Game Analytics

Learn about the basics of game data science, game analytics, and their advantages.


We may have heard of the term game analytics or game data science. We may have even picked up this course due to the use of the term in industry or academic circles. Game data science has become a cornerstone of game development in a brief period of time. In the 1990s, no one would have thought that game data would become a field of study and innovation in game research and industry. Back then, we were still developing better graphics, development tools, and design practices.

What is game data science?

Fast forward to now: game data science is emerging as a significant field of study due to the emergence of social games embedded in online social networks. The ubiquity of social games gives access to new data sources and impacts essential business decisions, given the introduction of freemiumis a monetization strategy where the barebone service is provided for free, but customers are expected to pay for additional elements such as vanity items, in-game currency, and faster cooldowns. business models.

Game data science is a broad domain covering all aspects of collecting, storing, and analyzing data, and communicating insights.

Freemium is a monetization strategy where the barebone service is provided for free, but customers are expected to pay for additional elements, such as vanity items, in-game currency, and faster cooldowns.

xIt can support any element of design and development, and it’s not only about player behavior, although that’s undoubtedly an important part of the process. With a mature data science framework in place, companies can gain objective knowledge about workflows and competitors, understand their communities and players, improve development processes, increase retention and revenue, and build the capacity to offer games for free to customers.

Purpose of game data science

Game data science fundamentally aims to add data-driven evidence to support decision making across operational, tactical, and strategic levels of game development, and this is why it’s so valuable. It allows researchers and the industry to move away from guesswork and make decisions based on carefully collected, curated, and analyzed data.

As the knowledge and practices in game data science are expanding rapidly, the ideas, methods, and tools presented will also likely expand as new solutions become available. We’ll discuss foundational approaches and theories that will help us understand current and future directions of game data science.

With this introductory chapter, we begin our journey in the field of game data science. In particular, this chapter will provide a high-level panoramic introduction to the processes used to analyze and make sense of game data and suggest actionable information with the scientific method as a base process. The material discussed is conceptual, providing us with the basics as we embark on the journey of understanding and practicing game data science.

Analysis in game data science

Fundamentally, game data science is the process of discovering and communicating patterns in data with the purpose of informed decision making in different domains, such as business or design, in the context of games. As such, game data science includes many types of analyses, such as summarizing the number of active players within a certain time unit, predicting when players will stop playing a game, or evaluating the performance of servers.

Game data science thus overlaps substantially with other data-informed processes in game development, including Games User Research (GUR), business intelligence as it’s applied in the games industry, and marketing and brand research. While there’s much ongoing discussion in the community about what exactly game data science is and is not, in this course, we’ll adopt an inclusive viewpoint rather than trying to set limits around the term.

To summarize, game data science is the term we use collectively for the process of providing data-driven evidence for decisions made at various parts of the game design, development, and production processes. We can apply the tools and techniques of game data science virtually across any aspect of the game design and development processes.

Games User Research (GUR) is a field of study that focuses on understanding user behaviors, needs, and motivations by analyzing how the design of a specific application or game impacts its audience. Researchers working in this area are also tightly coupled with game analysts as some of the processes used by game user researchers also use game data.