Teacher-Student Learning on Complexity in Intelligent Routing
Shu-Ting Pi Michael Yang Yuying Zhu Qun Liu
Customer service is often the most time-consuming aspect for e-commerce
websites, with each contact typically taking 10-15 minutes. Effectively routing
customers to appropriate agents without transfers is therefore crucial for
e-commerce success. To this end, we have developed a machine learning framework
that predicts the complexity of customer contacts and routes them to
appropriate agents accordingly. The framework consists of two parts. First, we
train a teacher model to score the complexity of a contact based on the
post-contact transcripts. Then, we use the teacher model as a data annotator to
provide labels to train a student model that predicts the complexity based on
pre-contact data only. Our experiments show that such a framework is successful
and can significantly improve customer experience. We also propose a useful
metric called complexity AUC that evaluates the effectiveness of customer
service at a statistical level.