Getting insights from the voices of customers: Conversation mining at a contact center

Abstract

Business-oriented conversations between customers and agents need to be analyzed to obtain valuable insights that can be used to improve product and service quality, operational efficiency, and revenue. For such an analysis, it is critical to identify appropriate textual segments and expressions to focus on, especially when the textual data consists of complete transcripts, which are often lengthy and redundant. In this paper, we propose a method to identify important segments from the conversations by looking for changes in the accuracy of a categorizer designed to separate different business outcomes. We then use text mining to extract important associations between key entities (insights). We show the effectiveness of the method for making chance discoveries by using real life data from a car rental service center. © 2008 Elsevier Inc. All rights reserved.