Customer Clustering Using Demographics for Sparse Purchase Histories
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Solution Overview
Problem
E-commerce websites face challenges in effectively classifying and grouping customers based on their purchase behavior and demographics to provide tailored services, especially when dealing with customers who have insufficient purchase history data.
Innovation Solution
A customer classification system that preprocesses purchase history and demographic data, applies bin quantile standardization, reduces dimensionality using principal component analysis, and employs clustering algorithms like PAM and CLARA to group customers, assigning low-purchasing customers to clusters based on demographic similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If clustering algorithms are applied to customers with insufficient purchase history data, then customer classification coverage is improved, but classification accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary approach by combining demographic data with available purchase history data to bridge the information gap for customers with insufficient purchase history. This intermediary data source enables classification of customers who would otherwise be excluded, while maintaining reasonable accuracy through the complementary nature of demographic characteristics.
Solution Approach 2:
The patent segments the customer base into different groups based on the availability and quality of purchase history data. Customers with sufficient purchase history are classified using traditional clustering on purchase behavior, while customers with insufficient purchase history are classified using demographic data, allowing each segment to be handled with the most appropriate method.
2Productivity
If dimensionality reduction is applied using principal component analysis, then computational efficiency is improved, but information loss increases
Solution Approach 1:
The patent transforms the original high-dimensional data space into a lower-dimensional space by changing the parameters through principal component analysis. This parameter transformation identifies the most significant variance directions in the data, retaining essential information while reducing dimensionality for more efficient clustering computation.
3Reliability
If multiple preprocessing steps are applied including bin quantile standardization and PCA, then clustering effectiveness is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary actions by performing multiple preprocessing steps before the actual clustering operation. Bin quantile standardization is applied first to normalize the data distribution, followed by principal component analysis to reduce dimensionality. These preliminary actions prepare the data in an optimal state for clustering, improving effectiveness while the modular structure manages complexity.
Data Source
AI summary
Methods and apparatus are disclosed regarding an e-commerce system that places customers into a plurality of clusters and tailors services provided to a customer based on the cluster in which the customer is placed. In one embodiment, the e-commerce system defines the clusters based on purchase history data for customers having sufficient purchase history data. The e-commerce system then places customers without sufficient purchase history data into one of the defined clusters based on demographic data for the customer and demographic data for the customers in the cluster.