Insurance customer segmentation using clustering approach
| Autor: | Shaghayegh Abolmakarem, Farshid Abdi, Kaveh Khalili Damghani |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
Customer retention Operations research Computer science business.industry k-means clustering 02 engineering and technology Customer relationship management 020901 industrial engineering & automation Market segmentation 0202 electrical engineering electronic engineering information engineering Feature (machine learning) General Earth and Planetary Sciences 020201 artificial intelligence & image processing Segmentation Cluster analysis Customer to customer business General Environmental Science |
| Zdroj: | International Journal of Knowledge Engineering and Data Mining. 4:18 |
| ISSN: | 1755-2095 1755-2087 |
| DOI: | 10.1504/ijkedm.2016.082072 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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