@article{oai:uec.repo.nii.ac.jp:00009114,
 author = {Ogawara, Wataru and Tsubaki, Michiko and Takashima, Jun},
 issue = {4},
 journal = {Journal of Advanced Management Science},
 month = {Dec},
 note = {Since services contain human factors of service providers and receivers, the quality and the value of services are essentially difficult to define, especially because of the two service characteristics of ‘heterogeneity’ and ‘simultaneity’. On the other hand, because there are many opportunities to provide services that are suitable for individual customer in job categories where employees directly serve customers, providing services that make use of know-how of individual employees and companies is considered to be important. However, studies which propose the optimal service method in cases featuring heterogeneity and predict its effect are unsatisfactory, despite the fact that heterogeneity of employees and customers is found to exist. The aim of this paper is to propose a sales support analysis method which can suggest reinforcement on which product or service sales is effective, by comparing the sales characteristics of the employee’s type and the current state of each employee’s sales behavior, and considering employees’ sales abilities. We constructed each employee’s sales-purchase Bayesian network model based on nationwide sales data, and proposed employees’ classification method by their sales style. Then, we calculated the conditional purchase probability of each product by stochastic reasoning, based on the constructed Bayesian network model for each type and individual employees, and proposed a sales support analysis method that enables each employee to focus on products that have not yet improved the purchase probability as recommended products, based on the comparison between the characteristics of each type’s purchase probability and that of each employee of that type.},
 pages = {182--189},
 title = {A Study on Type Classification of Employees and Sales Support Analysis Based on Similarity of Sales-purchase Bayesian Network Structure},
 volume = {6},
 year = {2018}
}