AgentBuddy: A Contextual Bandit based Decision Support System for Customer Support Agents

TL;DR

This paper introduces AgentBuddy, a decision support system for customer support agents that uses bandit algorithms to learn from agent interactions and improve productivity in real-time.

Contribution

It presents a novel human-in-the-loop system leveraging bandit algorithms for online learning in customer support, with early insights from user feedback.

Findings

Early feedback provided valuable insights for system improvement

Bandit algorithms enable real-time, adaptive suggestions for CSAs

The approach shows promise for enhancing support agent productivity

Abstract

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.