基于位置和时间维度的消费者细分使用从企业到客户零售市场的大数据
Fatemeh Ehsani1, Monireh Hosseini1
1Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
Big data
|October 30, 2023
概括
本研究使用RFMT方法对消费者进行细分,重点关注购物时间和地点. 这些发现揭示了关键的消费者行为,并改善了B2C零售商的产品建议.
科学领域:
- 业务分析 业务分析
- 营销科学 营销科学
- 数据挖掘 数据挖掘
背景情况:
- 在B2C零售中,消费者细分对于了解客户偏好至关重要.
- 传统的细分通常忽略了购物时间和地点的动机.
- 大数据分析提供了探索这些未充分利用的维度的机会.
研究的目的:
- 使用近期,频率,货币和所有权 (RFMT) 方法对B2C消费者进行细分,强调时间和地理特征.
- 识别关键的消费者行为,流行的产品类别,以及最佳的购买时间和地点.
- 开发一个数据驱动的产品推系统,以提高消费者参与度.
主要方法:
- 应用了RFMT方法,根据时间和空间数据将消费者分成10个不同的群体.
- 分析了市场,收入和消费者分布,以估计地理特点和消费者密度.
- 评估产品交付准确性,促销时间和产品受欢迎程度,以提供建议.
主要成果:
- 通过热图分析确定了消费者热点和最佳购买时间和地点.
- 根据消费者分布和购买模式,确定了最受欢迎的产品类别.
- 证明了RFMT细分在提供卓越的消费者行为洞察力的有效性.
结论:
- 拟议的基于RFMT的消费者细分为了解B2C客户行为提供了一种新的方法.
- 整合时间和地理维度大大提高了消费者细分的准确性.
- 开发的产品推系统提高了营销效率和消费者参与B2C零售.
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