Predictive Consumer Profile Modeling via Privacy-Preserving Indices
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Solution Overview
Problem
Existing methods for predicting consumer spending propensities rely on specific demographic information, which can violate privacy rights by revealing personal identities, as more detailed information increases the likelihood of identifying individuals.
Innovation Solution
A computer-implemented method using machine learning to aggregate and analyze demographic data, constructing a predictive model of purchasing propensity without revealing personal identity information by converting predicted values into indices of spending propensity, allowing for ranking of individuals within selected demographic groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed demographic and employment information is supplied to improve prediction accuracy, then the predictive modeling capability is improved, but the probability of identifying personal identities increases, violating privacy rights
Solution Approach 1:
The patent extracts and removes personally identifiable information from the dataset, retaining only aggregated demographic and employment statistics. This allows the system to maintain predictive capability through statistical patterns while eliminating direct references to individual identities, thus resolving the contradiction between prediction accuracy and privacy protection
Solution Approach 2:
The patent introduces an intermediary aggregation layer that transforms individual-level data into group-level statistical summaries. This intermediary structure enables the system to analyze consumer behavior patterns without exposing personal identities, serving as a mediator between the need for detailed data and the requirement for privacy protection
2Productivity
If more specific consumer information is collected to enable targeted marketing, then the marketing effectiveness is improved, but the risk of revealing personal identity information increases
Solution Approach 1:
The patent segments consumer data into distinct demographic categories and employment sectors, analyzing patterns at these segmented levels rather than at the individual level. This segmentation allows targeted marketing strategies to be developed based on group characteristics while preventing identification of specific individuals within those groups
3Quantity of substance
If individual-level data is used to create consumer profiles, then the detail and completeness of consumer information is improved, but the likelihood of violating privacy rights increases
Solution Approach 1:
The patent merges individual consumer data points into aggregated statistical profiles that represent broader demographic and employment categories. This merging process preserves the essential information needed for complete consumer profiling while distributing the data at higher aggregation levels, making it impossible to reconstruct individual identities from the combined data
Data Source
AI summary
A method, computer system, and computer program product that aggregates data regarding a plurality of factors correlated with demographic parameters; performs iterative analysis on the data using machine learning to construct a predictive model of purchasing propensity; populates, using the predictive model, a database with predicted values of spending propensity for selected demographic parameters; converts 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; and rank orders the people within the selected group according to their indices of spending propensity.