Nowadays the number of mobile games available to download from google play store or apple store is tremendous. It means that there is big and strong competition on the market, which pushes gaming companies to make smarter and more innovative decisions. Using Big Data in the mobile gaming industry you could extract hidden patterns and discover new correlations.
- How does one come up with “smarter decisions” to stay competitive?
- How to understand which features of the game are good?
- Do you know how to calculate and measure what is convincing users to play and pay and what’s not?
- If your users don’t know how to make some game moves or they are stuck on a particular level of your game, how this information could be delivered to your developers in a clear and comprehensive way?
- How can gameplay time be properly measured if the app is running in the background?
Answers to those questions are hidden in huge amounts of data you are collecting on your users and gamers. Some deeper data analysis can perform much more sophisticated queries on demand.
Mobile gaming analytics KPI’s
To properly measure the success of your game you need to understand how many users are active monthly? How many users you have acquired during the last month? Or even how much your users are spending playing your game? Calculated KPI’s such as MAU (Monthly Acquired Users), DAU (Daily Active Users), ARPU (Average Revenue Per User) can help answer those questions and understand the performance and success reasons of your mobile app.
The more players have your application, the bigger is the probability that those users will upgrade to paid accounts and start spending money or will refer your app to friends. Additionally, there is other KPI’s which can give a possibility to understand gamers` behavior and how they interact during the game. Games could be improved and updated using extracted insight from users` behavior hidden in collected big data sets. As a result, user experience can be improved and gameplay time could be expanded.
Track customer retention and LTV
It is important to retain as many acquired customers as possible in the mobile gaming industry. Companies can let their players leave. It is important to identify those users who will bring revenue. Here is where customer churn and LTV analysis comes into the game. The customer retention report will give you an opportunity to understand how long each particular user stays your client. Using Machine Learning we can predict, which particular customers are likely to leave based on historical data points.
Customer LTV (lifetime value) analysis gives you a clear understanding of how users have spent money during the gameplay historically. With the help of machine learning consulting, you could understand how much newly acquired users will spend in the future playing your game. It will help you to monetize on them more, concentrate only on potential buyers, reduce marketing and advertising costs.
Measuring user engagement
Gaming companies measure users` engagement with above mentioned KPI’s as also using retention, churn and interaction analysis. In most cases, mobile games have different levels of difficulty and different players with various demographics and cohorts. With the help of self-service BI tools, gaming companies can better understand and track user behavior during different game levels and make sure that a particular level is not to difficult for those users. When insights on gameplay and interactions are extracted, game developers could update particular levels so that users are not stuck anywhere and are moving forward through the game.
With mobile gaming analytics driving developers’ decisions, they can make better-informed decisions leading to more success.
Business Intelligence (BI) in mobile gaming (Tableau, Looker and Google Data Studio)
Today there are plenty of business intelligence services available on the market. All tools have similar functionalities and give an opportunity to end-users to analyze collected data without programming knowledge. In the gaming industry, most companies chose Tableau, Looker or Google Data Studio (GDS) because of its compatibility with big data sets, data lake architectures and data warehouses.
All this to say that a tool for visualization and data modeling layer depends on your current infrastructure. For example, if you are planning to build analytical layer on Google Cloud Platform (GCP) you will definitely need to consider Google Data Studio as a visualization tool. But if functionalities of GDS will not be enough we recommend considering Tableau because of its wide range of functionalities and compatibility with cloud platforms.
Mobile gaming companies with a good data strategy can grow faster, acquire more users and improve their games and applications. Such companies put themselves ahead on a very competitive market. However, every company has a unique business case, need and problems to solve in their way to perfect data and analytics strategy.
Achieve implementation success is much easier with an experienced partner that understands the industry problems and knows how to solve them. Read our recently published Case Study on how AI and analytics can help mobile game developers.