2025

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Browsing 2025 by Author "UMER PERVAIZ"

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    Leveraging Machine Learning for Enhanced Audience Segmentation and Campaign Optimization in Facebook Advertising
    (UMT, Lahore, 2025) UMER PERVAIZ
    This research investigates the application of machine learning to enhance digital advertising campaign performance within Facebook's ecosystem, addressing current limitations in transparency and real-time optimization. Analyzing thirteen months of Facebook campaign data across diverse objectives and targeting strategies, we employed unsupervised clustering for audience categorization based on exposure patterns and supervised regression modeling for expenditure-outcome relationships. Our clustering analysis revealed four distinct audience segments, each with unique reach- frequency profiles, enabling refined retargeting, frequency management, and personalization strategies. Regression modeling quantified the relationship between advertising investment and audience reach, supporting evidence-based budget allocation. The findings demonstrate that machine learning significantly improves social media advertising efficiency by enhancing audience comprehension and predictive capabilities. This methodology provides interpretable insights for campaign optimization, addressing transparency concerns in algorithmic advertising. This study empirically validates clustering applications in digital marketing and establishes frameworks for predictive campaign planning, highlighting the potential for data-driven segmentation and forecasting while upholding ethical considerations.