A Conversation-Mining System for Gathering Insights to Improve Agent Productivity

In this paper we look at the methods for analyzing transcriptions of recorded calls of customer-agent interactions in a contact center to see how agent productivity can be improved. The aim is to obtain actionable insights to improve agent performance by automatically analyzing such transcripts taken from a car rental help desk. These were analyzed to discover steps that agents are taking to convince customers to make bookings and pick up later. Customers book cars from a particular vendor if they are satisfied on various parameters such as rates, car models, pickup locations, etc. In particular we aim to discover specific traits of agents that result in car bookings and pickups. Based on the analysis it is shown that specific actions
by the agents result in better pickup. After implementing such actionables, over a period of one month the booked car pick-up rate improved by 1.72% after adjustment for seasonal effect. We also propose an automated technique to identify key segments of customer agent interactions and using these segments we demonstrated an effective way of identifying cases lacking compliance to prescribed guidelines.

By: Mikio Takeuchi; L V Subramaniam; Tetsuya Nasukawa; Shourya Roy; Sreeram Balakrishnan

Published in: IEEE CEC/EEE 2007, Tokyo in 2007


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