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