Longitudinal clustering for heterogeneous binary data
Longitudinal clustering for heterogeneous binary data
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DOI：
10.5705/ss.202018.0298
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发表时间：
2021
影响因子：
1.4
通讯作者：
Xiaolu Zhu;Xiwei Tang;A. Qu
中科院分区：
文献类型：
--
作者：
Xiaolu Zhu;Xiwei Tang;A. Qu
由于电子商务的成功和数字营销数据的可访问性，个性化营销已成为一种重要的营销策略。众所周知，不同的客户群体可能会对相同的营销策略做出不同的反应，这是由于他们的个人偏好。在本文中，我们提出了一个成对的子分组的方法来确定子组和类似的营销效果归类到组。具体来说，我们将客户的购买决策建模为广义线性模型框架下的二进制响应，同时将其纵向相关性。我们对异质效应的成对距离进行惩罚，以制定子组，其中不同的子组与不同的营销效应相关。在理论上，我们建立了一致性的子群识别的意义上，真正的底层分割结构可以成功地恢复，除了参数估计的一致性。我们进行数值研究和真实的数据应用程序使用IRI营销数据在店内展示营销效果，所提出的方法优于其他竞争方法的子组识别和营销效果估计。1 Statistica Sinica：最新录用论文（录用作者版本以英文编辑为准）
Personalized marketing has emerged as a critical marketing strategy due to the success of E-commerce and the accessibility of digital marketing data. It is well-known that different groups of customers might react rather differently to the same marketing strategy due to their individual preferences. In this paper, we propose a pairwise subgrouping approach to identify subgroups and categorize similar marketing effects into groups. Specifically, we model customers’ purchase decisions as binary responses under the generalized linear model framework while incorporating their longitudinal correlation. We impose penalization on pairwise distances of heterogeneous effects to formulate subgroups, where different subgroups are associated with different marketing effects. In theory, we establish the consistency of subgroup identification in the sense that the true underlying segmentation structure can be recovered successfully, in addition to parameter estimation consistency. We conduct numerical studies and a real data application using IRI marketing data on in-store display marketing effects, where the proposed method outperforms other competing methods in terms of subgrouping identification and marketing effects estimation. 1 Statistica Sinica: Newly accepted Paper (accepted author-version subject to English editing)