There are approximately three million apps in the Google Play Store and Apple App Store combined, and consumers are spending 84% of their time in only five of those apps. Furthermore, the average Android app loses about 80% of its daily active users within the first three days, and about 90% by the first month. What percentage of users do you think come back to your app three days after downloading it? It’s about time developers start focusing on retention rates over acquisition rates…but how?

The answer is simple: retention analysis. Sounds fun, right? While it’s not as flashy as creating an ad campaign for your business, it is far more important long term. Many people don’t know how to analyze their retention in any meaningful way, and end up losing valuable and potentially loyal users because they are unable to interpret what the user data actually means, and what to do about it.

If you are one of those people, fear not…retention analysis is pretty simple when you boil it down, and the benefits can be far greater than any marketing scheme. The basic idea with retention analysis is to:

  1. Understand the number of users that return to, or abandon your app after installation
  2. Understand when and why users leave your app
  3. Understand what causes users to return to and engage with your app
  4. Develop a well thought out strategy to improve app retention rate

So, how do I actually do retention analysis?

Let’s start with the basics, if you already have an app in the app store then you already have some data to work with. The key is understanding how to interpret your data:

1. What is my base retention?

Let’s say you are a developer, and you recently came out with a great new app to track your health and fitness called HealthHelper. The first, and simplest step to developing a retention strategy is to understand your current day N retention rate. In other words, how many people download HealthHelper and are still using it “N” days later?

There are many analytic tools you can use to calculate this, the most common being Google Analytics. This will help you to understand your app’s current retention rate, but drawing any conclusions from this data alone is ill-advised. Maybe you have a great marketing strategy so you get a lot of new users downloading your app daily, but this is hiding the fact that most people stop using HealthHelper after a few days. Instead of lumping all of your users into one category, you want to use acquisition cohorts to divide your users into specific groups based on when the user installed your app.

Using specific cohort analysis is particularly beneficial because it breaks your retention down so that you can see when exactly users are churning out—immediately, after a few days, or after a few weeks. It helps to build a retention curve to visualize when the average user stops using your app. Knowing when users abandon your app means that you can narrow down your focus to that particular area, and find out what is causing people to leave and how you can prevent it.

2. What causes users to be retained?

An important thing to look at in understanding how to optimize user retention is user behavior. To find out why users are staying in or leaving your app, you need to isolate the specific behaviors the individual users take, and see how these affect retention of those users over time. This can be done using behavioral cohorts, with which you can narrow your analysis to new users who performed a specific action. You can then look at this subset of users and see how that particular action they have in common is correlated with their retention.

As soon as a user downloads HealthHelper they have some different options: they can take a quiz to find their best health plan, enter meals to track their nutrition, enter exercise to track their fitness, or connect with friends to share your health goals and achievements. You want to know whether taking the quiz causes users to stick with the app longer, so you divide out the cohorts who took the quiz to look at the retention rate of those users. Say you find that the cohorts who take the quiz are consistently retained more than those who don’t, and with more analysis you find that users who don’t take the quiz at all have even lower retention rates than the average.

So what does this data mean? Well it tells that your should focus on getting more users to take the quiz…maybe make it a more obvious feature or a mandatory part of setting up an account. Either way, you now have information that can help you modify your app to optimize user retention. You can use this same method to check the effect of the other functions within your app to see what user behaviors increase or decrease retention.

3. What causes long-term growth?

Long-term growth typically occurs when an app has both retention and user engagement. Fortunately, the behaviors that lead to better retention are usually the same behaviors that increase user engagement. By carefully analyzing certain user behaviors and how they relate to retention, you can identify the main behaviors and actions that are correlated with long-term growth. Once you reach the point of discover the specific actions that lead to growth, you’ll need to put concrete numbers on these behaviors.

Consider Facebook, for example. Using similar retention analysis, they found that users who added 7 friends within the first 10 days of having Facebook were highly likely to continue use long-term. By not adding those 7 friends, users were more likely to churn out. In this case, performing the specific event of adding 7 friends in a certain timeframe was highly correlated with retention.

So, how do you find which action quantifiably causes long-term growth? You can do this by simple trial and error. Back to your HealthHelper app, say you have tested all the different user actions and found that the action that causes the most retention is in fact taking the quiz. So you know that overall everyone who takes the quiz is more likely to be retained, but you can farther narrow it down by dividing the quiz-taking cohorts into those that took the quiz within the first three days of downloading the app, and those that took it later. Continuing this narrowing down will eventually lead you to the one quantifiable action that leads to the most long-term growth.

Conclusion

With so much competition in the app store nowadays, its vitally important to be able to hang on to the users that you gain. This can be done with thorough careful analysis of your users actions and retention rates. While the tools I’ve outlines here are very important to retaining users, its important to keep in mind that your user base is always changing, and you should never stop analyzing the data and keeping up with your users changing demands.

 

 

 

 

 


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