Customer Classification and Decision Making in the Digital Economy based on Scoring Models

Abstract

The article presents the way of applying cluster models to customer classification and managerial decision on retaining the available clients and acquiring new ones. The objective of the research is to find out the relevant techniques for building scoring models in different fields. The main research was testing the hypothesis: if the number of point models is approximated in different spheres of activity, then the proposed methods will be universal. To check this hypothesis the vector method of k-nearest neighbors support was applied for decision making in the digital economy based on scoring models. In order to realize the principle of customer classification and revealing the client categories with risk of quitting, the client’s classification model was created. Moreover, a risk issue was shown in the example of fraud dynamic. Different fraud categories were studied to define their features. On the basis of the model building results, the authors proposed some recommendations on decision making in risk situations. The model shows how to retain existing clients and how to share client base through the client groups and how to deal with risks of losing clients.