2015 Volume 27 Issue 4 Pages 680-690
In the field of marketing, it is very important to design a marketing strategy based on the acquired data from customers. Statistical analysis techniques called data mining are utilized to acquire useful information from large amounts of data. Recently, customer groups with features different from others called minority groups are emphasized because of Customer Relationship Management (CRM). Thus, it is strongly needed to extract and analyze minority groups from customer data such as questionnaire data. Most of conventional methods, however, aim at grasping overall trends in data, so they are not suitable for extracting and analyzing minority groups. EWOCS is one of the methods to extract minority groups, and isolated clique is the definition of groups similar to minority. However, EWOCS does not consider the dissimilarity to others and most of isolated cliques have a small number of data. In this paper, we define Local Minority Factor (LMF) as the criterion of minority to solve the above problems. LMF is based on Local Outlier Facor (LOF) which is one of the outlier detection methods and has two criterions as the isolation and the agglomeration. LMF becomes high when the data set is isolated but each data in the group is concentrated. We also propose an exploratory extraction method of minority groups by optimizing LMF. We apply the proposed method to an actual questionnaire data and compare with the conventional methods based on the criterion and the visualization. The results show that the proposed method can extract the respondents having stronger features of minority than the conventional methods and we show the examples of marketing strategy for each acquired minority group.
