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@article{182977,
        author = {Tanmaykumar Shah},
        title = {Predictive Modeling for High-Value Audience Identification in Financial Services},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {4145-4153},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182977},
        abstract = {In the evolving landscape of financial services, the ability to accurately identify and prioritize high-value audiences is increasingly critical for competitive advantage. Traditional segmentation approaches, based on static demographic variables, no longer suffice in capturing the complexity and dynamism of consumer behavior. Predictive modeling offers a robust alternative, enabling marketers and risk managers to target customers with the highest potential lifetime value (CLV), responsiveness, and loyalty. This paper presents a comprehensive analysis of predictive modeling techniques used to identify high-value audiences, explores real-world applications in financial marketing, and examines key challenges such as data governance, model interpretability, and regulatory compliance.},
        keywords = {Predictive Analytics, Customers, Audience, Segmentation, Ethics, Customer Lifetime Value (CLV), compliance.},
        month = {July},
        }