(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 9, 2021 434 | Page www.ijacsa.thesai.org Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier Shuzlina Abdul-Rahman, Nurin Faiqah Kamal Arifin, Mastura Hanafiah, Sofianita Mutalib Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam, Malaysia Abstract—Customer segmentation and profiling has become an important marketing strategy in most businesses as a preparation for better customer services as well as enhancing customer relationship management. This study presents the segmentation and classification technique for insurance industry via data mining approaches: K-Modes Clustering and Decision Tree Classifier. Data from an insurance company were gathered. Decision Tree Algorithm was applied for customer profile classification comparing two methods which are Entropy and Gini. K-Modes Clustering segmentized the customers into three prominent groups which are “Potential High-Value Customers”, “Low Value Customers” and “Disinterested Customers”. Decision Tree with Gini model with 10-fold cross validation was found as the best fit model with average accuracy of 81.30%. This segmentation would help marketing team of insurance company to strategize their marketing plans based on different group of customers by formulating different approaches to maximize customer values. Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies. Keywords—Customer segmentation; customer profiling; decision tree; insurance domain; k-modes clustering I. INTRODUCTION Insurance industry has been in the global market for decades and it is a critical contributor to a country‟s long term economic growth. Life insurers improve their policyholders‟ quality of life by pooling the risk of mortality, morbidity, and longevity among a wide number of people and returning the benefits of this pooling in the form of guaranteed payments [1]. In insurance industry, maintaining current customers is a challenge. Customer retention is more important than acquisition of new customers. It is said that 20% of the customers contribute more to the revenue of the company than the rest, as according to Pareto principle [2]. Despite the belief that clients are important for insurance organizations in gaining income and enhance their profitability, acquiring and retaining clients are serious issues faced by insurance firms [3]. It is not easy to obtain and influence new clients because when compared to the current clients, generally, new clients purchase 10% fewer than them, fewer involvement in the purchasing procedure as well as association with the seller [4]. Additionally, acquisition of new clients is more expensive compared to the maintenance of existing clients of the company [5]–[7]. Besides that, the likelihood of effectively selling a good or service to existing active clients is approximately 60-70 percent, while the likelihood is just 5-20 percent for potential clients, which made a greater likelihood of success in selling a good or service to existing clients compared to the potential ones [8]. It is also worthy to note that different clients contribute different amount of revenue to insurance companies, and so it is vital to handle clients based on their profitability due to uneven revenue generated by them [9]. Insurance companies are growing in numbers and the diversity of services offered, in which the clients have full control of their decisions [7]. It is thus important to have a good customer relationship management to retain the existing customers. To achieve that, insurance companies need to identify their target markets by segmenting the customers into groups. This allows them to choose whichever services that match their needs from any service providers. Customer segmentation helps business people to customize marketing plans, identify trends, plan product development, advertising campaigns and deliver relevant products, as well as personalizing messages of individuals for better communication with the intended groups [10]. Consumer sectioning is a great instrument in separating the consumers into various groups and perform analysis on their traits [3], and thus organizations are able to focus on clients in distinct features and determine the most valuable clients by sectioning the clients [9]. Clustering methods have been employed in many studies to segmentize customers [3], [9], [11]–[14], while classification via Decision Tree has also been widely used in past studies [15]–[17]. The following are the contributions of this paper:  This research uses K-Modes Clustering and Decision Tree Classifier for customer segmentation and profiling for insurance domain.  Marketing team of insurance company will be able to strategize their marketing based on different group of customers by formulating different strategies to maximize customer values.  Customers can receive customization of insurance plans which satisfy their necessity as well as better assistance or services from insurance companies. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Shah Alam, Selangor, Malaysia.