Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with income and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of encountered income for a selected set of predefined signage locations; converts the predicted values of encountered income in the database into percentages of observed values of encountered income for signage locations within the selected set over a specified time period to create indices of encountered income; and rank orders the signage locations within the selected set according to their indices of encountered income.

Abstract: A method, apparatus, and computer program product for digitally presenting statistically-relevant business insights into a set of business metrics for an organization. A computer system identifies a set of organizational characteristics from human resources data of employees of a plurality of organizations, and applies a selected inclusion criteria to the set of organizational characteristics to identify a set of candidate organizations. The computer system identifies a set of benchmark organizations from the set of candidate organizations, and creates a fixed panel of the benchmark organizations. The computer system applies the fixed panel to the human resources data of employees of a plurality of organizations to create an analysis dataset that consists of human resources data of employees of the benchmark organizations. The computer system generates a business insight into the set of business metrics of the organization based on the analysis dataset, and digitally presents the business insight.

Abstract: A method, an apparatus, and a computer program product for digitally presenting a competitive human resources migration model for an organization. A computer system identifies human resources data regarding employees of a set of benchmark organizations. The human resources data comprises employee names and job types. The computer system indexes the human resources data according to job types. The computer system determines a set of geographic distribution trends for each of the job types. The set of geographic distribution trends is determined based on employee names of the employees. The computer system determines a competitive human resources migration model for the organization based on an effect of the set of geographic distribution trends on business metrics for the set of benchmark organizations. The computer system digitally presents the competitive human resources migration model for the organization.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with income and geographic location; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of average income for a selected set of predefined geographic regions; converts the predicted values of average income in the database into percentages of observed values of average income for geographic regions within the selected set over a specified time period to create indices of average income; and rank orders the regions within the selected set according to their indices of average income.

Abstract: A method, computer system, and computer program product that aggregates data regarding a plurality of factors correlated with income; performs iterative analysis on the data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted income values for a selected range of housing costs; converts the predicted income values in the database into percentages of observed income values for a selected group of people within the selected range of housing costs over a specified time period to create indices of financial stress; and rank orders the people within the selected group according to their indices of financial stress.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with employment; performs iterative analysis on the data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted employment values for predefined geographic regions; converts the predicted employment values in the database into percentages of observed employment values for the predefined geographic regions over a specified time period to create indices of workforce elasticity for each geographic region; and rank orders the predefined geographic regions according to their indices of workforce elasticity.

Abstract: A method, computer system, and computer program product that aggregates sample data regarding a plurality of factors associated with work scheduling, employee compensation, and employee tenure; performs iterative analysis on the sample data using machine learning to construct a predictive model; populates, using the predictive model, a database with predicted values of employee tenure in relation to work scheduling and employee compensation; converts the predicted values of employee tenure in the database into percentages of observed values of employee tenure for a selected group of employers over a specified time period to create indices of employee tenure; and rank orders the selected employers according to their indices of employee tenure.

Abstract: A method, computer system, and computer program product for on-demand job messaging to predicted employees. A machine intelligence application compares data for an employee to a job change interest model. Responsive to comparing the data for the employee to the job change interest model, the machine intelligence application determines whether the employee is a predicted employee. The predicted employee enables improved targeting of job messaging by a job messaging application in a computer system.

Abstract: A method and apparatus for reducing an amount of resources required for targeted advertising to bonus recipients accesses a payroll database, identifies from the payroll database an employee, a bonus payment date for the employee, and a bonus payment amount for the employee, determines a selected luxury item based on a luxury item preference score in a consumer profile for the employee, determines the amount of resources assigned to a targeted advertisement for the selected luxury item, and delivers the targeted advertisement for a selected luxury item on the amount of resources, wherein using the luxury item preference score enables reducing the amount of resources for delivering the targeted advertisement eliminates advertising for luxury items with less probability of causing the employee to spend the bonus amount on a luxury item than the selected luxury item.

Abstract: A method, a computer system, and a computer program product for managing inventory for an organization. The computer system identifies human resources data regarding employees of organizations. The human resources data comprises geolocation data based at least partially on a geolocation of the organization and geolocations of the employees. The computer system determines a change in market dynamics relating to a set of geographic regions based at least partially on the human resources data. The computer system identifies transaction data for transactions involving a business merchant within a particular geographic region and a consumer. The computer system generates a suggested inventory for the business merchant. The suggested inventory is based at least partially on the determined change in market dynamics for the set of geographic regions in the transaction data for the business merchant.