The Co-Production of Service: Modeling Service Times in Contact Centers Using Hawkes Processes

Abstract

In customer support contact centers, a successful service interaction involves a messaging dialogue between a customer and an agent. Both parties depend on one another for information and problem solving, and this interaction defines a co-produced service process. In this paper, we propose, develop, and compare new stochastic models for service co-production in a contact center. A key observation is that the customer and agent’s co-produced service has cross- and self-exciting dynamics within each conversation. The cross-excitation stems from the two parties responding to one another, and the self-excitation captures one party sending follow-ups to their own prior message. Hence, messages beget messages, and we capture this phenomenon by introducing Hawkes point process models of the conversational services. These models distinguish between the role of the customer and of the agent, reflect the service process's dynamic evolution over time based on its own history, and include additional behavioral and operational aspects, including the agent's number of simultaneous assignments and measures of the amount of information and sentiment each message contains. To evaluate our service co-production models, we apply them to an industry contact center dataset containing nearly 5 million messages. We show that the Hawkes models better represent the service dynamics than do the classic Poisson and phase-type models. Indeed, we find that service interactions are characterized by strong agent-customer dependency and the centrality of the process's cross- and self-excitation attributes. Finally, we use the proposed models to improve upon routing algorithms used in contact centers. We show how an activity-based dynamic routing based on predicted information easily computed from our models can outperform well-known and widely used concurrency-based routing rules and substantially reduce customer waiting time, demonstrating how these history-dependent stochastic models can improve operational decision making in practice.