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 language | English |
|---|---|
| Pages (from-to) | 7513-7518 |
| Number of pages | 6 |
| Journal | Expert Systems with Applications |
| Volume | 40 |
| Issue number | 18 |
| DOIs | |
| Publication status | Published - 2013 Aug 14 |
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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 journal › Article
TY - JOUR
T1 - Customer relationship management in the hairdressing industry
T2 - An application of data mining techniques
AU - Wei, Jo Ting
AU - Lee, Ming Chun
AU - Chen, Hsuan Kai
AU - Wu, Hsin Hung
PY - 2013/8/14
Y1 - 2013/8/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881282937&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881282937&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2013.07.053
DO - 10.1016/j.eswa.2013.07.053
M3 - Article
AN - SCOPUS:84881282937
VL - 40
SP - 7513
EP - 7518
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 18
ER -