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Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing
Abstract
This article explores the role of artificial intelligence (AI) in aiding personalized engagement marketing—an approach to create, communicate, and deliver personalized offerings to customers. It proposes that consumers are ready for a new journey in which AI is a tool for endless options and information that are narrowed and curated in a personalized way. It also provides predictions for managers regarding the AI-driven environment on branding and customer management practices in both developed and developing countries.
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“With the further application of AI technology, Kumar et al. (2019) proposed a comprehensive framework to understand the application of AI in personalized marketing: enterprises can use this technology to analyze consumer information and provide personalized products and services.”
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Metadata
- Authors
- Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J.
- Affiliations
- Georgia State University (US)
- University of Virginia (US)
- Northwestern University (US)
- Venue
- California Management Review
- Publisher
- SAGE Publications
- Published
- 2019-07-04
- Volume
- 61
- Issue
- 4
- Pages
- 135-155
- ISSN
- 0008-1256 (ISSN-L)
- 2162-8564
- OpenAlex
- W2953773016
- Cited by
- 797
- References
- 0
- Citations by year
- 2026: 136 · 2025: 154 · 2024: 208 · 2023: 111 · 2022: 76 · 2021: 73 · 2020: 33 · 2019: 4
- FWCI
- 21.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- California Management Review (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Keywords
- artificial intelligence, personalized marketing, customer engagement, personalization, customer management, branding, consumer behavior, information curation, AI-driven marketing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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