第三方消费者分析和受众传递的效果如何?:实地研究的证据

Nico Neumann, Catherine Tucker, T. Whitfield
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引用次数: 10

摘要

数据经纪人经常使用在线浏览记录来创建数字消费者档案,并将其作为预先定义的受众出售给营销人员,用于广告定位。然而,这个过程是一个“黑匣子”:人们对所创建的数字档案的可靠性知之甚少,也不知道购买平台提供的受众身份。在本文中,我们使用三种实地测试来调查各种人口统计和受众兴趣细分的准确性。我们检查了19个数据代理中的90多个第三方受众的准确性。受众群体的质量参差不齐,而且主要数据代理的数据往往不准确。与随机受众选择相比,使用黑盒数据配置文件平均可将具有所需属性的用户的识别度提高0-77%。结合优化软件,受众识别能力平均提升123%。然而,考虑到定位解决方案的高额外成本和相对不准确性,我们发现第三方受众通常在经济上没有吸引力,除了价格较高的媒体投放。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Effective Is Third-Party Consumer Profiling and Audience Delivery?: Evidence from Field Studies
Data brokers often use online browsing records to create digital consumer profiles they sell to marketers as pre-defined audiences for ad targeting. However, this process is a `black box': Little is known about the reliability of the digital profiles that are created, or of the audience identification provided by buying platforms. In this paper, we investigate using three field tests the accuracy of a variety of demographic and audience-interest segments. We examine the accuracy of over 90 third-party audiences across 19 data brokers. Audience segments vary greatly in quality and are often inaccurate across leading data brokers. In comparison to random audience selection, the use of black-box data profiles on average increased identification of a user with a desired attribute by 0-77%. Audience identification can be improved on average by 123% when combined with optimization software. However, given the high extra costs of targeting solutions and the relative inaccuracy, we find that third-party audiences are often economically unattractive, except for higher-priced media placements.
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