Teacher-Student Learning on Complexity in Intelligent Routing

• URL: http://arxiv.org/abs/2402.15665v1
• Date: Sat, 24 Feb 2024 00:40:40 GMT
• Title: Teacher-Student Learning on Complexity in Intelligent Routing
• Authors: Shu-Ting Pi, Michael Yang, Yuying Zhu, Qun Liu
• Abstract summary: We develop a machine learning framework that predicts the complexity of customer contacts and routes them to appropriate agents. Experiments show that such a framework is successful and can significantly improve customer experience. We propose a useful metric called complexity AUC that evaluates the effectiveness of customer service at a statistical level.
• Score: 19.32977689162711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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