Segmentation for factoring customers using unsupervised machine learning algorithms

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 using the local 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 Mixed Modeling methods, Fuzzy c - Means were used besides the most popular K-Means. The success criteria for each algorithm were examined and the appropriate cluster numbers were found, and the results were measured. When the clustering outcomes were examined, the optimal number of clusters was calculated very high with GMM, DBSCAN could not assign clusters, and Hierarchical clustering has been found to be very costly in terms of time. It was observed that the best results were obtained with the K - Means and FCM.