Silent Abandonment in Contact Centers: Estimating Customer Patience from Uncertain Data

In the quest to improve services, companies offer customers the opportunity to interact with agents through contact centers, where the communication is mainly text-based. This has become one of the favorite channels of communication with companies in recent years. However, contact centers face operational challenges, since the measurement of common proxies for customer experience, such as knowledge of whether customers have abandoned the queue and their willingness to wait for service (patience), are subject to information uncertainty. We focus this research on the impact of a main source of such uncertainty: silent abandonment by customers. These customers leave the system while waiting for a reply to their inquiry, but give no indication of doing so, such as closing the mobile app of the interaction. As a result, the system is unaware that they have left and waste agent time and capacity until this fact is realized. In this paper, we show that 30%-67% of the abandoning customers abandon the system silently, and that such customer behavior reduces system efficiency by 5%-15%. To do so, we develop methodologies to identify silent-abandonment customers in two types of contact centers: chat and messaging systems. We first use text analysis and an SVM model to estimate the actual abandonment level. We then use a parametric estimator and develop an expectation-maximization algorithm to estimate customer patience accurately, as customer patience is an important parameter for fitting queueing models to the data. We show how accounting for silent abandonment in a queueing model improves dramatically the estimation accuracy of key measures of performance. Finally, we suggest strategies to operationally cope with the phenomenon of silent abandonment.Comment: V

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

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

State-Dependent Estimation of Delay Distributions in Fork-Join Networks

Problem definition: Delay announcements have become an essential tool in service system operations: They influence customer behavior and network efficiency. Most current delay announcement methods are designed for relatively simple environments with a single service station or stations in tandem. However, complex service systems, such as healthcare systems, often have fork-join (FJ) structures. Such systems usually suffer from long delays as a result of both resource scarcity and process synchronization, even when queues are fairly short. These systems may thus require more accurate delay estimation techniques than currently available. Methodology/results: We analyze a network comprising a single-server queue followed by a two-station FJ structure using a recursive construction of the Laplace–Stieltjes transform of the joint delay distribution, conditioning on customers’ movements in the network. Delay estimations are made at the time of arrival to the first station. Using data from an emergency department, we examine the accuracy and the robustness of the proposed approach, explore different model structures, and draw insights regarding the conditions under which the FJ structure should be explicitly modeled. We provide evidence that the proposed methodology is better than other commonly used queueing theory estimators such as last-to-enter-service (which is based on snapshot-principle arguments) and queue length, and we replicate previous results showing that the most accurate estimations are obtained when using our model result as a feature in state-of-the-art machine learning estimation methods. Managerial implications: Our results allow management to implement individual, real-time, state-dependent delay announcements in complex FJ networks. We also provide rules of thumb with which one could decide whether to use a model with an explicit FJ structure or to reduce it to a simpler model requiring less computational effort.</p

Affect-as-Information: Customer and Employee Affective Displays as Expeditious Predictors of Customer Satisfaction

10.1177/10946705231194076JOURNAL OF SERVICE RESEARC

The role of specialized hospital units in infection and mortality risk reduction among patients with hematological cancers

MOTIVATION: Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome. METHODS: We have developed an approach allowing to retrieve infection-related information from unstructured electronic medical records of a tertiary center. Data on 2,330 adults receiving 13,529 chemotherapy treatments for hematological malignancies were identified and assessed. Infection and mortality hazard rates were calculated with multivariate models. Patients were randomly divided into 80:20 training and validation cohorts. To develop patient-tailored risk-prediction models, several machine-learning methods were compared using area under the curve (AUC). RESULTS: Of the tested algorithms, the probit model was found to most accurately predict the evaluated hazards and was implemented in an online calculator. The infection-prediction model identified risk factors for infection based on patient characteristics, treatment and history. Observation of patients with a high predicted infection risk in general wards appeared to increase their infection hazard (p = 0.009) compared to similar patients observed in hematology units. The mortality-risk model demonstrated that for infection events starting at home, admission through hematology services was associated with a lower mortality hazard compared to admission through the general emergency department (p = 0.007). Both models show that dedicated hematological facilities and emergency services improve patient outcome post-chemotherapy. The calculated numbers needed to treat were 30.27 and 31.08 for the dedicated emergency and observation facilities, respectively. Infection hazard risks were found to be non-monotonic in time. CONCLUSIONS: The accuracy of the proposed mortality and infection risk-prediction models was high, with the AUC of 0.74 and 0.83, respectively. Our results demonstrate that temporal assessment of patient risks is feasible. This may enable physicians to move from one-point decision-making to a continuous dynamic observation, allowing a more flexible and patient-tailored admission policy.status: publishe

The role of specialized hospital units in infection and mortality risk reduction among patients with hematological cancers

Motivation Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. The current study aimed to evaluate whether management of such patients in dedicated inpatient and emergency wards could provide superior infection prevention and outcome. Methods We have developed an approach allowing to retrieve infection-related information from unstructured electronic medical records of a tertiary center. Data on 2,330 adults receiving 13,529 chemotherapy treatments for hematological malignancies were identified and assessed. Infection and mortality hazard rates were calculated with multivariate models. Patients were randomly divided into 80: 20 training and validation cohorts. To develop patient-tailored risk-prediction models, several machine-learning methods were compared using area under the curve (AUC). Results Of the tested algorithms, the probit model was found to most accurately predict the evaluated hazards and was implemented in an online calculator. The infection-prediction model identified risk factors for infection based on patient characteristics, treatment and history. Observation of patients with a high predicted infection risk in general wards appeared to increase their infection hazard (p = 0.009) compared to similar patients observed in hematology units. The mortality-risk model demonstrated that for infection events starting at home, admission through hematology services was associated with a lower mortality hazard compared to admission through the general emergency department (p = 0.007). Both models show that dedicated hematological facilities and emergency services improve patient outcome post-chemotherapy. The calculated numbers needed to treat were 30.27 and 31.08 for the dedicated emergency and observation facilities, respectively. Infection hazard risks were found to be non-monotonic in time. Conclusions The accuracy of the proposed mortality and infection risk-prediction models was high, with the AUC of 0.74 and 0.83, respectively. Our results demonstrate that temporal assessment of patient risks is feasible. This may enable physicians to move from one-point decision-making to a continuous dynamic observation, allowing a more flexible and patient-tailored admission policy