AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents
This is an incremental application of existing bandit methods to a specific domain (customer support for accounting software).
The paper tackles the problem of improving customer support agent productivity by developing a decision support system using contextual bandit algorithms to provide suggestions and learn from agents online, with early insights from feedback motivating future work.
In this short paper, we present early insights from a Decision Support System for Customer Support Agents (CSAs) serving customers of a leading accounting software. The system is under development and is designed to provide suggestions to CSAs to make them more productive. A unique aspect of the solution is the use of bandit algorithms to create a tractable human-in-the-loop system that can learn from CSAs in an online fashion. In addition to discussing the ML aspects, we also bring out important insights we gleaned from early feedback from CSAs. These insights motivate our future work and also might be of wider interest to ML practitioners.