The emergence of new business models, technical innovations and the popularity of online games have resulted in a deluge of player behavior data. While this has enabled new opportunities, it also presents a serious challenge for game analytics, which must address the large, time-dependent and high-dimensional nature of these data sets. Clustering offers a promising method to explore this space, as it allows for the identification of patterns that reduce overall complexity and allow for more precise analysis.

Behavioral Pattern Identification

The identification of recurring patterns UFABET เข้าสู่ระบบ และเริ่มเล่นทันที in player behavior is one of the key aspects of game analytics. It can be used to build detailed profiles of players that map out their motivations and preferences, which in turn informs how these are reflected in the design of the game. This can include personality mapping (e.g. identifying player traits such as honesty/humility, emotionality, extraversion and agreeableness), lifestyle correlation (e.g. examining how in-game actions correlate with outside-game activities), and even gameplay loop analysis (e.g. determining which sequences of gameplay are most popular with players).

Another type of behavioral analytics is predictive, which involves analyzing player data to predict future behavior. This can be used to identify players who are at risk of quitting a game, which in turn can help developers implement measures to retain them (e.g. delivering gaming events and content that are suited to their playing style). Researchers at NC State have recently developed a novel method for doing just this, predicting player behavior with up to 80% accuracy.