IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v12y2024i4d10.1057_s41270-023-00284-w.html
   My bibliography  Save this article

Hey ChatGPT: an examination of ChatGPT prompts in marketing

Author

Listed:
  • Wondwesen Tafesse

    (United Arab Emirates University)

  • Bronwyn Wood

    (United Arab Emirates University)

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.

Suggested Citation

  • Wondwesen Tafesse & Bronwyn Wood, 2024. "Hey ChatGPT: an examination of ChatGPT prompts in marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(4), pages 790-805, December.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-023-00284-w
    DOI: 10.1057/s41270-023-00284-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-023-00284-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41270-023-00284-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    2. Djonata Schiessl & Helison Bertoli Alves Dias & José Carlos Korelo, 2022. "Artificial intelligence in marketing: a network analysis and future agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 10(3), pages 207-218, September.
    3. Mekhail Mustak & Joni Salminen & Loïc Plé & Jochen Wirtz, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Post-Print hal-03269994, HAL.
    4. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    5. Kumar, V. & Ramachandran, Divya & Kumar, Binay, 2021. "Influence of new-age technologies on marketing: A research agenda," Journal of Business Research, Elsevier, vol. 125(C), pages 864-877.
    6. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christopher Gerling & Stefan Lessmann, 2024. "Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis," Papers 2411.14463, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    2. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    3. Nur Areena Aqilah Mohd Sapri & Asma Nadia Zanol Rashid & Wan Ainol Mursyida Ahmad Tarmizi, 2024. "Leveraging AI for Effective Content Marketing in Libraries: Maximizing User Engagement," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(9), pages 3510-3521, September.
    4. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    5. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    6. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    7. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    8. Sule Birim & Ipek Kazancoglu & Sachin Kumar Mangla & Aysun Kahraman & Yigit Kazancoglu, 2024. "The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods," Annals of Operations Research, Springer, vol. 339(1), pages 131-161, August.
    9. O. C. Ferrell & Dana E. Harrison & Linda K. Ferrell & Haya Ajjan & Bryan W. Hochstein, 2024. "A theoretical framework to guide AI ethical decision making," AMS Review, Springer;Academy of Marketing Science, vol. 14(1), pages 53-67, June.
    10. Broekhuizen, Thijs & Dekker, Henri & de Faria, Pedro & Firk, Sebastian & Nguyen, Dinh Khoi & Sofka, Wolfgang, 2023. "AI for managing open innovation: Opportunities, challenges, and a research agenda," Journal of Business Research, Elsevier, vol. 167(C).
    11. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    12. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    13. Erik Karger & Marvin Jagals & Frederik Ahlemann, 2021. "Blockchain for Smart Mobility—Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    14. Ertugrul Uysal & Sascha Alavi & Valéry Bezençon, 2022. "Trojan horse or useful helper? A relationship perspective on artificial intelligence assistants with humanlike features," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1153-1175, November.
    15. Reyes-Menendez, Ana & Clemente-Mediavilla, Jorge & Villagra, Nuria, 2023. "Understanding STI and SDG with artificial intelligence: A review and research agenda for entrepreneurial action," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    16. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    17. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.
    18. Ding, Bin & Li, Yameng & Miah, Shah & Liu, Wei, 2024. "Customer acceptance of frontline social robots—Human-robot interaction as boundary condition," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    19. Hossain, Md Afnan & Akter, Shahriar & Yanamandram, Venkata, 2021. "Why doesn't our value creation payoff: Unpacking customer analytics-driven value creation capability to sustain competitive advantage," Journal of Business Research, Elsevier, vol. 131(C), pages 287-296.
    20. Jeon, Yongwoog Andrew, 2022. "Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes," Journal of Business Research, Elsevier, vol. 145(C), pages 892-904.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jmarka:v:12:y:2024:i:4:d:10.1057_s41270-023-00284-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.