Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.

Abstract: A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.

Abstract: A method and a system for identifying players likely to spend in an online game are described. Indicators corresponding to activities of the player and in-game friends of the player in an online game and in other online games are determined. A prediction model of the player's likelihood to spend in the online game is computed based on the indicators. The prediction model is used to identify players of the online game that are likely to spend in the online game.

Abstract: This disclosure describes systems and methods for including advertisements in outgoing electronic messages and rewarding the sender for every recipient who clicks on an included advertisement. The disclosure further describes various methods for selecting keywords used to generate the advertisement as well as a method for allowing the sender to directly select the advertisement from an advertisement listing. Furthermore, the disclosure discusses various forms of rewarding the sender including a revenue sharing method.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.

Abstract: A method and a system for predicting churn of a player of an online game is described. Online engagements of a group of players of the online game are monitored during a churn prediction model training period. Online engagement scores for the group of players are computed within a periodic number of days within the churn model training period. A weighted exponential moving average of the online engagement scores of the group of players of the online game is computed during the churn model training period. The weighted exponential moving average is used to determine an online engagement threshold value of a churn prediction model for the online game. The online engagement threshold value is applied to a weighted exponential moving average of a player during an observation period to determine a churn probability of the player within a prediction period.

Abstract: A communication analysis system for an online game is described. A communication history among players of the online game is retrieved. Communication tracking metrics are generated based on the communication history of the players of the online game. A retention analysis is performed with the communication tracking metrics to determine retention of players of the online game. A retention recommendation is generated based on the retention analysis.

Abstract: This disclosure describes systems and methods for including advertisements in outgoing electronic messages and rewarding the sender for every recipient who clicks on an included advertisement. The disclosure further describes various methods for selecting keywords used to generate the advertisement as well as a method for allowing the sender to directly select the advertisement from an advertisement listing. Furthermore, the disclosure discusses various forms of rewarding the sender including a revenue sharing method.