Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.

Abstract: Techniques for integrating two server systems are provided. A first server system receives, from a first computing device, provider data generated by a third-party tracking service. The first server system stores association data that associates the provider data with one or more content items. After storing the association data, the first server system receives, from a second computing device, a message that includes a first identifier and that indicates an event and a content item. In response to receiving the message, the first server system determines whether to report the event to the third-party tracking service. If so, the first server system determines a second identifier based on the first identifier, retrieves, based on the association data and the content item, the provider data that is associated with the content item, and transmits, over a network to the third-party tracking service, the second identifier and the provider data.

Abstract: Techniques for optimizing content item delivery for installations or activations of a mobile application are provided. In one technique, a machine-learned model is trained based on multiple training instances that individually indicate whether an entity performed a particular action relative to a mobile application. In response to receiving a content item request from a third-party content delivery exchange, it is determined whether a client device that initiated the content item request has activated a particular application. In response to determining that the client device has not activated the particular application, multiple feature values of the content item request are identified. Based on inputting the feature values into the model, a score is generated that indicates a likelihood that an entity of the client device will perform the particular action relative to the particular application. Based on the score, a content item is transmitted over a network to the client device.

Abstract: Techniques for integrating two server systems are provided. A first server system receives, from a first computing device, provider data generated by a third-party tracking service. The first server system stores association data that associates the provider data with one or more content items. After storing the association data, the first server system receives, from a second computing device, a message that includes a first identifier and that indicates an event and a content item. In response to receiving the message, the first server system determines whether to report the event to the third-party tracking service. If so, the first server system determines a second identifier based on the first identifier, retrieves, based on the association data and the content item, the provider data that is associated with the content item, and transmits, over a network to the third-party tracking service, the second identifier and the provider data.

Abstract: Generally discussed herein are methods, systems, and apparatuses for tracking user interaction with sponsored and/or unsponsored content. A method can include receiving a tracking event packet from a front end tracking client, the tracking event packet including data fields comprising a tracking event identifier, a type of user interaction with the content, a timestamp, a cost, or a campaign identifier, determining the tracking event packet has not been previously processed, interpreting and formatting data in the tracking event packet based on an application of the plurality of applications from which the event packet originated to create formatted tracking data, determining whether the tracking event packet corresponds to a revenue generating tracking event based on the formatted tracking data, and providing the formatted tracking data to a database based on whether the packet corresponds to revenue generating event.