Value-Based Routing and Preference-Based Routing in Customer Contact Centers

Sisselman, Michael E.; Whitt, Ward

Telephone call centers and their generalizations—customer contact centers—usually handle several types of customer service requests (calls). Since customer service representatives (agents) have different call-handling abilities and are typically cross-trained in multiple skills, contact centers exploit skill-based routing (SBR) to assign calls to appropriate agents, aiming to respond properly as well as promptly. Established agent-staffing and SBR algorithms ensure that agents have the required call-handling skills and that call routing is performed so that constraints are met for standard congestion measures, such as the percentage of calls of each type that abandon before starting service and the percentage of answered calls of each type that are delayed more than a specified number of seconds. We propose going beyond traditional congestion measures to focus on the expected value derived from having particular agents handle various calls. Expected value might represent expected revenue or the likelihood of first-call resolution. Value might also reflect agent call-handling preferences. We show how value-based routing (VBR) and preference-based routing (PBR) can be introduced in the context of an existing SBR framework, based on static-priority routing using a highly-structured priority matrix, so that constraints are still met for standard congestion measures. Since an existing SBR framework is used to implement VBR and PBR, it is not necessary to replace the automatic call distributor (ACD). We show how mathematical programming can be used, with established staffing requirements, to find a desirable priority matrix. We select the priority matrix to use during a specified time interval (e.g., 30-minute period) by maximizing the total expected value over that time interval, subject to staffing constraints.


Also Published In

Production and Operations Management

More About This Work

Academic Units
Industrial Engineering and Operations Research
Published Here
September 19, 2017