Modeling Customer Experience in a Contact Center through Process Log Mining

The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data

Modeling And Optimization Of Non-Profit Hospital Call Centers With Service Blending

This dissertation focuses on the operations problems in non-profit hospital call centers with inbound and outbound calls service blending. First, the routing policy for inbound and outbound calls is considered. The objective is to improve the system utilization under constraints of service quality and operators\u27 quantity. A collection of practical staffing assignment methods, separating and mixing staffing policy are evaluated. Erlang C queuing model is used to decide the minimum number of operators required by inbound calls. Theoretical analysis and numerical experiments illustrate that through dynamically assigning the inbound and outbound calls to operators under optimal threshold policy, mixing staffing policy is efficient to balance the system utilization and service quality. Numerical experiments based on real-life data demonstrate how this method can be applied in practice. Second, we study the staffing shift planning problem based on the inbound and outbound calls routing policies. A mathematical programming model is developed, based on a hospital call center with one kind of inbound calls and multiple kinds of outbound calls. The objective is to minimize the staffing numbers, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments based on actual operational data are included. Results show that the model is effective to optimize the shift planning and hence reduce the call centers\u27 cost. Third, we model the staffing shift planning problem for a hospital call center with two kinds of service lines. Each kind of service is delivered through both inbound calls and outbound calls. The inbound calls can be transferred between these two service lines. A mathematical programming model is developed. The objective is to minimize the staffing cost, by deciding the shift setting and workload allocation. The inbound calls service level and staffing utilization are taken into consideration in the constraints. Numerical experiments are carried out based on actual operational data. Results show that the model is effective to reduce the call centers\u27 labor cost

The Emergence of Interactive Behaviour: A Model of Rational Menu Search

One reason that human interaction with technology is difficult to understand is because the way in which people perform interactive tasks is highly adaptive. One such interactive task is menu search. In the current article we test the hypothesis that menu search is rationally adapted to (1) the ecological structure of interaction, (2) cognitive and perceptual limits, and (3) the goal to maximise the trade-off between speed and accuracy. Unlike in previous models, no assumptions are made about the strategies available to or adopted by users, rather the menu search problem is specified as a reinforcement learning problem and behaviour emerges by finding the optimal policy. The model is tested against existing empirical findings concerning the effect of menu organisation and menu length. The model predicts the effect of these variables on task completion time and eye movements. The discussion considers the pros and cons of the modelling approach relative to other well-known modelling approaches

Machine Learning Models for Network Intrusion Detection and Authentication of Smart Phone Users

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the Degree of Master of Science by S. Sareh Ahmadi on November 18, 2019