一种预测分析方法来改善电信的客户保留率.
Asem Omari1, Omaia Al-Omari2, Tariq Al-Omari3
1Computer Information Systems, Higher Colleges of Technology, Al Ain, United Arab Emirates.
Frontiers in artificial intelligence
|September 15, 2025
概括
这项研究为电信公司开发了一个客户流失预测模型. 支持矢量机 (SVM) 模型在识别可能离开的客户方面表现最好,从而实现了主动的保留策略.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 电信 电信服务 电信服务 电信服务
背景情况:
- 客户的保留是电信公司面临的重大挑战.
- 了解和预测客户流失对于业务战略改进至关重要.
- 需要采取积极的措施来留住客户并降低离职率.
研究的目的:
- 开发一个准确的预测模型来识别潜在的客户流失.
- 改善电信提供商的决策过程.
- 为了获得对客户行为更深入的见解,以提高服务.
主要方法:
- 使用机器学习算法开发和评估各种预测模型.
- 应用先进的数据分析技术用于流失预测.
- 将数据预处理,特征选择和可解释性整合到模型中.
主要成果:
- 进行了各种预测技术的比较分析.
- 支持矢量机 (SVM) 模型在流失预测中取得了最高的性能.
- 该研究成功地整合了有效的数据预处理,特征选择和可解释性.
结论:
- 开发的流失预测模型为电信公司提供了宝贵的见解.
- 支持矢量机器 (SVM) 是用于预测客户流失的高效方法.
- 该研究通过增强模型集成和可解释性来解决退出预测中的现有差距.
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