Abstract
This paper presents investigating the customer characteristics of mail order industry, especially the bad debt customers. These kinds of investigations have not made intensively, such as private default risks so far and conventional method for predicting such risks depend on the employee’s working experiences. For these backgrounds, we observed the bad debt list gathered from a mail order company and analyzed combined with the sales data. From the results of the research, we characterized the potential bad debt customers with the machine learning method. Intensive research has revealed that the characteristics of customers who might fall into the bad debt list. This result will make use for the revenue expansion with the improvement of the bad debts collection.KeywordsMail OrderCustomer AnalysisBad DebtsRandom ForestMachine LearningService Science and Management Engineering
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