Big Data For Predicting Mobile Gaming Trends
Analytics Big Data

Tips On Using Big Data For Predicting Mobile Gaming Trends

In our modern fast-paced world, everyone and everything is moving so quickly that you won’t have the time to blink when there is yet another update released. Every mobile game development company strives to be the best in its field and outperform their competitors by showing their customers that they are heard and their complaints are taken into account in order to improve the functionality of the company’s products.

Of course, the surest way to know the issues with your game in and out is to gather enough customer feedback and performance data. This will enable you to calculate what changes should be made to ensure that your clients are satisfied.

This is where big data statistics come in. In this article, we will discuss the most persuasive benefits of using big data for predicting mobile gaming trends and improving the usability of your company’s apps.

1. Mobile Gaming Analytics KPI’s

Key performance indicators or KPI’s are crucial for understanding various aspects of the success (or failure) of your mobile game. Start by asking yourself such questions:

  • Active monthly?
  • How many users you have acquired over the last month?
  • How much time do your users spend playing your game?

There are some basic big data analytics trends and statistics you must absolutely know in order to figure out the performance levels of your app and determine the future course of action. There are three main KPI’s to keep in mind, and they are:

Monthly Acquired Users (MAU):

This is the number of people who downloaded your game and registered (if that is required in your case). Moreover, if you have the opportunity to get the numbers of those who downloaded the game and are actively using it and those who downloaded it, but are not using it, then you should totally include them into your statistics report. They will be very valuable and will show how much interest your customers maintain once they have your game on their device available at any moment.

Daily Active Users (DAU):

This is the number of users who open and use your game on a daily basis. This number may fluctuate depending on the day of the week and any holidays that come up. DAU is slightly less important than MAU, but it is still a very good indicator for various aspects of your mobile game’s performance.

Average Revenue Per User (ARPU):

This is the amount your company earns from one user. Your app may be a paid one or can contain paid features. If it is a free app, it is very likely that you included ads. All the revenue comes from these sources and it’s a good indicator of how profitable your mobile game is.

Of course, these are not all the KPI’s but they are still some of the main ones that you should be aware of. The more users are playing your game, the more chances they will recommend it to their friends. Upgrade to a premium account or purchase other products by your company. This is key to developing your brand in the mobile gaming industry, so such factors must never be overlooked.

By analyzing the data you collect, you can further develop the different features of your game, satisfy your users even further, and increase the average gameplay duration.

2. Tracking Customer Retention & LTV

After a user has downloaded your game, the next is to persuade them to stay and use it. Your ultimate goal is to tell them about how awesome it will be if they purchase the premium features and hope they do just that. This is where customer retention and LTV come in.

Customer retention reports are meant to analyze how long every user stays your client. If they uninstalled the game, then it is highly unlikely that they will come back to download it again. Similarly, you must not only make sure that your users stay with you, but also that they buy the features you want them to purchase. Machine Learning can be used with customer retention statistics in order to calculate which users are more likely to leave at a certain point in the gameplay.

Customer lifetime value, commonly abbreviated as customer LTV, will tell you how much your users spent on the game during the whole gameplay historically. It is a crucial element in understanding ARPU. Combined with Machine Learning, LTV can help you predict which potential customers could become potential buyers and who it is worth advertising to.

3. Measuring User Engagement

One of the issues that players often experience is being stuck on one level and not being able to continue playing the game. This negative experience is something that can discourage users from enjoying your game altogether. They may even go as far as to uninstall the app, not to mention the potential revenue you are losing.

Some of the factors making such a situation happen are the user’s age, gender, and background. Of course, there is no way to satisfy all the multiple groups within the players mass, but there are still ways to adapt the game levels in a way so that they are not as complicated.

By using Business Intelligence (BI) tools, mobile gaming developers can track user behaviour to identify the problems clients experience. After that, particular game levels can be altered and developed in the right direction.

Moreover, you can further improve user engagement with the help of game localization. Use an online translation service such as PickWriters to translate and localize your game for audiences worldwide.

4. Business Intelligence (BI) In Mobile Gaming

In order to analyze the data you gathered, you must use specific BI tools created for making the most exact conclusions from the analysis of big data statistics.

The best BI tools you can consider are Tableau, Looker, and Google Data Studio. The latter one is compatible with big data sets, data lake architectures and data warehouses which gives it an edge over other tools. On the other hand, Tableau sports a wide range of functionalities and compatibility with cloud platforms.

While choosing the BI tool for your company, consider your needs. It mostly depends on your infrastructure. If you are planning to build analytical layers on Google Cloud Platform, then you will probably need to consider Google Data Studio as your primary visualization tool. However, if it’s not enough, Tableau might be a better choice.


To sum up, the more data you gather from your users and the better you analyze it, the more chances there are that you will be able to predict the future of the mobile gaming industry or at least its trends. Collect big data statistics and make new discoveries before others do them!

Additional Resources:

5 ways in which e-retailers can use Big Data in their favour

Big Data Architecture – Get acquainted with the Art of Handling Big Data

How “Big” Is Data In The Retail Sector

BigData cluster monitor – CDH how to enable HA for production deployments

Knowing the Apache Hadoop Ecosystem for Big Data Business Applications

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