Predictive Analytics in E-Commerce Leveraging Data Mining for Customer Insights

Abstract

The proposed work examines the application of predictive analytics and data mining to the e-commerce sector in order to gain a deeper understanding of what motivates online purchasers. While acknowledging the limitations of analytics, it emphasizes the value of more complex analytics for guiding business strategy and improving performance. In addition to examining the outcomes of the antecedent steps (data collection, pre-processing, model selection, and integration), data mining techniques are used to examine the outcomes of the preceding steps (data collection, pre-processing, model selection, and integration). The proposed work's empirical analysis of prediction models reveals significant insights regarding consumers' activities and preferences. It has been demonstrated that predictive analytics has a revolutionary effect on e-commerce operations, particularly in nurturing consumer engagement, optimizing resource allocation, and promoting more personalized interactions. Despite its limitations, The proposed work contributes to the current discourse on predictive analytics and offers a novel, intriguing framework for future research and development. Finally, predictive analytics emerges as a crucial asset for prospering in the dynamic e-commerce industry, where new avenues of opportunity are constantly opening and competitive advantages are continually forged.