  Customer satisfaction is an important factor in creating and maintaining
long-term relationships with customers. Near real-time identification of
potentially dissatisfied customers following phone calls can provide
organizations the opportunity to take meaningful interventions and to foster
ongoing customer satisfaction and loyalty. This work describes a fully
operational system we have developed at a large US company for predicting
customer satisfaction following incoming phone calls. The system takes as an
input speech-to-text transcriptions of calls and predicts call satisfaction
reported by customers on post-call surveys (scale from 1 to 10). Because of its
ordinal, subjective, and often highly-skewed nature, predicting survey scores
is not a trivial task and presents several modeling challenges. We introduce a
graph neural network (GNN) approach that takes into account the comparative
nature of the problem by considering the relative scores among batches, instead
of only pairs of calls when training. This approach produces more accurate
predictions than previous approaches including standard regression and
classification models that directly fit the survey scores with call data. Our
proposed approach can be easily generalized to other customer satisfaction
prediction problems.

Machine Learning;Computation and Language