Are you a product manager looking to boost your user retention, but something vague keeps coming in your way?
Run a well-thought and refined cohort analysis for the mobile app to identify what keeps your app from user acquisition.
In this form of mobile app analytics, instead of looking at data as a whole, you look at similar, comparable chunks of data to derive conclusions and make data-driven decisions.
But first, what is a cohort analysis? And how do you perform a cohort analysis for mobile app analytics?
We answer these and some other questions on the topic in our blog.
Learn about the use cases for digital analytics in this blog.
What is Cohort Analysis?
A cohort is a group of individuals sharing a common characteristic like age, acquisition date, language, or geographic location.
Slicing your users into these groups or cohorts and analyzing and comparing their behavior over a given period to identify trends, patterns, and shifts is called cohort analysis.
It is a type of behavioral analytics that facilitates data-driven decisions by removing the noise and only looking at chunks of data.
For example, you may divide your users along geographic lines into various cohorts, such as visitors from the USA, Canada, and Britain.
Comparing their behaviors within the same month, you notice that US visitors engage most with your website.
But then you learn that Canadian visitors convert after fewer engagements.
So, you increase your focus on the Canadian market with more aggressive marketing.
Does digital analytics confuse you? Read our blog about what could be wrong with the approach to digital analytics.
Types of Cohort Analysis
The most popular types of cohort analysis include the following:
Acquisition
It groups users according to their acquisition events, such as when they first signed up or shifted to a paid subscription.
Demographic
As the title suggests, it groups users into cohorts based on their geographic location, e.g., cohorts of visitors from the USA and Canada or different states in the same country.
Behavioral
This cohort groups people based on their behavior, such as user retention for individuals who opt for reminder emails and those who do not.
Let us discuss the main topic, cohort analysis for mobile app analytics.
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What is Cohort Analysis for Mobile App Analytics?
Cohort analysis for mobile app analytics involves breaking down the visitors into smaller segments and comparing their behaviors and interactions with your mobile app to identify trends in user retention and behavior over a given period.
For example, you break down users into groups based on the day they first launched your app on their mobiles.
Now, you wish to compare their return rate over a week.
Shared below is a hypothetical data set for the scenario.
|
Cohort |
Day 0 |
Day 1 |
Day 2 |
Day 3 |
Day 4 |
Day 5 |
Day 6 |
Day 7 |
|
March 1 |
100 % |
43 % |
25 % |
17 % |
15 % |
13 % |
12 % |
11 % |
|
March 2 |
100 % |
40 % |
20 % |
17 % |
13 % |
12 % |
11 % |
10 % |
|
March 3 |
100 % |
37 % |
18 % |
15 % |
12 % |
11 % |
10 % |
9 % |
|
March 4 |
100 % |
35 % |
17 % |
14 % |
11 % |
10 % |
9 % |
8 % |
|
March 5 |
100 % |
31 % |
16 % |
13 % |
10 % |
9 % |
8 % |
7 % |
|
March 6 |
100 % |
29 % |
15 % |
13 % |
9 % |
8 % |
7 % |
6 % |
Hypothetical table for cohort analysis for mobile app analytics
Such an analysis gives you valuable information about your churn rate.
You may then turn to Behavioral cohort analysis, plus investigate the user experience to identify what causes the user drop off.
Learn about the most valuable mobile app analytics to track in this blog.
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Benefits of Cohort Analysis for Mobile App Analytics
Before we explain how you can perform a cohort analysis, it must be intriguing why it interests analysts.
1. Identify User Behavior Patterns
With cohort analysis for mobile app analytics, you can identify the patterns exhibited by users who churn and those who convert.
For example, your findings are that users who use at least one feature on their sign-up tend to convert.
Or the users who do not make it to the third screen to enter their details churn most often.
2. Identify a Suitable Window for Re-engagement
With the patterns and trends that emerge from the mobile app cohort analysis, you can identify when the users churn the most.
Your first approach would be to investigate and fix if it is a technical issue. Otherwise, you would know the perfect window of opportunity to re-engage the customer to increase the chances of conversion.
For example, your findings state that users who do not log in until three days after their first visit churn.
So, you send a reminder or a notification with a personalized message on the second day.
3. Improve User Acquisition and Customer Lifetime Value
When you are aware of the patterns, positive and negative, you can work to remove the hurdles and optimize the app and user experience.
Similarly, targeted re-engagement, especially with high-quality customers, can enhance customer retention and lifetime value.
For example, the cohort analysis reveals that the layout of your app makes navigation complicated.
You work to optimize it and target your high-quality customers with custom communication in your email marketing campaigns, even offering some value in exchange for their review.
Learn about effective digital analytics in this blog.
How to do Cohort Analysis for Mobile App Analytics?
Now that you understand what is a cohort analysis for a mobile app, it is time we showed you how you can perform it.
1. Set up Goals
What do you want to achieve with a mobile app cohort analysis? List the questions you want to answer by the end of the analysis.
Defining the problem sets the tone for the rest of the analysis.
For example, do you wish to understand and reduce your mobile app churn rate, increase conversions, or move people from a free version of the mobile app to a paid version?
The result of this stage will automatically identify what you will do in the later stages of the analysis.
2. Select Metrics
You already know what you want to analyze. A natural next step is to choose the key performance metrics to track and measure for the cohort analysis.
For example, if your goal for the mobile app cohort analysis is to increase customer retention, track your retention and churn rate.
These metrics inform you about the valuable actions during their buyer journey or identify stages when they drop off most sharply.
If the aim is to increase the conversions, the conversion rate would be an excellent choice for a suitable metric.
3. Define Cohorts
Once you have decided on the KPIs for the cohort analysis for the mobile app, it is time you define the criteria for setting up cohorts.
Defining a cohort will include some of the following things.
- The date range for the cohort (weekly, monthly)
- Type of cohort, e.g., acquisition, demographic
- Cohort size (too small will not give actionable results)
- Channel (organic, paid, paid Facebook, or Google Ads)
For example, you have a fitness app.
You recently introduced a new feature where users can build and join in-app fitness communities, set up fitness goals, and share their successes.
You would want to run a behavior cohort analysis for user interaction, return, and download rate before and after the feature launch.
You would select a few months before and after the launch and compare the similar dates or days of the week in different months, pre and post-launch.
It is important to note that typically, you don’t stick to just one cohort analysis.
For example, you may run an acquisition cohort analysis first. Based on its findings, turn to behavior cohort analysis to spot the touchpoint where most drop-offs happen.
4. Run Cohort Analysis
You have all the cohorts ready. It is time to run the analysis.
You may do so in two ways:
i) Calculate in Excel Sheet
It was the predominant method for running cohort analysis before specialized tools became all the rage.
To perform the cohort analysis in the Excel sheets, pull the metrics data and arrange it into the cohorts.
Where required, plug the metrics into formulae to calculate the desired KPIs.
Generate tables and trend charts to find patterns and relationships between behaviors and variables on the mobile app.
ii) Use a Cohort Analysis Tool
You may also use specialized tools for mobile app cohort analysis. We briefly discuss some of these tools below.
Mixpanel – is a popular analytics tool for tracking and analyzing user behavior. To perform a cohort analysis in Mixpanel, you go through similar stages, only following the flow set up by the tool.
It offers additional information like demographics and comparative analysis for various cohorts, allowing for better insights into user behavior.
Amplitude – is an analytics tool exclusively for mobile analytics that offers cohort analysis for mobile app analytics. Amplitude tracks user interactions and behavior, custom event tracking, and has a variety of cohort analyses.
It is easier to use, sets up quickly, and offers considerable flexibility and customization.
Countly – is an open-source mobile and web analytics platform that tracks user behavior in real-time via various tools and metrics.
Conversely, it is coding intensive, and setting it up is time-consuming.
Adobe Analytics – is an enterprise-exclusive mobile and web analytics tool that offers multi-channel data collection, advanced calculation capabilities, and multi-source attribution.
It also offers real-time reporting for quick analysis and trend identification. Businesses can perform default and customized mobile app cohort analyses in Adobe Analytics.
Google Analytics 4 – is a web and mobile app analytics tool primarily for marketing. It allows businesses to track the performance of their websites and apps.
You can perform the Cohort analysis for mobile app analytics in GA4 in Cohort Exploration in the Explorations report.
However, it has limitations compared to other tools as it only reveals the top 15 values in the report, allows a maximum of 40 cohorts, and subjects demographic data to data thresholding.
5. Interpret Results
Once you have your cohort analysis for mobile app analytics, it is time to understand what the results communicate and draw conclusions.
For example, if you were looking to identify the cause of your churn rate, a cohort analysis should tell you when the return rate dropped off.
Another might tell you exactly where the user rate drops, called the inflection point.
Say, after the users update the app to a newer version, they tend to rage-click for a few seconds before leaving and not returning.
Or you notice that the retention rate for our hypothetical fitness app was higher for users who subscribed to notifications.
Also, you noticed a pattern. The retention was higher among women between the ages of 30 – and 40.
6. Iterate Cohort Analysis
Cohort analysis is one of the mobile analytics that feeds into the continual improvement of the user experience.
Once you have a set of results, investigate to find actionable insights and take data-driven actions.
For the fitness app example, you run further behavior cohort analyses and learn more about the demographic of the most active users.
Such as, you learn that the most active users of your fitness app are at-home moms and decide to offer remote fitness consultations to this demographic to increase engagement and retention.
So, you run another cohort analysis for this demographic and find these are at-home moms who appreciate the remote consultations.
Naturally, you would focus resources on catering to this demographic, even scale it as an exclusive in-app offer to increase user acquisition and customer lifetime value.
Conclusion
Cohort Analysis is the process of breaking down data into groups called cohorts and comparing them over time to identify trends, patterns, and user behaviors.
Cohort analysis for mobile app analytics involves slicing the app visitors into smaller segments and running a comparative analysis over a given period to identify their app interaction and behavior.
Its benefits include identifying valuable trends and patterns, revealing the ideal window for re-engagement, and improving user acquisition, retention, and lifetime value.
Performing a cohort analysis for mobile app analytics is a five-step process.
First, set up goals for the cohort analysis, outlining what questions you want answered. Next, select the metrics that align with your goals.
Then, define your cohorts. It includes deciding the date range, type, size, and channels to consider for the cohort.
Your next stop would be to perform the actual calculation. You may import your data into an Excel sheet and run the analyses there.
Or use specialized tools for mobile app cohort analysis.
Some examples of such tools include Mixpanel, Amplitude, Countly, Adobe Analytics, and Google Analytics 4.
Once you have your results, you interpret them to identify the inflection point or valuable patterns to investigate and optimize your app.
Finally, you may have to repeat the analyses a couple of times and rely on a variety of types of cohort analysis for mobile app analytics until you get actionable insights.
Find this helpful? Check out more articles about digital analytics from our blog.
