Analytics can be used to enhance a video game in many ways. Below are some examples of how data analytics can be used to improve a game.
Incorporating an analytics pipeline to your game can help you create more engaging games by doing data driven game development. You can learn how to optimize the game play experience so you can attract more players and increase player engagement. Understanding how players are currently playing a game is key to improving that game play.
You can use data analytics for fraud and player investigations. Data analytics can help identify abusers, cheaters, and can even decrease player churn. Once you have a data analytics pipeline in place and a good foundation of data in a data lake, you can extend that pipeline to include machine learning for more robust anomoly detection.
Analytics can help with revenue generation by encouraging purchases, targeted ads, and content recommendations. This is especially important if you are developing a free-to-play game with microtransactions.
Analytics can help you improve your game infrastructure by better understanding peak usage times so you know when to scale. You can better understand metrics like CPU and memory utilization to optimize your infrastructure. You can also better understand player latency, which is important if you are creating a latency-sensitive game like a first person shooter.
Real-time analytics can also be used to support live ops, such as community building, releasing new content, supporting live events, in-game promotions, and more. It is important to have a scalable analytics pipeline in place when doing live ops because it is very much a data-driven process.
There are many use cases for analytics in games that require batch or near real-time pipelines. If you would like to learn more you can check out the Why Analytics For Games AWS Training Course