Identifying GDPR Violation Patterns in Different Digital Marketing Channels: A Case-Based Analysis of Enforcement Decisions and Strategic Recommendations

Abstract

In this study, I investigate the enduring persistence of GDPR violations in digital marketing despite widespread awareness and substantial penalties. Drawing on a systematically filtered sample of 222 enforcement cases from the 1,000 most recent entries on EnforcementTracker.com, I focus on decisions issued by the five EU countries with the highest violation counts. To process multilingual legal documents efficiently, I employed large language models (LLMs) guided by rigorously engineered prompts for translation and semantic analysis. Hallucination was controlled through outline-based review checkpoints, and model outputs were cross-validated against a second, state-of-the-art LLM before all remaining discrepancies were manually verified. Quantitative analysis reveals that 19.8 % of GDPR violations occur in digital marketing contexts, with telephone/SMS campaigns presenting the highest risk. Root-cause coding uncovers that 77.3 % of cases stem from organizational governance failures rather than purely technical misconfigurations. By integrating rigorous AI-assisted methods with researcher-driven verification, this thesis offers both methodological innovation for cross-language legal-compliance analysis and actionable recommendations for strengthening governance frameworks in digital-marketing operations.