Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Large Language Models (LLMs) have brought unprecedented innovation opportunities to the marketing field. However, the practical applications of LLMs within the marketing landscape currently exhibit a fragmented and scattered nature. In this study, we aim to aggregate these scattered literature to create a holistic view of LLMs capabilities for marketing research. Specifically, we present an overview of LLMs using the evolution of LMs. Subsequently, we explore their application in the marketing domain across five distinct dimensions: data annotation, idea inspiration and content generation, substitution of human participants, user behavior learning and prediction, and evaluation of LLM feedback. Finally, we discuss the new trends and challenges for LLMs in marketing. This study enriches the theoretical foundations of integrating generative AI with marketing practices.
Recommended Citation
Qian, Yang; Chen, Shaoge; Wu, Chenyang; Yuan, Kun; Du, Yanan; Jiang, Yuanchun; and Liu, Yezheng, "Large Language Models for Marketing Research: A Survey and New Perspectives" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 2.
https://aisel.aisnet.org/hicss-58/da/smart_city/2
Large Language Models for Marketing Research: A Survey and New Perspectives
Hilton Waikoloa Village, Hawaii
Large Language Models (LLMs) have brought unprecedented innovation opportunities to the marketing field. However, the practical applications of LLMs within the marketing landscape currently exhibit a fragmented and scattered nature. In this study, we aim to aggregate these scattered literature to create a holistic view of LLMs capabilities for marketing research. Specifically, we present an overview of LLMs using the evolution of LMs. Subsequently, we explore their application in the marketing domain across five distinct dimensions: data annotation, idea inspiration and content generation, substitution of human participants, user behavior learning and prediction, and evaluation of LLM feedback. Finally, we discuss the new trends and challenges for LLMs in marketing. This study enriches the theoretical foundations of integrating generative AI with marketing practices.
https://aisel.aisnet.org/hicss-58/da/smart_city/2