Machine Learning Approaches to Enhance Revenue Capture in E-commerce Platforms

This thesis delves into the realm of machine learning, data analysis, and data acquisition systems, with a focus on the e-commerce sector. One of the primary objectives is to identify "missing revenue recognition" on e-commerce platforms. By leveraging various user interaction signals such as clicks, rage clicks, form submissions, and more, we aim to detect potential gaps and opportunities in revenue capture. Moreover, the research also extends to technological flaws that might hinder optimal user experience, such as slow-loading pages, problematic resources, JavaScript errors, and the like. By addressing these issues, this work aims to enhance user experience and consequently, revenue generation in the e-commerce space.