使用购买行为,社会文化因素和学习技术预测全道零售中的客户忠诚度
Shima Roosta1, Seyed Jafar Sadjadi1, Ahmad Makui1
1Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
PloS one
|August 21, 2025
概括
这项研究引入了一个智能框架,用于使用全面数据和先进的人工智能预测全道零售业的客户忠诚度. 提高了客户关系管理,提高了客户的保留力和市场竞争力.
科学领域:
- * 商业分析
- * 人工智能
- * 零售管理
背景情况:
- 在竞争激烈的全道零售中,客户忠诚度至关重要,但现有的研究往往仅单独研究影响因素.
- 传统研究缺乏灵活的多道环境中的忠诚度驱动器的整体观点.
- 在了解影响不同零售平台客户忠诚度的复杂相互作用方面存在差距.
研究的目的:
- 开发一个智能分析框架,用于预测客户忠诚度在全道零售环境.
- * 整合一个全面的数据集,包括销售,社会经济,行为和情绪数据.
- 加强企业信息系统 (EIS) 中的客户关系管理 (CRM) 子系统,以改善决策.
主要方法:
- 使用混合人工智能方法,将BERT用于情绪分析和强化学习用于行为分析.
- 使用图形卷积网络 (GCN) 和适应性学习进行趋势分析和忠诚度预测.
- 结合了微调技术,以提高个人和社区水平的预测准确性.
主要成果:
- 这项研究提供了一个新的框架,用于利用综合的全道数据预测客户忠诚度.
- 混合人工智能模型在分析复杂的客户行为和情绪方面表现出有效性.
- 这一框架可以在个人和社区层面进行预测,提供细微的洞察力.
结论:
- 建议的智能框架显著提高了客户忠诚度的预测.
- 通过该框架优化CRM策略可以改善定价,库存管理和个性化广告.
- 这项研究最终有助于增加客户的保留和提高市场竞争力.
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