startup and can only currently focus on one use case, your personas can help determine which user group to focus on based on impact. If you have a larger team and a more established product, you might identify a new use case and start making changes to improve that experience and broaden your user base.
Either way, defining behavioral personas will help you understand your users, their actions, and the opportunities to improve the overall user experience.
There’s a rule of thumb about online communities, called the “1% rule” or “1/9/90 rule”, that taps into this concept of behavioral personas. According to this rule, only 1% of users actively create content in any internet community or social network. Nine percent of users may engage with the content like commenting, sharing, upvoting, etc. And the remaining 90% only passively consume the content.
If a platform like YouTube only focused on improving the experience for the 1% of users who create content, and neglected the viewing experience, that could have a huge negative impact on their growth.
While these ratios may not hold true for all social networks, and certainly not for all product types, the general takeaway does. You should never assume that all users want the same benefit from your product. Make a conscious decision about what type of user you're optimizing for and their specific use cases.
Have you ever heard of Burbn? It was a location-based app, similar to Foursquare, with multiple features for checking in at locations, earning points, and posting pictures. Unfortunately, it wasn't seeing much growth.
When the founders studied their user behavior, they found that individuals weren’t using most features. Still, a small group of users consistently used one aspect of the app: posting and sharing photos.
Seeing that data, they scraped everything and focused solely on photo sharing. They made uploading photos fast and seamless, and in October 2010, they launched Instagram.
We know the rest of the story: Instagram quickly gained tens of millions of users, sold to Facebook for $1 billion in 2012, and continues to grow, now with over 2 billion users. Burbn could have been another failed startup. Instead, its founders identified a specific behavioral persona that was using its product and used it to shape it into one of the most popular apps today.
How to identify your behavioral personas
In this section, we'll discuss how to identify your behavioral personas, assess retention differences, and decide which personas to focus on.
Quantitative and qualitative approaches to finding personas
To determine if groups of people use your product in a specific way or for a certain use case, use both qualitative and quantitative approaches to derive the most complete answer.
Start by brainstorming some personas that you think or know to exist based on your current knowledge of users. Qualitative data from user interviews and testing can help in determining personas. Ask current users why they use your product and how it fits into their routine. You can also study user activity timelines to identify salient behavioral patterns. Often, qualitative data provides more context for trends observed in your user data.
It's important to supplement qualitative knowledge with quantitative methods, which are more scalable and provide a more accurate picture. Here are a few ways to discover behavioral personas through your product analytics data:
- Segment your user base by different user and event properties.
- Bucket users based on the frequency at which they perform certain key events.
Use a clustering algorithm, like the one that powers Amplitude's Personas feature, to group users based on similarities in behavior automatically.
Amplitude has a Personas feature that automatically clusters users based on actions they take and how frequently they take them. The Personas feature also enables you to compare the behavior of users in each cluster, and you can see tables of events that a given cluster performs more or less often than the other clusters. To investigate groups further, it also enables you to create cohorts directly from the personas. For more details on how to use Personas in Amplitude, we recommend checking out this help center video or support article.
Segmenting power, core, and passive users
For most products, you can consider bucketing users into power, passive, and core personas. This may not apply to every product, so determine whether it makes sense for you. You can also have multiple personas in each category. For example, two types of core users who have distinct use cases.
- Power users: Use product with very high frequency or use a "power" feature that most don't leverage.
- Core users: Use app at regular frequency and in the "expected" way.
- Passive users: May not be using app in the core manner for which it was designed, but are returning with regular frequency.
We've included real examples of behavioral personas in this chapter to better understand what power, core, and passive users look like.
Real-life example: How an on-demand delivery company determined behavioral personas
For an on-demand delivery company, personas could be based on factors like how often people place orders or other event properties. This company used its behavioral data to identify a few major personas by segmenting its user base by the event property amount spent per order.
They found the following personas:
- Individuals: Typical order size indicates they only order for themselves.
- Group orders: Typical order size is above a certain threshold, indicating they regularly order for a larger group, like a family, group of friends, or small company.
They also bucketed users based on the frequency of performing certain events. For this company, placing an order is a critical event.
This chart measures a metric called stickiness. In a 30-day period, it shows the percentage of users who placed an order on at least X-days out of 30. The chart shows that more than 75% of users place an order on two or more days out of 30, while only about 20% place an order on seven or more days out of 30.
Based on this data, the on-demand company determined these personas for order frequency:
- Occasional orderers: Placed an order one-to-three days per month.
- Frequent orderers: Placed orders more than four days per month. For these people, using the on-demand service is part of their routine rather than an occasional convenience.
Digging into retention: Your product analysis toolkit
Once you've created your lifecycle cohorts and identified your behavioral personas, you can better measure retention and retention drivers for each cohort. This section covers product analysis methods to help you answer questions like:
- Does a specific persona retain better than others or have a higher lifetime value (LTV)?
- What actions contribute to those metrics?
- How could you get more users into that persona?
You don't need to use all of these methods but can pick and choose what will be most enlightening for your product and users.
Measure and compare baseline metrics for each cohort and persona
First, plot your current user retention. Remember, you can use Day-N (Return On), unbounded (Return On or After), or Bracketed (Return On (Custom) retention.
Here are the recommended retention metrics based on your usage interval:
This chart shows the retention curve for current users of a music streaming app. The first and returning events are set to “Play Song,” this product's critical event.
Compare the retention curves of behavioral personas
Comparing the retention curves of different behavioral personas helps you decide which personas to focus on:
- If you should commit resources to convert more users to specific “power” personas
- If some of your core or power personas retain better or worse than others
- How much of a difference a persona has from your overall current user retention
By quantifying these retention differences, you'll better understand which personas to focus on as part of your retention strategy.
Retention curves make it easy to view these retention differences at a glance. Below are the daily retention curves for three different behavioral personas in Amplitude.
Persona 2 (green) has much better retention than the other two. To improve retention, you'd want to understand whether you can influence people in Persona 1 (blue) and 3 (orange) to behave more like Persona 2.
Investigate and segment retention by user properties
Looking at user properties, or attributes that provide additional context around your users, will give you a high-level understanding of your users.
User properties are characteristics describing who they are before they come to your platform, such as:
- Platform (iOS, Android, web)
- App version
- Referral source
- Number of friends
- Current level in a game
- Customer plan type
- Paying v. non-paying user
You can measure the breakdowns of fundamental user properties to help you identify trends and groups of users you should study more closely. Once you identify any user properties you want to study, segment your retention curve by these properties to help you identify any significant differences worth exploring.
Amplitude helps automate the process of defining user and event properties by tracking a default list and enabling customization. Learn more in our support documentation.
Real-life Example: How an on-demand delivery company segments users
An on-demand delivery company segmented its retention curve for current users by the Platform user property. Notice the significant differences in retention between platforms, with the lowest retention among iOS users. We recommend they look more closely at behavior across their three platforms and identify user experience improvements to elevate Android and iOS retention.
Use behavioral cohort analysis to measure the impact of different user actions
In user analytics, the broadest definition of a cohort is a group of users who share some common characteristic. There are two main types of cohorts:
- Acquisition cohorts group users by when they signed up for your product. You might break down your cohorts by the day, week, or month they signed up. By measuring the retention of these cohorts, you can see how long people continue to use your product from their initiation point.
- Behavioral cohorts group users by behaviors they perform in your product within a given timeframe. These could be any number of discrete actions that a user performs—sharing a photo, playing a song, buying gold coins, or any combination of these actions. A cohort will be a group of users who did those actions within a specified timeframe. You can then measure how long different cohorts stay active in your app after they perform those actions.
Forward-thinking companies today use behavioral cohorts to understand how different user actions or characteristics impact retention.
Although segmenting your retention curve by user properties can help you uncover valuable insights, it doesn't give you insight into how users' behavior within your product impacts retention. That's where behavioral cohorts come in. A behavioral cohort is a group of users who did or didn’t perform specific actions in your product within a defined period.
Behavioral cohorts are dimensions describing what a user does after they come to your platform, such as:
- Sign up
- Complete profile
- Consume content
- Create content
- Perform a search
- View support article
Behavioral cohorting is an analysis of user segments that are based on any combination of actions taken (or not) in the product. It can help you form and test hypotheses about necessary actions for retention.
For example, Facebook identified that users who added at least seven friends (a specific behavior) within their first ten days (defined time period) were more likely to be retained long-term.
We created a worksheet to help you frame these questions and guide your behavioral cohort definition.
Real-life example: Music App uses behavioral cohorting to improve retention
In Amplitude, it’s easy to create and apply behavioral cohorts across different charts to measure the impact of user actions on your metrics. This chart shows the retention curve for an Amplitude customer’s three cohorts of users:
- Started trial - first seven days
- Did not start trial - first seven days
- Played at least three songs - first seven days
As you can see, users who started a trial in their first week have significantly better retention than users who didn't, indicating that starting a trial can potentially contribute to better retention. Retention for users who play at least three songs is somewhere in between but nowhere near users who start a trial.
In this case of correlation but not causation, we recommend running a test in which you encourage more users to start a trial early in the user experience, such as in the onboarding flow or with an email reminder. Then, you can evaluate whether influencing more users to start a trial causes higher overall retention.
Conversion rate through your critical path funnel
A critical path funnel is the series of actions you anticipate users to take to complete your critical event. A funnel chart will enable you to visualize the drop-off along each step. You can also compare conversion rates for different user groups. Not every product will have a clearly defined desired path, so this analysis may not be necessary for every business.
Real-life example: On-demand delivery company critical path funnel
An on-demand delivery company we work with identified its critical funnel as:
App launched → Select vendor → Add item → Complete order
Here's the funnel for the company's current users—each step in the funnel shows the number of users who moved on from the previous step. Of everyone who entered the funnel by opening the app, 66% completed all the steps and executed the critical event, which was completing an order.
When we segmented the funnel by platform (iOS, Android, or web), we found that users on iOS had a higher conversion rate through the critical funnel than web and Android.
The most significant drop-off happens between the App launched → Select vendor step. We recommend our customers first focus on this step to improve the conversion rates for web and Android.
Find your most common user flows
Funnels are great for measuring well-defined sequences, but what if you'd like a broader picture of new user behavior? It's impossible to anticipate every single route a user might take in your product, and people often defy our expectations of 'normal user behavior.'
In these instances, user activity streams that show sequences of events can be beneficial. Depending on your analytics setup, you can get raw logs of all user events and study them to look for patterns. Some analytics platforms, including Amplitude, can give you out-of-the-box access to these user timelines. In addition, you can perform user testing to observe how people navigate through your product.
Creating your path analysis visualization can be very time-consuming. In Amplitude, the Journeys feature automates this process enabling you to explore user actions to or from any point in your product.
Important insights you can glean from path analysis include:
- Comparing the paths of retained and dormant users.
- Seeing what users who drop out of a funnel are doing instead.
- Identifying the main paths to an important event in your product, like creating a new account or making a purchase.
For example, you can use path analysis to discover the paths users take to your critical event.
In the image above, you see users' actions before purchasing a subscription in a music streaming app.
Stickiness provides another dimension of understanding user engagement. While retention measures the rate users return over time, stickiness looks at usage frequency—how often people use your product within a specific period.
To compare stickiness metrics, measure for:
- General usage: How many days out of a week or month did users open the app and do something?
- Your critical event: How many days out of a week or month did users perform the critical event?
Stickiness: The frequency at which people use our product. Specifically, stickiness measures the number of days out of a given time period that a user was active, or did a specific event (like your critical event).
In Amplitude, we have two options for measuring stickiness:
- Weekly Stickiness: The percentage of users who were active or performed a specific event at least N days out of a week.
- Monthly Stickiness: The percentage of users who were active or performed a specific event at least N days out of a month.
If your product has a daily or weekly usage interval, you should compare the weekly stickiness metrics of your persona cohorts. You should compare the monthly stickiness metrics if your product has a biweekly or monthly usage interval.
Real-life example: Social mobile game measures stickiness
Stickiness can help you identify your most engaged users. This chart shows stickiness for one of Amplitude’s customer’s social mobile game product. This graph measures stickiness for general usage, counting each day a user opens the app:
As you can see, Persona 4 is the most sticky: Almost 75% of the users in that cohort open the app 7 out of 7 days a week.
With results like this, we’d recommend focusing on Persona 4 to learn more about what makes them so engaged and then encourage those behaviors in other personas to get more people to match Persona 4.
Yet another way to supplement your understanding of user engagement is with session metrics. Loosely defined, a session is the period during which a person is actively using your product.
There’s one caveat: The length of time someone spends in your product may be a poor indicator of engagement for your business.
Accordingly, it’s best to only look at session metrics if it makes sense for your app. For example, an on-demand delivery service or an exercise class booking app don’t necessarily care how much time a user spends in the product. They care more about having a fast and seamless ordering or booking process. For social games or music streaming services, however, the amount of time spent in the app directly correlates with user engagement.
Amplitude has default ways of defining sessions, but you're free to modify this definition as it makes sense for your product. For example, a music streaming service would want a session to last as long as someone played music, even if the app is in the background.
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 daily.
Below, you can see a graph of session length distribution. The chart shows that the most significant number of sessions are 10 to 30 minutes long.
Real-life example: Lifestyle app behavioral persona's session length
In this average session length chart, we see that Power Users (blue) spend much longer in the app, with an average session length of around 30 minutes, compared to roughly 16 minutes for Passive Users (green) and Total Current Users (orange).
With an understanding of how to find your behavioral personas and these product analytics methods, it's finally time to roll up your sleeves and get to work. You'll apply these methods in the following chapters as we begin to understand how to improve retention throughout the user lifecycle.