财富 100 强技术制造商如何将支持工程师的搜索工作量减少 70%
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Cortical.io Support Intelligence
技术栈
- Unsupervised Machine Learning
实施规模
- Enterprise-wide Deployment
影响指标
- Productivity Improvements
- Customer Satisfaction
技术
- 分析与建模 - 机器学习
适用行业
- 电子产品
服务
- 数据科学服务
关于客户
客户是一家财富 100 强技术制造商。他们是一家拥有复杂网络环境的大型公司。他们的支持工程师处理大量支持案例，这些案例通常涉及复杂的技术问题。该公司的内部文档使用特定术语，这些术语通常与客户使用的语言不同。语言使用的这种差异进一步增加了支持案例的解决难度。该公司之前曾尝试使用其他基于搜索的解决方案来缩短找到有意义结果的时间，但这些尝试未能提高支持团队的工作效率。
挑战
该公司的支持工程师处理的支持案例很难解决，而且耗时，因为它们涉及复杂网络环境中的复杂技术问题。客户使用的术语通常与公司内部文档中使用的术语不同，这使得任务更加困难。高效处理支持案例的前提是从过去的案例中寻找解决方案，而不是从头开始解决问题。尝试使用其他基于搜索的解决方案来减少找到有意义结果的时间，但失败了，因为它们无法快速一致地识别类似的支持案例，并且没有提高支持团队的工作效率。
解决方案
Cortical.io 支持智能通过无监督机器学习方法使用支持案例快速训练。该解决方案的专利技术不仅分析关键词，还分析整个支持案例的含义，包括客户的书面请求、工程师的笔记、电子邮件交流以及支持文档（长篇或短篇）中文本部分的含义，从而克服了语言歧义和词汇不匹配的问题。这使得该解决方案能够快速为支持工程师提供支持案例最适用的文档。随着新材料的出现，它会被提取、索引并自动变为可搜索的。根据支持工程师的反馈，系统还不断学习评估适用文档的质量。
运营影响
数量效益
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