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We use a 575,000-subject, 28-day experiment to investigate monetary policy in a virtual setting. The experiment tests the effect of virtual currency endowments on player retention and virtual currency demand. An increase in endowments of a virtual currency should lower the demand for the currency in the short run. However, in the long run, we would expect money demand to rise in response to inflation in the virtual world. We test for this behavior in a virtual field experiment in the football management game Top11. 575,000 players were selected at random and allocated to different “shards” or versions of the world. The shards differed only in terms of the initial money endowment offered to new players. Money demand was observed for 28 days as players used real money to purchase additional virtual currency. The results indicate that player money purchases were significantly higher in the shards where higher endowments were given. This suggests that a positive change in the money supply in a virtual context leads to inflation and increased money demand, and does so much more quickly than in real-world economies. Differences between virtual and real currency behavior will become more interesting as virtual currency becomes a bigger part of the real economy. [ABSTRACT FROM AUTHOR]

Customer churn is a widely known term in many industries, including banking, telecommunications and gaming. By definition, churn represents the act of a customer leaving a product for good. Most commonly, late customer churn is addressed. In the dynamics of free to play games, most of newly registered users abandon the game in the first few days, so the main focus is on early customer churn. Therefore, successful early churn prevention methodology is vital to having a successful business in free to play gaming industry. To tackle this problem, we introduce a two stage intelligent system. It employs early churn prediction, formulated as a binary classification task, followed by a churn prevention technique using personalized push notifications. For early churn prediction, common machine learning models are trained and compared using a data set obtained from two million players of Top Eleven - Be A Football Manager online mobile game. To prevent churn, we track user activity, identify the game features that are potentially interesting to the user and then use that data to tailor personalized push notifications with a purpose to attract users back into the game. Using this approach, we are able to reduce churn up to 28%, which, at the scale of millions of users, represents a significant positive impact to business. [ABSTRACT FROM AUTHOR]