Customer relationship management in the hairdressing industry: An application of data mining techniques

Jo Ting Wei, Ming Chun Lee, Hsuan Kai Chen, Hsin Hung Wu

Research output: Contribution to journalArticle

46 Citations (Scopus)

Abstract

With the increase of living standards and the sustainable changing patterns of people's lives, nowadays, hairdressing services have been widely used by people. This paper adopts data mining techniques by combining self-organizing maps (SOM) and K-means methods to apply in RFM (recency, frequency, and monetary) model for a hair salon in Taiwan to segment customers and develop marketing strategies. The data mining techniques help identify four types of customers in this case, including loyal customers, potential customers, new customers and lost customers and develop unique marketing strategies for the four types of customers.

Original languageEnglish
Pages (from-to)7513-7518
Number of pages6
JournalExpert Systems with Applications
Volume40
Issue number18
DOIs
Publication statusPublished - 2013 Aug 14

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Data mining
Marketing
Self organizing maps
Industry

All Science Journal Classification (ASJC) codes

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

Cite this

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Customer relationship management in the hairdressing industry : An application of data mining techniques. / Wei, Jo Ting; Lee, Ming Chun; Chen, Hsuan Kai; Wu, Hsin Hung.

In: Expert Systems with Applications, Vol. 40, No. 18, 14.08.2013, p. 7513-7518.

Research output: Contribution to journalArticle

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