Leveraging Generative Artificial Intelligence to Create Visual Content in Digital Advertising

References

• Balandat M, Karrer B, Jiang D, Daulton S, Letham B, Wilson AG, Bakshy E (2020) BoTorch: A framework for efficient Monte-Carlo Bayesian optimization. Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, eds. Advances in Neural Information Processing Systems, vol. 33 (Curran Associates, Red Hook, NY), 21524–21538.Google Scholar
• Brochu E, Cora VM, De Freitas N (2010) A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Preprint, submitted December 12, https://arxiv.org/abs/1012.2599.Google Scholar
• Burda M, Daviet R (2023) Hamiltonian sequential Monte Carlo with application to consumer choice behavior. Econom. Rev. 42(1):54–77.Crossref, Google Scholar
• Burnap A, Hauser JR, Timoshenko A (2023) Product aesthetic design: A machine learning augmentation. Marketing Sci. 42(6):1029–1056.Link, Google Scholar
• Chaloner K, Verdinelli I (1995) Bayesian experimental design: A review. Statist. Sci. 10(3):273–304.Crossref, Google Scholar
• Dew R (2025) Adaptive preference measurement with unstructured data. Management Sci. 71(5):3996–4012.Link, Google Scholar
• Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
• Dzyabura D, El Kihal S, Hauser JR, Ibragimov M (2023) Leveraging the power of images in managing product return rates. Marketing Sci. 42(6):1125–1142.Link, Google Scholar
• Feit EM, Berman R (2019) Test & roll: Profit-maximizing A/B tests. Marketing Sci. 38(6):1038–1058.Link, Google Scholar
• He S, Zhang Y, Xie R, Jiang D, Ming A (2022) Rethinking image aesthetics assessment: Models, datasets and benchmarks. Proc. 31st Internat. Joint Conf. Artificial Intelligence, 942–948.Google Scholar
• Huang D, Luo L (2016) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci. 35(3):445–464.Link, Google Scholar
• Izmailov P, Vikram S, Hoffman MD, Wilson AGG (2021) What are Bayesian neural network posteriors really like? Marina M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 139 (JMLR.org), 4629–4640.Google Scholar
• Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 4401–4410.Google Scholar
• Liu L, Dzyabura D, Mizik N (2020) Visual listening in: Extracting brand image portrayed on social media. Marketing Sci. 39(4):669–686.Link, Google Scholar
• Park T, Liu MY, Wang TC, Zhu JY (2019) Semantic image synthesis with spatially-adaptive normalization. Proc. IEEE/CVF Conf. Comput. Vision Pattern Recognition (IEEE Computer Society, Washington, DC), 2337–2346.Google Scholar
• Peng T, Gui G, Brucks M, Merlau DJ, Fan GJ, Sliman MB, Johnson EJ, et al. (2025) Digital twins as funhouse mirrors: Five key distortions. Preprint, submitted September 23, https://arxiv.org/abs/2509.19088v1.Google Scholar
• Podell D, English Z, Lacey K, Blattmann A, Dockhorn T, Müller J, Penna J, Rombach R (2023) SDXL: Improving latent diffusion models for high-resolution image synthesis. Preprint, submitted July 4, https://arxiv.org/abs/2307.01952.Google Scholar
• Qwen Team (2025) Qwen3 technical report. Preprint, submitted May 14, https://arxiv.org/pdf/2505.09388.Google Scholar
• Schwartz EM, Bradlow ET, Fader PS (2017) Customer acquisition via display advertising using multi-armed bandit experiments. Marketing Sci. 36(4):500–522.Link, Google Scholar
• Shahriari B, Swersky K, Wang Z, Adams RP, De Freitas N (2016) Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE 104(1):148–175. Crossref, Google Scholar
• Sisodia A, Burnap A, Kumar V (2025) Generative interpretable visual design: Using disentanglement for visual conjoint analysis. J. Marketing Res. 62(3):405–428.Crossref, Google Scholar
• Thompson WR (1933) On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25(3/4):285–294.Crossref, Google Scholar
• Toubia O, Gui GZ, Peng T, Merlau DJ, Li A, Chen H (2025) Database report: Twin-2K-500: A data set for building digital twins of over 2,000 people based on their answers to over 500 questions. Marketing Sci. 44(6):1446–1455.Link, Google Scholar