您在此处:
为个案设置预测风险得分
通过使用机器学习来预测哪些个案可能违反他们的 SLA,从而最大限度地减少 SLA 违反。直接在个案记录上查看实时风险分数和关键影响因素,以便在违规发生之前采取措施。SLA 违约预测功能使用机器学习来分析个案数据,例如优先级、状态和历史,以预测个案错过其 SLA 截止日期的可能性。它提供实时风险得分,并确定影响该得分的前三个因素,使团队能够主动管理个案。

使用更普遍的搜索词。
选择更少的筛选器,并扩大搜索范围。
通过使用机器学习来预测哪些个案可能违反他们的 SLA,从而最大限度地减少 SLA 违反。直接在个案记录上查看实时风险分数和关键影响因素,以便在违规发生之前采取措施。SLA 违约预测功能使用机器学习来分析个案数据,例如优先级、状态和历史,以预测个案错过其 SLA 截止日期的可能性。它提供实时风险得分,并确定影响该得分的前三个因素,使团队能够主动管理个案。
| 查看支持版本。 |
| 所需用户权限 | |
|---|---|
| 要启用 AI 加速器,使用个案设置和权利: | 自定义应用程序 |

We use three kinds of cookies on our websites: required, functional, and advertising. You can choose whether functional and advertising cookies apply. Click on the different cookie categories to find out more about each category and to change the default settings.
Privacy Statement
Required cookies are necessary for basic website functionality. Some examples include: session cookies needed to transmit the website, authentication cookies, and security cookies.
Functional cookies enhance functions, performance, and services on the website. Some examples include: cookies used to analyze site traffic, cookies used for market research, and cookies used to display advertising that is not directed to a particular individual.
Advertising cookies track activity across websites in order to understand a viewer’s interests, and direct them specific marketing. Some examples include: cookies used for remarketing, or interest-based advertising.