User Behavior Analysis and Clustering in a MMO Mobile Game: Insights and Recommendations

TL;DR

This paper analyzes user behavior in a mobile battle royale game using data mining techniques to identify player segments, revealing insights for personalized experiences and retention strategies.

Contribution

It introduces a comprehensive methodology combining temporal and static data mining for user segmentation in mobile games, with novel visualization and analysis of player clusters.

Findings

Identified five distinct user segments with varying engagement and skill levels.

Revealed correlations between cluster cohesion and player activity.

Provided data-driven recommendations for game personalization and marketing.

Abstract

This study presents a comprehensive analysis of user behavior and clustering in a popular mobile battle royale game, employing temporal and static data mining techniques to uncover distinct player segments. Our methodology encompasses time series K-means clustering, graph-based algorithms (DeepWalk and LINE), and static attribute clustering, visualized through innovative hybrid charts. Key findings reveal significant variations in player engagement, skill levels, and social interactions across five primary user segments, ranging from highly active and skilled players to inactive or new users. We also analyze the impact of external factors on user retention and the network structure within clusters, uncovering correlations between cluster cohesion and player activity levels. This research provides valuable insights for game developers and marketers, offering data-driven recommendations for personalized game experiences, targeted marketing strategies, and improved player retention in online gaming environments.