How to conduct a cohort analysis to increase your game's retention rate

Aysu Ayık
Aysu Ayık
Product Marketing Manager
How to conduct a cohort analysis to increase your game's retention rate

Mobile marketing has come a long way, especially within the last couple of years; with the social effects such as gen Z entering the scene and staying in phase during the COVID-19 pandemic, the industry has organically boomed and, as a result, become an extremely competitive field. Thousands of apps in every vertical are trying to expand their user base. So therefore, it’s a must to be attentive and observant when it comes to making decisions for your user acquisition and monetization campaigns and applying them. However, there’s another side to the medallion; the POV of mobile marketers in the booming app marketing scene. While trying to be quick and stand out amongst other apps, it’s quite easy to get lost between sheets of campaign data, which can lead to poor decisions and ineffective campaigns, especially when you’re not sure how to look into it. This is where conducting a cohort analysis comes in. It gives growth marketers and user acquisition professionals a clearer picture and charges them with the power of knowledge.

But what is cohort analysis? 

Cohort analysis simplifies the process of understanding data. Applying a cohort analysis allows you to group the users of your app and make more informed decisions. By conducting a cohort analysis, you can not only find the optimal user base to target but also the breaking points in the customer journey, areas that can be improved, and the best channels for promoting your app. Finding these will improve the experience and shorten the way to hitting your KPIs. 

Types of cohort analysis 

Cohort analysis can be done based on time or segment, depending on your business vertical and what you’re measuring. 

Time-based cohort analysis

Time-based cohort analysis helps you analyze the behavior of your user base in a selected time range. It might change depending on what kind of behavior you’d like to analyze or your goals. Its length might be a day, week, month, 3-months or even a year. 

Segment-based cohort analysis

This cohort analysis type lets you analyze users in different segments, the  LTV or gameplay periods comparisons of users acquired from different campaigns, platforms or ad networks. It can help find better-performing ad networks, platforms and creatives, allowing you to optimize your user acquisition strategy to increase engagement and revenue.

How to conduct a cohort analysis

In conducting a cohort analysis, the first step is pointing out the problem and coming up with the questions to be answered by the end of the analysis. For instance, it might be why you’re losing revenue when your app is continuously acquiring the same amount of new users. After you decide on the question to be answered, you need to define the metrics, time range or actions that would help you find the answer to your question at hand. When these preparations are completed, you can proceed with applying the cohort analysis.

Let us explain the process with the example issue mentioned above: you’re losing revenue while your game is acquiring approximately the same amount of new users each month since it’s published. So how should you proceed? 

  1. Select the time range and extract the data. 

Gather the data from all ad networks and put them together in one place (such as an Excel sheet). For the time range, let’s say that your day zero for your analysis is June 1st. Each metric you look at after this is based on how many days have passed for every user after day zero (June 1st).

  1. Find your cohort identifiers and check your selected metrics. 

You can check metrics such as Cohort Size, Retained Users, Retention Rate, Average Revenue per User (ARPU), and ROAS metrics on different levels: ad network or campaign. You can check Average Revenue per User and Retained Users Day X results, X meaning the number of days passed since the day you’ve taken as your day zero. 

  1. Measure the lifecycle stages of your cohorts. 

You can check if each new user is opening the app after the install by looking at Day X Retention Rate and Cohort Size. 

  1. Visualize data with graphs and tables to compare data more efficiently and test the findings to make sure they make sense. 

After evaluating your metrics for each ad network and campaign, you can find that you’re acquiring users that cost higher than your users’ Lifetime Value (LTV), or the users are acquired from ad networks with lower LTVs. So you can take action and adjust the bids to get users with lower LTVs.

In mobile game marketing, looking at data in cohorts trims the understanding process and helps make more data-driven decisions, as a direct consequence, increases ROAS. But conducting a cohort analysis for a game has a process of its own where you pull the data of multiple campaigns from several ad networks and MMPs, put them together in sheets, and find that meaningful piece of data. Sounds a bit tiring, right? Hopefully, there are tools that reduce the time spent on manual work and empower the user acquisition professionals by offering freedom to play with data, such as UAhero. 

UAhero is a compact yet powerful platform that enables mobile game marketers to see all their campaign data from various sources in one place. It both saves marketers from the hassle of jumping between tabs while trying to make sense of the data and offers recommendations powered by AI to increase the profits of their campaigns. 

Using the dashboard of the UAhero platform, game marketers can compare time ranges, check their essential metrics, and get the data quickly for their cohort analyses. They can even perform analyses with a couple of clicks on the easily adjustable pivot table and check the metrics on the graph for visualized results. Furthermore, it is possible to check the metrics such as Predicted ARPU, Predicted Retention Rate, and Predicted ROAS provided by the UAhero platform to see which ad groups in campaigns can give better outcomes. The platform also offers bid, budget and ROAS recommendations according to the predicted metrics, which can be applied either automatically or manually to keep the marketer in control of the last decision. 

In a nutshell, you can increase campaign performance and hit your targets effortlessly by utilizing the UAhero platform in every step of the campaign management process, from analysis to applying decisions.

To benefit more from data with AI, shorten campaign management processes and increase ROAS with UAhero, drop us a line at

Stay updated
Get the latest news on UAhero and the mobile marketing world.
Thank you! Check your inbox to confirm your submission.
Oops! Something went wrong.
Your privacy is important to us, see how we handle your data in our Privacy Policy.