Challenges and Future Directions of Computational Advertising Measurement Systems

Joseph T. Yun, Claire M. Segijn, Stewart Pearson, Edward C. Malthouse, Joseph A. Konstan, Venkatesh Shankar

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)446-458
Number of pages13
JournalJournal of Advertising
Volume49
Issue number4
DOIs
StatePublished - Aug 7 2020

Bibliographical note

Publisher Copyright:
© 2020 The Author(s). Published with license by Taylor and Francis Group, LLC.

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