Predicting Customer Lifetime Value Based on Financial and Demographic Characteristics Using GMDH Neural Network Case Study: Individual Customers of a Private Bank of Iran

The role of customer relationship management as a strategic tool in development of manufacturing and service organizations, and also acquisition and retention customers in competitive industries, is undeniable. Identification, valuation and classification of customers and allocating resources to them based on their value for organization are the main concerns in customer relationship management. One of the most important tool in this direction, is calculating and predicting customer lifetime value (CLV). “CLV” is a value which is expected customer bring to the organization in specified period.
In this paper, calculating and predicting customer lifetime value is as a key tool in the implementation of customer relationship management in banking. The GMDH neural networks due to its high performance in terms of prediction, is applied and with genuine customer demographic and transactional information of a private Iranian bank , the CLV forecasting is evaluated. The results show that this tool can be used to accurately predict over 90% of customer lifetime value.