Online call quality monitoring for automating agent-based call centers

One of the challenges in automating a call center is the tradeoff between customer satisfaction and the cost of human agents: i.e., most callers prefer human agents to automated systems, but adding human agents substantially increases call center operating costs. One possible compromise is to let callers use automation at the beginning of the call and bring in a human agent if they have problems. The key problem here is, obviously, how to detect the problematic calls promptly before it is too late. This paper proposes a novel method for monitoring call quality, aiming to salvage callers having problems with automation by bringing in a human agent in a timely manner. We propose to use finite state machines to automatically label call data for training and use the log likelihood ratio for monitoring calls to detect bad calls. We demonstrate, by experiments, that it is possible to detect bad calls before callers give up the call, which increases customer satisfaction and minimizes costs.