Automated Video Analytics in Marketing Research: A Systematic Literature Review and a Novel Multimodal Large Language Model Method

Christopher Schraml

No 63nbc_v1, OSF Preprints from Center for Open Science

Abstract: The exponential growth of online video content presents significant opportunities for marketing researchers to analyze online consumer behavior. However, effectively using this data requires advanced methods for managing the complexity and sheer volume of video data. This research presents a systematic literature review of video analytics in marketing research, outlining the types of information extracted automatically from video data and examining the specific methodologies employed by researchers. Furthermore, we evaluate a multimodal large language model, specifically ChatGPT-4o, for complex, zero-shot image coding tasks in marketing research. Building on these image coding capabilities, we introduce a novel multimodal large language model-based pipeline for zero-shot video analysis. This innovative method allows researchers to efficiently extract meaningful information from video data without prior training or labeled datasets. Our findings highlight how multimodal large language models can advance video analytics into a scalable and cost-effective tool for marketing research.

Date: 2025-05-21
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:63nbc_v1

DOI: 10.31219/osf.io/63nbc_v1

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