A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business

Der Chiang Li, Wen Li Dai, Wan Ting Tseng

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

In order to obtain comprehensive information about customers, this study aims to use a systematized analytic method to examine customers. This study uses LRFM customer relationship model, which consists of four dimensions: relation length (L), recent transaction time (R), buying frequency (F), and monetary (M), to carry out customer clusters. We proceed with this clustering analysis to classify customers in order to set discriminative marketing strategies. In addition, this study further employed a cross analysis over three predetermined dimensions: area, sales, and new/old characteristics to enhance the clustering analysis. The results obtained from the real textile business show that the customer groups formed using the four-factor (LRFM) clustering all has statistical significant differences, and with meaningful explanations in terms of marketing strategy. Thus, this study considers useful for discriminative customer relationship management.

Original languageEnglish
Pages (from-to)7186-7191
Number of pages6
JournalExpert Systems With Applications
Volume38
Issue number6
DOIs
Publication statusPublished - 2011 Jun 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business'. Together they form a unique fingerprint.

Cite this