Abstract:

In the realm of e-commerce, understanding and predicting consumer behaviour are essential for optimizing online retail strategies. This research delves into the intricate dynamics of consumer interactions on e-commerce platforms, with a specific focus on the influence of engagement metrics—such as session duration, clicks, and page views—on purchasing decisions. Analysing a comprehensive dataset of transaction logs, we uncover significant correlations that underscore the pivotal role of prolonged engagement in fostering user interaction and potentially driving conversions.Our study employs advanced machine learning techniques, particularly leveraging the Random Forest Classifier, to forecast user behaviours with high precision. The model demonstrates exceptional accuracy in predicting purchasing decisions based on engagement metrics and other relevant features. Comparisons with Logistic Regression and Decision Tree models further validate the Random Forest's efficacy in enhancing predictive capabilities and informing targeted marketing strategies.The findings highlight actionable insights for e-commerce businesses seeking to refine user engagement strategies and personalize customer experiences. By harnessing these insights, businesses can optimize content placement, refine product offerings, and tailor marketing initiatives to effectively meet consumer preferences and enhance overall conversion rates. This research contributes substantively to the field by bridging empirical data analysis with predictive modelling, offering a practical framework for leveraging consumer engagement data to drive strategic decisions and achieve sustainable growth in competitive digital markets.

Description:

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