Marketing Strategies and Consumer Analysis Using AI Algorithm-Based Digital Content Recommendations: Effects and Side Effects

The integration of artificial intelligence (AI) into digital content recommendation systems has fundamentally transformed marketing strategies and consumer behavior. (1) Background/Purpose: This study explores the dual impact of AI algorithm-based recommendation systems—examining both their marketing effectiveness and their unintended side effects, including algorithmic bias, privacy violations, and the reinforcement of market monopolies. (2) Methodology/Approach: A mixed-methods design was employed, combining a quantitative survey of 500 consumers across diverse demographic profiles, qualitative case studies of Netflix, Amazon, Facebook (Meta), and Instagram, and a systematic literature review. Environmental analysis assessed the regulatory, technological, and social dimensions in which these systems operate. (3) Findings: Survey results show that 68% of respondents are satisfied with AI-based content recommendations, and 62% report having made unplanned purchases due to AI recommendations—rising to 75% among consumers aged 18–35. However, 73% express discomfort with the volume of personal data required, and 65% demand greater transparency in data usage. Case study findings confirm that Netflix, Amazon, and Facebook achieve significant gains in user engagement, sales conversion, and subscription retention through personalized recommendation systems. Concurrently, filter bubble effects, consumer autonomy erosion, and misinformation diffusion risk emerge as notable challenges. (4) Originality/Value: This study provides an integrated dual-perspective framework on AI recommendation systems, offering actionable insights for businesses, and policy recommendations to mitigate negative impacts while maximizing the benefits of AI-driven marketing.