Challenges and Future Directions of Computational Advertising Measurement Systems. Issue 4 (7th August 2020)

• Record Type: Journal Article
• Authors: Yun, Joseph T.; Segijn, Claire M.; Pearson, Stewart; Malthouse, Edward C.; Konstan, Joseph A.; Shankar, Venkatesh
• Is Part Of: Journal of advertising. Volume 49:Issue 4(2020)
• Page Start: 446
• Page End: 458
• Publication Date: 2020-08-07
• Subjects: Advertising -- Periodicals; Advertising -- Research -- Periodicals; 659.105
• DOI: 10.1080/00913367.2020.1795757
• Languages: English
• ISSNs: 0091-3367

Abstract

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.