Constructing Automatic Customer Segmentation in an Institute of Higher Education

Abstract

Customer relationship management is practiced widely in different kind of organizations, in addition in institutes of higher educations. In this environment customer relationship management is useful even though it has its special characteristics. In a central role in developing customer relationship management is to deepen the understanding about the customers and their behavior, and the goal is similar in the context of institute of higher education. This study has indeed arose from the need to create a better understanding from the customer base. The research was conducted utilizing design science research approach.

The objective of this research was to create a segmentation about research’s case organization’s customers automatically using machine learning. The aim was to form a better understanding about the customer base with this segmentation and in addition conduct an experiment about using machine learning in customer relationship management system. In the research an artefact – or segmentation – was constructed and evaluated using an iterative process. The most central part of designing the artefact was determining the features by which the customers would be divided into segments. The parameters chosen were formed from internal data of the case organization. The artefact was developed based on these selected parameters. The possible segments were searched using several mathematical models. After the first iteration round, it was discovered that with the selected parameters, no segments exist. Therefore a second iteration round was conducted where a segmentation was formed with previously defined parameters. The manual segmentation was conducted using the same parameters as automatic segmentation, although the principals of forming the segments were different.

As a result of this research customer segmentation was achieved for the client organization and additionally, deepened understanding about customer segmentation in the context of institute of higher education was accomplished. Customer segmentation in the client organization was possible to be reached with the features formed in workshop, in which different user groups of customer relationship management were represented. The segmentation created illustrates clearly the meaningfulness of the client organization’s customers, which was the main goal of the segmentation. Although the segmentation couldn’t be successfully created using machine learning methods, a platform of procedures was created for the client organization for forming the automatic segmentation. Another result of this study is the deeper understanding about customer relationship management and especially about organizational customer segmentation in the context of higher institutes of education, in which little research has been conducted. During the research it was discovered that special characteristics related to the functioning of institute of higher education are creating challenges in segmentation. These challenges are especially related to illustrating the overall picture of single customer relationships, maintenance of data consistency throughout the organization and the variety of important measurements related to customer relationships. In addition to institutes of higher educations, the research serves organizations and communities outside of this context, whose customer base is similarly unusual compared to the more traditional ones. The next step for the client organization of the study is to experiment segmentation by defining different output goals and the parameters accordingly. Generally more research about customer segmentation in the context of institutes of higher education is needed.