Mining customer knowledge for product line and brand extension in retailing
學年 96
學期 2
出版（發表）日期 2008-04-01
作品名稱 Mining customer knowledge for product line and brand extension in retailing
作品名稱（其他語言）
著者 廖述賢; Liao, Shu-hsien; Chen, Chyuan-meei; Wu, Chung-hsin
單位 淡江大學經營決策學系
出版者 Oxford: Pergamon
著錄名稱、卷期、頁數 Expert Systems with Applications 34(3), pp.1763-1776
摘要 Retailing consists of the final activities and steps needed to place a product in the hands of the consumer or to provide services to the consumer. In fact, retailing is actually the last step in a supply chain that may stretch from Europe or Asia to the customer's hometown. Therefore, any firm that sells a product or provides a service to the final consumer is performing the retailing function. On the other hand, product line extension, which adds depth to an existing product line by introducing new products in the same product category, can give customers greater choice and help to protect the firm from flanking attack by a competitor. In addition, a product line extension is marketed under the same general brand as a previous item or items. Thus, to distinguish the brand extension from the other item(s) under the primary brand, the retailer can either add secondary brand identification or add a generic brand. This paper investigates product line and brand extension issues in the Taiwan branch of a leading international retailing company, Carrefour, which is a hypermarket retailer. This paper develops a relational database and proposes Apriori algorithm and K-means as methodologies for association rule and cluster analysis for data mining, which is then implemented to mine customer knowledge from household customers. Knowledge extraction by data mining results is illustrated as knowledge patterns/rules and clusters in order to propose suggestions and solutions to the case firm for product line and brand extensions and knowledge management. (C) 2007 Published by Elsevier Ltd.
關鍵字 retailing; product line extension; brand extension; data mining; association rules; cluster analysis; knowledge extraction
語言 en
ISSN 0957-4174
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者
審稿制度
國別 GBR
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