Quantitative Analysis of Strategic Communication and Media Relations: Data-Driven Approaches for Professional Excellence in Public Engagement

Abstract

Nsentip George Afangideand Abasi-ada Nnabuk Eddy

Received: 13 August 2025/Accepted: 14 October 2025

This study investigates the role of quantitative approaches in strategic communication and media relations, with a strong emphasis on data-driven decision-making for enhancing professional practice and public engagement. Adopting a descriptive and analytical framework, the study integrates empirical evidence from recent literature with simulated datasets to evaluate key communication performance indicators, including engagement rate, media reach, sentiment score, and reputation index. A multiple regression analysis reveals that strategic communication variables significantly predict organizational reputation, with sentiment emerging as the strongest predictor (β = 0.63, p < 0.001), followed by engagement (β = 0.45, p = 0.001) and media reach (β = 0.32, p = 0.003). The model demonstrates strong explanatory power (R² ≈ 0.68), indicating that approximately 68% of the variance in reputation is explained by the predictors. Correlation analysis further shows a strong positive relationship between sentiment and reputation (r = 0.79), engagement and reputation (r = 0.71), and engagement and sentiment (r = 0.72). Additionally, descriptive statistics indicate a high overall media visibility score (mean = 3.85/5), reflecting effective communication reach across platforms. The findings also highlight the significant mediating role of artificial intelligence (AI) in enhancing communication efficiency and predictive accuracy, as well as the growing importance of ESG communication in strengthening stakeholder trust. Overall, the results provide robust quantitative evidence supporting the integration of analytics, AI, and KPI-driven frameworks into strategic communication practices to improve effectiveness, accountability, and organizational reputation.