Challenges and Future Directions of Computational Advertising Measurement Systems

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...

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Bibliographic Details
Published inJournal of advertising Vol. 49; no. 4; pp. 446 - 458
Main Authors Yun, Joseph T., Segijn, Claire M., Pearson, Stewart, Malthouse, Edward C., Konstan, Joseph A., Shankar, Venkatesh
Format Journal Article
LanguageEnglish
Published Abingdon Routledge 07.08.2020
Taylor & Francis Ltd
Subjects
Advertising
Attribution
Bias
Challenges
Consumer behavior
Customers
Fraud
Measurement
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Summary: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.
ISSN:0091-3367
1557-7805
DOI:10.1080/00913367.2020.1795757