A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data
A Flexible Method for Protecting Marketing Data: An Application to Point-of-Sale Data
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保护营销数据的灵活方法：销售点数据的应用
DOI：
10.1287/mksc.2017.1064
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Yan Yu
中科院分区：
文献类型：
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作者：
Matthew J. Schneider;Sharan Jagpal;Sachin Gupta;Shaobo Li;Yan Yu
我们开发了一种灵活的方法来保护商业生态系统中的营销数据，其中数据提供者寻求满足数据用户的信息需求，但希望阻止潜在入侵者对数据的无效使用。在这种情况下，我们提出了一个贝叶斯概率模型，产生受保护的合成数据。我们提出的方法的一个关键特征是，数据提供者可以在数据保护导致的信息丢失和向入侵者披露的风险之间取得平衡。我们将我们的方法应用于零售销售点数据供应商所面临的问题，其客户使用这些数据来估计价格弹性和促销效果。同时，数据提供者希望保护样本存储的身份免受可能的入侵。我们定义度量来衡量数据保护方法所隐含的平均和最大保护损失。我们表明，通过使数据提供者能够选择向合成数据中注入的保护程度，……
We develop a flexible methodology to protect marketing data in the context of a business ecosystem in which data providers seek to meet the information needs of data users, but wish to deter invalid use of the data by potential intruders. In this context we propose a Bayesian probability model that produces protected synthetic data. A key feature of our proposed method is that the data provider can balance the trade-off between information loss resulting from data protection and risk of disclosure to intruders. We apply our methodology to the problem facing a vendor of retail point-of-sale data whose customers use the data to estimate price elasticities and promotion effects. At the same time, the data provider wishes to protect the identities of sample stores from possible intrusion. We define metrics to measure the average and maximum loss of protection implied by a data protection method. We show that, by enabling the data provider to choose the degree of protection to infuse into the synthetic data, o...