Abstract: The present disclosure is directed toward systems and methods for optimizing view-through conversion rates. For example, systems and methods described herein train and utilize a machine learning model that predicts whether providing a digital impression to a particular networking system user will result in a conversion. Systems and methods described herein identify view-through conversions by generating a vector associated with the provision of a digital impression to a networking system user and receiving third-party conversion information during an attribution window. The systems and methods described herein then utilize the vector and conversion information to train the machine learning model to predict future conversions.

Abstract: The present disclosure is directed toward systems, methods, and non-transitory computer readable media that use multi-point optimization for delivery of digital content by way of a digital content distribution platform. In particular, one or more embodiments described herein receive a content item from a content provider to be displayed to users of the platform. The embodiments optimize delivery to obtain a first target event and determine metrics that delivery of the content item is expected to satisfy. If the actual metrics of delivery fail to satisfy the expected metrics, delivery is re-optimized to obtain either the first target event or a second target event.

Abstract: The present invention provides techniques for use in providing advertisers and other entities with information relating to target audiences. Techniques are provided in which, in reply to an advertiser query, the advertiser is provided with, in connection with a specified target audience, topics of interest, levels of interest per topic, and a level or levels of engagement with the advertiser. Other information may also be provided, including topic of interest trending information, as well as topics of interest that best differentiate between the target audience and a specified comparison audience.