Customer relationship management in the hairdressing industry: An application of data mining techniques
Expert Systems with ApplicationsPublished 23 July 2013
Jo-Ting Wei, Ming‐Chun Lee, Hsuan-Kai Chen, Hsin‐Hung Wu
Citations108
SJR quartileQ1
SJR score1.85
SNIP2.55
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TL;DR
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.
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.
Keywords
Business, Management and Accounting
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