Enhanced on-target rate optimization for video using machine learning

A method for audience targeting across multiple content mediums, the method comprising:

• receiving, by at least one processor of a system, streaming video advertisement impression data comprising metadata and content of advertisement bid requests, the metadata indicative of scheduled streaming video presentation;
• receiving, by the at least one processor, survey data indicative of day-part times when television viewers watch content;
• determining, by the at least one processor, a number of non-overlapping demographic groups comprising age ranges;
• generating, by the at least one processor, based on the streaming video advertisement impression data and the survey data, using a first machine learning model, a demographic probability vector for the streaming video advertisement impression data and the survey data, wherein each entry of the demographic probability vector is indicative of a probability that a viewer is in a respective age range of the non-overlapping demographic groups, and wherein the probability is a cross-channel probability for both streaming video and television;
• generating, by the at least one processor, using the first machine learning model, without deterministic identities of the viewers represented by the demographic probability vector, an audience recognition model comprising the demographic probability vector;
• generating, by the at least one processor, based on the streaming video advertisement impression data and the survey data as inputs to a second machine learning model, a synthetic audience model predicting future advertisement viewing behavior associated with multiple viewers including the viewer;
• generating, by the at least one processor, based on the audience recognition model and the synthetic audience model as inputs to a third machine learning model, an assignment of an advertisement bid to a respective demographic group of the non-overlapping demographic groups;
• and
• generating, by the at least one processor, based on the assignment, a ranked list of target demographic groups of the non-overlapping demographic groups for a bid request associated with the advertisement bid.

A system for audience targeting, the system comprising at least one processor coupled to memory, the at least one processor configured to:

• receive streaming video advertisement impression data comprising metadata and content of advertisement bid requests, the metadata indicative of scheduled streaming video presentation;
• receive survey data indicative of day-part times when television viewers watch content;
• determine a number of non-overlapping demographic groups comprising age ranges;
• generate, based on the streaming video advertisement impression data and the survey data, using a first machine learning model, a demographic probability vector for the streaming video advertisement impression data and the survey data, wherein each entry of the demographic probability vector is indicative of a probability that a viewer is in a respective age range of the non-overlapping demographic groups, and wherein the probability is a cross-channel probability for both streaming video and television;
• generate, using the first machine learning model, without deterministic identities of the viewers represented by the demographic probability vector, an audience recognition model comprising the demographic probability vector;
• generate, based on the streaming video advertisement impression data and the survey data as inputs to a second machine learning model, a synthetic audience model predicting future advertisement viewing behavior associated with multiple viewers including the viewer;
• generate, based on the audience recognition model and the synthetic audience model as inputs to a third machine learning model, an assignment of an advertisement bid to a respective demographic group of the non-overlapping demographic groups;
• and
• generate, based on the assignment, a ranked list of target demographic groups of the non-overlapping demographic groups for a bid request associated with the advertisement bid.