Leveraging Machine Learning for Enhanced Audience Segmentation and Campaign Optimization in Facebook Advertising
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Date
2025
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Publisher
UMT, Lahore
Abstract
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.