AI and audience development: From footfall to forecasting cultural participation

This study investigates the intersection of artificial intelligence (AI) and audience development strategies within the cultural sector. Building on existing research on cultural participation, this article draws from a nationally funded project which employs AI-powered computer vision to monitor and predict analytics to forecast visitor and non-visitor flow outside Malta's National Centre for Creativity. By mapping movement patterns outside the creative centre complemented with a survey research, the study provides insights not only into who visits and when, but also into who remains outside and why. This study explores how big data on people flow can be useful for decision-making, improving and promoting the site and the overall visitor experience. This study examines how neural network-based footfall monitoring and machine learning forecasting models support cultural sites in analysing visitor behaviour, movement, and patterns, enabling better resource allocation and targeted promotional activities. Central research questions include: How can AI-driven methodologies support the conversion of non-visitors into visitors? By integrating AI-driven insights with traditional audience research, this study offers data-based recommendations for audience development. In summary, this article contributes to insight on the use of computer vision and machine learning algorithms in enhancing cultural site management by providing valuable insights and increasing visitations and cultural participation.