The rapid development of digital technology has transformed marketing strategies, enabling companies to leverage big data analytics to enhance personalized marketing approaches. With the increasing volume of customer interaction data collected from various digital platforms, businesses can now gain deeper insights into consumer preferences and behaviors. This study aims to analyze the impact of big data analytics on personalized digital marketing and evaluate the role of data visualization in improving decision-making processes. The research employs an exploratory approach by analyzing secondary data from multiple digital sources, including e-commerce platforms, social media, and company websites. The study applies data-driven segmentation models and machine learning-based predictive analytics to assess customer engagement and conversion rates. The findings reveal that implementing big data analytics leads to a 48.57% increase in customer engagement and a 132% improvement in conversion rates compared to traditional marketing methods. Furthermore, the integration of data visualization techniques enables marketers to interpret complex consumer patterns effectively, contributing to a 46.67% rise in average transaction value per customer. These results indicate that data-driven personalization significantly enhances marketing effectiveness and customer loyalty. This research contributes to the field by providing empirical evidence on the advantages of utilizing big data analytics in digital marketing and highlighting the importance of interactive dashboards for real-time customer trend analysis. Future research is encouraged to explore the automation of personalized marketing through machine learning algorithms and the optimization of real-time data-driven strategies.
