REGRESSION-BASED MODELING AND PREDICTION OF USER ENGAGEMENT IN MOBILE APPLICATIONS: ANALYZING BEHAVIORAL PATTERNS, APP USAGE METRICS, AND PERSONALIZATION FACTORS

Abstract

User engagement with mobile applications determines whether digital goods succeed and the revenue generated, user happiness, and retention. For this work, regression based modeling is done on behavioral patterns, app use data and customization elements in attempts to predict user engagement. The report tries to tackle a major problem for developers and companies, that is which is the decline in user engagement in mobile apps. Using sophisticated regression techniques, the project aims to find important factors of user engagement and to develop the prediction models that might lead the app improvement methods. The method consist of collecting data on the actual app use, feature engineering, and utilizing multivariate regression models to find relationships among user behavior and engagement metrics. Results show that session length, app use frequency and tailored content are all signs of user engagement. These results supply practical advice to app developers regarding how to boost user experience and retention. This research is added to the evolving corpus of research on user engagement by providing a data driven method for understanding and projecting user behavior in mobile applications.