Segmentation for factoring customers: using unsupervised machine learning algorithms

Abstract

Nowadays the fact that technology facilitates data collection is an important opportunity, as well as making the management of all this data difficult and makes no sense unless it is well processed. This stored data is extremely important, and companies use data provided by their customers. Catching the needs of the customer profiles of the changing world is now a necessity and takes the first place for companies. With the increase in the amount of stored data over time, it has become difficult to establish a relationship between the data and to separate them from each other. At this point, machine learning methods have become more involved in our lives. In this study, what segmentation is and its change over the years are mentioned. It has been mentioned which machine learning techniques will be useful in data selection. Then, possible machine learning methods are shown in real life segmentation problem by using the domestic factoring company’s customer check data. Since this study aims to group unlabeled data, unsupervised learning techniques are emphasized. Among these methods, Hierarchical Clustering, DBSCAN, Gaussian Mixture Modeling methods, Fuzzy c- Means were used as well as the most popular K-Means algorithm. When the clustering results were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering could not produce expected results. It was observed that the best results were obtained with the K-Means and Fuzzy c - Means algorithms.