Optimizing Digital Marketing Through Cross-Platform Data Integration: A Focus on Facebook Campaign Efficiency
1. Dishu Patel, Sardar Patel University, Student, India
This research delves into a strategic approach to optimize
digital marketing campaigns, mainly focusing on Facebook Lead generation. The
primary challenges involved integrating audience data from Google Analytics and
proprietary Machine Learning models with Facebook's advertising platform. The
solution approach entailed a seamless integration of Facebook Pixel and Click
ID data into Google Analytics, followed by sophisticated data processing and
audience segmentation in BigQuery. The key objective was to improve the
efficiency and effectiveness of Facebook campaigns by utilizing advanced
lead-scoring models for more accurate audience targeting. This strategy
significantly reduced Cost Per Lead (CPL), demonstrating the effectiveness of
cross-platform data integration and analytics in enhancing digital marketing
campaign performance.
Digital Marketing Facebook Advertising Google Analytics BigQuery Lead Scoring Audience Segmentation Cost Per Lead Optimization Data Integration
The findings from this research underscore the
transformative impact of integrating and analyzing data across multiple digital
marketing platforms. The team successfully optimized campaign performance by
harnessing the strengths of Google Analytics, BigQuery, and Facebook's
advertising capabilities, notably reducing the CPL. The strategic use of Lead
Scoring models for audience segmentation proved pivotal in achieving higher
efficiency and effectiveness in Facebook advertising campaigns. This approach highlights
the potential of data-driven strategies in digital marketing, offering valuable
insights for organizations looking to enhance their online advertising ROI and
campaign effectiveness.
1. None
The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.
The authors did not receive any specific grants from funding agencies in the public, commercial, or non-profit sectors for the research, authorship, and/or publication of this article.
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All authors declare that they have no conflicts of interest.
I thank the following individuals for their expertise and assistance in all aspects of our study and for their help in writing the manuscript. I am also grateful for the insightful comments given by anonymous peer reviewers. Everyone's generosity and expertise have improved this study in myriad ways and saved me from many errors.
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