People play game with different motives and expectations. Yet, game developers tend to rely on a single predictive model to segment and understand their players. TENTUPLAY breaks from the norm by building multiple empirically proven models that each correspondingly map distinct behavioral tendencies of players. Such approach overhauls existing drawbacks in single modeling paradigm by yielding adaptive and comprehensive player analytics. Multi-model analysis systematically identifies the most suitable models to employ for each game genre and its lifecycle stage. The models then analyze player behavior throughout their journey from D1 to synthetically reveal the reasoning behind their churn and purchase. Understanding these individual retention and consumption behavior is integral to leveraging further engagement with hyper-personalizing gaming experience for each player. This session dives deeper into the evidence of multi-model's advantage and how it is substantively implemented to encourage more purchase and longer play time.