Abstract

User engagement is critical to online community success and sustainability in an ever-changing digital landscape. However, the active user base tends to shrink, as more users remain inactive or dormant after their initial log-in. This paper thus focuses on developing predictive models of detecting sleeper users in digital communities and identifies optimized re-engagement strategies that mitigate user churn. Leverage machine-learning techniques such as random forests, support vector machines (SVM), and logistic regression, among others, to predict dormancy by users at different points of interaction patterns, behavior, and engagement metrics. The paper will also elaborate on the personalization of re-engagement strategies, including targeted notifications, incentive programs, and personalized content for enhancing user retention. This work in this domain should continue by integrating data across various channels, conducting real-time predictive modelling about external factors influencing these communities, and refining such models and strategies.