Abstract: A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.

Abstract: A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.

Abstract: A system and method for conducting a parameter update event including one or more processors for transmitting first parameter settings to a program used by multiple users, such as a mobile device application at a plurality of mobile devices, receiving performance information indicating performance of the program after the first parameter setting, the performance information for each user being separately identifiable, and for each individual user of the plurality of users, determining a parameter setting update based at least in part on the performance information of the individual user and transmitting the parameter setting update to the program.

Abstract: The present disclosure provides systems and methods for intervention optimization. A computing system obtain an entity history of each of a plurality of entities of a computer application. For each of the plurality of entities, the computing system can determine a respective probability that each of a plurality of available interventions will improve an objective value that is determined based at least in part on a measure of continued use of a computer application by the entity. The computing system can provide interventions of the plurality of available interventions to entities of the plurality of entities based at least in part on the respective probabilities determined via the machine-learned intervention selection model. Thus, a computing system can employ a machine-learned intervention selection model to select, on an entity-by-entity basis, interventions that are predicted to prevent the entity from churning out of the computer application.