Dynamically Adjusting Digital Component Campaign Values For Disengaged Consumers

A system, comprising:

• one or more processors, the one or more processors configured to:
• receive digital component campaign information comprising an initial conversion value, one or more additional conversion values, an initial bid value, one or more additional bid values, and ground truth data identifying one or more qualifying consumers;
• receive a request for a digital component;
• generate, by executing a first stage model of an artificial intelligence (AI) model trained using the ground truth data, one or more consumer embedding representations;
• generate, by executing a second stage model of the AI model trained using the ground truth data, one or more merchant embedding representations, wherein at least one of the first or second stage model is trained offline;
• provide, as input to the AI model the request for the digital component, the one or more consumer embedding representations, and the one or more merchant embedding representations;
• determine, by executing the AI model online, at least one of a new consumer probability or a new qualifying consumer probability, wherein:
• the new consumer probability corresponds to a probability a consumer to be presented the digital component is a new consumer, and
• the new qualifying consumer probability corresponds to a probability the consumer to be presented the digital component is a new qualifying consumer;
• and
• dynamically adjust, based on at least one of the one or more additional bid values or the one or more additional conversion values, and at least one of the new consumer probability or the new qualifying consumer probability, at least one of a resulting bid value or a resulting conversion value associated with the digital component.

One or more non-transitory computer-readable media storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving digital component campaign information comprising an initial conversion value, one or more additional conversion values, an initial bid value, one or more additional bid values, and ground truth data identifying one or more qualifying consumers;
• receiving a request for a digital component;
• generating, by executing a first stage model of an artificial intelligence (AI) model trained using the ground truth data, one or more consumer embedding representations;
• generating, by executing a second stage model of the AI model trained using the ground truth data, one or more merchant embedding representations, wherein at least one of the first or second stage model is trained offline;
• providing, as input to the AI model the request for the digital component, the one or more consumer embedding representations, and the one or more merchant embedding representations;
• determining, by executing the AI model online, at least one of a new consumer probability or a new qualifying consumer probability, wherein:
• the new consumer probability corresponds to a probability a consumer to be presented the digital component is a new consumer, and
• the new qualifying consumer probability corresponds to a probability the consumer to be presented the digital component is a new qualifying consumer;
• and
• dynamically adjusting, based on at least one of the one or more additional bid values or the one or more additional conversion values, and at least one of the new consumer probability or the new qualifying consumer probability, at least one of a resulting bid value or a resulting conversion value associated with the digital component.