Big Data Analytics for Predicting Customer Behavior in Digital Marketplaces

Abstract

The rapid growth of digital commerce has generated vast amounts of data from customer interactions within online marketplace platforms. This study aims to analyze the role of Big Data Analytics in predicting customer behavior in digital marketplaces. The research employs a qualitative research approach with a descriptive design to explore how data-driven strategies are used to understand consumer behavior and support business decision-making. Data were collected through in-depth interviews with digital marketing managers and data analysts, questionnaires distributed to licensed employees working in digital marketing and analytics roles, as well as observational analysis of customer interaction patterns on digital marketplace platforms. The results indicate that big data analytics plays a crucial role in identifying customer preferences, predicting purchasing patterns, and improving marketing effectiveness. Predictive models based on transaction history, browsing behavior, and customer reviews enable companies to develop personalized recommendation systems and targeted promotional strategies. These analytical capabilities significantly enhance customer engagement and increase sales performance in digital marketplaces. Furthermore, the findings highlight that organizations implementing advanced data analytics technologies gain competitive advantages through improved customer insights and more efficient marketing strategies. Despite several limitations related to sample size and qualitative data interpretation, this study provides valuable insights into the strategic importance of big data analytics for understanding consumer behavior in digital commerce ecosystems. The study also offers practical implications for businesses seeking to implement data-driven decision-making in the rapidly evolving digital economy.