Context aware routing of enterprise user communications

This paper develops a context aware framework to address the diverse communication needs of a modern enterprise. Such enterprises are characterized by workers in different locations, subject to different policies, using different communication devices, and having varying degrees of skill sets. This diversity poses challenges in finding the most effective human worker (agent) for tasks like fielding a customer request, helping another agent with additional expert knowledge, or more generally help complete a task like a supply chain exception. We focus on the problem of routing communications to the most effective agent using a spectrum of contextual knowledge: availability, media type, activity, expertise, and location. We determine an optimal 'request-to-agent' routing based on several metrics of effectiveness depending on the communication context. The optimal agent is selected to communicate on a specific media who minimizes the expected duration of interaction while maximizes the probability of successful call completion. Based on our model we have conducted simulations involving context aware and noncontext aware routing scenarios. The results indicate that the context aware routing outperforms other conventional requestrouting techniques. The work presented here can impact routing algorithms, as well as address problems related to enterprise staffing and temporal variation of context for the agent.