Importers Value Discovery Using Data Mining Techniques

• Main Authors: Ming-Jui Kao, 高明瑞
• Other Authors: 盧以詮
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/85174480441730344807

碩士 === 元智大學 === 資訊管理學系 === 98 === As the market of shipping by sea is flourishing, liner container carriers are raising their volume of freight and enhancing service quality in order to meet customers'' demands. However, the customers with higher contribution margin are very important to be acquired from a large number of customers in the global market. To determine those customers with higher contribution margin, this research combined RFM Model for measuring the value of customers and K-means algorithm for clustering analysis. Since Trans-Pacific trade of US import from the Far East is more indicative, we adopted US market data for this research. The results showed that the proposed clustering model has the essence of efficiency. The customers were divided into five categories, they are (A)Best , (B)Spender, (C)Frequent, (D)Uncertain and (E)Loss/negative. Among those five categories, the Best and Spender account for 75 % of overall Trans-Pacific trades. The corresponding marketing strategies for the above two groups are proposed in this research as well.