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基于数据挖掘技术的 SPRINT 分类算法的客户关系管理。

Customer Relationship Management Based on SPRINT Classification Algorithm under Data Mining Technology.

机构信息

Department of Economic Management, Pingdingshan Polytechnic College, Pingdingshan 467000, Henan, China.

School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, Henan, China.

出版信息

Comput Intell Neurosci. 2022 Apr 14;2022:6170335. doi: 10.1155/2022/6170335. eCollection 2022.

Abstract

Under the advance of computational intelligence, customer relationship management system based on data mining technology can not only bring more economic benefits to an enterprise but also improve the management and decision-making level of Chinese enterprises. In this paper, the application of data mining technology in customer relationship management (CRM) is analyzed, and four data mining modes are realized: customer classification, cross-marketing, customer acquisition, and customer retention. In the data mining module, SPRINT classification algorithm is used in customer classification. At the same time, FP-growth, an association rule algorithm without candidate set, is applied in cross-marketing, which enhances the practicability of the system. The algorithm of optimal customer retention strategy under digital intelligence technology is adopted in customer retention, which makes up for the shortcomings of traditional CRM system and helps enterprises to better operate and adjust marketing strategies.

摘要

在计算智能的推动下,基于数据挖掘技术的客户关系管理系统不仅能为企业带来更多的经济效益,还能提高中国企业的管理和决策水平。本文分析了数据挖掘技术在客户关系管理(CRM)中的应用,实现了四种数据挖掘模式:客户分类、交叉营销、客户获取和客户保留。在数据挖掘模块中,使用 SPRINT 分类算法进行客户分类。同时,在交叉营销中应用了无候选集的关联规则算法 FP-growth,增强了系统的实用性。在客户保留中采用了数字智能技术下的最优客户保留策略算法,弥补了传统 CRM 系统的不足,帮助企业更好地运营和调整营销策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83a1/9023209/796cbe27a988/CIN2022-6170335.001.jpg

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