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Prodigal Technologies Inc. > 实例探究 > Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service
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Unifin Employs Prodigal’s Natural Language Engine for Improved Customer Service

技术
  • 分析与建模 - 自然语言处理 (NLP)
  • 分析与建模 - 实时分析
适用行业
  • 金融与保险
适用功能
  • 人力资源
  • 销售与市场营销
用例
  • 语音识别
服务
  • 数据科学服务
挑战
The coronavirus pandemic led to a sudden loss of jobs for approximately 15 million Americans. This situation led to a surge in stressful discussions between Unifin's agents and borrowers, particularly concerning loss of employment and health concerns among the elderly. Unifin identified a problem where some agents failed to empathize with the borrowers' stressful situations, which was considered a Level 4 infraction. Unifin needed a quick mechanism to identify these infractions among thousands of calls each day and educate the agents to drive more empathy towards borrowers.
关于客户
Unifin is a company that provides support, assistance, and education to borrowers throughout the loan process. The company prides itself on its highly trained agents who engage and collaborate with borrowers over the phone. Unifin is committed to maintaining a high level of empathy towards borrowers, particularly those affected by the pandemic. The company has established a moral code of conduct for its agents to follow during such calls, and any violation of this code is taken seriously.
解决方案
Prodigal’s Natural Language Engine was employed to identify stressful discussions related to loss of employment and health concerns among the elderly. The engine, with its expertise in the collections domain, was able to surface insights without specifically being asked to search for a list of keywords. It flagged all conversations of this nature and made the trends prominently visible on its interface. This made it easy to identify agents who were repeat offenders or needed coaching. Prodigal’s smart reports were shared with all clients for free within two weeks of the shutdown being enforced.
运营影响
  • Unifin was able to swiftly incentivize recommended behaviors among its agents.
  • The company identified a dozen calls where the agents had shown great consideration and empathy with the borrower.
  • Prodigal enabled Unifin to identify certain agents who had committed severe infractions and these agents were guided to improve.
数量效益
  • 98%+ Correct Call Tags
  • 24 hrs Implemented in a day
  • 40+ ARM domain tags

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