Multi-Perspective Customer Segmentation Engine
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Solution Overview
Problem
Current customer segmentation techniques rely on single attribute sets, fail to reveal insights from multiple groupings across different attribute subsets, and are sensitive to the specification of the number of clusters, leading to poor clustering results and lack of interactive analysis of customer behavior patterns.
Innovation Solution
A system and method for generating multiple sets of clusterings based on customer attributes, using a segmentation engine with a clustering module, consolidation module, and visualization engine to automatically determine views, handle outliers, and combine clusterings into a hierarchical structure for interactive visualization and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If customer segmentation is performed using a single attribute set, then the segmentation process is simple, but the ability to reveal insights from multiple customer behavior facets is lost
Solution Approach 1:
The patent divides the customer segmentation process into multiple independent clustering operations, each operating on a specific attribute subset (view). Instead of performing one complex clustering on all attributes, the system segments the attribute space into multiple views (e.g., demographic, behavioral, transactional) and performs separate clusterings on each view, then combines the results to preserve both simplicity and comprehensive insights.
Solution Approach 2:
The patent adds a new dimension to the segmentation process by introducing the concept of 'views' as an additional layer of organization. Rather than analyzing all attributes in a single flat space, the system organizes attributes into multiple dimensional views and performs clusterings across these dimensions, enabling discovery of patterns that are scattered across different facets of customer behavior.
2Productivity
If the number of clusters is specified in advance using k-means algorithm, then the clustering process is efficient, but the quality of clustering results deteriorates when the specified number is incorrect
Solution Approach 1:
The patent enables the clustering algorithm to automatically determine the optimal number of clusters for each view without requiring manual specification. The system performs self-assessment of the data structure and autonomously selects the number of clusters that best represents each customer segment across different views, eliminating the need for user intervention while maintaining high clustering quality.
Solution Approach 2:
The patent makes the number of clusters dynamic rather than static. Instead of fixing the number of clusters beforehand, the system allows the cluster count to adapt and change based on the characteristics of each view and the data distribution, enabling the clustering process to respond dynamically to the underlying customer segment structure.
3Ease of manufacture
If traditional segmentation techniques are used, then the implementation is straightforward, but interactive analysis of multiple customer groupings is not possible
Solution Approach 1:
The patent creates a multi-functional segmentation system that can perform multiple operations: automatic outlier detection, multiple independent clusterings on different attribute subsets, combination of clustering results, and interactive visualization. This universal system handles diverse analytical tasks within a unified framework, enabling both straightforward implementation and versatile interactive analysis of customer segments from multiple perspectives.
Data Source
AI summary
Systems and methods for management of multi-perspective customer segments. This invention relates to customer management, and more particularly to management of customer segments, wherein the customer segments can be multi-perspective. Embodiments herein disclose methods and systems for generating multiple sets of clusterings of customers based on at least one customer attribute, and combining and interactively visualizing the clusterings, to derive insights.