Analyzing the Problems of Ayandeh Bank Branches across the Country Using Data Mining Technique

In order to manage problems and complaints of customers and branches, many banks in the country outsource parts of their customer relationship management to companies such as call centers. Since this important unit is managed out of the banks, analyzing the data and evaluating the performance of call centers are very important. On the other hand, many banks are not able to analyze and do not know how to use hidden patterns in the data. Hence, by presenting RFS model in this paper, we have tried to cluster bank branches based on R factor (recently announced problem), F (frequency or number of difficulties) and S (branches satisfaction with call center) and find the relationship between these factors and mentioned problems. Moreover, call center's ability to resolve problems of branches of each cluster can be assessed using S Factor. Branches were distributed into four optimized clusters based on their behavior pattern. Finally, the results were analyzed and the recommendations were presented to improve the performance of call centers.

Keywords:

CRM , Clustering , Customer Satisfaction , k-means algorithm