Dissertation/Thesis Abstract

User retention and classification in a mobile gaming environment
by Ruffin, Michael, M.S., California State University, Long Beach, 2014, 52; 1527021
Abstract (Summary)

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.

Indexing (document details)
Advisor: Korosteleva, Olga
Commitee:
School: California State University, Long Beach
School Location: United States -- California
Source: MAI 54/02M(E), Masters Abstracts International
Source Type: DISSERTATION
Subjects: Applied Mathematics, Mathematics, Statistics
Keywords: Business intelligence, Cluster analysis, Machine learning techniques, Mobile games, Non-parametric classification, Statistics
Publication Number: 1527021
ISBN: 978-1-321-37055-3
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