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

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 for processing massive, complex, and complicated data from financial transactions in industrial markets. Furthermore, they often overlook the overlapping nature of segments, where a firm might belong to multiple market segments. Financial transaction networks (FTNs) can be depicted as graphs, where nodes and edges represent firms and transactions, respectively. A limited number of network analysis studies have been employed for industrial market analysis, but those efforts failed 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 an 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) networks.