Abstract:

The advent of AI-powered personalization is revolutionizing digital commerce by tailoring customer experiences through real-time interactions, leading to enhanced customer satisfaction and increased sales. By utilizing data-driven insights, businesses can now predict customer preferences and behaviors, offering a more customized shopping experience.Traditional personalization methods often rely on static, rule-based algorithms, which fail to effectively adapt to the dynamic nature of consumer behavior. These methods can result in irrelevant recommendations, data overload, and missed opportunities to engage with customers in meaningful ways.To address these challenges, we propose the Dynamic AI Personalization Framework (DAPF), which leverages machine learning and natural language processing to analyze and adapt to customer interactions in real time. The framework employs predictive analytics, sentiment analysis, and contextual recommendations to continuously update the personalization models, ensuring they evolve alongside customer preferences and trends.The DAPF offers a scalable solution for businesses to implement more sophisticated, context-aware personalization strategies that drive engagement and optimize conversion rates. It can be applied across various e-commerce platforms, from retail websites to mobile applications, enhancing user experience by providing highly relevant and personalized content, offers, and recommendations.The proposed method has shown significant improvements in both customer engagement and sales performance. By dynamically adapting to each user's preferences, it has resulted in a higher rate of personalized recommendations, better customer retention, and a reduction in customer churn. The outcome suggests that AI-driven personalization can lead to more effective digital commerce strategies, with measurable benefits in both customer satisfaction and business profitability.