Experienced mobile marketers know: real success isn’t about getting someone to download your app.
It’s about convincing them to come back.
Vanity metrics like downloads and active users don’t give you the whole picture. It’s easy to look at these numbers and think you’ve got healthy growth. But in reality, you’re losing most of your users after just a few days.
You’re spending precious time and resources acquiring new customers — all for nothing.
So how do you find out exactly what makes some users leave while others stay?
Don’t resort to guesswork!
To find the answers, dig deeper into your app’s metrics using a method called Cohort Analysis.
What is Cohort Analysis?
Cohorts? Segments? Shifting curves? We get it — this stuff gets confusing fast.
But here’s the deal:
It’s actually pretty simple.
To start, we’ll strip away the jargon and define cohort analysis in plain English.
“Cohort” is just a fancy word for group.
Instead of looking at all users in one broad view, cohort analysis breaks them down into groups. Think platform, acquisition date or channel, specific user behavior — anything you want.
Cohort analysis measures user engagement over time, making it easy to spot friction points and behavioral patterns.
Essentially, it gives you a data-driven approach to understanding exactly what makes users fall in love with your app — so you can keep making it happen.
Cohort analysis is a powerful way to see how users are engaging with your app — and get actionable insights into specific changes you can make to dramatically improve user engagement.
Here’s an example: create a cohort (group) of new users who have launched an app for the first time. Then see how many of them come back to the app over the next 10 days.
User Retention Cohort
This retention table may look complicated, but stick with me!
We can see that:
32,961 users launched the app on August 30. Of these users, 31.3% came back on Day 1, 22% on Day 4, and 8.1% on Day 7. That means on the 7th day after using the app, only 1 in 12 users are still active.
Out of all new users acquired between August 30 and September 6 (134,529 total), only 26.1% of them are retained on Day 1, 11.5% on Day 4, and 8.1% on Day 7. That means we’re losing 92% of our hard-earned customers in the first seven days.
PRO TIP: Find which cohorts had better retention rates to uncover possible reasons why. Did you launch a new marketing campaign that day? Offer a promotion or discount? Release a new feature? Add a video tutorial to your product tour? Apply those successful tactics to other users to improve retention for more and more users.
Retention by Acquisition Day: By comparing different cohorts, we can see trends about how many users are coming back to the app after 4 days, 7 days, etc. This data can give you important insights into your onboarding experience, product quality, user experience, and product/market fit.
Retention Over Time: By looking at the number of days that people in each cohort are coming back to your app, you can see how healthy each cohort is not only in the first few days but over the long term.
From there, you can pinpoint where users are dropping off. And you can see what your most engaged user groups are doing, so you can influence new users to follow the same path.
Mobile engagement retention
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How to Do Cohort Analysis to Improve User Retention Rates
As we’ve discussed, cohort analysis involves looking at groups of users to:
see how their behavior changes over time
look for patterns of behavior that influence retention
compare user groups to identify best practices and use cases
But when it comes to using cohorts to improve Colombia Phone Numbers List your user retention, where should you start?
We recommend beginning with two types of cohorts:
Acquisition Cohorts: Group users by the day, week, or month that they first downloaded your app. By measuring retention for each of these cohorts, you can determine how long people are using your app and where you’re losing them.
Behavioral Cohorts: Group users by the specific behaviors they have (or haven’t) taken in your app within a given time frame. (For example, App Install, App Launch, Complete User Profile, Add a Friend, Complete a Purchase, etc., — or any combination of these actions.)
You can then track how long different cohorts stay active in your app after they perform certain actions, and see which actions have a positive or negative effect on retention.
PRO TIP: We recommend creating a behavioral cohort based on your ideal user journey. For a movie ticketing app, this might be: App Install –> App Launch –> View Product –> Add to Cart –> Complete a Purchase within the first week of installing the app. This will help you understand how many users are completing these steps, how long it takes them to do so, and wher
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