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

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 b...

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Autores principales:	Yuki Horita, Haruka Yamashita
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2019
Materias:	plsa bootstrap rfm analysis bayesian network business analytics Business HF5001-6182 Management. Industrial management HD28-70
Acceso en línea:	https://doaj.org/article/5cef4004cd84422d8ab250ea865a27d0
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spelling	oai:doaj.org-article:5cef4004cd84422d8ab250ea865a27d02021-12-02T16:42:10ZBayesian network considering the clustering of the customers in a hair salon2331-197510.1080/23311975.2019.1641897https://doaj.org/article/5cef4004cd84422d8ab250ea865a27d02019-01-01T00:00:00Zhttp://dx.doi.org/10.1080/23311975.2019.1641897https://doaj.org/toc/2331-1975The 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.Yuki HoritaHaruka YamashitaTaylor & Francis Grouparticleplsabootstraprfm analysisbayesian networkbusiness analyticsBusinessHF5001-6182Management. Industrial managementHD28-70ENCogent Business & Management, Vol 6, Iss 1 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	plsa bootstrap rfm analysis bayesian network business analytics Business HF5001-6182 Management. Industrial management HD28-70
spellingShingle	plsa bootstrap rfm analysis bayesian network business analytics Business HF5001-6182 Management. Industrial management HD28-70 Yuki Horita Haruka Yamashita Bayesian network considering the clustering of the customers in a hair salon
description	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.
format	article
author	Yuki Horita Haruka Yamashita
author_facet	Yuki Horita Haruka Yamashita
author_sort	Yuki Horita
title	Bayesian network considering the clustering of the customers in a hair salon
title_short	Bayesian network considering the clustering of the customers in a hair salon
title_full	Bayesian network considering the clustering of the customers in a hair salon
title_fullStr	Bayesian network considering the clustering of the customers in a hair salon
title_full_unstemmed	Bayesian network considering the clustering of the customers in a hair salon
title_sort	bayesian network considering the clustering of the customers in a hair salon
publisher	Taylor & Francis Group
publishDate	2019
url	https://doaj.org/article/5cef4004cd84422d8ab250ea865a27d0
work_keys_str_mv	AT yukihorita bayesiannetworkconsideringtheclusteringofthecustomersinahairsalon AT harukayamashita bayesiannetworkconsideringtheclusteringofthecustomersinahairsalon
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Bayesian network considering the clustering of the customers in a hair salon

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