AI-Enhanced Consumer Behavior Analysis in Digital Environments with BERT Optimization

This study presents an innovative approach to enhance consumer behavior analysis in digital environments through the integration of AI techniques, specifically leveraging BERT optimization. We preprocess digital interaction data and fine-tune the BERT model on labeled consumer behavior datasets to capture the nuances of consumer sentiment and preferences expressed in online interactions. Our experiments demonstrate a substantial improvement in predictive performance, with an average increase of 15% over traditional methods. The BERT-optimized model outperforms existing state-of-the-art models in various consumer behavior analysis tasks, showcasing its robustness across different types of digital interactions. This research underscores the effectiveness of BERT optimization in providing accurate insights into consumer preferences and trends in digital environments, thereby facilitating more informed decision-making for businesses and marketers.