Extracting knowledge of customers' preferences in massively multiplayer online role playing games

Due to fierce competition in game markets, to identify customers' true needs is one of the crucial factors in online game industry. Traditionally, game producers heavily rely on game testers, who are primarily responsible for analyzing computer games, finding software defects and being a part of quality control process, to achieve this goal. But, it is not often reliable. To ensure the investment can be returned, game producers need an effective approach to discover frequently shifted customer preferences in time. Recently, Kano model and data mining techniques have been successfully applied to recognize customers' preferences and implement customer relationship management tasks, respectively. However, in traditional Kano analysis, only basically statistical analysis techniques are used, and they are insufficient to provide advanced knowledge to enterprisers. Therefore, in order to discover the relationship between/among quality elements in Kano model and to extract knowledge related to customer preferences, this study proposes a knowledge acquisition scheme that integrates several data mining techniques including association rule discovery, decision tree, and self-organizing map neural network, into traditional Kano model. An actual case of customer satisfaction survey regarding massively multiplayer online role playing game has been provided to demonstrate the effectiveness of our proposed scheme.