| 摘要: | All kinds of information technologies have been converging rapidly in recent few years from simple traditional computers to diversified multimedia information appliances, creating unprecedented technologies and devices, such as personal digital assistants (PDAs), smart cell phones, portable media players (PMPs), online console games, and set top boxes, among others. These electronic functionalities converged devices with powerful contents and functions, such as the World Wide Web, videoconferencing, e-mail, internet telephony, online gaming, digital television, and net banking, are easier to use than traditional computers but not less capable of performing daily tasks. These information technology revolutions along with rapid growing of network technology not only increased the amount of internet applications and digital contents, but also led to diversified consumer behaviors, increased competition, and opportunities. On the other hand, one-to-one marketing is different from traditional marketing methods because it focuses on customer satisfaction and is customer-oriented rather than focusing on marketing mass consumers; thus a one-to-one marketer tries to find more different products and services for the same customer. Therefore, how to establish potential cross-selling and one-to-one offers through product mix analysis, enhance relationship with customers by means of personalized offers through product knowledge, and understand users’ needs and making useful suggestions for new product developments and one-to-one marketing become critical issues to information appliance firms. This paper proposes association rules, clustering analysis and CART as methodologies of data-mining, which is implemented for mining product and marketing knowledge from information users. Knowledge extraction from information users is illustrated as knowledge patterns, rules, clusters, and trees in order to propose suggestions on one-to-one marketing for information appliance firms. |
