Cohort Analysis on Google Analytics for Mobile Apps

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Being an app developer or a digital marketer, convincing your target users to download and install your app is one part – making them stick to your app is another. For that, you need to go beyond those DAUs or MAUs, or any other statistical and numeric data that you have at your disposal.

For that, you need to use an analytical methodology that measures user engagement – in short, you need Cohort Analysis.

Cohort Analysis is undoubtedly one of the most effective Google Analytics tools in understanding a particular segment of your customers and studying their behavioral traits over a specific time period. This is due to the fact that it gives you a thorough understanding and visibility of your user’s interaction with your mobile app.

 

What is Cohort Analysis?

Before going into any details, let us first understand what this concept is all about.

In the simplest of definitions, a cohort is defined as a set or group of users with shared behavioral traits or characteristics. A ‘cohort analysis’ is the assessment or comparison of more than one cohorts over a specific time period. You can find the lowest and highest performing cohorts within your data set and study all the factors that have an impact on their performance.

 

Understanding Cohort Analysis in Detail

In order to have a better picture of this concept, let us consider the following example – a cohort of users who used an app for the first time and then revisited the app during a 10-day period after that.

The above chart shows the following results:

  • On Jan 26th, 1,358 users launched the app - Day 1 retention was 31.1%. Retention rate on the 7th day was 12.9%. Similarly, the retention rate on 9th day was 11.3%. It can be deduced from the aforementioned example that 7 days after using the app, 1 in 8 users were still active app users.
  • The new users from this time perdio (total of 13,487 users), 27% are successfully retained on the first day, 12.5% on the 7th day and 12.1% on the 10th day.

 

Important Benefits of Cohorts

There are two major positive highlights from this example:

  • We have a visibility over the percentage of people in a specific cohort who return to the app after 3 days, 4 days, 5 days and so on. The lifetime period can be associated with the customer experience or user interface factors that might be playing a vital role in this.
  • Secondly, user lifetime (right of the table) can be studied and compared as well. This shows the long-term relationship with users in a specific cohort. It gives you a clear idea of how strong a cohort is relatively. Depending on your app business model or industry that you are part of, you can associate this with the product quality, customer service, etc.

Keeping the above example into consideration, we can safely say that cohort analysis on Google Analytics allows you to view how various metrics perform over the customer or product lifetime. These comparisons and statistics can eventually assist you in making strategic business decisions.

 

Categorization of Cohorts

Cohort analysis is all about assessing user behavior in groups over a specific period of time and identifying key elements that lead to behavioral changes within that group. 

 

For example, if you roll out a bulk email to your new or existing customers, some of them might buy your product on day 1, some might buy on day 2 and so on. However, if you send another email to your customers, they might be buying your product or service on day ‘0’ and the previously sent email will show its impact on the customer’s buying decision. In order to make your cohort analysis more impactful and precise, you can split the cohort groups in two ways:

 

Acquisition Cohorts 

This is a classification where you can divide your users since their first sign up. For instance, if you are an app owner, you can track your users by splitting your cohorts by day, week or month since the first launched your app. These daily, weekly or monthly cohorts will give you an important statistic regarding app usage which you can later use for various decision-making processes. Consider the following example:

Cohort Analysis on Google Analytics

This image shows a retention curve and the biggest drop that takes place after the first day – around 75% of users quit using the app. The other big drop is on day 7, when it goes below 10% before the curve levels off with the x-axis.



Behavioral Cohorts 

This is a categorization where you can divide your users by their behavioral traits. This could be the actions or activities that they did in your app over a specific time period. These actions could include app install, app launch, app delete, transaction made, etc. This cohort will eventually give you a group of users who made certain favorable or unfavorable actions with their app. This data will be quite useful for you as you would know what are the different set of actions or activities that a user initiates on certain stages of their lifecycle. Here is an example of behavioral cohort:

 

Cohort Analysis on Google Analytics for mobile apps



This is an example of PayPal account holders who connected their PayPal to buy movie tickets via a single click. In this example, we can get answers to questions like: (1) Is there a high retention rate for users who enable PayPal? (2) Are these users making more purchases or orders? 

 

Using Cohort Analysis – 4 Important Elements 

Let us now have a brief overview of what you need to be aware of before you initiate a cohort analysis in your mobile app:

 

Type of Cohort

This is the vertical axis and it usually means the acquisition date.

 

Size of Cohort

This is the size of your cohorts that entirely depends on your app business model. Do you want your cohorts to be in days, weeks or months

 

Metric

This is the benchmark metric that you need your cohorts to be compared or evaluated against. Your metrics could include retention, total revenue, session length, etc.

 

Time Period

This is the span of time during which you want to make your cohort observations. Try not to have too lengthy cohort time spans, as it could affect the accuracy of your results.

 

Where Can Cohort Analysis be Useful?

As a digital marketer or an app developer, you must know when and where cohort analysis can benefit your mobile app. Below are some major factors that can leave a huge effect on your user’s behavior:

  • Ad Content
  • Target audience
  • Campaigns & Strategies
  • UX/UI Redesigns
  • Promotional Campaigns

In theory, yes you can analyze the aforementioned factors with cohort analysis, however, not all analytics tools might allow you to assess the effect of these factors on user experience or user behavior.

 

How to Build a Retention Strategy with Cohort Analytics

As far as the mobile app business is concerned, if you are able to define certain parameters well, your cohort analysis can give you visibility in a number of key areas. So, what are those parameters? Let’s have a look:

Set Realistic Goals – You need to know your specific goals or targets. 

Use Existing Data – Make use of your current app data in order to make necessary changes.

Brainstorming – Come up with all the possible questions and ambiguities that you might have from your experimentation.

Rigorous Testing – Thoroughly test your hypothesis.

Analyze Results – Evaluate the results and compare with your original goals that you set in step number 1.

Modify and Repeat – If there are irregularities, go back, make changes and start with your testing process again.

 

Improvement of Retention Over Time

With the passage of time, you being an app developer, digital marketer or a business owner, will realise that there are numerous features that collectively play a role in keeping your users engaged. These features or elements will continuously keep evolving as per your user behavior, market competition, industry needs or expectations, etc. 

 

Provided that you know and have figured out those elements that keep your users engaged and give you the ‘stickiness’ numbers that you are expecting from your app, the next step would be to continuously test and experiment with user engagement methodologies and techniques. 

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