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Data-Driven Marketing Innovation: Solving Revenue Stagnation and Efficiency Problems in Media and Broadcasting Sectors
Subject area: Management and Commerce · Area of research: Marketing Innovation
Abstract
The media and broadcasting sectors are undergoing transformative shifts due to evolving consumer behaviors, technological advancements, and increased competition from digital platforms. These challenges have resulted in revenue stagnation and operational inefficiencies for many traditional players. This study explores the role of data-driven marketing innovation as a strategic approach to address these issues. By leveraging advanced analytics, artificial intelligence, and machine learning, organizations can harness vast volumes of consumer data to personalize content delivery, optimize advertising strategies, and enhance viewer engagement. Key insights are derived from case studies, highlighting the successful implementation of predictive analytics for audience segmentation, programmatic advertising for revenue optimization, and real-time performance monitoring to improve operational agility. Furthermore, the paper examines the integration of cross-platform data to create unified consumer profiles, enabling targeted campaigns that resonate with diverse audience demographics. The findings underscore that a data-centric approach not only revitalizes revenue streams but also streamlines workflows, reduces operational costs, and fosters innovation. This research provides actionable recommendations for media and broadcasting firms to adopt data-driven strategies, thereby positioning themselves for sustainable growth in an increasingly competitive landscape. The implications of these innovations extend beyond financial performance, offering a blueprint for enhancing customer satisfaction and industry resilience in the digital age.
Keywords
Revenue optimization, marketing automation, performance analytics, predictive analytics, audience segmentation
References
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How to cite this paper
@article{1706009,
author = {Olanrewaju Awoyemi, Rita Uchenna Attah, Joseph Ozigi Basiru, Iveren M. Leghemo},
title = {Data-Driven Marketing Innovation: Solving Revenue Stagnation and Efficiency Problems in Media and Broadcasting Sectors},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {1},
pages = {650-661},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706009.pdf},
abstract = {The media and broadcasting sectors are undergoing transformative shifts due to evolving consumer behaviors, technological advancements, and increased competition from digital platforms. These challenges have resulted in revenue stagnation and operational inefficiencies for many traditional players. This study explores the role of data-driven marketing innovation as a strategic approach to address these issues. By leveraging advanced analytics, artificial intelligence, and machine learning, organizations can harness vast volumes of consumer data to personalize content delivery, optimize advertising strategies, and enhance viewer engagement. Key insights are derived from case studies, highlighting the successful implementation of predictive analytics for audience segmentation, programmatic advertising for revenue optimization, and real-time performance monitoring to improve operational agility. Furthermore, the paper examines the integration of cross-platform data to create unified consumer profiles, enabling targeted campaigns that resonate with diverse audience demographics. The findings underscore that a data-centric approach not only revitalizes revenue streams but also streamlines workflows, reduces operational costs, and fosters innovation. This research provides actionable recommendations for media and broadcasting firms to adopt data-driven strategies, thereby positioning themselves for sustainable growth in an increasingly competitive landscape. The implications of these innovations extend beyond financial performance, offering a blueprint for enhancing customer satisfaction and industry resilience in the digital age.},
keywords = {Revenue optimization, marketing automation, performance analytics, predictive analytics, audience segmentation},
month = {July},
}