Abstract
Advancements in computing, technology, and their applications to advertising enable marketers to deliver brand messages tailored to individuals and consumer segments. The growth of computational advertising (CA) has created new opportunities but also poses risks in the use of algorithms to generate and optimize the impact of such messages. This article addresses a particular domain influenced by these advancements, namely, automated brand-generated content. We offer an automated brand-generated content (ABC) model that posits two advances. First, rather than solely optimizing consumer data for enhanced impact of automated content, we submit, and provide extra key variables to further illustrate, that there is a desirable balance of both consumer and brand data as inputs to algorithms to serve short- and long-term impact goals. Second, this article guides research by addressing tensions between understanding the relationship between inputs and desired impacts (both short and long term) and proposing a research agenda for future work.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 411-427 |
| Number of pages | 17 |
| Journal | Journal of Advertising |
| Volume | 49 |
| Issue number | 4 |
| DOIs | |
| State | Published - Aug 7 2020 |
Funding
The authors thank the participants of the Thought Leadership Forum (TLF) on Computational Advertising for their stimulating comments, hosted in 2020 by the University of Minnesota Hubbard School of Journalism and Mass Communication. We specially thank Professors Jisu Hu and Edward Malthouse for organizing this event; Professor Bart Goethals for his substantive suggestions and interesting discussions, which led to this article; and the three anonymous Journal of Advertising reviewers for their constructive feedback.
ASJC Scopus subject areas
- Business and International Management
- Communication
- Marketing