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Virtual agents are becoming a prominent channel of interaction in customerservice. Not all customer interactions are smooth, however, and some can becomealmost comically bad. In such instances, a human agent might need to step inand salvage the conversation. Detecting bad conversations is important sincedisappointing customer service may threaten customer loyalty and impactrevenue. In this paper, we outline an approach to detecting such egregiousconversations, using behavioral cues from the user, patterns in agentresponses, and user-agent interaction. Using logs of two commercial systems, weshow that using these features improves the detection F1-score by around 20%over using textual features alone. In addition, we show that those features arecommon across two quite different domains and, arguably, universal.

Detecting Egregious Conversations between Customers and Virtual Agents