Bayesian network considering the clustering of the customers in a hair salon

Yuki Horita and Haruka Yamashita

Cogent Business & Management, 2019, vol. 6, issue 1, 1641897

Abstract: The service industry, which includes hair salons, currently accounts for almost 70% of Japan’s GDP(Gross Domestic Product). Although hair salons are frequently used, over the years, the industry has decreased in size. However, the number of hair-salon facilities and the number of hairdressers have both continued to increase, thus leading to the overcrowding of salons. Consequently, about 90% of hair salons close within 3 years after they first open; this is a significant issue. Today, various business approaches, such as using coupons, have been positively adopted by the Japanese hair-salon industry. However, some customers use a salon only once, while others use them repeatedly. Consequently, the effectiveness of different business measures can vary greatly, so it is necessary to conduct analyses of the various approaches. Therefore, from a management perspective, it is important to use actual data analysis to determine what types of menu items are most effective. In this study, we have identified soft clusters of customers by using an extension of the recency-frequency-monetary (RFM) analysis that is based on soft clustering. We used a Bayesian network to construct a causal model for each class that was obtained in this way. We also proposed a method that uses sensitivity analysis to determine an optimal menu for business measures.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/23311975.2019.1641897 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:oabmxx:v:6:y:2019:i:1:p:1641897

Ordering information: This journal article can be ordered from
http://cogentoa.tandfonline.com/journal/OABM20

DOI: 10.1080/23311975.2019.1641897

Access Statistics for this article

Cogent Business & Management is currently edited by Len Tiu Wright and Tahir Nisar

More articles in Cogent Business & Management from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().