Teacher-Student Learning on Complexity in Intelligent Routing
This addresses time inefficiencies in e-commerce customer service, though it is incremental as it applies a teacher-student learning approach to an existing routing challenge.
The paper tackles the problem of routing customer contacts in e-commerce by predicting complexity to reduce transfer times, resulting in a framework that significantly improves customer experience with a proposed complexity AUC metric for evaluation.
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.