We get it—retaining your customers is hard work. When it comes to mobile, health and fitness apps boast the highest retention rates of any category. That follows years-long trends toward healthy living, fitness, and overall wellness that yielded an explosion (upwards of 300% growth) in the sheer number of users and apps at play in the industry.
However, a recent uptick in demand thanks to the effects of the COVID-19 pandemic notwithstanding, user growth in the segment has slowed in recent years. Closures of physical locations necessitated by the resulting lockdowns have also caused a large number of health and fitness brands to launch or double-down on mobile apps to retain their customer base.
That leaves a large and ever-growing number of health, wellness, and fitness apps competing fiercely for a relatively stagnant number of customers—meaning product teams have to work hard to improve onboarding and engagement in order to retain customers.
However, improving user retention is an uphill task because retaining app users and a low churn rate is rarely a singular team’s effort. Marketing, customer success, and product teams need to work together to provide value for customers to keep them returning to your app. Consistent effort in retaining customers also keeps customer acquisition costs low and increases lifetime value.
Enter Cohort Analysis
Understanding the usage patterns of your existing customer base is critical. Analyzing your customer’s journey can unlock key areas of friction and provide opportunities and benchmarks to improve your application or product.
Product teams split customer segments either based on their onboarding or actions, then break them into groups or ‘cohorts’ for a given time period. Splitting these cohorts by period and observing changes in behavior over similar periods in the customer lifecycle is a great way to analyze which events or triggers customers respond to and improve your product accordingly.
This process is known as cohort analysis.
Cohort Analysis: Asking the Right Questions
Cohort analysis helps product teams break up customer segments by initial onboarding or by user flow. This helps product teams understand how customers adopt and use the app to achieve their own goals, which led them to sign up.
Product design and engineering teams may have a distinct workflow in mind while creating the product. Cohort analysis serves as a reality check, enabling them to understand how customers go through this workflow and what stops them from making their decisions over a given time period.
Let’s walk through a simple example. Say you are building a fitness or wellness app. You ask yourself the question:
Does Saving Credit Cards on File Lead to More Upgrades?
We could evaluate the cohorts of app users who were onboarded in the past three months. We then split these cohorts by users having a saved card on file versus those who did not.
Once we split these cohorts, we can analyze their subsequent purchase and subscription behavior. If saving cards on file leads to longer user retention, then it might be worth nudging customers to auto-save cards in the signup process.
On the other hand, if users who delay saving their card details spend more time in the app during the trial and justify a higher tier membership, then it might be worthwhile to postpone this action, so customers are more comfortable committing to a larger monthly subscription.
This split analysis could help you check if customers in these cohorts end up spending more on higher-priced subscription plans.
One thing to note: We are simplifying all the other variables at play during a mobile app transaction. We do not know in this scenario if the customer came to the app through a discounted promotion or if their spending patterns in the industry vary seasonally. Comparing these two cohorts should help you understand how you can improve your product to influence buying behavior in a positive way.
Cohort Analysis in Action
In the above example, we talked about how analyzing two customer cohorts over similar time periods can help us understand behavioral patterns. Let us now look at how these cohorts are broken down and visualized.
For example, we want to know the answer to the following question:
How Soon Do New Users Convert to Paid Subscriptions?
We first find cohorts of customers who created a profile in the last three months and their date of conversion to paid. This investigation path could help us understand if nudging users to create profiles could make them subscribe faster.
So we start defining our cohort in question by choosing a group of users based on their first step in the workflow—in this case, ‘Create Profile.’
Then we select this cohort’s target behavior, which is the end state of their actions in this workflow—in this case, ‘Subscribe.’
Note: The above visual uses sample data, but this latter variable can be any event that makes sense for your app—for example, upgrading from a free plan or trial to a paid plan.
Like in the previous example of saved credit cards, there might be several variables in the buying decision. You can define all those behavioral nuances in this cohort to answer your question.
This visualization shows that the first week is crucial to enable your new customers to convert to a paid plan. Forming a feedback loop right after users upgrade to a paid plan, for example, could lead to longer retention, a lower churn rate and further upgrades. You can further refine your process by carving out smaller segments of customers in a particular week, and find out how they arrive in the app.
- Did they click on a social media ad?
- Did they respond to your welcome email sequence?
- Did a particular notification on their mobile app appeal to them?
- Did a particular app update lead to a buggy user experience?
The nuances mentioned above can help us understand the paths your users take to become paid subscribers. This will help your marketing team use these benchmarks to improve their campaigns and expedite buying decisions.
How to Use Cohort Analysis for Retention
Cohort analysis helps your product teams ask specific questions to understand customer behavior in buying decisions. The quality of your investigation depends on the depth of the questions you ask your data.
Let’s look at the primary scenarios for how you could improve your product by using cohort analysis.
Cost of New Acquisition Channels
If your team is exploring new acquisition channels, then cohorts can help your teams optimize acquisition costs and focus on profitable channels.
For example, the cohort below shows us purchases made by users after subscribing to the product. It helps us understand direct channels like push notifications might be the biggest driver of the buying decision for your customers in the first week than other channels like email or referral marketing. So a concentrated effort in refining your notification approach could nudge users to engage with your app more frequently and lead to additional purchases or upgrades.
Product Update Performance
If your team launches frequent app updates, launch cohorts can help you probe your churn rate and engagement. There could be app sections where your customers experience poor performance, which your team can fix before it is too late.
Launch cohorts can help examine customer behavior before and after the update to drill down into massive pattern changes.
Their data suggests that “challenges” lead users to 22% higher step counts. Using cohort analysis, Samsung will be able to gauge whether the new update and usage of Group Challenges have a similar positive effect on app engagement, usage, and subscriptions.
Increase Customer Lifetime Value
Analyzing cohorts of your most engaged customers will help you understand their usage patterns over time. You could then use these metrics to create behavioral nudges in new customers to help them make quicker buying decisions. This will increase lifetime value for your product and support your product growth over time.
According to data from Flurry, mobile apps in the health, wellness, and fitness industry see some of the highest percentages of frequent users, with as many as 26% of active users opening the app more than 10 times per week. With a large segment of users exhibiting such high engagement, product teams in this space have a huge opportunity to learn from these benchmarks.
That’s why Equinox let their most dedicated and engaged customers lead the development and expansion of the new Variis mobile app while the COVID-19 pandemic had physical locations shuttered or restricted.
Although the app is now widely available to the public, Equinox can still keep a close eye on that cohort of engaged users. Monitoring these metrics could help Equinox identify how to best communicate the core value the Variis app provides in the quickest way to the newest of users, thus increasing customer lifetime value.
In this manner, customer data cohorts could reveal surprising insights with minimal effort required to fix them. Such steps lead to your organization’s sustainable revenue growth since engaged customers are much more likely to purchase from you than your passive customers.
Effect of Employer-Sponsored Memberships on Retention
Employer-sponsored memberships and subscriptions represent a growing factor in the health, wellness, and fitness space. Whether to reduce employee healthcare costs or entice employees with additional perks and benefits, more and more employers are sponsoring app subscriptions for their employees.
As part of those efforts, growing companies could employ cohort analysis to identify changes in engagement, usage, and churn rates across users with employer-sponsored subscriptions.
Summary: Cohort Analysis is a Powerful Tool for Health, Wellness, and Fitness Apps
Cohort analysis is no silver bullet to give your product superpowers—but if you ask more in-depth questions of your data, cohort analysis can dissect that data to provide you with the answers you need to improve your app, boost retention, and grow revenue.
Once you have the right set of questions, analytics platforms like Indicative provide robust capabilities to create your cohorts. You can use several metrics of your customer profile like open email events, mobile app opens, or even custom events to tweak your data cohorts.
Are you interested in using cohort analysis to ask deeper questions about your data? We at Indicative provide powerful cohort analysis capabilities by connecting directly to your data warehouse.
Try our product demo today to find out how!