探测忠诚卡数据中购买模式的时间轨迹的张量主要组件分析:回顾性队列研究
Reija Autio1, Joni Virta2, Klaus Nordhausen3
1Faculty of Social Sciences (Health Sciences), Tampere University, Tampere, Finland.
Journal of medical Internet research
|December 15, 2023
概括
忠诚卡数据透露了使用张数主要组件分析 (PCA) 的与健康相关的购买模式. 这种方法可以识别出不同的客户行为,并有助于促进更健康的食物选择.
科学领域:
- 消费者行为分析分析
- 数据科学和数据分析
- 公共卫生研究 公共卫生研究
背景情况:
- 零售忠诚卡数据为分析客户健康相关购买习惯提供了丰富的来源.
- 客户购买数据,包括支出和时间,可以构成3D张量数据.
研究的目的:
- 将张量主要成分分析 (PCA) 应用于忠诚卡数据,以发现与健康相关的购买模式.
- 识别具有独特购买行为的客户细分.
- 为了将张量PCA与标准PCA的实用性进行比较,用于此应用.
主要方法:
- 从2016年起,利用了来自7251名芬兰零售客户的忠诚卡数据.
- 将购买重新分类为55个产品组,并将数据汇总为52周.
- 应用张量PCA同时减少时间和产品组尺寸,使用增强的主要组件选择.
主要成果:
- 张量PCA有效地识别了跨时间和产品类别的典型食品购买模式.
- 在客户群体中检测到不同的购买行为,以肉类产品消费模式 (稳定,增加,减少或季节性) 为例.
结论:
- 张量PCA通过同时处理时间和产品尺寸,提供了比传统方法更详细的购买行为检查.
- 未来的研究可以将确定的模式与社会经济因素和外部影响联系起来,以引导消费者选择更健康的食物.
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