Analyzing Consumer Behavior Using Big Data Analytics in Digital Advertising

Abstract

In the era of digital transformation, big data analytics has become a vital tool for understanding consumer behavior and enhancing the effectiveness of digital advertising. This study aims to explore how big data analytics can be utilized to analyze consumer behavior patterns and improve advertising strategies across digital platforms. By integrating behavioral analytics, machine learning techniques, and customer segmentation models, the research examines data from various sources such as social media, e-commerce transactions, clickstream data, and online browsing behavior. A mixed-method approach is employed, combining quantitative data analysis with qualitative insights from industry practitioners. The findings reveal that big data analytics enables real-time tracking of consumer preferences, predictive modeling of purchase intentions, and hyper-personalized ad targeting, leading to higher engagement rates and conversion. Additionally, the study identifies critical success factors in implementing data-driven advertising, including data quality, privacy compliance, and analytical capability. This research contributes to the field of digital marketing by providing a comprehensive framework for leveraging big data in consumer behavior analysis, offering strategic implications for marketers aiming to achieve competitive advantage in the digital advertising landscape.