基于大数据的药房会员管理系统优化:使用ANN建模的睡眠会员激活和唤醒方法
Jing Liang1, Xin Zhou1, Chong Yuan2
1School of Artificial Intelligence, Hubei Business College, Wuhan, 430079, China.
Heliyon
|December 17, 2024
概括
这项研究优化了药房会员管理,使用大数据和机器学习来重新吸引不活跃的会员. 这种方法成功地增加了会员活动,并促进了药店的整体销售和收入.
科学领域:
- 零售管理 零售管理
- 数据科学数据科学数据科学
- 客户关系管理 客户关系管理
背景情况:
- 有效的会员管理对于零售业至关重要,特别是药店,以提高客户忠诚度和销售.
- 药房面临着会员流失和不活跃会员的困难,限制了计划的潜力.
- 大数据技术和机器学习为优化这些系统提供了解决方案.
研究的目的:
- 使用大数据技术优化药房会员管理系统.
- 为了应对会员退出和不活跃会员的挑战.
- 增加休眠会员活动,提高整体销售额.
主要方法:
- 利用大数据技术和机器学习算法对来自药房连锁店的会员数据进行分析.
- 采用人工神经网络 (ANN) 来预测重新激活成功,会员活动和销售收入.
- 评估网站/应用程序使用频率和成员参与度作为输入因素.
主要成果:
- 在不活跃的会员中,活动显著增加.
- 揭示了药店会员的关键行为模式,偏好和购买习惯.
- 对关键指标的ANN分析显示了显著的相关性和可接受的预测错误.
结论:
- 基于会员个人资料的数据驱动策略有效地重新吸引客户并培养忠诚度.
- 通过大数据和机器学习优化会员管理,可以提高客户参与度和零售业务.
- 该研究为改善制药零售行业的会员战略和营销努力提供了宝贵的见解.
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