A cross-national market segmentation of online game industry using SOM

To compete successfully in today's global online game markets, a cross-national analysis for market segmentation is becoming a more important issue, by which companies are able to understand their domestic and foreign loyal customers and concentrate their limited resources into the target customers. However, previous research methodologies for market segmentation were difficult to be conducted on a cross-national analysis because they were performed within a nation. Additionally, the traditional clustering methodologies have not provided a unique clustering nor determined the precise number of clusters. The purpose of our research is to develop a new methodology for cross-national market segmentation. We propose a two-phase approach (TPA) integrating statistical and data mining methods. The first phase is conducted by a statistical method (MCFA: multi-group confirmatory factor analysis) to test the difference between national clustering factors. The second phase is conducted by a data mining method (a two-level SOM) to develop the actual clusters within each nation. A two-level SOM is useful to effectively reduce the complexity of the reconstruction task and noise. Especially, our research tested the model with Korean and Japanese online game users because they are the frontier of global online game industries.