Purchase-Behavior Product Recommendations for Privacy-Aware Target Marketing
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Solution Overview
Problem
Existing target marketing methods based on demographic information often lead to incorrect product recommendations, such as suggesting baby diapers to individuals without children.
Innovation Solution
A user-centric hyper-personalized product recommendation system that collects user-specific purchase information, generates a list of recommended products, and cross-checks this information to provide targeted marketing insights without relying on personal data, using an AI system that processes purchase information from multiple merchants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If target marketing is conducted based on demographic information, then product recommendations can be generated easily, but the accuracy of recommendations deteriorates
Solution Approach 1:
The system changes the parameters used for recommendation from demographic information (age, gender) to behavioral purchase information. By transforming the basis of recommendation from static demographic parameters to dynamic purchase behavior parameters, the system achieves both ease of generation and high accuracy, resolving the contradiction between ease of manufacture and measurement precision.
2Reliability
If separate systems are introduced for product recommendation and target marketing, then each function can be optimized independently, but system complexity increases
Solution Approach 1:
The system merges the product recommendation function and target marketing information generation function into a single integrated system. The processor simultaneously generates both recommended product information for users and target marketing information for merchants from the same purchase information database, reducing system complexity while maintaining functional optimization through modular processing components.
3Loss of information
If demographic information is used for marketing, then user privacy is preserved, but recommendation accuracy deteriorates
Solution Approach 1:
The system extracts only the necessary purchase behavior information from user data while deliberately excluding sensitive demographic information such as age, gender, and personal identifiers. By taking out only the essential purchase history data needed for accurate recommendations and leaving out sensitive personal information, the system achieves both privacy protection and high recommendation accuracy.
Data Source
AI summary
A user-centric hyper-personalized product recommendation and target marketing system is provided. The system includes: an input unit that collects user-specific purchase information; a memory that stores a program for generating recommended product information and target marketing information for a target customer on the basis of the user-specific purchase information; and a processor that executes the program stored in the memory, wherein the processor generates a list of user-specific recommended products on the basis of the recommended product information for the target customer, and generates target marketing information by cross-checking the list of user-specific recommended products on a product basis.