  Customer ratings are valuable sources to understand their satisfaction and
are critical for designing better customer experiences and recommendations. The
majority of customers, however, do not respond to rating surveys, which makes
the result less representative. To understand overall satisfaction, this paper
aims to investigate how likely customers without responses had satisfactory
experiences compared to those respondents. To infer customer satisfaction of
such unlabeled sessions, we propose models using recurrent neural networks
(RNNs) that learn continuous representations of unstructured text conversation.
By analyzing online chat logs of over 170,000 sessions from Samsung's customer
service department, we make a novel finding that while labeled sessions
contributed by a small fraction of customers received overwhelmingly positive
reviews, the majority of unlabeled sessions would have received lower ratings
by customers. The data analytics presented in this paper not only have
practical implications for helping detect dissatisfied customers on live chat
services but also make theoretical contributions on discovering the level of
biases in online rating platforms.
