Customer Segmentation Using CLV Elements | Zendy

Mitra Bokaei Hosseni | Zendy; Mohammad Jafar Tarokh | Zendy

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Customer Segmentation Using CLV Elements

Author(s) -

Mitra Bokaei Hosseni,

Mohammad Jafar Tarokh

Publication year - 2011

Publication title -

journal of service science and management

Language(s) - English

Resource type - Journals

eISSN - 1940-9907

pISSN - 1940-9893

DOI - 10.4236/jssm.2011.43034

Subject(s) - customer base , customer lifetime value , computer science , customer equity , market segmentation , customer retention , customer value , customer relationship management , customer profitability , profit (economics) , business , value (mathematics) , logistic regression , customer to customer , segmentation , marketing , artificial intelligence , machine learning , microeconomics , service (business) , service quality , economics

To have an effective customer relationship management, it is essential to have information about the different segments of the customers and predict the future profit of them. For this reason companies can use customer lifetime value that consists of three factors-current value of customers, potential value, and customer churn. Potential value of customers focuses on the cross-selling opportunities for current customers. Therefore, cross selling models are built on the total customers of the database that is not interesting. To overcome this, we presented a framework that estimates the current value and churn probability for the customers and then segmented them base on these two elements and select the most profitable segment for the cross-selling models. In this study we predict the customer churn base on logistic regression as a case study on the insurance database

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