Key-Factor-Aware Customer Value Prediction on Multi-View Hypergraphs

Customer value prediction is an essential task for effective customer relationship management, particularly for business-to-business (B2B) service platforms. In B2B scenarios, customer value is jointly shaped by customer preferences and platform service characteristics. Prior research mainly focuses on modeling pairwise relationships between individual customers, failing to capture the multifaceted factors influencing customer value on B2B platforms. To fill this gap, we propose a Multi-view Hypergraph Convolutional Network with Counterfactual Optimization (MHCC) to achieve key-factor-aware customer value prediction. First, to capture hierarchical high-order relationships among business customers, we propose a residual multi-view hypergraph convolutional module. It leverages distinct hypergraphs to model customers’ industry categories and service interactions. An attention-based residual fusion layer integrates these relational features with historical behavioral features. Second, we propose a counterfactual pruning optimization module that employs counterfactual perturbation and reasoning to quantify the causal influence of individual connections in the hypergraph. This enables the pruning of redundant connections and the identification of key factors driving customer value. Extensive experiments on a large-scale dataset from a real-world B2B platform demonstrate the superior performance of our model, achieving an 8.59 % improvement in MAE over state-of-the-art methods. We further identify distinctive key factors for customers at different value tiers, thereby offering actionable insights for data-driven customer relationship management.

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Recommended citation: Li, A., Lin, L., Zhang, X., Xia, K., Zhang, Q., &Wang, S. (2025, December). Key-Factor-Aware Customer Value Prediction on Multi-View Hypergraphs. In IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS). 2025.