With the rapid development of artificial intelligence technology, its application in market forecasting and marketing decision-making has gradually become an important means for enterprises to achieve competitive advantage. Market forecasting models extract valuable information from massive amounts of data through machine learning and deep learning algorithms, providing enterprises with more scientific market insights and decision support. This article systematically elaborates on the core principles of market forecasting models based on artificial intelligence, including key processes such as data collection, feature engineering, model training, and optimization. It also outlines the specific application methods of common AI technologies such as time series analysis, supervised learning, and reinforcement learning in market forecasting. At the same time, typical application scenarios of AI in marketing decision-making were analyzed, such as customer demand forecasting, market segmentation, dynamic pricing, and advertising optimization, demonstrating how artificial intelligence can improve the accuracy, flexibility, and response speed of marketing strategies. By combining successful cases of companies such as Alibaba and Coca Cola, illustrate the application effect of artificial intelligence in practical business environments. In addition, this article also explores the limitations of artificial intelligence in market forecasting models, such as data quality issues, insufficient model interpretability, and privacy and ethical challenges. It also looks forward to future development trends and proposes possible paths to enhance multimodal data integration capabilities and strengthen human-machine collaboration, providing reference and inspiration for enterprises to apply artificial intelligence.
Jianan Wen (Tue,) studied this question.