A conversation-mining system for gathering insights to improve agent productivity

Abstract

We describe a method to analyze transcripts of conversations between customers and agents in a contact center. The aim is to obtain actionable insights from the conversations to improve agent performance. Our approach has three steps. First we segment the call into logical parts. Next we extract relevant phrases within different segments. Finally we do two dimensional association analysis to identify actionable trends. We use real data from a contact center to identify specific actions by agents that result in positive outcomes. We also show that implementing the actionable results in improved agent productivity. © 2007 IEEE.