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Challenges and Future Directions of Computational Advertising Measurement Systems
Journal of Advertising ( IF 6.528 ) Pub Date : 2020-08-05 , DOI: 10.1080/00913367.2020.1795757
Joseph T. Yun 1 , Claire M. Segijn 2 , Stewart Pearson 3 , Edward C. Malthouse 4 , Joseph A. Konstan 5 , Venkatesh Shankar 6
Affiliation  

Abstract Computational advertising (CA) is a rapidly growing field, but there are numerous challenges related to measuring its effectiveness. Some of these are classic challenges where CA offers a new aspect to the challenge (e.g., multi-touch attribution, bias), and some are brand-new challenges created by CA (e.g., fake data and ad fraud, creeping out customers). In this article, we present a measurement system framework for CA to provide a common starting point for advertising researchers to begin addressing these challenges, and we also discuss future research questions and directions for advertising researchers. We identify a larger role for measurement: It is no longer something that happens at the end of the advertising process; instead, measurements of consumer behaviors become integral throughout the process of creating, executing, and evaluating advertising programs.

中文翻译:

计算广告测量系统的挑战和未来方向

摘要计算广告(CA)是一个快速发展的领域,但是在衡量其有效性方面存在许多挑战。其中一些是经典挑战,CA为挑战提供了新的方面(例如,多点触摸归因,偏见),另一些是CA提出的全新挑战(例如,伪造数据和广告欺诈,蠕动客户)。在本文中,我们为CA提供了一个度量系统框架,为广告研究人员开始解决这些挑战提供了一个通用的起点,并且我们还讨论了广告研究人员未来的研究问题和方向。我们确定了更大的衡量角色:在广告流程结束时不再发生这种情况;相反,对消费者行为的衡量在整个创建,执行,
更新日期:2020-08-05
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