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 |
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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Cite this
Wei, J. T., Lee, M. C., Chen, H. K., & Wu, H. H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513-7518. https://doi.org/10.1016/j.eswa.2013.07.053