数字营销中的客户细分使用基于Q学习的差异演变算法,与K-means集群集成.
Guanqun Wang1,2
1College of Accounting, Ningbo University of Finance & Economics, Ningbo, China.
PloS one
|February 7, 2025
概括
本研究介绍了一种基于人工智能的客户细分框架,使用强化学习和K-means集群. 该模型准确地识别了客户特征,增强了营销策略并提高了业务利.
科学领域:
- 人工智能的人工智能
- 数字营销 数字营销 数字营销
- 数据科学数据科学数据科学
背景情况:
- 有效的客户细分对于有针对性的营销策略至关重要.
- 人工智能 (AI) 提供了分析复杂客户数据的高级功能.
- 现有的细分方法在准确识别不同客户需求方面面临挑战.
研究的目的:
- 为数字营销提出一个人工智能集成的客户细分框架.
- 提高客户细分流程的准确性和效率.
- 利用先进的算法来获得更深入的客户洞察力和更好的营销成果.
主要方法:
- 使用主要组件分析 (PCA) 进行特征无声化和维度减小.
- 集成了一个基于强化学习的差异演化算法与K-means集群.
- 采用Q学习进行自适应参数调整以提高K-means的性能.
主要成果:
- 拟议的框架在细分客户数据方面实现了超过95%的分类准确性.
- 主要组件分析有效地减少了客户特征中的噪音和多线性.
- Q-learning显著提高了K-means算法的集群性能.
结论:
- 开发的AI框架提供了准确的客户特征识别和细分.
- 这种方法提高了营销效率,客户满意度和企业利增长.
- 这种方法为数字营销中复杂的客户细分挑战提供了强大的解决方案.
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