In this era of abundant digital information, customer satisfaction has become one of the prominent factors in the success of any business. Customers want a one-click solution for almost everything. They tend to get unsatisfied if they have to call about something which they could have done online. Moreover, incoming calls are a high-cost component for any business. Thus, it is essential to develop a framework capable of mining the reasons and motivators behind customer calls. This paper proposes two models. Firstly, an attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering for extracting these reasons from transcripts of inbound calls. Secondly, a set of ensemble models based on probabilities from Support Vector Machines and Logistic Regression. It is capable of detecting factors that led to these calls. Extensive evaluation proves the effectiveness of these models.
翻译:在数字信息丰富的时代,客户满意度已成为任何企业取得成功的突出因素之一。客户几乎每件事情都想要一击即决的解决办法。如果他们不得不在网上打电话,他们往往会不满意。此外,接到的电话对任何企业来说都是费用高昂的组成部分。因此,必须制定一个能够挖掘顾客电话背后的原因和动力的框架。本文件提出了两种模式。首先,一个基于关注的堆叠双向长时程记忆网,然后由等级分组从传呼记录中提取这些原因。第二,一套基于支持媒介机器和后勤支助倒退概率的混合模型,能够发现导致这些呼吁的因素。广泛评估证明了这些模型的有效性。