A strategic framework for artificial intelligence in marketing

Ming-Hui Huang () and Roland T. Rust ()
Additional contact information
Ming-Hui Huang: National Taiwan University
Roland T. Rust: University of Maryland

Journal of the Academy of Marketing Science, 2021, vol. 49, issue 1, No 3, 30-50

Abstract: Abstract The authors develop a three-stage framework for strategic marketing planning, incorporating multiple artificial intelligence (AI) benefits: mechanical AI for automating repetitive marketing functions and activities, thinking AI for processing data to arrive at decisions, and feeling AI for analyzing interactions and human emotions. This framework lays out the ways that AI can be used for marketing research, strategy (segmentation, targeting, and positioning, STP), and actions. At the marketing research stage, mechanical AI can be used for data collection, thinking AI for market analysis, and feeling AI for customer understanding. At the marketing strategy (STP) stage, mechanical AI can be used for segmentation (segment recognition), thinking AI for targeting (segment recommendation), and feeling AI for positioning (segment resonance). At the marketing action stage, mechanical AI can be used for standardization, thinking AI for personalization, and feeling AI for relationalization. We apply this framework to various areas of marketing, organized by marketing 4Ps/4Cs, to illustrate the strategic use of AI.

Keywords: Artificial intelligence; Machine learning; Mechanical AI; Thinking AI; Feeling AI; Strategic AI marketing; Marketing strategy; Standardization; Personalization; Relationalization; Segmentation; Targeting; Positioning; 4Ps; 4Cs (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (76)

Downloads: (external link)
http://link.springer.com/10.1007/s11747-020-00749-9 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:spr:joamsc:v:49:y:2021:i:1:d:10.1007_s11747-020-00749-9

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/11747

DOI: 10.1007/s11747-020-00749-9

Access Statistics for this article

Journal of the Academy of Marketing Science is currently edited by John Hulland, Anne Hoekman and Mark Houston

More articles in Journal of the Academy of Marketing Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().