Social Media Analytics and Consumer Behavior in Digital Fashion: A Narrative Review of Data-Driven Marketing Strategies

Authors

  • Hery Maulana Arif Universitas Negeri Makassar, Indonesia

DOI:

https://doi.org/10.59971/meta-journal.v3i3.423

Keywords:

Social Media Analytics, Fashion Marketing, Machine Learning, Game Theory, Consumer Behavior

Abstract

The rapid growth of social media in the digital era has fundamentally transformed fashion marketing and consumer engagement, enabling businesses to adopt more personalized and data-driven strategies. Despite the expanding availability of big data, many organizations continue to face challenges in translating social media information into meaningful strategic insights. This article presents a narrative review of recent empirical and conceptual studies to examine how data analytics, Artificial Intelligence (AI), and predictive modeling are being applied to optimize fashion marketing performance within digital environments. The review synthesizes findings from peer-reviewed literature on Natural Language Processing (NLP), Latent Dirichlet Allocation (LDA), sentiment analysis, Game Theory, and machine learning algorithms, including XGBoost and Random Forest. Evidence drawn from reviewed studies indicates a significant positive relationship between digital engagement indicators and sales performance, with predictive models achieving accuracy rates as high as 94.73% in identifying high-engagement content (Ju, 2024). Game Theory modeling further suggests that sustained aggressive social media engagement strategies can yield a competitive market share advantage of approximately 30% over passive competitors under certain conditions (Ju, 2024). However, the reviewed literature consistently identifies a “personalization–privacy paradox,” in which highly personalized marketing strategies may simultaneously increase consumer discomfort regarding privacy and data usage. This review concludes that data analytics and AI have become critical instruments in the transition from mass marketing toward micro-targeting approaches in fashion. Sustainable success in digital fashion marketing depends on balancing technological innovation, ethical transparency, and human creativity to maintain long-term consumer trust and competitive advantage.

Downloads

Download data is not yet available.

References

Almashaleh, O., Wicaksono, H., & Valilai, O. F. (2025). A framework for social media analytics in textile business circularity for effective digital marketing. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), 100544. https://doi.org/10.1016/j.joitmc.2025.100544

Chertov, O. (2025). Video game sales prediction based on social media data using machine learning: A survey and future directions. I.J. Information Technology and Computer Science, 17(4), 49-57. https://doi.org/10.5815/ijitcs.2025.04.05

Deng, Q., Hine, M. J., Ji, S., & Wang, Y. (2021). Consumer engagement with brand posts on social media: A multi-level analysis. Electronic Commerce Research and Applications, 48, 101068. https://doi.org/10.1016/j.elerap.2021.101068

Deza-De-Souza-Ferreyra, S., Ramos-Cavero, M. J., & Cordova-Buiza, F. (2025). Organic positioning strategies and digital consumer behavior: A study in Peru’s real estate sector. Innovative Marketing, 21(1), 119-128. https://doi.org/10.21511/im.21(1).2025.10

Ismayilova, S. (2025). Fashion meets data: Enhancing brand strategies with analytics. Scientific Work International Scientific Journal, 19(4), 198-201. https://doi.org/10.36719/2663-4619/114/198-201

Ju, X. (2024). A social media competitive intelligence framework for brand topic identification and customer engagement prediction. PLoS ONE, 19(11), e0313191. https://doi.org/10.1371/journal.pone.0313191

Kechri, K., Kleisiari, C., Kyrgiakos, L. S., Vasileiou, M., Tosiliani, D. D., Angelopoulos, V., Kleftodimos, G., & Vlontzos, G. (2025). Emerging technologies for investigating food consumer behavior: A systematic literature review. Comprehensive Reviews in Food Science and Food Safety, 24, e70340. https://doi.org/10.1111/1541-4337.70340

Liang, X., Hussain, W. M. H. W., & Salem, M. R. M. (2025). Mapping the digital Silk Road: Evolution and strategic shifts in Chinese social media marketing (2015–2025). Cogent Business & Management, 12(1), 2546086. https://doi.org/10.1080/23311975.2025.2546086

Luong, V. H., Tarquini, A., Anadol, Y., Klaus, P., & Manthiou, A. (2024). Is digital fashion the future of the metaverse? Insights from YouTube comments. Journal of Retailing and Consumer Services, 79, 103780. https://doi.org/10.1016/j.jretconser.2024.103780

Masrianto, A., Hartoyo, H., Hubeis, A. V. S., & Hasanah, N. (2022). Digital marketing utilization index for evaluating and improving a company's digital marketing capability. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 153. https://doi.org/10.3390/joitmc8030153

Mohammad, A. A. S., Mohammad, S. I. S., Al Oraini, B., Hindieh, A., Vasudevan, A., & Alshurideh, M. T. (2025). Decoding consumer behaviour: Leveraging big data and machine learning for personalized digital marketing. Data and Metadata, 4, 700. https://doi.org/10.56294/dm2025700

Morales-Muñoz, A., Iniesta-Bonillo, M. Á., Estrella-Ramón, A., & Herrada-Lores, S. (2026). Artificial intelligence and consumer behaviour in social media: Systematic literature review and future research agenda. International Journal of Consumer Studies. https://doi.org/10.1111/ijcs.70173

Nasrabadi, N., Wicaksono, H., & Valilai, O. F. (2024). Shopping marketplace analysis based on customer insights using social media analytics. MethodsX, 13, 102868. https://doi.org/10.1016/j.mex.2024.102868

Okyere Sefa, A. A., Rezaei, M., & Valilai, O. F. (2026). An analytics-driven method for building ethical customer digital twins using neuromarketing and social media data. Decision Analytics Journal, 18, 100689. https://doi.org/10.1016/j.dajour.2026.100689

Pahari, S., Bandyopadhyay, A., Kumar V., V. M., & Pingle, S. (2024). A bibliometric analysis of digital advertising in social media: The state of the art and future research agenda. Cogent Business & Management, 11(1), 2383794. https://doi.org/10.1080/23311975.2024.2383794

Penkova, O., Kitchenko, O., Sogorin, A., Klimovych, O., & Tesak, O. (2025). Video content and strategic marketing: Overcoming barriers and enhancing engagement. Management (Montevideo), 3, 259.

Reklitis, D. P., Terzi, M. C., Sakas, D. P., & Konstantopoulou, C. K. (2025). Customer behaviour in response to disaster announcements: A big data analysis of digital marketing in hospitality. Tourism and Hospitality, 6(2), 112. https://doi.org/10.3390/tourhosp6020112

Sharifi, Z., & Shokouhyar, S. (2021). Promoting consumers’ attitude toward refurbished mobile phones: A social media analytics approach. Resources, Conservation & Recycling, 167, 105398. https://doi.org/10.1016/j.resconrec.2021.105398

Sharma, T., & Jha, S. (2025). AI-powered analysis of brand-consumer engagement in the digital era: Insights from Indian millennials. ShodhKosh: Journal of Visual and Performing Arts, 6(4s), 538-547. https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6930

Sykora, M., Elayan, S., Hodgkinson, I. R., Jackson, T. W., & West, A. (2022). The power of emotions: Leveraging user-generated content for customer experience management. Journal of Business Research, 144, 997–1006. https://doi.org/10.1016/j.jbusres.2022.02.016

Teng, S., & Khong, K. W. (2021). Examining actual consumer usage of E-wallet: A case study of big data analytics. Computers in Human Behavior, 121, 106778. https://doi.org/10.1016/j.chb.2021.106778

Theodorakopoulos, L., Theodoropoulou, A., & Klavdianos, C. (2025). Interactive viral marketing through big data analytics, influencer networks, AI integration, and ethical dimensions. Journal of Theoretical and Applied Electronic Commerce Research, 20, 115. https://doi.org/10.3390/jtaer20020115

Theodoridis, P. K., & Gkikas, D. C. (2025). Maximizing social media user engagement through predictive analytics in retail tourism: Identifying key performance indicators that trigger user interactions. Applied Sciences, 15, 11720. https://doi.org/10.3390/app152111720

Wang, L., Jing, Z., Li, H., Li, C., & Su, Y. (2025). The influence of AI-driven personalization in social media marketing on consumer purchase decisions and behavior. International Journal of Accounting and Economics Studies, 12(5), 438-444. https://doi.org/10.14419/dcggbj32

Weng, Y., Isleem, H. F., Hindi, K. E., & Ezugwu, A. E. (2025). Natural language processing for extracting consumer sentiment dynamics through multimodal social media analysis to predict microeconomic consumption pattern shifts. Journal of Big Data, 12, 254. https://doi.org/10.1186/s40537-025-01315-2

Zhan, Y., Han, R., Tse, M., Ali, M. H., & Hu, J. (2021). A social media analytic framework for improving operations and service management: A study of the retail pharmacy industry. Technological Forecasting & Social Change, 163, 120504. https://doi.org/10.1016/j.techfore.2020.120504

Downloads

Published

2026-03-29

How to Cite

Arif, H. M. (2026). Social Media Analytics and Consumer Behavior in Digital Fashion: A Narrative Review of Data-Driven Marketing Strategies . Management, Economics, Trade, and Accounting Journal (META-JOURNAL), 3(3), 651–660. https://doi.org/10.59971/meta-journal.v3i3.423

Issue

Section

Article