Efficient Customer Service Combining Human Operators and Virtual Agents
This work addresses the problem of improving customer service efficiency and satisfaction for businesses by integrating human and virtual agents, though it appears incremental in applying queuing theory to a known bottleneck.
The paper tackles the challenge of creating an effective hybrid customer service system combining human operators and virtual agents, demonstrating through simulations and a user study that proper parameter optimization can increase the number of served clients while reducing waiting time and boosting satisfaction.
The prospect of combining human operators and virtual agents (bots) into an effective hybrid system that provides proper customer service to clients is promising yet challenging. The hybrid system decreases the customers' frustration when bots are unable to provide appropriate service and increases their satisfaction when they prefer to interact with human operators. Furthermore, we show that it is possible to decrease the cost and efforts of building and maintaining such virtual agents by enabling the virtual agent to incrementally learn from the human operators. We employ queuing theory to identify the key parameters that govern the behavior and efficiency of such hybrid systems and determine the main parameters that should be optimized in order to improve the service. We formally prove, and demonstrate in extensive simulations and in a user study, that with the proper choice of parameters, such hybrid systems are able to increase the number of served clients while simultaneously decreasing their expected waiting time and increasing satisfaction.