This study focuses on the analysis of purchased items' relationship occurred by consumers' purchasing behavior observed in on-line shopping mall based on social network analysis. In order to find relational characteristics of each item for establishing marketing strategy, we apply three definitions of centrality in network, which are degree centrality, betweenness centrality, and closeness centrality in the purchased items' network. Thus, the research results provide the criteria for selection of market segmentation variables. Furthermore, the details of case has been introduced to validate the analyzed results in terms of marketing strategy, and supporting evidences are provided accordingly.

In recent, hybrid market segmentation techniques have been widely adopted, which conduct segmentation using both general variables and transaction based variables. However, the limitation of the techniques is to generate incorrect results for market segmentation even though its methodology and concept are easy to apply. In this paper, we propose a novel scheme to overcome this limitation of the hybrid techniques and to take an advantage of product information obtained by customer's transaction data. In this scheme, we first divide a whole market into several unit segments based on the general variables and then agglomerate the unit segments with higher QAP correlations. Each product network represents for purchasing patterns of its corresponding segment, thus, comparisons of QAP correlation between product networks of each segment can be a good measure to compare similarities between each segment. A case study has been conducted to validate the proposed scheme. The results show that our scheme effectively works for Internet shopping malls.

No abstract