Player retention is one of the biggest challenges in game development, and churn prediction models can be game-changers. I'm interested in learning what indicators you monitor to identify players at risk of leaving. Do you track metrics like session frequency decline, engagement drop-offs, or social disconnection? What machine learning models have you found most effective - logistic regression, random forests, or more advanced neural networks? How far in advance can you reliably predict churn, and what intervention strategies have worked best when you identify at-risk players? Have you had success with personalized notifications, special offers, or content recommendations? I'd also love to know how you balance prediction accuracy with the cost of false positives in your retention campaigns.