A System Framework for Optimizing the Localization Media Strategy of Multinational Corporations Based on Multi-Source Data Fusion

Abstract

 In the era of globalization, multinational corporations (MNCs) face the critical challenge of tailoring their media strategies to resonate with diverse local markets, particularly within dynamic creative cities. Traditional approaches to localization are often based on siloed data and qualitative judgments, lacking the agility and precision required in fast-paced digital environments. This paper proposes a novel system framework for optimizing the localized media strategies of MNCs through the systematic fusion of multi-source data. The framework is designed to integrate internal enterprise data, such as sales and marketing metrics, with a wide array of external data, including social media analytics, public opinion data, local cultural and policy information, and competitor intelligence. The core of the system is a data fusion and analytics engine that employs machine learning and predictive modeling to analyze the complex interplay between various factors (e.g., local cultural trends, policy shifts, user sentiment) and media strategy performance. By modeling these relationships, the system provides data-driven decision support, generating actionable recommendations for content themes, platform selection, and optimal timing for media deployment. This framework offers a structured, scalable, and intelligent solution to enhance the effectiveness of cross-border communication, enabling MNCs to achieve deeper market penetration and stronger brand resonance.