Summary of Contact Complexity in Customer Service, by Shu-ting Pi et al.
Contact Complexity in Customer Service
by Shu-Ting Pi, Michael Yang, Qun Liu
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed machine learning model aims to accurately predict the complexity of customer service issues by mimicking the behavior of human agents. The model defines complexity based on how an AI expert responds to a given contact transcript. If the AI expert is uncertain or lacks skills, it considers the issue high-complexity. This approach eliminates the need for consensus-based data annotation and has proven reliable, scalable, and cost-effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new machine learning method that can predict the complexity of customer service issues. This helps to route contacts to the right agents, reducing multiple transfers or repeated contacts. The model is based on how an AI expert responds to a transcript, considering it high-complexity if uncertain or lacking skills. |
Keywords
* Artificial intelligence * Machine learning
