Predictive Analytics in eCommerce: AI-Driven Insights for Market Trends and Consumer Behavior
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V2I3P104Keywords:
Predictive Analytics, e-commerce, Artificial Intelligence, Machine Learning, Consumer Behavior, Market Trends, Demand Forecasting, Customer Lifetime Value, Explainable AIAbstract
The digitalization that has taken place today has made it easier to identify markets and how customers behave. Predictive analytics in the modern world is one of the most valuable tools in the development of artificial intelligence, allowing decisions to be made based on big data. This paper aims to highlight options in the use of AI-based predictive models to predict market trends, optimize prices, and create customized experiences for consumers. Using structured and unstructured patterns from transactional databases and a product registry or social media logs, lurking, browsing behavior, and so on. AI can also discover relevant patterns, identify a shift, and make accurate estimations. The paper understands traditional market analysis methods, compares them with artificial intelligence, and compares modern approaches such as recommendation systems, demand forecasting systems, and customer lifetime value systems. It also covers a conceptually elegant model of real-time prediction for eCommerce, leveraged by tools of neural networks, NLP, and ensemble learning. Some evaluation criteria and case examples are presented, along with a discussion of ethical issues and potential difficulties in deployment. Lastly, the paper discusses potential work and further research ideas such as Explainable Artificial Intelligence, integration of Multiple Data Sources, and Real-Time Analysis. This study also seeks to provide practical information to practitioners and researchers interested in applying predictive analytics to competition in the current digital environment
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