Transforming E-commerce with a Novel Multifaceted Data-Decision Framework

Abstract

With the quickly evolving nature of e-commerce, numerous businesses face a range of challenges, such as constrained investment, customer dissatisfaction, delivery delays, product-market misalignment, and a lack of understanding of customer preferences and satisfaction. These issues often contribute to the failure of many enterprises. This paper proposes a Novel Multifaceted Data-Decision Framework designed to navigate these challenges by guiding businesses from data collection to actionable insights using advanced analytics. The framework integrates various essential elements: data collection and processing, descriptive analytics to discern past occurrences, diagnostic analytics to unveil causative factors, and predictive analytics to estimate what could happen in the future. Prescriptive analytics provides detailed advice on how to respond and machine learning classifiers to analyze complex datasets. The framework's effectiveness is illustrated using an e-commerce dataset, showing how businesses of all sizes can leverage analytics for informed decision-making. By adopting this Multifaceted Data-Decision Framework, e-commerce businesses, from small to large-scale, can make informed decisions using data that enhance customer fulfilment, streamline operations, and promote sustainable growth, enabling them to overcome challenges and succeed in a competitive environment.