Player churn analysis is crucial for arcade game developers to understand and reduce user attrition, ultimately enhancing game longevity and profitability. Several common types of churn analysis are widely employed in the industry. First, behavioral segmentation analysis categorizes players based on in-game actions, such as session frequency, spending patterns, and level progression, to identify at-risk groups. Second, churn prediction models utilize machine learning algorithms to forecast which players are likely to leave, enabling proactive interventions like personalized offers or notifications. Third, cohort analysis examines player retention over time by grouping users based on their sign-up date or first play session, revealing trends and the impact of game updates. Fourth, funnel analysis tracks player drop-offs at key stages, such as tutorial completion or first purchase, to pinpoint friction points. Lastly, feedback-driven analysis incorporates player surveys and reviews to gather qualitative insights into churn reasons, such as boredom or technical issues. By combining these methods, developers can implement targeted retention strategies, improve game design, and sustain a vibrant player community.