Nowadays, the number of mobile games available to download from google play store or apple store is tremendous. It means that there is a big and strong competition on the market, which pushes gaming companies to make smarter and innovative decisions. By using big data in mobile gaming industry you can extract hidden patterns and discover new correlations.
- How does one come up with “smarter decisions”?
- How to understand which features of the game are good?
- Tip on how to measure what is convincing users to play and pay and what’s discouraging?
- If users don’t understand how to make some game moves or are stuck on a particular level of the game, how this information could be passed 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 these questions are hidden in huge amounts of collected data about your users and gamers. Some deeper data analysis can perform much more sophisticated queries on demand.
You may also find it interesting – Data Analytics BI Tools.
Big data in mobile gaming: Analytics KPI’s
To properly measure the success of your game you need to know how many users are active monthly. How many users you have acquired during the last month? Or even how much time your users spend 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 reasons of your mobile app success.
The more players have your application, the bigger is the probability that those users will upgrade to paid accounts and start spending money or recommend your app to friends. Above all, there is other KPI’s which can give a possibility to understand gamers` behavior and how they interact during the game. Therefore, games could be improved and updated by using extracted insights from users` behavior hidden in collected big data sets. As a result, user experience can be improved and gameplay time might expand.
Track customer retention and LTV
It is important to retain as many acquired customers as possible in the mobile gaming industry. Companies can’t let their players leave. It is important to identify those users who will bring revenue. Here is where customer churn and LTV analysis come into the game. The customer retention report will give you an opportunity to understand how long each particular user remains your client. Thinks to using machine learning we can predict which customers are likely to leave based on the historical data points.
Customer LTV (lifetime value) analysis gives a clear understanding of how users spent money in the past during the gameplay. With the help of machine learning consulting, you can understand how much newly acquired users will spend in the future while playing your game. It will help you to monetize them, concentrate only on potential buyers, and reduce marketing and advertising costs.
Big data in mobile gaming: Measuring user engagement
Gaming companies measure users` engagement with above mentioned KPI’s and by retention, churn, and interaction analysis. In most cases, mobile games have a few 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 too difficult for them. When insights on gameplay and interactions are extracted, game developers should 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 take up more effective actions leading to success.
Business Intelligence (BI) in mobile gaming (Tableau, Looker, and Google Data Studio)
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 having programming knowledge. However, in the gaming industry, most companies chose Tableau, Looker, or Google Data Studio (GDS) because of their compatibility with big data sets, data lake architectures, and data warehouses.
All this to say that a visualization tool and data modeling layer depends on the current infrastructure. For example, if you are planning to build an analytical layer on Google Cloud Platform (GCP) you will definitely need to consider Google Data Studio as a visualization tool. But if the 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 unique needs and problems to solve their way to obtain a perfect data and analytics strategy.
Achieving implementation success is much easier with an experienced partner that understands the industry challenges and knows how to overcome them. In addition, read our recently published Case Study on how AI and analytics can help mobile game developers.