Automatic performance-triggered campaign adjustment

Abstract

Automatic performance triggered campaign adjustment. A hierarchical feature tree is generated. Each child node's feature is more specific than its respective parent node's feature. The discovery system creates a behavioral model comprising features of the feature tree which is used in the operation of an advertising campaign. A degraded model feature is detected at the discovery system by comparing a performance metric of a model feature from two different time windows. The discovery system matches a node of the feature tree with the degraded feature and selects a prospective model feature from an ancestor node of the matching feature's node. An estimated performance metric for the prospective model feature is determined and the results are used to decide if the prospective model feature should be incorporated into an updated model or not. The model can be updated with a new model feature selected from one or more prospective model features.