In customer support centers, a successful service interaction involves a  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 the co-production of service in a contact center. Using insights from service communication data, we model the service interactions using self-exciting and mutually exciting bivariate Hawkes processes, so that a correspondence from one party increases the likelihood of a response from the other party soon after. Moreover, our models incorporate both dynamic busyness factors that depend on the agent workload as well as dynamic factors that depend on the inner-mechanics of the interaction. To understand how well our Hawkes modelsdescribe the message-timestamps, we compare the goodness-of-fit of these models on contact center data from industry . We show that the word-count bivariate Hawkes model, which takes into account the mutual interaction and the amount of information provided by each party, fits the data the best. In addition to a great goodness-of-fit, the Hawkes models allow us to construct explicit expressions for the relationship between the correspondence rates of each party and the conversation progress. These formulae illustrate that the agent is more dominant in pacing the service along in the short term, but that the customer has a more profound effect on the duration of the conversation in the long run . Finally, we use our models to predict the future level of activity within a given conversation, through which we find that the bivariate Hawkes processes that incorporate the amount of information provided by each party or the sentiment expressed by the customer give us the most accurate predictions .
In customer contact centers, a successful service interaction involves a messaging dialogue between a customer and agent. Both parties depend on one another for information and problem solving . 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. We capture this phenomenon by introducing Hawkes process models of the 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 .
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Using insights from service communication data, we model the service interactions using self-exciting and mutually exciting bivariate Hawkes processes, so that a correspondence from one party increases the likelihood of a response from the other party soon after. Moreover, our models incorporate both dynamic busyness factors that depend on the agent workload as well as dynamic factors that depend on
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. We capture this phenomenon by introducing Hawkes process models of
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inner-mechanics of the interaction. To understand how well our Hawkes modelsdescribe the message-timestamps, we compare the goodness-of-fit of these models on contact center data from industry
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
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word-count bivariate Hawkes model, which takes into account the mutual interaction and the amount of information provided by each party, fits the data the best. In addition to a great goodness-of-fit, the Hawkes models allow us to construct explicit expressions for the relationship between the correspondence rates of each party and the conversation progress. These formulae illustrate that the agent is more dominant in pacing the service along in the short term, but that the customer has a more profound effect on the duration of the conversation in the long run
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
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our models to predict the future level of activity within a given conversation, through which we find that the bivariate Hawkes processes that incorporate the amount of information provided by each party or the sentiment expressed by the customer give us the most accurate predictions
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