Using Data Mining in CRM to Understand the Insurance Market
The British University in Dubai (BUiD)
Understanding customers’ interests is an important concept for designing marketing campaigns to improve businesses and increase revenue. The rapid growth of high dimensional databases and data warehouses, such as Customer Relationship Management (CRM), stressed the need for advanced data mining techniques. In this paper we investigate different data mining algorithms, specifically K-Means, SOM, and CHAID 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 good visualization. Also we discuss how both techniques segmentation analysis can be useful in studying customer’s interest. CHAID helps us to predict new target for customers’ interest based on their demographic data.
DISSERTATION WITH DISTINCTION
insurance market, data mining, Customer Relationship Management (CRM)