Method and system for predictive modeling of consumer profiles

A machine learning predictive modeling system, comprising:

• a computer system;
• one or more processors running on the computer system, wherein the one or more processors aggregate sample data regarding a plurality of factors correlated with demographic parameters;
• perform iterative analysis on the data using machine learning to construct a predictive model of purchasing propensity;
• populate, using the predictive model of purchasing propensity, a database with predicted values of spending propensity for selected demographic parameters, wherein the spending propensity is a metric representing a propensity to buy specific consumer products or types of products in relation to the demographic parameters;
• convert the predicted values of spending propensity in the database into percentages of observed values of spending propensity for a selected group of people within the selected demographic parameters over a specified time period to create indices of spending propensity without enabling individual person identification of any person in the selected group of people;
• rank order the people within the selected group according to their indices of spending propensity in order to identify target markets based on the selected demographic parameters;
• wherein the one or more processors running on the computer system compare the rank ordering of indices of spending propensity to observed spending propensity of the selected group over a second specified time period;
• aggregate updated sample data over the second specified time period;
• and update the predictive model using machine learning incorporating the updated sample data for the second specified time period.

A computer program product for machine learning predictive modeling, the computer program product comprising:

• a persistent computer-readable storage media;
• first program code, stored on the computer-readable storage media, for aggregating data regarding a plurality of factors correlated with demographic parameters;
• second program code, stored on the computer-readable storage media, for performing, by one or more processors, iterative analysis on the data using machine learning to construct a predictive model of purchasing propensity;
• third program code, stored on the computer-readable storage media, for populating, using the predictive model of purchasing propensity, a database with predicted values of spending propensity for selected demographic parameters, wherein the spending propensity is a metric representing a propensity to buy specific consumer products or types of products in relation to the demographic parameters;
• fourth program code, stored on the computer-readable storage media, for converting the predicted values of spending propensity in the database into percentages of observed values of spending propensity for a selected group of people within the selected demographic parameters over a specified time period to create indices of spending propensity without enabling individual person identification of any person in the selected group of people;
• fifth program code, stored on the computer-readable storage media, for rank ordering the people within the selected group according to their indices of spending propensity in order to identify target markets based on the selected demographic parameters;
• sixth program code, stored on the computer-readable storage media, for comparing the rank ordering of indices of spending propensity to observed spending propensity of the selected group over a second specified time period;
• seventh program code, stored on the computer-readable storage media, for aggregating updated sample data over the second specified time period;
• and
• eighth program code, stored on the computer-readable storage media, for updating the predictive model using machine learning incorporating the updated sample data for the second specified time period.