Scoring Customers in Banking Services Marketing: A Case Study on Two Iranian Banks

This paper aims to find an approach for evaluating and ranking customers, in order to help a bank take effective and strategic decisions in providing services to each customer. Recognition of different ranks of customers based on financial, demographical, and residential features, as effective factors in Customer Lifetime Value (CLV), will help design an appropriate mechanism, which would provide each customer with customized services. In order to calculate the CLV and design such mechanism, the GMDH neural network methodology is used, where both residential and commercial type of demographical and financial variables for customers from two Iranian banks are used as the neural network inputs. Results reveal that financial variables have the most impact on the CLV.