This is a guest post by Tom Talpir, Software Developer at ironSource. ironSource is as an Advanced AWS Partner Network (APN) Technology Partner and an AWS Big Data Competency Partner.
Ever wondered what it takes to keep a user from leaving your game or application after all the hard work you put in? Wouldn’t it be great to get a chance to interact with the users before they’re about to leave?
Finding these users can be difficult, mainly because most churn happens within the first few minutes or hours of a user’s gameplay. However, machine learning (ML) can make this possible by providing insights to help developers identify these users and engage with them to decrease the churn rate.
Upopa is a gaming studio that creates cool games (that you should definitely check out), and they were a great fit for our new project, leveraging Amazon Machine Learning (Amazon ML) to offer game developers an ability to predict the future actions of their players, and ultimately reduce churn without having to learn the complex ML algorithms.
Upopa sends all their data to Amazon Redshift, using ironSource Atom, a data flow management solution that allows developers to send data from their application into many different types of data targets (including Amazon Redshift, Amazon S3, Amazon Elasticsearch Service, and other relational databases) with great ease.
Amazon ML turned out to be the right solution for Upopa, because it integrates easily with Amazon Redshift, and makes everything much easier with visualization tools and wizards that guides you through the process of creating ML models.
All software developers strive to build products that are functional, robust, and bug-free, but video game developers have an extra challenge: they must also create a product that entertains. When designing a game, developers must consider how the various elements—such as characters, story, environment, and mechanics—will fit together and, more importantly, how players will interact with those elements.
It’s not enough to just assume that those interactions occur as intended—is a particular level too difficult? Are the controls responsive enough? Is the combat balanced? While in-house and focus testing can help answer those questions during development, nothing provides a better answer than actual data from real world players.
We’re currently developing Amazon Game Studios’ new title Breakaway; an online 4v4 team battle sport that delivers fast action, teamwork, and competition. We’re releasing Breakaway in a live alpha state on December 15, 2016, so that we can iterate and improve the game by analyzing massive amounts of gameplay data from our player community.
Nate Wiger is Principal Gaming Solutions Architect for AWS.
Dave Lang, Senior Product Manager for Amazon DynamoDB, also contributed to this article.
Amazon DynamoDB is rapidly becoming the go-to database for many of the fastest-growing games in the world. Games like Fruit Ninja (from Halfbrick Studios) and Battle Camp (from PennyPop) have leveraged Amazon DynamoDB’s push-button scalability to seamlessly grow their games to millions of players. Amazon DynamoDB has also been adopted by developers like Supervillain Studios, maker of games like Tower Wars and Tron Evolution.
In this post, you’ll learn how Amazon DynamoDB can help you quickly build a reliable and scalable database tier for a mobile game. We’ll walk through a design example and show how to power a sizable game for less than the cost of a daily cup of coffee. We’ll also profile a fast-growing customer who has scaled to millions of players while saving time and money with Amazon DynamoDB.