An Analysis of How to Enhance E-Commerce Transaction Rates Using Big Data

Abstract

With the rapid advancement of Internet and big data technologies, user behavior data and product information on e- commerce platforms have experienced exponential growth. Leveraging big data to mine and analyzing massive volumes of user data provides technical support for precision recommendation and precision marketing, thereby boosting the transaction rates of e-commerce platforms. This study employs a literature review approach to systematically synthesize the current research status—both domestically and internationally—regarding the role of big data in driving e-commerce conversion rates. First, from a data perspective, it elaborates on the integration of multi-source user behavior data and the dynamic construction of user portraits; second, it explores the application of optimized recommendation algorithm technologies (e.g., collaborative filtering, Graph Neural Networks [GNNs]) in recommendation systems; subsequently, it analyzes the function of real-time feedback mechanisms in the iterative updating of recommendation systems; and it further discusses strategies for balancing personalized recommendation and recommendation diversity. Finally, an analysis is conducted by incorporating practical cases from typical platforms such as Amazon, Taobao, and Netflix. The research demonstrates that constructing dynamic user portraits, adopting advanced recommendation algorithms, and implementing real-time iterations can effectively improve recommendation quality and user experience, which in turn enhances platform conversion rates. The conclusion summarizes the key research findings and offers an outlook on the future development directions of big data-driven personalized recommendation systems.