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.

本研究通过attention-based stacked bidirectional Long Short Term Memory Network followed by Hierarchical Clustering和Support Vector Machines、Logistic Regression模型，开发出一个挖掘客户呼叫原因和驱动因素的框架，以提高数字信息环境下客户满意度并降低业务成本。

运用自然语言处理理解金融领域客户呼叫背后的原因与动机