Personalized market response analysis for a wide variety of products from sparse transaction data
Personalized market response analysis for a wide variety of products from sparse transaction data
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DOI：
10.1007/s41060-018-0099-9
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发表时间：
2018-06-01
影响因子：
2.4
通讯作者：
Allenby, Greg M.
中科院分区：
文献类型：
--
作者：
Ishigaki, Tsukasa;Terui, Nobuhiko;Allenby, Greg M.
先进的数据库营销是为了确定个人客户的市场反应与折扣或显示广泛的各种产品从交易数据。然而，在超市或电子商务中记录的交易数据基本上是稀疏的，因为大多数顾客只购买商店中所有产品中的几种产品。现有的方法不适用于阐明个性化的反应，因为缺乏购买的数据的样本量。本文提出了一种个性化的市场反应估计方法，广泛的客户和产品，从这些稀疏的数据。该方法将稀疏的交易数据与营销变量的响应相关的信息压缩到一个降维空间中进行可行的参数估计。然后，他们被解压到原始空间使用增广的潜变量，以获得个人的响应参数。结果表明，该方法可以找到合适的营销促销为个人客户的每一个分析的产品。
Advanced database marketing is designed to ascertain individual customers' market responses with a discount or display of widely various products from transaction data. However, transaction data recorded in a supermarket or electric commerce are fundamentally sparse because most customers purchase only a few products from all products in shops. Existing methods are not applicable to elucidate the personalized response because of a lack of sample size of purchased data. This paper proposes a personalized market response estimation method for a wide set of customers and products from these sparse data. The method compresses a sparse transaction data with information related to response to marketing variables into a reduced-dimensional space for feasible parameter estimation. Then, they are decompressed into original space using augmented latent variables to obtain individual response parameters. Results show that the method can find suitable marketing promotions for individual customers to every analyzed product.