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彻底改变市场监督:使用机器学习的客户关系管理

Xiangting Shi¹, Yakang Zhang¹, Manning Yu²

¹Industrial Engineering and Operations Research Department, Columbia University, New York, United States.

PeerJ. Computer science

|February 3, 2025

概括

预测客户流失对于电信公司来说至关重要. 使用合并方法的SmartSurveil CRM模型显著提高了流失预测的准确性和适应性,以更好地保留客户.

科学领域:

电信电信服务电信服务电信服务
数据科学数据科学数据科学
机器学习机器学习

背景情况:

预测客户流失对于电信部门的利能力至关重要.
传统的客户关系管理 (CRM) 系统与静态模型扎,无法适应动态的客户行为.
现有的CRM模型缺乏有效,不断发展的客户保留策略所需的适应性.

研究的目的:

开发一种先进的CRM模型,用于在电信行业中增强客户流失预测.
为了提高流失预测的准确性和适应性,超出传统的CRM系统能力.
将预测模型集成到决策支持系统中,以获得可操作的客户保留见解.

主要方法:

开发了SmartSurveil CRM模型,这是一个整体系统,结合了随机森林,梯度增强和支持矢量机算法.
使用全面的电信数据集进行培训和验证.
将预测模型集成到决策支持系统 (DSS) 中,以提供可操作的见解.

主要成果:

智能Surveil CRM 模型实现了高性能指标,包括0.89的准确性和0.91.9的ROC-AUC.
与基线方法相比,表现出优异的预测准确性和适应性.
成功提供了可操作的洞察力,用于在客户保留方面定制动态战略.

结论:

智能Surveil CRM 模型在预测准确性和CRM 系统的实际应用性方面取得了重大进展.
整体方法提高了对不断变化的客户行为反应的响应能力,改善了客户的保留.
该模型解决了伦理方面的考虑,确保了强大而负责任的CRM战略.

关键词:

客户关系管理客户关系管理机器学习是机器学习.市场监督是指市场监督.个性化个性化预测分析是一种预测分析.

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