Graph-Based Customer Churn Prediction Using Matrix Decomposition
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Solution Overview
Problem
Customer churn prediction is challenging due to the influence of external factors beyond organizational control and the difficulty in determining the impact of churned customers on existing customers, which conventional methods struggle to address effectively.
Innovation Solution
A method and system that create a graph representing customers with edges connecting multiple nodes, determining a similarity matrix, decomposing it into matrices, and using a scaling parameter to identify potential churners by analyzing customer influence and behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional churn prediction methods are used, then implementation is simple, but accuracy in predicting churn influenced by external factors and customer influence is poor
Solution Approach 1:
The patent segments the churn prediction problem into multiple components: (1) building a graph structure where nodes represent customers and edges represent relationships, (2) computing similarity matrices to capture customer similarities, (3) decomposing the similarity matrix into eigenvalues and eigenvectors to extract influence patterns, and (4) combining multiple features including external factors and customer influence scores. This segmentation transforms a complex prediction task into manageable computational steps, improving accuracy while maintaining implementability.
Solution Approach 2:
The patent transitions from traditional tabular customer data to a graph-based representation, adding a structural dimension to the analysis. By representing customers as nodes and their relationships as edges in a graph, the system captures influence propagation and network effects that conventional methods miss. The graph structure enables computation of similarity matrices and influence scores that reflect multi-dimensional customer interactions, significantly improving churn prediction accuracy.
2Loss of information
If graph-based similarity analysis is performed, then customer influence detection improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary computation of the similarity matrix and its eigenvalue decomposition before the actual churn prediction task. By pre-computing these mathematical structures that capture customer similarities and influence patterns, the system avoids repeated heavy computations during prediction. The decomposed eigenvalues and eigenvectors serve as reusable features that efficiently represent customer influence information, reducing computational burden during inference while preserving critical influence data.
3Reliability
If external factors are considered in churn prediction, then prediction completeness improves, but control over remedial actions decreases
Solution Approach 1:
The patent extracts and separately models external factors from the churn prediction framework. By identifying and isolating external factors (such as market conditions, competitor actions, and macroeconomic variables) as distinct features in the graph model, the system can account for their influence on churn while maintaining focus on internal factors that organizations can control. This separation allows predictive completeness without obscuring the actionable internal levers that remedial strategies should target.
Data Source
AI summary
A method and a system are provided for customer churn prediction. The method includes creating a graph comprising a plurality of nodes and a plurality of edges. At least one edge of the plurality of edges in the graph connects more than two nodes of the plurality of nodes. Each of the plurality of nodes represents a customer. Thereafter, a similarity matrix representative of a similarity between each of a plurality of customers is determined based on the graph. Further, the similarity matrix is decomposed into a first matrix and a second matrix. A third matrix is determined based on the first matrix, the second matrix, and a scaling parameter. The third matrix is utilized to identify a set of potential churn customers from the plurality of customers. Thereafter, the set of potential churn customers is presented on a user-computing device.