DATA ANALYTICS IN AUDIENCE ENGAGEMENT AND CULTURAL PARTICIPATION
DOI:
https://doi.org/10.29121/shodhkosh.v7.i3s.2026.7315Keywords:
Cultural Data Analytics, Audience Engagement, Cultural Participation, Digital Transformation, Cultural Institutions, Machine Learning, Smart Cultural EcosystemsAbstract [English]
The digital revolution in the cultural and creative industries is the factor, which has radically changed the way the audience communicated with the cultural content and organizations. Digital platforms are becoming a more and more significant instrument which cultural organizations utilize in order to connect with more people, become more participatory, such as websites, social media, mobile applications, cultural archives on the Internet. It has been observed that data analytics are a huge source, in this case, to understand the audience behavior, increase the engagement strategy, and evidence-based decision-making. The paper research analyzes how data analytics is used in the engagement and cultural participation of the audience and further offers a comprehensive outline of data analytics that should be used by culture institutions. The first analysis of the paper is the currently existing data-driven models of engagement, including audience analytics frameworks, smart cultural analytics systems, and engagement modeled on the basis of personalization. The elementary limitations and gaps in research will be conducted through comparison analysis to identify gaps in research in the current systems. Based on such findings, a hierarchical data analytics framework can be proposed, which includes many components, including cultural data sources, data collection and data integration module, scalable data storage platform, analytics and machine learning, visualization dashboards, and decision-making operations. The proposed framework can enable cultural institutions to accumulate, process, and analyze the audience data on different online platforms to generate meaningful insights into the audience preferences and activity patterns. Evaluation and key performance indicators measurements are also provided to show whether the given framework was successful in improving the outcomes of cultural participation and engagement. The results prove that data-driven cultural analytics has the potential to enhance audience engagement, strategy-oriented approaches to cultural decisions significantly, and the sustainability of cultural organizations. The other implementation challenges are also discussed in the study and the future research directions are outlined as applying artificial intelligence, predictive analytics, immersive technologies, and smart cultural ecosystems
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