Predicting member churn in dynamic groups: A multi-dimensional analysis

Predicting churn in dynamic groups remains a challenge in the field of social network analysis and group behavior modeling. While previous research has focused predominantly on static networks or individual-level predictors, the complex interplay of group dynamics over time is still underexplored. This work proposes a multidimensional framework for analyzing and predicting member churn in dynamic groups by integrating structural and temporal dimensions. Drawing on theories from social networks, group cohesion, and organizational behavior, we examine how fluctuations in individual behavior, group stability and performance, as well as network characteristics relate to member retention and change. We apply key concepts such as individual engagement, group stability, and performance using a comprehensive set of features derived from a dataset of the massively multiplayer online game (MMOG) Travian, which covers over 4700 groups (alliances) and 18,000 players, providing a rich setting to observe dynamic group processes. By applying machine learning models, we demonstrate the predictive value of these multidimensional features, achieving robust performance across various classifiers. This study contributes to the theoretical understanding of churn as an emergent group-level phenomenon and offers practical insights for designing interventions in online communities and team-based environments.