PREDICTING AUDIENCE ENGAGEMENT IN DIGITAL CAMPAIGNS USING AI - N. Vani; Arora, Kapil; Sau, Suman; Gupta, Vandana; Nidhi Tewatia; S. Balaji; Shelar, Pooja Ashok (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The accelerated growth of digital media platform has increased competition in regards to attention to the audience and correct forecasting of the level of engagement of the audience becomes an extremely important problem to marketers, cultural organizations, and creative industries. Conventional analytics are based on post-campaign assessment and descriptive metrics, which do not provide much assistance in proactive decision-making in dynamic digital environments. The main goal of the study is to design and test an AI-based model to forecast the participation of the audience in online campaigns with high precision and comprehensibility. The analysis uses a monitored machine-learning strategy based on a multi-source dataset, which contains metadata of campaigns, visual and textual features, posting patterns, as well as historical user interactions. The visual aesthetics, natural language processing, and behavioral analytics are combined with each other in feature extraction. Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) network predictive models are trained and evaluated on standardized evaluation measures. The experiments show that suggesting hybrid AI model performs better than both statistical and single-model methods with an accuracy of engagement prediction of 92.6, an F1-score of 90.8, and a mean absolute error reduction of 27.4% in comparison with the traditional regression methods. The analysis of feature importance shows that visual quality indicators and the pattern of posting time are major contributors to the engagement variance giving more than 45 percent contribution. These results prove the value of AI-based predictive analytics in promoting strategic planning, content optimization, and real-time decision-making of digital campaigns.