資料探勘應用於資訊家電之一對一行銷之研究

蕭世忠

doi:10.6846/TKU.2007.00607

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資料探勘應用於資訊家電之一對一行銷之研究

The Research of Data Mining Techniques Applied To One to One Marketing of Information Appliances

蕭世忠 , Masters　　Advisor：廖述賢　　Co-advisor ：劉艾華

英文 DOI： 10.6846/TKU.2007.00607

資料探勘；一對一行銷；資訊家電；關聯性資料庫； Apriori演算法； Two-step集群分析； CART決策樹； Data mining ； one to one marketing ； information appliances ； relational database ； Apriori algorithmn ； Two-step clustering analysis ； Classification and Regression Trees

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As information technologies converges and information devices become more powerful, along with the continuing network technology’s boom, the 3C (Computer, Communication, and Consumer electronics) industry’s products have became from a luxurious product to an essential product of daily needs for most people in the 21st  century. This digital revolution accompanied with today’s increasingly globalized market have not only raised the competition difficulty within the 3C industry, but also lowered the effectiveness of the traditional mass marketing due to changing households, complex technology-based products, new ways to shop and pay, intense competition, additional channels, and declining advertising effectiveness. Personal marketing is what the customers want nowadays. 
Accordingly, practitioners who want to reach a win-win strategy under such conditions must somehow distinguish their products and services from those of their competitors, and increase customers’ satisfaction by fulfilling their needs and preferences, thus obtaining higher loyalties and profits.
The purpose of this research was to assist marketers of 3C industry at improving their competitiveness by reaching such win-win strategy in today’s highly competitive markets through effective utilization of customers’ data. This research has built a relational database and utilized various data mining techniques to help analyze, understand, and visualize the huge amounts of stored data about customers. Valuable information, knowledge patterns and rules have been extracted, analyzed, and interpreted from customers’ database by using Apriori algorithmn, Two-step clustering analysis, and CART (Classification and Regression Trees) as needed.
Consequently, reports, conclusions, and map of marketing have been drawn at the end suggesting to the marketers the need to obtain customer knowledge and feedback from the demand side and use them as a knowledge resource for establishing suitable one to one marketing offers, mix of products, and future product developments.

Reference ( 120 ) 〈TOP〉

Agrawal, R., Imilienski, T., & Swami, A. (1993). Mining Association Rules between Sets of Items in Large Database. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207-216.
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