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Generative AI and Usage in Marketing Classroom

Min Ding et al.

Customer Needs and Solutions2024https://doi.org/10.1007/s40547-024-00145-2article
ABDC B
Weight
0.65

Abstract

This article examines the role of Generative Artificial Intelligence (GenAI) in the context of marketing education, highlighting its substantial impact on the field. The study is based on an analysis of how GenAI, particularly through the use of Large Language Models (LLMs), functions. We detail the operational mechanisms of LLMs, their training methods, performance across various metrics, and the techniques for engaging with them via prompt engineering. Building on this foundation, we explore popular GenAI platforms and models that are relevant to marketing, focusing on their key features and capabilities. We then assess the practical applications of GenAI in marketing tasks and educational settings, considering its utility in tasks such as providing information, extracting data, generating content, conducting simulations, and performing data analysis. By examining these areas, this paper demonstrates the integral role of GenAI in reshaping both marketing strategies and teaching methodologies and argues for its adoption as a critical resource for forward-thinking marketers and educators.

10 citations

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https://doi.org/https://doi.org/10.1007/s40547-024-00145-2

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@article{min2024,
  title        = {{Generative AI and Usage in Marketing Classroom}},
  author       = {Min Ding et al.},
  journal      = {Customer Needs and Solutions},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1007/s40547-024-00145-2},
}

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Generative AI and Usage in Marketing Classroom

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Evidence weight

0.65

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.77 × 0.4 = 0.31
M · momentum0.75 × 0.15 = 0.11
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.