Improvement of call center customer service in a thai bank using disco fuzzy mining algorithm

Poohridate Arpasat, P. Porouhan, W. Premchaiswadi
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引用次数: 17

Abstract

The main objective of the study was to benchmark performance, control discrepancies, and investigate variations of a bank customer service call center data dealing with incoming calls of its clients and customers. To do this, initially an event log consisting of a total of 625,767 process instances (i.e., events) was collected from a private bank in Thailand for the month of July 2015. Using Disco fuzzy mining technique as a process mining tool enabled us to simulate and create authentic visual models/maps from the collected event log in form of fuzzy mining graphs. To better investigate the behavior of the bank's clients/customers and administrators/operators dealing with inappropriate (not successful) customer service and calls, only “Failure” or “Not Responded” types of process instances/events were chosen and selected for the study. The findings showed that the number of the incoming calls made into the call center (customer service section) due to the “over card limit” problem was the highest (i.e., with 4,667 process instances in total) compared with the other problems within the call center event log. On the other hand, the results showed that almost 32%of the “Over Card Limit” type of the problems were not solved at the first attempt (i.e., clients/customers have to re-dial and re-contact the operators in charge of the section again for the second, third or sometimes multiple-times). Similarly, our results showed that the problems occurred due to the reason why the “card number is already activated” allocated the second highest number of problems (i.e., with a total of 2,068 process instances in total) within the call center event log. Interestingly, 10% of the incoming calls facing the “card number is already activated” were not solved/fixed at the first attempt and clients/customers need to re-contact the call center operators again afterwards. With the same token “record not found” (with 52% of the calls not solved at the first attempt), “account not found” (with 68% of the calls not solved at the first attempt) and “account does not exist” (with 61% of the calls not solved at the first attempt) types of the problems allocated the third, fourth and fifth most frequent incoming calls made into the call center customer service section. Eventually, the results of the study can be used in order to enhance and improve the performance of the customer service processes in a more efficient, effective and timely manner.
利用迪斯科模糊挖掘算法改进泰国某银行呼叫中心客户服务
该研究的主要目的是对性能进行基准测试,控制差异,并调查银行客户服务呼叫中心处理其客户和客户的来电数据的变化。为此,首先从泰国的一家私人银行收集了2015年7月共625,767个流程实例(即事件)组成的事件日志。使用Disco模糊挖掘技术作为过程挖掘工具,使我们能够从收集的事件日志中以模糊挖掘图的形式模拟和创建真实的可视化模型/地图。为了更好地调查银行客户/顾客和管理员/操作员处理不适当(不成功)客户服务和呼叫的行为,只选择了“失败”或“未响应”类型的流程实例/事件进行研究。调查结果显示,与呼叫中心事件日志中的其他问题相比,由于“超过卡限额”问题而进入呼叫中心(客户服务部门)的来电数量最高(即总共有4,667个流程实例)。另一方面,结果显示,近32%的“超限卡”类型的问题在第一次尝试时没有得到解决(即客户/客户必须再次拨打和重新联系负责该部分的操作员,进行第二次,第三次或有时多次)。类似地,我们的结果显示,问题发生的原因是“卡号已激活”在呼叫中心事件日志中分配了第二高数量的问题(即,总共有2,068个流程实例)。有趣的是,10%面对“卡号已激活”的来电在第一次尝试时没有解决/修复,客户/客户需要再次联系呼叫中心接线员。使用相同的令牌“未找到记录”(52%的呼叫在第一次尝试时未解决),“未找到帐户”(68%的呼叫在第一次尝试时未解决)和“帐户不存在”(61%的呼叫在第一次尝试时未解决)类型的问题分配到呼叫中心客户服务部门的第三,第四和第五最频繁的来电。最终,研究的结果可以用来提高和改善客户服务流程的表现,以更有效率、更有效和更及时的方式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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