Management Research in Iran

Abstract Customer relationship management provides the basis for customer optimal communication, customer loyalty and customer retention. This requires the design of customer-based strategies, the proper implementation and applying of technology, employee empowerment and increasing the level of customers knowledge. Given the advances in e-banking and the reduction of face-to-face contact points, the use of business intelligence tools to effectively use large volumes of customer information also seems necessary. The purpose of present study is to provide a conceptual framework for customer relationship management in electronic banking using business intelligence tools in Sepah Bank and merged banks. For this purpose, a qualitative research has been conducted and first, by reviewing the studies, the initial framework has been identified and then, Theme Analysis has been used for its development in electronic banking. The research population is 7 experts of case study. Semi-structured interviews were used to collect data and credibility and confirmability were used to assess the validity of the results. After analyzing the data, the customer relationship management framework in e-banking is classified into 6 dimensions, 16 components and 35 indicators. The main dimensions of which are Customer reach and acquisition, Customer identification, Customer attraction, Customer relationship development, Customer retention and customer relationship review.

Abstract Interaction of companies with customers in the form of customer relationship management has changed significantly. Identifying characteristics of different customers and allocating resources to them according to their value to the firm has become one of the main concerns in customer relationship management. The purpose of this paper is to provide an appropriate model for customer segmentation based on some of the most important financial and demographics characteristics influencing factors of customer lifetime value (CLV). The process proposed in this study was performed in Saman insurance company. After determining RFM model indices, which include date, frequency and monetary of purchase, AHP method used for weighting them among 180000 customers. The optimal number of clusters based on the silhouette and impact of RFM indicators was done by using Two-step algorithm and then customers classified through K-Means clustering algorithm. Results provided a platform to analyze the characteristics of customers in three main sections. Also, by prioritizing clusters based on the RFM indices, valuable customers were identified. Finally, some suggestions were presented to the company to improve its customer relationship management system.

Abstract Customer value refers to the potential interaction of customer and enterprise in the certain periods of time. As companies recognize customer value it can provide customized services for different customers, they can achieve to an effective customer relationship management. This research focuses on Banking Industry and integrates data mining techniques and management issues in order to systematically analyze the customer values. First it applies Fuzzy Analytical Hierarchy Processing (FAHP) in order to weighting variables and then imports DFMT model to the k-means technique, for clustering customers according to the specific criteria. Using proposed scoring model establishes the customer value pyramid and categorizes customers in four spectrums. The customer value pyramid helps to separately determining of each customer value to giving appropriate services to them in proportion with the class value. The statistical population was 285 customers of Tejarat bank branches of Zanjan city in Iran. In the resulted customer pyramid, the first spectrum is the Platinum customer which is composed of two rows of the pyramid called H1 and H2. These two rows in pyramid have the highest value and have the most profitability for the bank. Second spectrum, is called golden customers which has three rows in pyramid called H3, H4, H5. Third spectrums are Silver customers which are laid in H6, H7, H8 rows of spectrum. Forth spectrum, are leaden customers that are H9 and H10 rows of customer pyramid. This spectrum receives and wastes resources of the bank and bank should respect and bear high risks.