Deep Analysis of Digital Marketing Trends and Social Media Strategies: Case Studies in Short-Video Promotion, E-commerce Advertising, and AI-Personalized Marketing

Purpose: This study proposes a comparative research framework designed for systematic analysis of effect differences and mechanisms of three mainstream digital marketing strategies in social media platform settings, addressing the research gap in cross-strategy comparative analysis. Methodology: A research design with a data analysis paradigm originated from specific cases and industry datasets available on the Kaggle competition website, covering three social marketing platforms, namely TikTok's promotion of short videos, ecommerce ads marketing among Facebook users, and A/B group testing datasets derived from an A/B testing engine involving artificial intelligence. Findings: Results showed story-driven content performed better in terms of social interaction effects than merchandised content in a comparison of social marketing strategy effects for promoting short videos, click-through rates being a significant determinant in ecommerce ads conversion, and a significant performance difference in conversion effectiveness with leading internet users preferring personalized marketing advice offered by the A/B engine when compared with existing platforms. Conclusion: This study generates empirical findings fused with principles seeking synergistic marketing strategy characteristics by comprehending content properties’ role in marketing strategy deployment with specific growth mechanisms influencing Internet marketing space performance. Practical Applications: Every business needs to use differentiated strategy configurations according to defined objectives, namely narrative-driven video content for brand engagement objectives and AI-personalized recommendation systems for conversion objectives. A promising area is the combination of high interactivity content with precise algorithmic recommendation systems with a view to maximize marketing effectiveness.