Abstract: Various systems and methods for providing a tool to entities that determines the optimal usage of data items are disclosed. For example, the tool can generate a model that uses various characteristics to predict how likely it is that a viewer will watch (or listen to) the media program being promoted. The model can then determine an increase in revenue that would result from the assignment of a media promo using the predicted likelihood and subtracting a known opportunity cost from this determined revenue increase to determine a net revenue value. The model can repeat this determination for any number of viewers and aggregate the determined net revenue values to generate an aggregated net revenue value. The tool may include a user interface in which a content provider can adjust various variables to see how adjusting one or more variables affects the aggregated net revenue value.

Abstract: Various systems and methods for providing a tool to entities that determines the optimal usage of data items are disclosed. For example, the tool can generate a model that uses various characteristics to predict how likely it is that a viewer will watch (or listen to) the media program being promoted. The model can then determine an increase in revenue that would result from the assignment of a media promo using the predicted likelihood and subtracting a known opportunity cost from this determined revenue increase to determine a net revenue value. The model can repeat this determination for any number of viewers and aggregate the determined net revenue values to generate an aggregated net revenue value. The tool may include a user interface in which a content provider can adjust various variables to see how adjusting one or more variables affects the aggregated net revenue value.

Abstract: Various systems and methods for providing a tool to entities that determines the optimal usage of data items are disclosed. For example, the tool can generate a model that uses various characteristics to predict how likely it is that a viewer will watch (or listen to) the media program being promoted. The model can then determine an increase in revenue that would result from the assignment of a media promo using the predicted likelihood and subtracting a known opportunity cost from this determined revenue increase to determine a net revenue value. The model can repeat this determination for any number of viewers and aggregate the determined net revenue values to generate an aggregated net revenue value. The tool may include a user interface in which a content provider can adjust various variables to see how adjusting one or more variables affects the aggregated net revenue value.

Abstract: In one embodiment, a data processing method comprises generating impression estimate data that specifies an estimated total number of impressions of a plurality of non-program media items that are possible during future transmission of a plurality of media programs via a plurality of media channels; based on the impression estimate data, generating campaign data specifying a target number of impressions of a particular non-program media item and specifying one or more particular media channels to receive the particular non-program media item; based on the campaign data, and based upon transmission schedule data that specifies future times of transmission of the plurality of media programs via the plurality of media channels, automatically determining digitally stored promotional schedule data that specifies a plurality of times to transmit the particular non-program media item to achieve the target number of impressions; wherein the method is performed by one or more computing devices.

Abstract: In one embodiment, a data processing method comprises generating impression estimate data that specifies an estimated total number of impressions of a plurality of non-program media items that are possible during future transmission of a plurality of media programs via a plurality of media channels; based on the impression estimate data, generating campaign data specifying a target number of impressions of a particular non-program media item and specifying one or more particular media channels to receive the particular non-program media item; based on the campaign data, and based upon transmission schedule data that specifies future times of transmission of the plurality of media programs via the plurality of media channels, automatically determining digitally stored promotional schedule data that specifies a plurality of times to transmit the particular non-program media item to achieve the target number of impressions; wherein the method is performed by one or more computing devices.

Abstract: In one embodiment, a data processing method comprises generating impression estimate data that specifies an estimated total number of impressions of a plurality of non-program media items that are possible during future transmission of a plurality of media programs via a plurality of media channels; based on the impression estimate data, generating campaign data specifying a target number of impressions of a particular non-program media item and specifying one or more particular media channels to receive the particular non-program media item; based on the campaign data, and based upon transmission schedule data that specifies future times of transmission of the plurality of media programs via the plurality of media channels, automatically determining digitally stored promotional schedule data that specifies a plurality of times to transmit the particular non-program media item to achieve the target number of impressions; wherein the method is performed by one or more computing devices.

Abstract: In one embodiment, a data processing method comprises generating impression estimate data that specifies an estimated total number of impressions of a plurality of non-program media items that are possible during future transmission of a plurality of media programs via a plurality of media channels; based on the impression estimate data, generating campaign data specifying a target number of impressions of a particular non-program media item and specifying one or more particular media channels to receive the particular non-program media item; based on the campaign data, and based upon transmission schedule data that specifies future times of transmission of the plurality of media programs via the plurality of media channels, automatically determining digitally stored promotional schedule data that specifies a plurality of times to transmit the particular non-program media item to achieve the target number of impressions; wherein the method is performed by one or more computing devices.