Integer Programming Customer Clustering from Purchase and Demographic Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
E-commerce platforms struggle to effectively classify and group customers based on their purchasing habits and demographic data to provide personalized services and promotions.
Innovation Solution
An Integer Programming-based method is used to cluster customers by transforming purchase history and demographic data into a transaction and feature space, employing techniques like bin quantiles standardization and Minkowski distance to identify similar purchasing behaviors, and iteratively partitioning the customer base into meaningful groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional customer classification methods are used, then implementation is simple, but classification precision and personalization effectiveness are insufficient
Solution Approach 1:
The patent transforms raw customer data into standardized feature spaces using bin quantiles standardization, changing the parameter scale and distribution to enable precise mathematical comparison. This parameter transformation allows the system to achieve high classification precision by converting heterogeneous data (purchase history, demographics) into comparable numerical features that can be processed by integer programming algorithms
Solution Approach 2:
The patent replaces conventional heuristic-based customer classification methods with integer programming, a mathematical optimization approach. This substitution of mechanical/classical methods with advanced mathematical algorithms enables precise clustering by formulating customer segmentation as an optimization problem that maximizes intra-cluster similarity while minimizing inter-cluster similarity
2Loss of information
If detailed purchase history and demographic data are analyzed, then customer understanding improves, but data processing complexity increases
Solution Approach 1:
The patent extracts essential features from extensive customer data including purchase history, demographic information, and behavioral patterns. By selecting and extracting only the most relevant features needed for clustering, the system retains critical customer information while eliminating redundant data, thus reducing processing complexity without losing meaningful insights
Solution Approach 2:
The patent applies bin quantiles standardization to transform raw data parameters into standardized features. This parameter transformation compresses diverse data types (transaction amounts, frequencies, demographic categories) into normalized numerical representations, making the data suitable for mathematical optimization while preserving the essential information needed for accurate customer clustering
3Measurement precision
If traditional clustering methods are used, then computational resources are saved, but clustering accuracy and meaningful group identification are insufficient
Solution Approach 1:
The patent replaces traditional clustering algorithms (such as k-means or hierarchical clustering) with integer programming-based clustering. This substitution provides superior clustering accuracy by formulating the segmentation as an optimization problem that explicitly maximizes cluster quality metrics. The integer programming approach ensures optimal cluster assignments by considering all constraints and objectives simultaneously, achieving higher precision than heuristic-based traditional methods
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.