马尔科夫网络方法用于复制在便利店观察到的购买行为
Dan Johansson1,2, Hideki Takayasu2,3, Misako Takayasu4
1Department of Physics, Chalmers Institute of Technology, MSc. Complex Adaptive Systems, 412 96, Gothenburg, Sweden.
Scientific reports
|May 7, 2024
概括
了解日本便利店的客户购买行为是关键. 一个新的马尔科夫网络模型模拟购买,揭示了购买一个物品如何影响其他物品,比如营养棒减少了13%的烟草购买.
科学领域:
- 消费者行为分析 消费者行为分析
- 零售分析 零售分析
- 网络建模 网络建模
背景情况:
- 日本的便利店行业很重要.
- 了解客户购买模式对于零售业的成功至关重要.
- 现有的模型可能无法完全捕捉复杂的购买相互依赖.
研究的目的:
- 开发和验证一种新的马尔科夫网络模型,用于模拟便利店的客户购买行为.
- 用"驱动力"指标量化购买一个产品类别对其他产品类别的影响.
- 将模型生成的购买模式与现实世界的销售数据进行比较.
主要方法:
- 应用马尔科夫网络模型,从实际销售数据中推导出停止概率.
- 将产品类别定义为节点,并量化类别间的购买影响 ("驱动力").
- 用5400万份7-Eleven收据的大规模数据集进行验证,重点关注产品类别.
主要成果:
- 该模型准确地复制了宏观水平的购买行为和购买规模分布 (99.9%).
- 确定了特定产品的影响,例如,营养棒与随机模式相比,烟草购买量减少了13%.
- 证明了该模型在捕捉各种产品类别的广泛购买模式方面的有效性.
结论:
- 拟议的马尔科夫网络模型是有效的模拟和理解客户购买行为在便利店.
- "驱动力"概念为产品关系提供了可量化的见解.
- 该模型与大量现实数据的验证证实了其对零售战略的实际适用性.
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