Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.

Abstract: Systems and methods for optimizing content performance using creative extensions are provided. A content generation system receives request for a content item for presentation on a client device. The request includes an indication of a serving context for the content item. The content generation system uses a creative extension performance model and the serving context for the content item to calculate a predicted performance metric for the content item for multiple different potential creative extensions. Each of the potential creative extensions defines a different action that occurs in response to a user interaction with the content item. The content generation system selects one of the potential creative extensions based on the predicted performance metrics and generates a content item having the selected creative extension using data assets extracted from various data sources. The creative extension performance model is updated using event data from the client device.