E-commerce Evolution: Unveiling Consumer Trends through Dark Data Analysis

Abstract: The significance of Dark data analytics in the realm of e-commerce has garnered increasing attention in recent years. However, its theoretical and practical development remains relatively nascent, impeding its full potential. This paper delves into the discussion surrounding Dark data analytics within e-commerce through a comprehensive literature analysis. Initially, it establishes a conceptual framework elucidating the essence of Dark data analytics, encompassing its definitions, characteristics, methodologies, market significance, and pertinent issues in e-commerce. Additionally, the paper initiates a broader exploration into prospective research avenues and theoretical as well as practical challenges. The study's findings amalgamate various Dark data analytics principles, such as Dark data categorization, typologies, methodologies, market relevance, and associated theories. These insights offer valuable perspectives on versatile analytical instruments within the e-commerce landscape, providing a nuanced understanding of how Dark data analytics can be leveraged effectively.

Keywords: Data analytics, E-commerce, Data categorization, Methodologies, Conceptual framework, Methodologies, Typologies