AI-based content analysis in marketing: methods, processes, and evidence from an empirical study
DOI:
https://doi.org/10.32015/Keywords:
AI-based Content Analysis; Text Analysis; Image Analysis; Video Analysis; Multimodal AnalysisAbstract
The exponential growth of digital content has fundamentally transformed how organizations can observe, understand, and predict human attitudes and behaviors. In marketing and related domains, artificial intelligence (AI)–based content analysis has emerged as a critical analytical capability for extracting insights from unstructured data such as text, images, audio, and video. While prior research has extensively discussed individual methods, less is known about how organizations actually implement these approaches and integrate them into decision-making processes.
Addressing this gap, this study pursues two objectives. First, it provides a structured overview of AI-based content analysis methods and proposes an end-to-end process model that emphasizes organizational integration rather than algorithmic performance alone. Second, it reports findings from an empirical survey of marketing managers and experts that examines the adoption, maturity, objectives, perceived benefits, and barriers associated with AI-based content analysis in practice.
The results show that text-based methods are most widely adopted, whereas image, video, and multimodal approaches remain at an early stage of diffusion. Although respondents perceive substantial benefits from AI-based content analysis, process maturity, governance structures, and analytical capabilities are often underdeveloped.
References
Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal Machine Learning: A Survey and Taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(2), 423-443. https://doi.org/10.1109/TPAMI.2018.2798607
Bär, I., & Zerres, C. (2024). Social-Media-Monitoring. In: Zerres C. (Ed.), Handbuch Social-Media-Marketing, Springer Gabler, pp. 41-60.
Barari, M., & Eisend, M (2024) Computational content analysis in advertising research. Journal of Advertising 53(5), 681–699. https://doi.org/10.1080/00913367.2024.2407642
Berger, J., Humphreys, A., Ludwig, S., Moe, W.W., Netzer, O., & Schweidel, D.A. (2019). Uniting the tribes: Using text for marketing insight. Journal of Marketing 83(5), 1-25. https://doi.org/10.1177/0022242919873106
Chakraborty, I., Chiong, K., Dover, H., & Sudhir, K. (2024). Can AI and AI-Hybrids Detect Persuasion Skills? Salesforce Hiring with Conversational Video Interviews. Marketing Science 44(1), 30-53. https://doi.org/10.1287/mksc.2023.0149
De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K.-U., & von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing 51, 91-105. https://doi.org/10.1016/j.intmar.2020.04.007
Grewal, R., Gupta, S., & Hamilton, R. (2021). Marketing Insights from Multimedia Data: Text, Image, Audio, and Video. Journal of Marketing Research 58(6), 1025-1033. https://doi.org/10.1177/00222437211054601
Harzig, P., Brehm, S., Lienhart, R., Kaiser, C., & Schallner, R. (2018). Multimodal Image Captioning for Marketing Analysis. IEEE Conference on Multimedia Information Processing and Retrieval. https://doi.org/10.48550/arXiv.1802.01958
Li, H., & Zhang, N. (2024). Computer Vision Models for Image Analysis in Advertising Research. Journal of Advertising 53(5), 771-790. https://doi.org/10.1080/00913367.2024.2407644
Li, T., Zeng, Z., Li, Q., & Sun, S. (2024). Integrating GIN-based multimodal feature transformation and multi-feature combination voting for irony-aware cyberbullying detection. Information Processing & Management 61(3), 103651. https://doi.org/10.1016/j.ipm.2024.103651
Li, X., Shi, M., & Wang, X. (2019). Video mining: Measuring visual information using automatic Methods. International Journal of Research in Marketing 36(2), 216-231. https://doi.org/10.1016/j.ijresmar.2019.02.004
Lin, Z., Xie, J, & Li, Q. (2024). Multi-modal news event detection with external Knowledge. Information Processing & Management 61(3), 103697. https://doi.org/10.1016/j.ipm.2024.103697
Liu, X., Lee, Y., & Srinivasan, K. (2019). Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. Journal of Marketing Research 56(6), 918-943. https://doi.org/10.1177/0022243719866690
Mahadevkar, S.V., Khemani, B., Patil, S., Kotecha, K., Vora, D.R., & Abraham, A. (2022). A Review on Machine Learning Styles in Computer Vision - Techniques and Future Directions. IEEE Access, 10, 107293-107329.
Mahadevkar, S.V., Patil, S., Kotecha, K., Soong, L.W., & Choudhury, T. (2024). Exploring AI-driven approaches for unstructured document analysis and future horizons. Journal of Big Data 11(92), 1-54. https://doi.org/10.1186/s40537-024-00948-z
Moon, S., & Kamakura, W.A. (2017). A picture is worth a thousand words: Translating product reviews into a product positioning map. International Journal of Research in Marketing 34(1), 265-285. https://doi.org/10.1016/j.ijresmar.2016.05.007
Mroueh, Y., Marcheret, E., & Goel, V. (2015). Deep multimodal learning for Audio-Visual Speech Recognition. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, QLD, Australia, 2130-2134.
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science 31(3), 521-543. https://doi.org/10.1287/mksc.1120.0713
Peng, Y., Lock, I., & Ali Salah, A. (2023). Automated Visual Analysis for the Study of Social Media Effects: Opportunities, Approaches, and Challenges. Communication Methods and Measures 18(2), 163-185. https://doi.org/10.1080/19312458.2023.2277956
Philp, M., Jacobson, J., & Pancer, E. (2022). Predicting social media engagement with computer vision: An examination of food marketing on Instagram. Journal of Business Research 149, 736-747. https://doi.org/10.1016/j.jbusres.2022.05.078
Schraml, C. (2025, May 21). Automated Video Analytics in Marketing Research: A Systematic Literature Review and a Novel Multimodal Large Language Model Method. https://doi.org/10.31219/osf.io/63nbc_v1
Vlačić, B., Corbo, L., Costa e Silva, S., & Dabić, M. (2021). The evolving role of artificial intelligence in marketing: A review and research Agenda. Journal of Business Research 128, 187-203. https://doi.org/10.1016/j.jbusres.2021.01.055
Volkmar, G., Reinecke, S., & Fischer, P.M. (2021). Künstliche Intelligenz im Marketing: Möglichkeiten und Herausforderungen. Die Unternehmung 75(3), 359-375.
Xiao, L., Wu, X., Wu, W., Yang, J., & He, L. (2022). Multi-Channel Attentive Graph Convolutional Network with Sentiment Fusion for Multimodal Sentiment Analysis. IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 4578-4582.
Xu, D., Chen, T., Pearce, J., Mohammadi, Z., & Pearce, P.L. (2021). Reaching audiences through travel vlogs: The perspective of involvement. Tourism Management 86, 104326. https://doi.org/10.1016/j.tourman.2021.104326
Yang, J., Zhang, J., & Zhang, Y. (2025). Engagement that sells: Influencer video advertising on TikTok. Marketing Science 44(2), 247–267. https://doi.org/10.1287/mksc.2021.0107
Zerres, C., & Israel, K. (2025). KI im Marketing in Breyer-Mayländer, T., Drechsler, D., Zerres, C. (Hrsg.), KI-Transformation in Deutschland, utb Verlag, S. 289-307.
Zhou, M., Chen, G.H., Ferreira, P., & Smith, M.D. (2021). Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware. Journal of Marketing Research 58(6), 1079-1100. https://doi.org/10.1177/00222437211042013
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