Churn can be interpreted as customer defection and can be considered one of the most critical challenges in the Game Analytics domain because of its impact on the game industry's profit. When predicting churn, the first step is defining what is considered churn, which can change depending on the players' behaviors and approaches. This work studied related works and revealed two recurrent issues in the labeling process: limitations on the adopted labeling approaches (1) and the static definition of churn (2). To mitigate the first issue, an individualized labeling approach was deployed. To address the second one, a novel evaluation method, based on the impact of a change in the churn definition, was proposed. This method allowed the proposition of two new labeling approaches, which were included in the analysis. By comparing the labeling approaches in two games using a profit perspective, it was identified that the new ones present statistically significant benefits compared to the traditional ones. Regarding the evaluation method, its usage can justify when the redefinition of churn and the classifier's retraining should happen to improve profit. The results are valuable for the game context, potentially extended to other contexts by delivering more reliable labels and more validated classification performance.

Players can change their interest in continuing playing due to many reasons, such as the game content available to them. Therefore, game upgrades play an important role as they have the potential to influence players, being it a "double-edged sword", as players may like the new challenges or not. Among the active players, "whales" are those players that are the most profitable ones. The goal of this paper is to answer the following research question: "What are the influences of game upgrades on profitable players behavior?". To do that, we propose to apply and jointly interpret the results of four metrics (or KPIs): Commitment, Key Risk Indicator, Available Motivational Growth, and Game Refinement Value. This approach was applied to an MMORPG dataset that contains four kinds of upgrades. As results, the proposed joining identified three key influences and two players aspects, showing the potential to be used by game producers to evaluate the acceptance and influences of their upgrades in real situations.

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