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 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.