Mining customer knowledge for product line and brand extension in retailing

  • Authors:

  • Shu-Hsien Liao;Chyuan-Meei Chen;Chung-Hsin Wu

  • Affiliations:

  • Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei 251, Taiwan, ROC;Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei 251, Taiwan, ROC;Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei 251, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2008

Quantified Score

Hi-index 12.06

Visualization

Abstract

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.