Discovering Customer Intent in Real-time for Streamlining Service Desk Conversations

摘要

Businesses require the contact center agents to meet pie-specified customer satisfaction levels while keeping the cost of operations low or meeting sales targets, objectives that end up being complementary and difficult to achieve in real-time. In this paper, we describe a speech enabled real-time conversation management system that tracks customer-agent conversations to delect user intent (e.g. gathering information, likely to buy, etc.) that can help agents to then decide the best sequence of actions for that call. We present an entropy based decision support system that parses a text stream generated in real-time during a audio conversation and identities the first instance at which the intent becomes distinct enough for the agent to then take subsequent actions. We provide evaluation results displaying the efficiency and effectiveness of our system.