THEORETICAL EXPLORATION OF DATA-DRIVEN DECISION-MAKING IN DIGITAL MARKETING

Abstract

In the rapidly evolving landscape of digital marketing, the utilization of data-driven decision-making strategies has become imperative for organizations to stay competitive. This study employs a theoretical exploration methodology to investigate the intricate dynamics of data-driven decision-making in digital marketing. Through a systematic review and analysis of existing theories, concepts, and frameworks, the research aims to construct a comprehensive understanding of the theoretical underpinnings guiding decision-making processes in the digital marketing domain. Key theoretical constructs and models relevant to data analysis techniques, consumer behavior, market trends, and marketing strategy formulation are critically evaluated to elucidate the conceptual foundations of data-driven decision-making. By synthesizing insights from multiple disciplines, including marketing, information technology, and decision sciences, the study develops an integrative theoretical framework that provides a nuanced understanding of the complexities inherent in contemporary digital marketing practices. The findings contribute to the advancement of theoretical knowledge in the field and offer valuable insights for both academia and industry practitioners seeking to leverage data-driven approaches in their marketing strategies.