本研究預期研究成果如下：
(1) 報告本研究聯合使用多種柔性計算方法來解決資料庫行銷中一貫性整體議題的成果及績效評估。
(2) 以業界實際資料驗證本研究之實務可行性。
(3) 發表國際研討會論文。
(4) 發表論文於知名國際期刊。
Understanding customers in the market and satisfying their needs are among crucial success factors in marketing management. The capability for a company to collect comprehensive information of individual incumbent customers and prospects, and store it into a database empowers the company’s potency in maintaining relationship with customers and selling products/services via database marketing activities. It does so by integrating customer database and product database to elaborate building relationship, contacting, customizing offerings, and dealing with customers and finally leads to a transaction. Massive information in customer database as well as primary data rendered by a market survey from the existing customer base enables the execution of data-mining task to extract knowledge about customers’ needs segmentation and purchase behavior trend.
Apart from conventional multivariate statistical approach, soft computing techniques like artificial neural network, fuzzy logic and genetic algorithm that are derived from human biological instinct such as cognition, learning, memory and judgment are also applicable for data-mining task. However, most soft computing research literatures in business application are manifested in the form of single-technique for single-case approach, rather than congruently combining several techniques to offer the solution that can tackle the contextual-apt problems that always happen in the real world business environment. This arouses the motivation in responding the urge of Professor Zadeh (UC Berkeley) to study how to present an innovative methodology that are supported by the consortium of various soft computing techniques, and justify its feasibility in the area of database marketing activities all the way from customer needs identification, segmentation and future purchase behavior prediction. A case study on a development project for information/communication product of radical innovation will be presented to evaluate the contribution of this to-be research work.
The expected outcomes of this research work are listed as follows:
(1) A presentation of the research outcome thanks to the consortium of various soft computing techniques as well as the performance evaluation report.
(2) Justification for the feasibility of this research work via an industry survey data set that could be acquired through an information/communication product development project.
(3) Presentation of the research work finding in the international academic conference.
(4) Contribution of research paper to the highly-acclaimed international academic journal.
