Characteristic Analysis of Each Store in Japanese Hair Salon

There are 223,645 Hair salons in Japan in 2011. Those same about quadruple amount of Japanese convenience store. From these things, we can know that there are many Hair salon in Japan. Hair salon moved and there are close stores about 9,000 a year. However new open chain stores about 12,000 therefore Hair salon increasing about 3,000 stores a year of Hair salon in Japan. Hair salon were group at Kanto region and Kinki region because they are high population density in Japan. The struggle for existence Hair salon are store excess also a lot of hair salon are small scale and they are micro enterprises. In that sense, Hair salon are faces severe competition. Customer’s hair salon usages frequency is woman 4.5 times a year, men 5.38 times a year. The amount of usage per one times is women 6429 yen, men 4067 yen. Men are more frequently used, and the usage amount is increasing. However, the usage women’s rate frequency of Hair salon more than men’s rate frequency of hair salon. We use the data is all over Japan of a certain hair salon chain stores of this study. This was provided by Joint Association Study group of Managements Science (JASMAC) 2017 Data Analysis Competition. According to basic statistics, there are many customers with one visit to the store in this hair salon thus high customer rates. A certain hair salon have many people who are 30 to 60 years old. One of 12 stores are a male salon. We used Quantification category 3 that infer the characteristics of customers also, we predict store characteristics from the characteristics of customers. However, it was mixed Mathematical data and Qualitative data thus we had to unify the scale. Therefore, we converted Mathematical data and Qualitative data. As a result, we used Quantification category 3. We Interpreted compound variable answer1 is “Neighboring a working woman and office worker who want to quietness”. Answer2 is a customer who emphasize of high temperature and high-quality care. Cluster analysis has Hierarchical approach and Nonhierarchical approach. Nonhierarchical approach be able used for small data on the other hands Hierarchical approach can be used to big data. Therefor we adopted Hierarchical approach. We needed to decide on a group when we used Hierarchical approach. However, it was objectively lacking, and it had disadvantages of reduced reliability if you have decided the number of groups before this analysis. Therefore, we did cluster analysis in several times in addition we decided the number of groups by squared residual sum of squares in cluster. We created an elbow diagram. The difference in the residual sum of squares within the group is It was shrinking sharply smaller than Groups 4 to 5 thus we decided the number of groups to 5 in addition we did k-means method. When initial value of each result, big differences occur in size of group and convergence value. K-means method needs the best solution in multiple analyzes. In this research, we had the purpose to stores characteristic. We developed one-way analysis of variance and multiple comparison by compound variable, answer1 and answer2. We had the purpose to develop that analysis. The purpose was if we did not have significant difference, we would not classify every characteristic. One-way analysis of variance used compound variable, answer1 and answer2. Therefore, we used nonparametric method because those were not normal distribution. Nonparametric method has multiple test method. We used Games-Howell method in this research. Games-Howell method was matching in this research because it did not assumption homoscedastic. We used result hierarchical approach cluster analysis from First time to fourth time. We developed one-way analysis of variance and multiple comparison. The result, we adopted 2’nd time because it is classified definite characteristic. In addition, first time, 3’rd time and fourth time had significant difference one-way analysis of variance. However, there were combination in multiple comparison that did not have significant difference. We developed one-way analysis of variance with result of 2’nd time and compound variable. If answer1 shows a large value to minus, we infer that the customer want glamorous and has enough time to coming from afar. If answer2 shows a large value to minus, we infer that customer important care for low temperature and low humidity. We founded by one-way analysis of variance that there was a difference each group. We divided for each store the group. Then we were find difference in store characteristics. In addition, we suggested optimal marketing strategy based on result of analysis against each store. We will increase the synthesis and make finer interpretations.

After that, we will make suggestions for winning potential customers.