Simulation-based Assessment of Data-Driven Processes in Customer Support Systems

The use of digital technology and data-driven techniques in companies has become an engine to transform operations, respond rapidly to customer demands, and enable new business models. However, companies face the challenge of measuring and evaluating how the introduction of new technology will impact operational processes. This paper takes a systems approach to study how to assess the trade-offs of adopting data-driven mechanisms to improve operational processes in a multichannel customer support system. We investigate potential cost savings from two technology applications: classification methods to direct customers to efficient self-service communication channels and routing methods to match customers with agents based on the query type and available skill set. Discrete event simulation experiments model and evaluate how lower-level technology adoption, characterized purely on a functional level, affects system-level performance. Resulting discussions include practical examples of how operational managers could use experimental information to make strategic operational decisions when adopting data-driven technologies. What-if scenarios combine distinct configurations of the customer classification mechanisms and the available communication channels, to evaluate the reduction in the total number of agents needed to meet a target service quality level.