Evaluating AI-Driven Personalization Strategies and Their Effects on Digital User Engagement

Abstract

This article examines how AI-driven personalization strategies shape digital user engagement across platforms such as social media, mobile apps, and omnichannel retail. It asks which types of AI-based personalization are most commonly used, how engagement is defined and measured, and under what conditions personalization enhances or undermines user responses. The study adopts a systematic review of peer-reviewed research published between 2017 and 2021, synthesizing evidence from marketing, information systems, and digital communication. The results indicate that personalized recommendations, curated content feeds, and adaptive interfaces can increase clicks, time spent, and repeat use when they deliver relevant, contextually appropriate experiences that users perceive as helpful and legitimate. However, privacy concerns, perceived surveillance, and lack of control can dampen or reverse these effects, particularly in highly data-intensive settings. The article discusses these patterns by organizing studies by personalization strategy and engagement dimension, and concludes that effective AI-driven personalization must balance optimization goals with transparency, user agency, and long-term relationship outcomes.