The Social Intelligence Nexus – Leveraging Social Media Analytics for Comprehensive Brand Performance Optimization

Simon Suwanzy Dzreke; Semefa Elikplim Dzreke

The Social Intelligence Nexus – Leveraging Social Media Analytics for Comprehensive Brand Performance Optimization

Author(s)	Dr. Simon Suwanzy Dzreke, Ms. Semefa Elikplim Dzreke
Country	United States
Abstract	The Social Brand Intelligence Framework (SBIF), an integrated architecture that converts dispersed social data into useful strategic intelligence, was developed and empirically validated in this study to address important gaps in marketing analytics. Unlike traditional methods that treat data collection, processing, and activation as separate processes, SBIF creates four interconnected layers: Nestlé's 65% latency reduction through API integration exemplifies how the Input Layer resolves platform heterogeneity via middleware-enabled data harmonization. To measure causal relationships between sentiment and market outcomes, the Processing Layer employs transformer-based natural language processing. Notably, it demonstrates that sales declines are predicted to be preceded by an increase in anger intensity with high statistical significance. The output layer enhances existing brand equity models by establishing sector-specific brand equity dynamics and revealing sentiment, which directly influences CPG sales but is irrelevant for luxury brands. As demonstrated by Unilever's 11-week product reformulation cycle, the Optimization Layer achieves 44% improvements in engagement efficiency by institutionalizing organizational learning through quasi-experimental feedback loops. Importantly, triangulated evidence shows that algorithmic accountability and organizational absorptive capacity—where cross-functional teams achieve 2.4× faster iteration—are crucial to SBIF's success, with bias audits significantly increasing consumer trust. By integrating Information Processing Theory, Attribution Theory, and Dynamic Capabilities into a coherent framework and identifying boundary conditions such as network mapping limitations in low-involvement categories, SBIF advances scholarly understanding. Practically, it reinterprets social intelligence as a relational infrastructure where consumers and brands collaboratively generate value through conversations mediated by algorithms, requiring ongoing adjustments across ethical, human, and technical aspects. Future research must develop culture-specific NLP taxonomies, model resilience amid platform turbulence, and extend SBIF to B2B environments.
Keywords	Relational Infrastructure, Algorithmic Accountability, Sentiment Elasticity, Organizational Absorptive Capacity, Algorithmic Co-Creation, and Social Brand Intelligence Framework (SBIF)
Field	Business Administration
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-06

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The Social Intelligence Nexus – Leveraging Social Media Analytics for Comprehensive Brand Performance Optimization

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