AI-based digtial customer twins

Research into consumer behavior is undergoing significant development through the use of new technologies. Digital customer twins simulate the opinions, preferences and future behavior of real customers. The basis for the creation of digital customer twins is extensive data collection and analysis of digital consumer behavior and user preferences. Digital customer twins are said to have great potential for use in marketing practice and research, as they can replace costly and time-consuming surveys of real people. The development of new technological solutions is accompanied by the central question of how reliable AI-generated customer data is and whether it is actually a reliable substitute for interviewing real people.

Digital customer twins open up new and exciting fields of research for marketing: How can data be used effectively to better understand and predict the needs of real customers? How do digital customer twins interact with various established market research methods? How can the reliability of customer twins be optimized? How can customer twins be used effectively in marketing practice and research?

Note: To attend this seminar, no programming skills are required.

Recommended starting literature:

• Argyle, L. P., Busby, E. C., Fulda, N., Gubler, J. R., Rytting, C. & Wingate, D. (2023). Out of One, Many: Using Language Models to Simulate Human Samples. Political Analysis. 31(3), 337-351. doi:10.1017/pan.2023.2

• Brand, J., Israeli, A. & Ngwe, D. (March 21, 2023). Using GPT for Market Research. Harvard Business School Marketing Unit Working Paper. No. 23-062. http://dx.doi.org/10.2139/ssrn.4395751

• Chen, Y., Andiappan, M., Jenkin, T. & Ovchinnikov, A. (May 20, 2023). A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do? http://dx.doi.org/10.2139/ssrn.4380365

• Demszky, D., Yang, D., Yeager, D.S. et al. (2023). Using Large Language Models in Psychology. Nature Review Psychology. 2, 688–701. https://doi.org/10.1038/s44159-023-00241-5

• Dillon, D., Tandon, N., Gu, Y. & Gray, K. (2023). Can AI Language Models Replace Human Participants? Trends in Cognitive Sciences. 27(7), 597-600. https://doi.org/10.1016/j.tics.2023.04.008

• Hagendorff, T., Fabi, S. & Kosinski, M. (2023). Human-like Intuitive Behavior and Reasoning Biases Emerged in Large Language Models but Disappeared in ChatGPT. Nature Computational Science. 3, 833–838. https://doi.org/10.1038/s43588-023-00527-x

• Hutson M. (2023). Guinea Pigbots. Science. 381(6654), 121-123. 10.1126/science.adj6791

• Kirschner, S. N. (2024). GPT and CLT: The Impact of ChatGPT’s Level of Abstraction on Consumer Recommendations. Journal of Retailing and Consumer Services. 76, 103580. https://doi.org/10.1016/j.jretconser.2023.103580

• OpenAI. (2023). GPT‐4 Technical Report. arXiv. https://arxiv.org/pdf/2303.08774

• Paul, J., Ueno, A., & Dennis, C. (2023). ChatGPT and Consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies. 47(4), 1213–1225. https://doi.org/10.1111/ijcs.12928

• Rahwan, I., Cebrian, M., Obradovich, N., et al. (2019). Machine Behaviour. Nature. 568(7753), 477–486. https://doi.org/10.1038/s41586-019-1138-y

• Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., & Hashimoto, T. (2023). Whose Opinions do Language Models Reflect? arXiv. https://arxiv.org/pdf/2303.1754

• Sarstedt, M., Adler, S. J., Rau, L. & Schmitt, B. (2024). Using Large Language Models to Generate Silicon Samples in Consumer and Marketing Research: Challenges, Opportunities, and Guidelines. Psychology & Marketing. 1-17. https://doi.org/10.1002/mar.21982