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Examines how AI influences consumer behavior through predictive modeling and real-time marketing, suggesting significant impacts on engagement.
Purpose – This paper examines how artificial intelligence (AI) influences consumer behavior through predictive modeling and real-time data-driven marketing strategies. It aims to understand how businesses leverage AI to anticipate consumer needs and personalize marketing efforts. Design/Methodology/Approach – The study adopts a conceptual and analytical approach based on existing literature, industry practices, and emerging AI-driven marketing frameworks. It synthesizes existing research and integrates insights into predictive analytics and real-time data processing. Findings – The findings indicate that AI significantly enhances marketing precision, improves customer engagement, and increases conversion rates. Predictive modeling allows businesses to forecast consumer preferences, while real-time data enables immediate and personalized interactions. Practical Implications – Organizations can use AI tools to optimize marketing strategies, improve customer retention, and increase operational efficiency. The integration of AI into marketing systems helps firms respond dynamically to changing consumer behaviors. Originality/Value – This paper provides a structured perspective on AI-driven marketing by combining predictive modeling with real-time analytics, offering a comprehensive view of its role in shaping modern consumer behavior.
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Dr. V. Antony Joe Raja (2026) studied this question.
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