Predictive Revenue Modeling for New Market Segments Using Data Fusion and Big Data Analytics

Shalmali Patil, Abdul Sajid Mohammed

Abstract: The integration of big data analytics and data fusion techniques has revolutionized the way organizations identify and tap into new market segments. This paper proposes a novel framework for predictive revenue modeling that combines original data sources with third - party data through advanced fusion methodologies. Leveraging machine learning algorithms, this framework addresses challenges such as data heterogeneity, scalability, and accuracy in revenue prediction. The research systematically explores methodologies like ensemble modeling, feature selection, and hybrid fusion techniques to construct robust predictive models. Data validation mechanisms ensure the reliability and consistency of results, highlighting the potential for practical applications in sectors such as healthcare, marketing, and industrial IoT. This conceptual framework provides a foundational approach for businesses to uncover new revenue opportunities and optimize resource allocation in dynamic market environments. The findings align with existing studies that emphasize the importance of integrating diverse data streams to enhance decision - making processes. The research also builds on techniques discussed in the literature for managing challenges of big data such as veracity and variability. This paper offers valuable insights for both researchers and practitioners in predictive analytics, providing a springboard for future empirical validations and industrial applications.

Keywords: Predictive revenue modeling, data fusion, big data analytics, machine learning, data validation, customer segmentation, revenue forecasting, market segmentation, data integration, scalability, business intelligence