Teacher-Student Learning on Complexity in Intelligent Routing
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866913243919810560 |
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| author | Pi, Shu-Ting Yang, Michael Zhu, Yuying Liu, Qun |
| author_facet | Pi, Shu-Ting Yang, Michael Zhu, Yuying Liu, Qun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Teacher-Student Learning on Complexity in Intelligent Routing Pi, Shu-Ting Yang, Michael Zhu, Yuying Liu, Qun Machine Learning Artificial Intelligence 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. |
| title | Teacher-Student Learning on Complexity in Intelligent Routing |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2402.15665 |