Graph-Based Approach for Industrial Market Segmentation Using Financial Transaction Network Structure and Firm’s Attribute

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Abstract
Market segmentation is a challenging and necessary task for improving marketing strategies and allocating resources effectively in a complex scenario of industrial or business-to-business (B2B) markets. Most of the traditional methods have not provided comprehensive and detailed guidelines to process massive, complex, and complicated data from financial transactions in industrial market. Furthermore, they often overlook the overlapping nature of segments, where a firm might belong to multiple segments of market. Financial transaction networks (FTNs) can be depicted as graph, where nodes and edges represent firms and transactions, respectively. A few network analysis studies have been employed for industrial market analysis, but those efforts are missed to capture the important features of financial transactions, such as the number of transactions, transaction volume, direction of transactions, and firm-specific attributes. In this study, we propose a novel method for industrial market segmentation that utilizes both financial multiple transactions and attribute of firms. By integrating a graph-based generative probabilistic model, we reveal and understand the complex and overlapping relationships within financial transaction data. Subsequently, we apply the proposed method to a real-world dataset. The results show that the proposed method outperforms established methods in network analysis, such as clustering and community detection in transactional industrial market (B2B) network.
Funding Information
  • National Research Foundation of Korea (NRF) grant
  • Korea Government [Ministry of Science and ICT (MIST)] (NRF-2022R1F1A1063273)

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