Game analytics is a fast growing field where game studios are allocating valuable resources to develop sophisticated statistical models to understand user behavior and monetization habits to optimize game play and performance. Game developers' ability to understand user retention allows for game features that will generate high engagement leading to stronger overall monetization and increased lifetimes of players.
One important industry adopted metric is the percentage of users who log back into the game one day after installation, otherwise known as a one-day retention. Although this is an important metric, game studios typically allocate little resources to determining what user transactions are typically conducted on the day of installation that drive a one-day retention.
In this project, we first conduct a cluster analysis in an attempt to uncover meaningful subgroups based on players' transaction history on their first day of installation. Secondly, we use various classification methods including decision trees, logistic regression, and k-Nearest Neighbor algorithm to determine which behaviors are important in identifying whether a new user will return the following day.
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 54/02M(E), Masters Abstracts International|
|Subjects:||Applied Mathematics, Mathematics, Statistics|
|Keywords:||Business intelligence, Cluster analysis, Machine learning techniques, Mobile games, Non-parametric classification, Statistics|
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