Real-time Predicting Analytics for Financial Marketing Trends Using Big Data

Abstract

The growing availability of the extensive, including the Internet of Things (IoT), social media platforms, and search engines, has presented B2B (business-to-business)            industrial              marketing organizations with significant opportunities to use analytical methods for crafting programmatic marketing strategies in online display advertising. However, the tasks of cleansing, processing, and analyzing these large datasets come with considerable challenges, especially in the context of real-time decision-making and its broader implications. Despite these opportunities, research exploring these interactions remains scarce. This study leverages a problematization approach to investigate the relationships between big data, programmatic marketing, real-time data processing, and data-driven decision-making within the scope of B2B industrial marketing. It focuses on identifying major big data sources and analyzing batch and real-time processing methods for structured and unstructured datasets, evaluating their implication for data to processing techniques. By doing so, the research not only highlights the Potential areas but also integrates perspectives from multiple disciplines, connecting technological frameworks like Storm and the Hadoop to B2B Marketing practices and assessing their influence on modern marketing strategies.