چکیده:
Many banks in the country outsource part of their customer relationship management unit to companies such as call centers in order to manage problems and complaints of customers and branches. Considering that this important unit is managed outside the banks, analyzing its data and evaluating the performance of call centers is very important. On the other hand, many banks do not have the ability to analyze and how to use hidden patterns in the data. Therefore, in this article, we try to cluster the bank branches based on the similarity of factors R, meaning the novelty of the problem, F frequency or number of problems, and S the satisfaction of branches with the call center, by presenting the RFS model, and find the relationship between existing factors and the type of reported problems. Also, by examining factor S, the ability of the call center to solve the problems of each cluster's branches can be evaluated. Branches are optimally distributed into four clusters based on their behavior pattern, the results of the analysis are presented, and finally, suggestions are made to improve the performance of the call center.
خلاصه ماشینی:
Analysis of the problems of Bank-e-Aayande branches across the country using Data mining method Shabnam Mohammadi 1, Somayeh Alizadeh 2 Many banks in the country outsource part of their customer relationship management unit to companies such as call centers in order to manage customer and branch problems and complaints.
On the other hand, many banks do not have the ability to analyze and how to use hidden patterns in the data, so in this article we try to cluster bank branches based on the similarity of factors R meaning the novelty of the problem statement, F frequency or number of problems, and S the level of branch satisfaction with the call center, by presenting the RFS model, and find the relationship between existing factors and the type of problems reported.
Therefore, in this research, we try to use the RFS model and cluster branches based on their behavior pattern to better understand the type of problems of each branch and the level of satisfaction of branches with the performance of the branch support department.
Many studies have been conducted to manage customer complaints in various industries and using different methods, but so far no similar research has been done to recognize and manage complaints using data mining tools in bank branch support, as a unit outsourced to another organization.
The most important goal of this research is to identify the problems of branches and their level of satisfaction with the performance of branch support using the presented model, which will be discussed in the following sections.
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