Review of Critical Components of Data-Driven Nudge Theory on Enhancing Conversion Rates in Digital Marketing Campaigns

This study explores the application of nudge theory to enhance conversion rates in digital campaigns, highlighting the gap between its proven effectiveness and suboptimal implementation. Despite its potential, many digital campaigns fail to fully leverage nudge strategies due to a lack of comprehensive frameworks. This systematic literature review (SLR) addresses two key research questions: What are the critical components for effectively integrating data-driven nudges? What approaches and techniques are efficient in utilizing data-driven nudge strategies to improve conversion rates? The review identifies essential components such as data quality, audience understanding, appropriate AI and ML model development, and segmentation and personalization. It also highlights efficient approaches and techniques, including behavioral insights, context-aware nudges, and multi-channel integration. The novelty of this study lies in proposing an integrated framework that systematically addresses these components and techniques, providing a structured strategy for optimizing data-driven nudges. This framework aims to bridge the existing implementation gaps and maximize the effectiveness of nudges in digital campaigns, ultimately enhancing conversion rates.