A novel approach to identify problematic call center conversations

Voice based call centers enable customers to query for information by speaking to agents in the call center. Most often these call conversations are recorded for analysis with the intent of trying to identify things that can help improve the performance of the call center to serve the customer better. Today the recorded conversations are analyzed by humans by listening to call conversations, which is both time consuming, fatigue prone and not accurate. Additionally, humans are able to analyze only a small percentage of the total calls because of economics. In this paper, we propose a visual method to identify problem calls quickly. The idea is to sieve through all the calls and identify problem calls, these calls can then be further analyzed by human. We first model call conversations as a directed graph and then identify a structure associated with a normal call. All call conversations that do not have the structure of a normal call are then classified as being abnormal. In this paper, we use the speaking rate feature to model call conversation because it makes it easy to spot potential problem calls. We have experimented on real call center conversations acquired from a call center and the results are encouraging.