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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM50512
Volume:
9 |
6 |
Jun 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
At the fastest speed of digital landscapes, social media has proven to be a central platform for interaction between branded consumers. With billions of users worldwide, platforms like Instagram, X (formerly Twitter), Facebook and LinkedIn brands offer unprecedented opportunities for commitment. However, mere presence on social media is no longer sufficient. The effectiveness of brand building is increasingly dependent on the strategic use of social media analytics. In this article, we explore ways social media analytics can improve brand commitment by understanding the brand of audience behavior, optimizing content strategies, and promoting sensible interactions. Through mood analysis, engagement metrics, trend identification, and audience segmentation, brands can use data control strategies beyond estimates. This study examines the effectiveness of these analytical tools in designing brand stories, establishing communities, and increasing customer loyalty. The role of real-time feedback in adapting brand messaging and improving customer satisfaction is also emphasized. The research is based on a mixed method approach that includes case studies, industry reports and research data from marketing specialists. The results show that companies using advanced analytics experiences have significantly higher commitment rates and improved brand awareness. Furthermore, analysis allows brands to personalize communications, recognize potential crises early on, and ensure continuous, active engagement. This paper highlights the transformational effects of analysis on brand commitments and provides strategic recommendations for brands who want to make the most of their potential.
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Publication Date
6/14/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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