bayesian model

Costumer information can be take an important basis in the analysis of costumer behavior. It's obvious that one of the most essential characteristics of the customer behavior is the customer risk modelling in order to the customer risk assessment. Today, it's very important to keep up stable costumers to confront with the rival market and get it. In this research, at the first we introduced introduction in order to survey prior research about risk modelling and assessment in work field. There are a lot of procedure to risk modelling because the usage of this methodology is very comprehensive whiles there is not any distinctive structure to risk assessment and modelling. In section two, we are considered descriptive variables of customer such as age, weight, usage, prehistory and occupation to analyze past behavior of customer with respect to the future behavior by definition experimental function from determinative historical data. It approach used to model customer future behavior. Then we are assessed purchase risk in order to predict the future behavior of customer. At first, according to the many characteristics that driven from the specific sample of new strongbox company customers, experimental functions generated and are compared to gather with the information that gains from the descriptive statistics and distribution diagrams on this data and then, purchase risk is evaluated experimentally. In the next section, the Bayes risk of customer is analyzed and used to classify customers according to the prior data. After it, we proposed guidance for improve the production programing and sale management decision tree technique. The approach mentioned in this research is used as a case study about the products of Kaveh strongbox company that readers can be realize the practical usage of this research as much. All data in this research that obtains from thoroughbred replier is done by expert questioner. The software that we used in this research are MINITAB and Expert Choice.