Customer Clustering via Integer Programming Hyperplane Partitioning
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Solution Overview
Problem
E-commerce websites face challenges in effectively classifying and grouping customers based on their interests and purchasing behaviors, limiting their ability to provide tailored services and recommendations.
Innovation Solution
An integer programming-based method is employed to cluster customers using purchase history and demographic data, transforming this data into a transaction and feature space, and applying a hyperplane partitioning technique to identify clusters with similar purchasing habits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer classification methods are used, then the system is simpler to implement, but the classification accuracy and ability to provide tailored services is limited
Solution Approach 1:
The patent transforms customer data into a standardized feature space by converting purchase history and demographic information into numerical vectors with specific dimensions (e.g., purchase frequency, category preferences, price sensitivity). This parameter transformation enables the use of integer programming to achieve accurate customer segmentation while maintaining systematic approachability.
Solution Approach 2:
The patent divides the customer base into distinct segments or clusters based on similar purchasing behaviors and characteristics. By segmenting customers into groups such as value seekers, quality oriented, or impulse buyers, the system can provide tailored services to each segment while managing complexity through structured classification categories.
2Adaptability or versatility
If more customer data is collected and analyzed, then the ability to provide personalized services improves, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing by pre-transforming raw customer data into standardized feature vectors and pre-identifying key purchase patterns. This preliminary action prepares the data in advance for the integer programming optimization, reducing the computational time required during the actual customer segmentation process while maintaining comprehensive personalization capability.
Solution Approach 2:
The patent extracts only the most relevant and informative features from the extensive customer data. By identifying and selecting key purchase behavior indicators, demographic factors, and preference patterns that are most predictive of customer segments, the system reduces the data processing burden while maintaining high personalization accuracy.
3Manufacturing precision
If integer programming is used to cluster customers, then the classification precision and cluster identification improve, but the computational complexity and resource requirements increase
Solution Approach 1:
The patent reformulates the customer clustering problem as an integer programming optimization problem with specific objective functions and constraints. By transforming the clustering task into a mathematical optimization framework with defined parameters (e.g., maximizing within-cluster similarity, minimizing between-cluster differences), the system achieves precise cluster identification while providing a systematic approach to managing computational complexity.
Data Source
AI summary
Methods and apparatus are disclosed regarding an e-commerce system that clusters customers based on demographic data and purchase history data for the customers. In some embodiments, the e-commerce system solves an Integer Program that accounts for the demographic data and purchase history data in order to identify a hyperplane that splits a selected cluster of customers.