Solving customer insurance coverage sales plan problem using a multi-stage data mining approach

Abstract

Purpose Customer insurance coverage sales plan problem, in which the loyal customers are recognized and offered some special plans, is an essential problem facing insurance companies. On the other hand, the loyal customers who have enough potential to renew their insurance contracts at the end of the contract term should be persuaded to repurchase or renew their contracts. The aim of this paper is to propose a three-stage data-mining approach to recognize high-potential loyal insurance customers and to predict/plan special insurance coverage sales. Design/methodology/approach The first stage addresses data cleansing. In the second stage, several filter and wrapper methods are implemented to select proper features. In the third stage, K-nearest neighbor algorithm is used to cluster the customers. The approach aims to select a compact feature subset with the maximal prediction capability. The proposed approach can detect the customers who are more likely to buy a specific insurance coverage at the end of a contract term. Findings The proposed approach has been applied in a real case study of insurance company in Iran. On the basis of the findings, the proposed approach is capable of recognizing the customer clusters and planning a suitable insurance coverage sales plans for loyal customers with proper accuracy level. Therefore, the proposed approach can be useful for the insurance company which helps them to identify their potential clients. Consequently, insurance managers can consider appropriate marketing tactics and appropriate resource allocation of the insurance company to their high-potential loyal customers and prevent switching them to competitors. Originality/value Despite the importance of recognizing high-potential loyal insurance customers, little study has been done in this area. In this paper, data-mining techniques were developed for the prediction of special insurance coverage sales on the basis of customers’ characteristics. The method allows the insurance company to prioritize their customers and focus their attention on high-potential loyal customers. Using the outputs of the proposed approach, the insurance companies can offer the most productive/economic insurance coverage contracts to their customers. The approach proposed by this study be customized and may be used in other service companies.

Newly Proposed k-NN Method for More Efficient Classification

“… F1 score Apart from accuracy, we also use F1 score to evaluate the results of both classic k-NN and Mk-NN(TA) when applied to all datasets. Thus, we are looking how Mk-NN(TA) performs in terms of precision and recall compared to k-NN.The score is computed for all 18 datasets and for each value of k (1,3,5,7,9,11,13,15,30,45, 60 and √n).…”

“…[15].The idea of k-NN is applicable to a wide range of problemsbusiness, medicine, media and others. Classic k-NN can be applied in customer relations processes by filtering potential buyers of a specific product or service more effectively, as it can classify them as either buyers or non-buyers [1]. Spatial database is another area where k-NN techniques have an important application.…”

Customer Segmentation and Profiling for Life Insurance using K-Modes Clustering and Decision Tree Classifier

“…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].…”

“…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.…”

Research on relationship between government innovation funding and firms value creation using clustering-rough sets

“…Data mining techniques (such as support vector machine, artificial neural networks, decision tree) have been used for extraction of useful information from complex systems with a large amount of data (Abdi et al , 2018; Lin et al , 2017). For example, the evidence theory has gained increasing importance for the solution of problems involving uncertainty (Zhu et al , 2018a, 2018b), although it yields questionable results when evidence is strongly conflicting.…”