Modeling Uncertainty in Decision Support Systems for Customer Call Center

Abstract

Customer call centers are the preferred and prevalent way for many companies to communicate with their customers. The customer call center industry is thus vast and rapidly expanding in terms of both workforce and economic scope. Most major companies have reengineered their communication with customers via one or more call centers, either internally managed or outsourced. Call centers constitutes a set of resources which enable the delivery of services via telephone, email or web portal access. Customer inquiries contains different types of uncertainties regarding the problem description, the recommended system solution and precise cause study. We develop a decision support system for customer call centers using soft computing techniques for automating, maintaining and maximizing the value of the decision process. Fuzzy logic as soft computing technique is a methodology for the representation and manipulation of imprecise and vague information. Bayesian networks are formal graphical languages for the representation and communication of decision scenarios requiring reasoning under uncertainty. We discuss decision support system scenarios under uncertainty using Bayesian networks and fuzzy logic. Real customer requests as support cases contain cause action coherence under uncertainty. We will model these types of uncertainty scenarios in a decision support system selecting the appropriate technique of supporting the decision process.