The Analysis of Generative AI Prompts for Selling Images on the Adobe Stock Website

Main Article Content

Anupon Bangbor
Patama Satawedin

Abstract

A/B test over 1,100 images and collect behavioral metrics from the Adobe Stock Contributor Dashboard, including Impressions, Clicks, CTR, Downloads, Revenue, and Time to First Download (TTFD), weekly across six weeks (February 1–April 10, 2025).


Results show emotional prompts consistently outperform technical prompts: CTR 27.1% vs. 14.9%, clicks per image 12.8 vs. 3.2, and TTFD 2.5 vs. 6.5 days, while total downloads remain comparable. These findings indicate that emotionally resonant prompt design enhances user engagement and strengthens marketing effectiveness for stock imagery. We provide practical guidance on prompt composition and using behavioral metrics—especially CTR and TTFD—for content planning and long-term revenue optimization.

Article Details

Section
บทความวิจัย

References

Adobe. (2025). Adobe Stock contributor guide: Improve your content performance.

https://stock.adobe.com/.

AI Prompt Institute. (2023). Strategic prompt design for generative AI. AI Prompt Institute Research Series.

Burmagina, K. (2025). Artificial intelligence usage statistics and facts. Elfsight.

https://elfsight.com/blog/ai-usage-statistics/.

Davenport, T.H., Guha, A., & Grewal, D. (2021, July - August). How to design an AI marketing strategy. HARVARD BUSINESS REVIEW. https://hbr.org/2021/07/how-to-design-an-ai-marketing-strategy.

Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P.V., Janssen. M., Jones, P., Kar, A.K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R. & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(2021), 101994.

Feng, Y., Wang, X., Wong, K. K., Wang, S., Lu, Y., Zhu, M., Wang, B & Chen, W. (2023). Promptmagician: Interactive prompt engineering for text-to-image creation. IEEE Transactions on Visualization and Computer Graphics, 30(1), 295-305.

Gozalo-Brizuela, R., & Garrido-Merchán, E. C. (2024). A survey of generative AI applications. Journal of Computer Science, 20(8), 801-818.

Hargunani, C. (2025, June).The study of AI vision for images and video marketing campaign. ResearchGate GmbH.

https://www.researchgate.net/publication/393786049_The_study_of_AI_Vision_for_Imag

es_and_Videos_Marketing_Campaigns.

Harsanto, P.W., Udayana, A.A.G.B., & Satuti, K.R. (2025). The influence of artificial intelligence

image for product advertisements (case study of using model photos in Levi’s advertisements). Mudra: Jurnal Seni Budaya, 40(1), 81-94.

Kapitan, S., & Silvera, D. H. (2022). Human-centric messaging: Enhancing engagement and conversion in digital advertising. Journal of Digital Marketing, 38(2), 125–139.

Miller, A.P. (2021). The art & science of A/B testing. Wharton AI for Business.

Mintz. (2023). AI Legislation. Mintz Insights.

Morozova, I. & Nikolaienko, V. (2024). Rhetoric of education and innovation. Res Rhetorica, 11(4), 154-174.

Nielsen Norman Group. (2024). UX and AI: How generative models affect user experience. https://www.nngroup.com/articles/generative-aiux/.

Oppenlaender, J., Silvennoinen, J., Paananen, V., & Visuri, A. (2023, June). Perceptions and realities of text-to-image generation. ResearchGate GmbH. https://www.researchgate.net/publication/371605878_Perceptions_and_Realities_of_Text-to-Image_Generation.

Petty, R. E., & Cacioppo, J. T. (2020 A). The elaboration likelihood model of persuasion. In Communication and persuasion. Springer.

Petty, R. E., & Cacioppo, J. T. (2020 B). Communication and persuasion: Central and peripheral routes to attitude change (2nd ed.). Springer.

Shen, X., Qu, Y., Backes, M., & Zhang, Y. (2023, February 20). Prompt stealing attacks against textto image generation models. Cornell University. https://arxiv.org/abs/2302.09923.

Vinay, R., Spitale, G., Biller-Andorno, N., & Germani, F. (2025). Emotional prompting amplifies

disinformation generation in AI large language models. Frontiers in Artificial Intelligence, 8(1543603), 1-8. https://doi.org/10.3389/frai.2025.1543603.

Wong, Y. Y., & Faikhamta, C. (2023). Expanding the border of science education through the lens of Buddhist mindfulness. Culture Study Science Education,18(2), 345-358.

Zhang, Y., Li, H., & Wu, T. (2023). Visual relevance and familiarity in user responses to AI-generated content. Journal of Visual Communication, 42(3), 211–229.