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线下数据驱动的推系统,用于改善小企业的营销策略

Hwijae Son¹, Sung Woong Cho², Hyung Ju Hwang³

¹Department of Mathematics, Konkuk University, Gwangjin-gu, The Republic of Korea.

|February 12, 2026

概括

这项研究引入了一种新的推系统,使用线下数据来识别可能使用优惠券的客户,提高转化率 (CR) 并降低小型企业的营销成本. 该系统有效地应对数据稀疏性和冷启动问题等挑战.

科学领域:

计算机科学计算机科学
营销营销分析分析
商业情报 (BI) 是一种商业情报.

背景情况:

推系统可以提高各个部门的用户参与度.
小企业在线营销时面临高的每行动成本 (CPA) 和低的转化率 (CR).
个性化营销活动是识别高价值客户的关键.

研究的目的:

为小型企业开发一种新的推系统,以降低营销成本.
利用离线交互数据来识别可能使用折扣券的客户.
提高营销努力中的转化率 (CR) 和降低每行动成本 (CPA).

主要方法:

使用离线交互数据,包括商店级优惠券和点日志数据.
实施定制的数据增强技术,以解决冷启动问题和数据稀疏性.
使用CPA,CR和根平均平方误差等指标评估系统性能.

主要成果:

拟议的推者系统显著优于传统的在线营销平台.
该系统有效地识别了倾向使用折扣券的客户,从而增加了CR.
通过提高效率,证明了营销成本的降低.

结论:

合并线下数据与适当的增强对于成本有效的营销是有价值的.
这种新的推系统为那些在线营销费用难以支付的小企业提供了可行的解决方案.
数据增强技术对于克服线下推系统的挑战至关重要.

关键词:

通过冷启动启动.数据增强数据增强机器学习是机器学习.线下营销线下营销在线营销推者系统推者系统

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