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Moving Beyond ChatGPT: Applying Large Language Models in Marketing Contexts

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Suggested Citation

Schweidel, D., Reisenbichler, M., & Reutterer, T. (2024). Moving Beyond ChatGPT: Applying Large Language Models in Marketing Contexts. NIM Marketing Intelligence Review, 16(1) 24-29. https://doi.org/10.2478/nimmir-2024-0004

Year

2024

Authors
David A. Schweidel,
Martin Reisenbichler,
Thomas Reutterer
Publication title
Moving Beyond ChatGPT: Applying Large Language Models in Marketing Contexts
Publication
NIM Marketing Intelligence Review
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Moving Beyond ChatGPT: Applying Large Language Models in Marketing Contexts

Keywords: Large Language Models, Content Marketing, Search Engine Optimization, Human-Machine Collaboration

Abstract:
Large language models (LLMs) like ChatGPT have seemingly sentient capabilities but are better understood as “stochastic parrots.” They excel at “inside the box” tasks, where they are able to leverage existing data patterns. Creating SEO content falls into this category. This article introduces a hybrid approach involving dynamic fine-tuning of existing LLMs for SEO content creation, combining general language patterns with application-specific information and human oversight. The performance evaluation reveals that this hybrid solution outperformed SEO experts, achieving superior online visibility at a fraction of the cost. However, it emphasizes the indispensability of human oversight for brand consistency and accuracy. Early adopters attest to the practical benefits. Additional successful hybrid applications include search engine advertising, display advertising and social media posts. Marketers should, however, not be lulled into a “set it and forget it” mindset. AI is a tool that can boost productivity and performance, but it is not (yet) a replacement for marketers’ knowledge and skillset.

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