Insurance customer segmentation using clustering approach

Customers segmentation enables companies to identify the high-profit customers. Clustering algorithms are commonly used for customer segmentation. In this study, K-means clustering algorithms are employed to identify profitable customers in an insurance company. The optimum number of clusters is determined using 'NbClust' package in R software through calculating 23 clustering evaluation metrics. The clustering is accomplished on insurance customers on the basis of 16 customers' features, and ten insurance feature using CRISP methodology. The results show that the customers of insurance company are divided into three groups labelled as 'profitable customers', 'potential profitable customers', and 'disinterested customers'. On the basis of the results of this study, associated customer relationship management (CRM) strategies are proposed to establish suitable marketing and communication plans for each cluster of customers.