lynx   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v42y2025i1p13-31.html
   My bibliography  Save this article

The power of generative marketing: Can generative AI create superhuman visual marketing content?

Author

Listed:
  • Hartmann, Jochen
  • Exner, Yannick
  • Domdey, Samuel
Abstract
Generative AI’s capacity to create photorealistic images has the potential to augment human creativity and disrupt the economics of visual marketing content production. This research systematically compares the performance of AI-generated to human-made marketing images across important marketing dimensions. First, we prompt seven state-of-the-art generative text-to-image models (DALL-E 3, Midjourney v6, Firefly 2, Imagen 2, Imagine, Stable Diffusion XL Turbo, and Realistic Vision) to create 10,320 synthetic marketing images, using 2,400 real-world, human-made images as input. 254,400 human evaluations of these images show that AI-generated marketing imagery can surpass human-made images in quality, realism, and aesthetics. Second, we give identical creative briefings to commissioned human freelancers and the AI models, showing that the best synthetic images also excel in ad creativity, ad attitudes, and prompt following. Third, a field study with more than 173,000 impressions demonstrates that AI-generated banner ads can compete with professional human-made stock photography, achieving an up to 50% higher click-through rate than a human-made image. Collectively, our findings suggest that the paradigm shift brought about by generative AI can help advertisers produce marketing content not only faster and orders of magnitude cheaper but also at superhuman effectiveness levels with important implications for firms, consumers, and policymakers. To facilitate future research on AI-generated marketing imagery, we release GenImageNet that contains all of our synthetic images and their human ratings.

Suggested Citation

  • Hartmann, Jochen & Exner, Yannick & Domdey, Samuel, 2025. "The power of generative marketing: Can generative AI create superhuman visual marketing content?," International Journal of Research in Marketing, Elsevier, vol. 42(1), pages 13-31.
    • Handle: RePEc:eee:ijrema:v:42:y:2025:i:1:p:13-31
    DOI: 10.1016/j.ijresmar.2024.09.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811624000843
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2024.09.002?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    2. Shunyuan Zhang & Dokyun Lee & Param Vir Singh & Kannan Srinivasan, 2022. "What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features," Management Science, INFORMS, vol. 68(8), pages 5644-5666, August.
    3. John Hulland & Mark Houston, 2021. "The importance of behavioral outcomes," Journal of the Academy of Marketing Science, Springer, vol. 49(3), pages 437-440, May.
    4. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    5. Jan Ole Krugmann & Jochen Hartmann, 2024. "Sentiment Analysis in the Age of Generative AI," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 11(1), pages 1-19, December.
    6. Peres, Renana & Schreier, Martin & Schweidel, David & Sorescu, Alina, 2023. "On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 269-275.
    7. Alex Burnap & John R. Hauser & Artem Timoshenko, 2023. "Product Aesthetic Design: A Machine Learning Augmentation," Marketing Science, INFORMS, vol. 42(6), pages 1029-1056, November.
    8. Daria Dzyabura & Siham El Kihal & Renana Peres, 2022. "Image Analytics in Marketing," Springer Books, in: Christian Homburg & Martin Klarmann & Arnd Vomberg (ed.), Handbook of Market Research, pages 665-692, Springer.
    9. Hartmann, Jochen & Heitmann, Mark & Siebert, Christian & Schamp, Christina, 2023. "More than a Feeling: Accuracy and Application of Sentiment Analysis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 75-87.
    10. Hartmann, Jochen & Huppertz, Juliana & Schamp, Christina & Heitmann, Mark, 2019. "Comparing automated text classification methods," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 20-38.
    11. Lidan Xu & Ravi Mehta, 2022. "Technology devalues luxury? Exploring consumer responses to AI-designed luxury products," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1135-1152, November.
    12. Robert E. Smith & Scott B. MacKenzie & Xiaojing Yang & Laura M. Buchholz & William K. Darley, 2007. "Modeling the Determinants and Effects of Creativity in Advertising," Marketing Science, INFORMS, vol. 26(6), pages 819-833, 11-12.
    13. Kevin Lane Keller & Donald R. Lehmann, 2006. "Brands and Branding: Research Findings and Future Priorities," Marketing Science, INFORMS, vol. 25(6), pages 740-759, 11-12.
    14. Carlson, Keith & Kopalle, Praveen K. & Riddell, Allen & Rockmore, Daniel & Vana, Prasad, 2023. "Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis," International Journal of Research in Marketing, Elsevier, vol. 40(1), pages 54-74.
    15. Xiang Hui & Oren Reshef & Luofeng Zhou, 2023. "The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market," CESifo Working Paper Series 10601, CESifo.
    16. Bonaccorsi, Andrea & Rossi, Cristina, 2003. "Why Open Source software can succeed," Research Policy, Elsevier, vol. 32(7), pages 1243-1258, July.
    17. Mengxia Zhang & Lan Luo, 2023. "Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp," Management Science, INFORMS, vol. 69(1), pages 25-50, January.
    18. Martin Reisenbichler & Thomas Reutterer & David A. Schweidel & Daniel Dan, 2022. "Frontiers: Supporting Content Marketing with Natural Language Generation," Marketing Science, INFORMS, vol. 41(3), pages 441-452, May.
    Full references (including those not matched with items on IDEAS)

    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. Jan Ole Krugmann & Jochen Hartmann, 2024. "Sentiment Analysis in the Age of Generative AI," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 11(1), pages 1-19, December.
    2. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.
    3. Li, Yaqiu & Meg Lee, Hsin Hsuan & Blasco-Arcas, Lorena, 2025. "Computer vision in branding: A conceptual framework and future research agenda," Journal of Business Research, Elsevier, vol. 193(C).
    4. de Haan, Evert & Padigar, Manjunath & El Kihal, Siham & Kübler, Raoul & Wieringa, Jaap E., 2024. "Unstructured data research in business: Toward a structured approach," Journal of Business Research, Elsevier, vol. 177(C).
    5. Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    6. Mustak, Mekhail & Hallikainen, Heli & Laukkanen, Tommi & Plé, Loïc & Hollebeek, Linda D. & Aleem, Majid, 2024. "Using machine learning to develop customer insights from user-generated content," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    7. Daria Dzyabura & Siham El Kihal & John R. Hauser & Marat Ibragimov, 2023. "Leveraging the Power of Images in Managing Product Return Rates," Marketing Science, INFORMS, vol. 42(6), pages 1125-1142, November.
    8. Hui Li & Jian Ni & Fangzhu Yang, 2024. "Product Design Using Generative Adversarial Network: Incorporating Consumer Preference and External Data," Papers 2405.15929, arXiv.org, revised May 2025.
    9. Ma, Liye & Sun, Baohong, 2020. "Machine learning and AI in marketing – Connecting computing power to human insights," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 481-504.
    10. Laura Sáez-Ortuño & Ruben Huertas-Garcia & Santiago Forgas-Coll & Eloi Puertas-Prats, 2023. "How can entrepreneurs improve digital market segmentation? A comparative analysis of supervised and unsupervised learning algorithms," International Entrepreneurship and Management Journal, Springer, vol. 19(4), pages 1893-1920, December.
    11. Alireza Aghasi & Arun Rai & Yusen Xia, 2024. "A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 616-634, March.
    12. Peres, Renana & Schreier, Martin & Schweidel, David & Sorescu, Alina, 2023. "On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice," International Journal of Research in Marketing, Elsevier, vol. 40(2), pages 269-275.
    13. Christopher Gerling & Stefan Lessmann, 2024. "Leveraging AI and NLP for Bank Marketing: A Systematic Review and Gap Analysis," Papers 2411.14463, arXiv.org.
    14. Duong, Quang Huy & Zhou, Li & Van Nguyen, Truong & Meng, Meng, 2025. "Understanding and predicting online product return behavior: An interpretable machine learning approach," International Journal of Production Economics, Elsevier, vol. 280(C).
    15. Jiemiao Chen & Robert E. Smith, 2018. "The boundaries for ad creativity: effects of type of divergence and brand processing and responses," Journal of Brand Management, Palgrave Macmillan, vol. 25(6), pages 561-576, November.
    16. Jürgensmeier, Lukas & Skiera, Bernd, 2024. "Generative AI for scalable feedback to multimodal exercises," International Journal of Research in Marketing, Elsevier, vol. 41(3), pages 468-488.
    17. Ruijie Sun & Feng Liu & Yinan Li & Rongping Wang & Jing Luo, 2024. "Machine Learning for Predicting Corporate Violations: How Do CEO Characteristics Matter?," Journal of Business Ethics, Springer, vol. 195(1), pages 151-166, November.
    18. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    19. Simon J. Blanchard & Theodore J. Noseworthy & Ethan Pancer & Maxwell Poole, 2023. "Extraction of visual information to predict crowdfunding success," Production and Operations Management, Production and Operations Management Society, vol. 32(12), pages 4172-4189, December.
    20. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:ijrema:v:42:y:2025:i:1:p:13-31. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    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.
    Лучший частный хостинг