Research on Digital Platform User Retention Strategies and Marketing Model Optimization from a Data-Driven Perspective

This review paper synthesizes existing research on digital platform user retention strategies and marketing model optimization from a data-driven perspective. It examines various approaches employed by platforms to enhance user engagement and loyalty, leveraging data analytics to personalize experiences and improve marketing effectiveness. The review encompasses an historical overview of user retention techniques, delving into the evolution of marketing models in the digital age. Core themes explored include data-driven personalization, behavioral targeting, and dynamic pricing strategies. A comparative analysis of different retention models is presented, highlighting their strengths and weaknesses, and addressing the challenges associated with data privacy and algorithmic bias. Furthermore, the paper explores future research directions, anticipating the impact of emerging technologies such as AI and blockchain on user retention and marketing practices. This review aims to provide a comprehensive understanding of the data-driven landscape of user retention, offering insights for both academics and practitioners.