Abstract
Marketing is one of the areas where large language models (LLMs) such as ChatGPT have found practical applications. This study examines marketing prompts—text inputs created by marketers to guide LLMs in generating desired outputs. By combining insights from the marketing literature and the latest research on LLMs, the study develops a conceptual framework around three key features of marketing prompts: prompt domain (the specific marketing actions that the prompts target), prompt appeal (the intended output of the prompts being informative or emotional), and prompt format (the intended output of the prompts being generic or contextual). The study collected hundreds of marketing prompt templates shared on X (formerly Twitter) and analyzed them using a combination of natural language processing techniques and descriptive statistics. The findings indicate that the prompt templates target a wide range of marketing domains—about 16 altogether. Likewise, the findings indicate that most of the marketing prompts are designed to generate informative output (as opposed to emotionally engaging output). Further, the findings indicate that the marketing prompts are designed to generate a balanced mix of generic and contextual output. The study further finds that the use of prompt appeal and prompt format differs by prompt domain.
Similar content being viewed by others
Data availability
The data used in this study is available upon request from the corresponding author.
References
Ashley, C., and T. Tuten. 2015. Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychology & Marketing 32 (1): 15–27.
Bach, S.H., Sanh, V., Yong, Z.X., Webson, A., Raffel, C., Nayak, N.V., Rush, A.M., et al. 2022. Promptsource: An integrated development environment and repository for natural language prompts. ar**v preprint http://arxiv.org/2202.01279.
Banerjee, M., M. Capozzoli, L. McSweeney, and D. Sinha. 1999. Beyond kappa: A review of interrater agreement measures. Canadian Journal of Statistics 27 (1): 3–23.
Batra, R., and O.T. Ahtola. 1991. Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters 2: 159–170.
Brown, T., B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal, D. Amodei, et al. 2020. Language models are few-shot learners. Advances in Neural Invnformation Processing Systems 33: 1877–1901.
Cao, Y., Li, S., Liu, Y., Yan, Z., Dai, Y., Yu, P.S., and Sun, L. (2023). A comprehensive survey of AI-generated content (AIGC): A history of generative AI from GAN to chatgpt. ar**v preprint http://arxiv.org/2303.04226.
Chintalapati, S., and S.K. Pandey. 2022. Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research 64 (1): 38–68.
Davenport, T., A. Guha, D. Grewal, and T. Bressgott. 2020. How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science 48: 24–42.
Dwivedi, Y.K., N. Kshetri, L. Hughes, E.L. Slade, A. Jeyaraj, A.K. Kar, R. Wright, et al. 2023. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management 71: 102642.
Hariri, W. 2023. Unlocking the potential of ChatGPT: a comprehensive exploration of its applications, advantages, limitations, and future directions in natural language processing. ar**v preprint http://arxiv.org/2304.02017.
Harmeling, C.M., J.W. Moffett, M.J. Arnold, and B.D. Carlson. 2017. Toward a theory of customer engagement marketing. Journal of the Academy of Marketing Science 45: 312–335.
Hassani, H., and E.S. Silva. 2023. The role of ChatGPT in data science: How AI-assisted conversational interfaces are revolutionizing the field. Big Data and Cognitive Computing 7 (2): 62.
Hirschman, E.C., and M.B. Holbrook. 1982. Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing 46 (3): 92–101.
Huang, M.H., and R.T. Rust. 2021. A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science 49: 30–50.
HubSpot. 2022. The who, what, why, & how of digital marketing. https://blog.hubspot.com/marketing/what-is-digital-marketing
Kaplan, A., and M. Haenlein. 2019. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons 62 (1): 15–25.
Koubaa, A., W. Boulila, L. Ghouti, A. Alzahem, and S. Latif. 2023. Exploring ChatGPT capabilities and limitations: A critical review of the NLP game changer. Preprints. https://doi.org/10.20944/preprints202303.0438.v1.
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Kazienko, P., et al. 2023. ChatGPT: Jack of all trades, master of none. ar**v preprint http://arxiv.org/2302.10724.
Kojima, T., S.S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in Neural Information Processing Systems 35: 22199–22213.
Kumar, V., D. Ramachandran, and B. Kumar. 2021. Influence of new-age technologies on marketing: A research agenda. Journal of Business Research 125: 864–877.
Liu, P., W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig. 2023a. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55 (9): 1–35.
Liu, V., and Chilton, L.B. 2022, April. Design guidelines for prompt engineering text-to-image generative models. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1–23).
Liu, Y., Han, T., Ma, S., Zhang, J., Yang, Y., Tian, J., Ge, B., et al. 2023a. Summary of chatgpt/gpt-4 research and perspective towards the future of large language models. ar**v preprint http://arxiv.org/2304.01852.
Logan IV, R.L., Balažević, I., Wallace, E., Petroni, F., Singh, S., and Riedel, S. 2021. Cutting down on prompts and parameters: Simple few-shot learning with language models. ar**v preprint http://arxiv.org/2106.13353.
McHugh, M.L. 2012. Interrater reliability: The kappa statistic. Biochemia Medica 22 (3): 276–282.
Minculete, G., and P. Olar. 2018. Approaches to the modern concept of digital marketing. In International Conference Knowledge-Based Organization 24 (2): 63–69.
Mustak, M., J. Salminen, L. Plé, and J. Wirtz. 2021. Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research 124: 389–404.
Nvidia. 2023. An introduction to large language models: Prompt engineering and P-tuning. https://developer.nvidia.com/blog/an-introduction-to-large-language-models-prompt-engineering-and-p-tuning/
Oppenlaender, J. 2022. A taxonomy of prompt modifiers for text-to-image generation. ar**v preprint http://arxiv.org/2204.13988.
Puto, C.P., and W.D. Wells. 1984. Informational and transformational advertising: The differential effects of time. Advances in Consumer Research 11: 638–643.
Ray, P.P. 2023. ChatGPT: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet of Things and Cyber- Physical Systems. 3: 121–154.
Reynolds, L., and McDonell, K. 2021, May. Prompt programming for large language models: Beyond the few-shot paradigm. In Extended abstracts of the 2021 CHI conference on human factors in computing systems (pp. 1–7).
Schiessl, D., H.B.A. Dias, and J.C. Korelo. 2022. Artificial intelligence in marketing: A network analysis and future agenda. Journal of Marketing Analytics 10 (3): 207–218.
Shahriar, S., and Hayawi, K. 2023. Let's have a chat! A Conversation with ChatGPT: Technology, applications, and limitations. ar**v preprint http://arxiv.org/2302.13817.
Sorensen, T., Robinson, J., Rytting, C.M., Shaw, A.G., Rogers, K.J., Delorey, A.P., Wingate, D., et al. 2022. An information-theoretic approach to prompt engineering without ground truth labels. ar**v preprint http://arxiv.org/2203.11364.
Statista. 2023. Artificial intelligence (AI) use in marketing - Statistics & Facts. https://www.statista.com/topics/5017/ai-use-in-marketing/#editorsPicks
Tafesse, W., and A. Wien. 2018. Using message strategy to drive consumer behavioral engagement on social media. Journal of Consumer Marketing 35 (3): 241–253.
Tamkin, A., Brundage, M., Clark, J., and Ganguli, D. 2021. Understanding the capabilities, limitations, and societal impact of large language models. ar**v preprint http://arxiv.org/2102.02503.
Tellis, G.J., D.J. MacInnis, S. Tirunillai, and Y. Zhang. 2019. What drives virality (sharing) of online digital content? The critical role of information, emotion, and brand prominence. Journal of Marketing 83 (4): 1–20.
Teubner, T., C.M. Flath, C. Weinhardt, W. van der Aalst, and O. Hinz. 2023. Welcome to the era of chatgpt et al. the prospects of large language models. Business & Information Systems Engineering 1: 1–7.
Wei, J., X. Wang, D. Schuurmans, M. Bosma, F. **a, E. Chi, D. Zhou, et al. 2022. Chain-of- thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35: 24824–24837.
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Schmidt, D.C., et al. 2023. A prompt pattern catalog to enhance prompt engineering with ChatGPT. ar**v preprint http://arxiv.org/2302.11382.
Wu, T., Terry, M., and Cai, C.J. 2022, April. Ai chains: Transparent and controllable human-AI interaction by chaining large language model prompts. In Proceedings of the 2022 CHI conference on human factors in computing systems (pp. 1–22).
Zamfirescu-Pereira, J.D., Wong, R.Y., Hartmann, B., and Yang, Q. 2023, April. Why Johnny can’t prompt: How non-AI experts try (and fail) to design LLM prompts. In Proceedings of the 2023 CHI conference on human factors in computing systems (pp. 1–21).
Zhao, Z., Wallace, E., Feng, S., Klein, D., and Singh, S. 2021, July. Calibrate before use: Improving few-shot performance of language models. In International Conference on Machine Learning (pp. 12697–12706). PMLR.
Zhang, C., Zhang, C., Li, C., Qiao, Y., Zheng, S., Dam, S.K., Hong, C.S., et al. 2023a. One small step for generative AI, one giant leap for AGI: A complete survey on ChatGPT in AIGC era. ar**v preprint http://arxiv.org/2304.06488.
Zhang, C., Zhang, C., Zheng, S., Qiao, Y., Li, C., Zhang, M., Hong, C.S., et al. 2023b. A complete survey on generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need? ar**v preprint http://arxiv.org/2303.11717.
Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Sun, L., et al. 2023b. A comprehensive survey on pretrained foundation models: A history from BERT to CHATGPT. ar**v preprint http://arxiv.org/2302.09419.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest in this study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tafesse, W., Wood, B. Hey ChatGPT: an examination of ChatGPT prompts in marketing. J Market Anal (2024). https://doi.org/10.1057/s41270-023-00284-w
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1057/s41270-023-00284-w