Media spend management using real-time predictive modeling of touchpoint exposure effects

A computer readable medium, embodied in a non-transitory computer readable medium, the non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by a processor causes the processor to perform a set of acts, the acts comprising:

• storing in a computer, stimuli data for a plurality of touchpoint encounters that represent a plurality of messages exposed to a plurality of users, wherein the stimuli data comprises a plurality of attributes that characterize the deployment of the messages,
• storing, in a computer, response data for the touchpoint encounters that comprise converting user data, which identifies touchpoint encounters for the users that exhibited a positive response to the message, and non-converting user data that identifies touchpoint encounters for the users that exhibited a negative response to the message;
• training, using machine-learning techniques in a computer, the attributes of the stimuli data with the converting user data and the non-converting user data of the response data to generate a touchpoint response predictive model that correlates the attributes for deployment of the message to the response of the message;
• generating, in a computer, a touchpoint exposure predictive model that models the relationship between the number of messages deployed and the response so as to model diminishing returns on the response due to the number of messages;
• rendering, on a display of a user computer, from the touchpoint exposure predictive model, at least one predicted message deployment—response curve for the messages that depicts the effectiveness of the response to the messages as a function of one or more of the attributes of the messages;
• receiving, through an interface of the user computer, input to increase the deployment of the messages;
• and
• rendering, on the display of the user computer, from the touchpoint exposure predictive model, a modified message deployment—response curve for the messages that incorporates diminishing returns as a result of the increase in deployment of the messages.

A system comprising:

• a storage medium, having stored thereon, a sequence of instructions;
• at least one processor, coupled to the storage medium, that executes the instructions to cause the processor to perform a set of acts comprising:
• storing in a computer, stimuli data for a plurality of touchpoint encounters that represent a plurality of messages exposed to a plurality of users, wherein the stimuli data comprises a plurality of attributes that characterize the deployment of the messages;
• storing, in a computer, response data for the touchpoint encounters that comprise converting user data, which identifies touchpoint encounters for the users that exhibited a positive response to the message, and non-converting user data that identifies touchpoint encounters for the users that exhibited a negative response to the message;
• training, using machine-learning techniques in a computer, the attributes of the stimuli data with the converting user data and the non-converting user data of the response data to generate a touchpoint response predictive model that correlates the attributes for deployment of the message to the response of the message;
• generating, in a computer, a touchpoint exposure predictive model that models the relationship between the number of messages deployed and the response so as to model diminishing returns on the response due to the number of messages;
• rendering, on a display of a user computer, from the touchpoint exposure predictive model, at least one predicted message deployment response curve for the messages that depicts the effectiveness of the response to the messages as a function of one or more of the attributes of the messages;
• receiving, through an interface of the user computer, input to increase the deployment of the messages;
• and
• rendering, on the display of the user computer, from the touchpoint exposure predictive model, a modified message deployment—response curve for the messages that incorporates diminishing returns as a result of the increase in deployment of the messages.