Intelligent e-commerce framework for consumer behavior analysis using big data Analytics

Internet shopping gradually surpassed conventional retail shopping; it has been taken up internationally by many customers. However, e-commerce in emerging markets remains at an early stage, and thus, the factors that lead to its acceptance must be discovered. The main objective is to combine the expected theory of behavior, rational activity, and the technology model's acceptability using big data analysis (BDA) to evaluate the main predictors of internet buying plans. The growth in online shopping raises rivalry in the area of e-commerce between various organizations. The existing enterprise planning methods became obsolete with the advent of technology. Enterprises must adapt market intelligence through big data analysis to improve the business process in e-commerce. In the e-commerce market world, the influence of BDA plays a crucial part. The proposed model addresses different methodologies and methods for data analysis e-commerce. Users are proposing several new ways of enhancing market intelligence using BDA in the e-commerce sector. The findings show the relevance of the reasonable action theory and technology acceptance model, for explaining online shopping intentions, confirmation that e-commerce behavior often determines intentional purchases online, which in turn can be explained in terms of perceived utility, how easy it is to use, and how subjective the online shopping rule is perceived. This perspective of online shopping intention has contributed to a paradigm change in the e-commerce industry, as data is no longer viewed as the outcome of their market practices but as their greatest asset: a vital insight into the needs of consumers, a prediction of customer behavior patterns, the democracy of publicity to match consumer preferences, and success assessment to determine efficiency in meeting customers. The experimental outcomes of suggested BDA enhance the order delivery ratio (95.2%), customer behavior analysis ratio (92.6%), product quality ratio (97.6%), customer satisfaction ratio (95.9%), and demand prediction ratio (96.3%).