挖掘可盈利CRM的最佳行动

C. Ling, Tielin Chen, Qiang Yang, Jie Cheng
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引用次数: 55

摘要

数据挖掘已经在许多行业中应用于CRM(客户关系管理),但取得了有限的成功。大多数数据挖掘工具只能发现客户模型或配置文件(例如可能是吸引者的客户和忠诚的客户),而不能发现可以改善客户关系的操作(例如将吸引者更改为忠诚的客户)。我们描述了一种新的算法,该算法建议采取行动将客户从不希望的状态(如吸引者)改变为期望的状态(如忠诚度)。我们的算法考虑了行动的成本,并进一步尝试最大化预期净利润。据我们所知,目前还没有数据挖掘算法或工具可以在CRM中完成这一重要任务。该算法的实现,与许多先进的功能,在一个专门的和高效的数据挖掘软件称为主动解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining optimal actions for profitable CRM
Data mining has been applied to CRM (Customer Relationship Management) in many industries with a limited success. Most data mining tools can only discover customer models or profiles (such as customers who are likely attritors and customers who are loyal), but not actions that would improve customer relationship (such as changing attritors to loyal customers). We describe a novel algorithm that suggests actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal). Our algorithm takes into account the cost of actions, and further it attempts to maximize the expected net profit. To our best knowledge, no data mining algorithms or tools today can accomplish this important task in CRM. The algorithm is implemented, with many advanced features, in a specialized and highly effective data mining software called Proactive Solution.
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