AI technologies in the analysis of visual advertising messages: survey and application

Larisa Sharakhina, Irina Ilyina, Dmitrii Kaplun, Tatiana Teor () and Valeria Kulibanova
Additional contact information
Larisa Sharakhina: Saint Petersburg Electrotechnical University “LETI”
Irina Ilyina: Saint Petersburg Electrotechnical University “LETI”
Dmitrii Kaplun: Saint Petersburg Electrotechnical University “LETI”
Tatiana Teor: Saint Petersburg Electrotechnical University “LETI”
Valeria Kulibanova: RANEPA St. Petersburg (The Branch of the Presidential Academy of National Economy and Public Administration)

Journal of Marketing Analytics, 2024, vol. 12, issue 4, No 21, 1066-1089

Abstract: Abstract Artificial intelligence technologies are improving the marketing toolkit, making it possible to process large amounts of data faster and more efficiently than ever before. Machine learning, a subset of AI, uses algorithms that can predict which ads will be most effective in specific situations, allowing for optimized ad targeting. This research explores the issues of coevolution and distribution of machine and human intelligence in various social practices, including marketing and advertising. The authors describe the key approaches to studying the visual component of advertising and suggest revising traditional methods of analyzing advertising messages. The tracking of biometric data combined with AI-based methods that capture human emotions while viewing video content is proposed as a promising direction for such analysis. This paper presents the results of a pilot study based on analytical face-tracking technology using AI, where the subject of the experiment was the analysis of video fragments that may have an impact on the emotional state of the viewer. The AI software platform used was Amazon Rekognition, and the results show that AI analytics provide the ability to track the level of audience engagement in perceiving video content, which helps to improve communication effectiveness. This allows the use of AI to make recommendations for the development of more directed and engaging advertising messages.

Keywords: AI-based video analysis; Emotions tracking; Advertising effectiveness; AI analytical tools; Video content; Emotions (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-023-00255-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-023-00255-1

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-023-00255-1

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().