In customer support contact centers, every service interaction involves a
messaging dialogue between a customer and an agent. Both parties depend on one
another for information and problem solving, hence this interaction defines a
co-produced service process. In this paper, we develop and compare new
stochastic models for service co-production in contact centers. A key
observation is that this service interaction features 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, which depend on the conversation's history, on the
customer-agent relationships, and on the state of the system.
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 classic Poisson and
phase-type models do. Indeed, we find that service interactions are
characterized by strong agent-customer dependency and by the centrality of the
process's cross- and self-excitation attributes. Finally, we use the proposed
models to improve upon popular routing algorithms used in contact centers. We
show how dynamic routing based on Hawkes process predictions outperforms
well-known concurrency-based routing rules. Large data-driven simulation
experiments show that this Hawkes-based routing significantly reduces customer
waiting time, demonstrating how these history-dependent stochastic models can
improve operational decision making in practice