Customers Segmentation in the Insurance Company (TIC) Dataset.

Abstract Customers' Segmentation is an important concept for designing marketing campaigns to improve businesses and increase revenue. Clustering algorithms can help marketing experts to achieve this goal. The rapid growth of high dimensional databases and data warehouses, such as Customer Relationship Management (CRM), stressed the need for advanced data analytics techniques. In this paper we investigate different data analytics algorithms, specifically K-Means and SOM, using the TIC CRM dataset. While K-Means has shown promising clustering results, SOM has outperformed in the sense of: speed, quality of clustering, and visualization. Also we discuss how both techniques segmentation analysis can be useful in studying customer's interest. The purpose of this paper is to provide a proof of concept (based on a small publicity of data) of how big data analytics can be used in customer segmentation. [ABSTRACT FROM AUTHOR]