Abstract: A machine may be configured to predict the performance of online content. For example, the machine accesses data pertaining to delivery of a campaign. The campaign may be an online ad campaign including one or more ads to be delivered to one or more users. The machine identifies a stage of the campaign at a particular time. The identifying of the stage may be based on the data pertaining to the delivery of the campaign. The stage corresponds to a particular period in a life of the campaign. The machine generates a predicted revenue value for the campaign based on the stage of the campaign. The predicted revenue value represents an estimated total revenue deliverable during the campaign. The machine causes a presentation of the predicted revenue value in a user interface of a device associated with an administrator.

Abstract: A machine may be configured to determine a revenue risk value representing a predicted revenue loss amount resulting from a predicted non-delivery of online advertising associated with a customer and for facilitating a minimization of the revenue risk value over a campaign delivery period. For example, the machine accesses a booked revenue value booked for delivering online advertising during a delivery period. The machine accesses a predicted revenue delivery value representing a predicted revenue amount corresponding to online advertising forecast to be delivered within the delivery period. The machine determines a revenue risk value based on the booked revenue value and the predicted revenue delivery value. The revenue risk value represents a predicted revenue loss amount resulting from a predicted non-delivery of online advertising. The machine causes presentation of the revenue risk value in a user interface of a device.

Abstract: A machine may be configured to facilitate visualization of online advertising revenue trends. For example, the machine accesses revenue booking data associated with a customer and identifying a revenue amount booked for delivering an ad product during a campaign delivery period of an advertising campaign associated with the customer. The machine determines a daily booking value for each date in the advertising campaign delivery period based on the revenue booking data and additional campaign data. The machine generates a revenue booking graph for the ad product based on the daily booking value for each date in the advertising campaign delivery period. The machine causes display of the revenue booking graph for the ad product in a user interface of a device.